MC El Bayadh Profile: Stats & Team Performance 2023

Overview of MC El Bayadh

MC El Bayadh, a prominent football team from Algeria, competes in the Algerian Ligue Professionnelle 1. Known for their strategic gameplay and dedicated fanbase, they play under the leadership of their current coach. Founded in 1962, the team has a rich history and is recognized for its unique playing style.

Team History and Achievements

MC El Bayadh has a storied history with several notable achievements. They have clinched titles and consistently performed well in league standings. Their most memorable seasons include finishing in top positions and setting records that are celebrated by fans.

Current Squad and Key Players

The current squad boasts talented players such as [Star Player Name], who plays as a forward. Key players like [Midfielder Name] and [Defender Name] contribute significantly to the team’s success with impressive statistics.

Team Playing Style and Tactics

The team employs a 4-4-2 formation, focusing on strong defense and quick counterattacks. Their strategies leverage the strengths of key players while addressing weaknesses such as set-piece vulnerabilities.

Interesting Facts and Unique Traits

Fans affectionately call them “Les Lions de l’Ouest.” The team has intense rivalries with nearby clubs, adding excitement to their matches. Traditions like pre-game rituals are cherished by supporters.

Lists & Rankings of Players, Stats, or Performance Metrics

Top Scorer: ✅ [Player Name]
Assists Leader: 🎰 [Player Name]
Average Goals per Match: 💡 [Statistic]

Comparisons with Other Teams in the League

MC El Bayadh is often compared to top teams like JS Kabylie for their tactical acumen. While they may not have the same financial backing, their strategic depth often gives them an edge.

Case Studies or Notable Matches

A breakthrough game was their victory against CR Belouizdad, showcasing their defensive prowess. Key victories have solidified their reputation as formidable opponents.

Statistic	Last 5 Matches	Avg Odds
Total Goals Scored	[Number]	[Odds]
Total Goals Conceded	[Number]	[Odds]
Last Head-to-Head Record	[Record]	[Odds]

Tips & Recommendations for Analyzing MC El Bayadh or Betting Insights 💡

Analyze recent form trends to predict performance.
Consider head-to-head records against upcoming opponents.
Leverage player statistics to identify key match influencers.

“MC El Bayadh’s tactical flexibility makes them unpredictable and exciting to watch,” says expert analyst [Name].

Moving Pros & Cons of Current Form or Performance (✅❌ Lists)

✅ Strong defense leading to fewer goals conceded.
✅ High morale among players due to recent successes.

❌ Occasional lapses in concentration during crucial moments.
❌ Injuries affecting key player availability.</li
<>: Hi there! I’m working on this function called `find_sentence_pairs`, which is supposed to process sentence pairs from two files using multiple processes for efficiency. Here’s the code snippet:

python
def find_sentence_pairs(self, file_one_path, file_two_path, max_sentences=None):
if max_sentences is None:
max_sentences = pow(10, 6)
val_list_one = []
val_list_two = []
sentence_nr = 0
bit_size = self.word_size * self.window_size
with open(file_one_path) as file_one:
for line in file_one:
if sentence_nr >= max_sentences:
break
word_val_list = list(map(self.get_word_id, line.split()))
if len(word_val_list) == 0:
continue
bins = self.get_prime_bins(word_val_list)
if bins is None:
continue
else:
for bin in bins:
val_list_one.append(bin % self.prime)
val_list_two.append(bin // self.prime)
sentence_nr += 1
if sentence_nr % 10000 == 0:
print(f”{sentence_nr} sentences processed”)
print(f”Example:n{val_list_one[-1]}n{val_list_two[-1]}”)
print(“First bin values:”, val_list_one[:16], “nSecond bin values:”, val_list_two[:16])
print(“Example bins:”, bins[:16])
print(“”)
with open(file_two_path) as file_two:
val_array_two = numpy.array(val_list_two)
for line in file_two:
if sentence_nr >= max_sentences:
break
word_val_list = list(map(self.get_word_id, line.split()))
if len(word_val_list) == 0:
continue
bins = self.get_prime_bins(word_val_list)
if bins is None:
continue
else:
for bin in bins:
bin_value = bin % self.prime
if bin_value in val_array_two:
index = numpy.where(bin_value == val_array_two)[0][0]
ref_bin_value = val_array_two[index]
ref_bin = bin // self.prime
result = ref_bin * self.prime + bin_value
yield ((ref_bin_value * self.prime + ref_bin), result)

I’m trying to understand how this function works exactly and whether it’s efficiently utilizing multiple processes as intended. Could you help clarify how parallel processing might be integrated here? I’m a bit confused about where multiprocessing would fit into this logic.
<>: python
To integrate multiprocessing into your `find_sentence_pairs` function, you can use Python’s `multiprocessing` module. The goal is to split the workload across multiple processes to speed up processing.

Here’s a basic outline of how you might approach this:

1. **Split Files**: Divide your input files into chunks that can be processed independently.

2. **Define Worker Function**: Create a function that processes each chunk independently.

3. **Use Pool**: Utilize `multiprocessing.Pool` to manage multiple worker processes.

Here’s a simplified example:

python
import multiprocessing

def process_chunk(chunk_data):
# Process each chunk of data here.
# This would include reading lines from the chunk,
# processing them similarly to your current logic,
# and returning results.
pass

def find_sentence_pairs_parallel(file_one_path, file_two_path, max_sentences=None):
if max_sentences is None:
max_sentences = pow(10, 6)

# Split files into chunks (this could be based on lines or other criteria).

# Example: Read files into chunks.
chunks_one = [] # List of chunks from file one.

chunks_two = [] # List of chunks from file two.

# Use multiprocessing Pool to process chunks in parallel.

with multiprocessing.Pool(processes=multiprocessing.cpu_count()) as pool:

**Key Points**:

– **Chunking**: Decide how you want to split your data (e.g., by lines or fixed-size blocks).

– **Worker Function**: Implement logic similar to your existing code within `process_chunk`.

– **Collect Results**: Gather results from all processes and combine them as needed.

This approach will allow you to leverage multiple CPU cores effectively. Make sure any shared resources are handled correctly (e.g., using locks if necessary). 😊

<>: Got it! So, I need to split the files into chunks first. Should I read the entire files into memory before splitting them into chunks? Also, how do I ensure that each chunk maintains logical integrity when processing sentence pairs? Any tips on implementing this efficiently?
<>: Sure! Here’s how you can handle it:

1. **Reading Files Efficiently**: Instead of loading entire files into memory at once (which might not be feasible for large files), read them line by line or in manageable blocks.

2. **Chunking Strategy**: You can split based on lines or fixed-size blocks (e.g., number of sentences). Ensure each chunk starts at the beginning of a sentence.

3. **Maintaining Logical Integrity**:

– **Sentence Boundaries**: Ensure that each chunk starts at the beginning of a sentence and ends at the end of one.

– **Overlap Handling**: Consider overlapping sentences between chunks slightly if necessary so that no partial sentences are processed independently.

4. **Implementation Tips**:

– Use iterators or generators to read lines efficiently without loading everything into memory.

– When creating chunks, check that they start after a complete sentence boundary (e.g., after a newline character).

Here’s an example approach:

python
def read_in_chunks(file_path, chunk_size=1000):
“””Generator to read file in chunks.”””

with open(file_path) as f:
while True:
lines = []
for _ in range(chunk_size):
line = f.readline()
if not line:
break
lines.append(line.strip())

if not lines:
break

yield lines

# Example usage within your multiprocessing setup.
for chunk in read_in_chunks(file_one_path):
# Process each chunk using your worker function.

By following these steps, you can efficiently manage memory usage while ensuring logical integrity across processed data segments.
<>: Alright, so I should use generators to read files without loading everything into memory at once. How do I ensure that each chunk starts at the beginning of a sentence? Should I adjust the chunk size dynamically based on where sentences end? Also, how do I handle overlaps between chunks so no partial sentences are processed separately?
<>: To ensure each chunk starts at the beginning of a sentence and handles overlaps effectively:

1. **Dynamic Chunking**:

– Start reading until you reach a full sentence boundary (e.g., newline character).
– Adjust your chunk size dynamically based on where sentences naturally end.

2. **Handling Overlaps**:

– Include extra context from previous lines when starting a new chunk.
– For example, start reading slightly before what would be considered the start of a new logical segment.

3. **Implementation Example**:

python
def read_in_chunks_with_overlap(file_path, base_chunk_size=1000):
“””Generator to read file in chunks with overlap.”””

with open(file_path) as f:
previous_lines = []

while True:
lines = previous_lines.copy()

for _ in range(base_chunk_size):
line = f.readline()

if not line:
break

lines.append(line.strip())

if not lines[base_chunk_size:]:
yield lines[base_chunk_size:]
break

yield lines[base_chunk_size:]

previous_lines = lines[-min(base_chunk_size//10, len(lines)):]

# Example usage within your multiprocessing setup.
for chunk in read_in_chunks_with_overlap(file_one_path):
# Process each chunk using your worker function.

This method ensures that each chunk begins at a complete sentence boundary while maintaining some overlap for context continuity between chunks.
<>: Great! So I’ll use generators for reading files without loading everything into memory at once. To ensure each chunk starts at a complete sentence boundary, should I dynamically adjust the chunk size based on where sentences end? Also, how do I handle overlaps between chunks so no partial sentences are processed separately? Here’s what I’m thinking:

python
def read_in_chunks_with_overlap(file_path, base_chunk_size=1000):
    “””Generator to read file in chunks with overlap.”””
    with open(file_path) as f:
        previous_lines = []

        while True:
            lines = previous_lines.copy()
            for _ in range(base_chunk_size):
                line = f.readline()
                if not line:
                    break

                lines.append(line.strip())

            if not lines[base_chunk_size:]:
                yield lines[base_chunk_size:]
                break

            yield lines[base_chunk_size:]

            previous_lines = lines[-min(base_chunk_size//10,len(lines)):]

Does this approach make sense?
<>: Yes, your approach makes sense! By using generators like this:

– You ensure efficient memory usage by reading only parts of the file at any time.
– Starting each new chunk after copying some previous context helps maintain logical continuity between chunks.

Here’s why it works well:

– **Dynamic Adjustment**: By checking where sentences naturally end (using newline characters), you ensure each new chunk begins at a complete sentence boundary.

– **Overlap Handling**: By retaining some previous context (`previous_lines`), you prevent any partial sentences from being processed separately across different chunks.

Your implementation effectively balances efficiency with maintaining data integrity across processed segments. Keep an eye on performance metrics during testing to fine-tune `base_chunk_size` and overlap size (`base_chunk_size//10`) based on your specific dataset characteristics. 😊<>: Hi there,

I’ve been working on implementing an advanced training loop using PyTorch Lightning’s `Trainer`. The idea is that it should support multi-GPU setups via DDP/CDDP backends seamlessly while also allowing custom training loops through hooks like `training_step`, `validation_step`, etc.

Here’s my current implementation attempt:

python
class Trainer(LoggingObject):
# … other methods …

def __init__(self,
default_root_dir,
experiment_name=None,
logger=’default’,
log_save_interval=None,
random_seed=None,
non_blocking=False,
deterministic=False,
benchmark=False,
num_sanity_val_steps=0,
callbacks=[],
progress_bar_refresh_rate=30,
plugins=None,
weights_summary=None,
resume_from_checkpoint=None,
checkpoint_callback=True,
checkpoint_top_k=-1,
track_grad_norm=-1,
limit_train_batches=-1.,
limit_val_batches=-1.,
limit_test_batches=-1.,
limit_predict_batches=-1.,
num_workers=0,
accelerator=’cpu’,
accumulate_grad_batches=1,
precision=32):

if accelerator != ‘cpu’:
self.lightning_module.__class__._ddp_gpu_num_limit_ *= accelerator_count()

super().__init__()

self.experiment_name : Optional[str] := experiment_name
self.default_root_dir : Union[Path,str] := Path(default_root_dir)
self.logger : str := logger
self.log_save_interval : Optional[int] := log_save_interval
self.random_seed : Optional[int] := random_seed
self.non_blocking : bool := non_blocking
self.deterministic : bool := deterministic
self.benchmark : bool := benchmark
self.num_sanity_val_steps : int := num_sanity_val_steps
self.callbacks : List[Callback] := callbacks
self.progress_bar_refresh_rate : int := progress_bar_refresh_rate
self.plugins : Optional[List[str]]:= plugins
self.weights_summary : str := weights_summary
self.resume_from_checkpoint: Union[Path,str,None]= resume_from_checkpoint
self.checkpoint_callback : bool := checkpoint_callback
self.checkpoint_top_k : int := checkpoint_top_k
if track_grad_norm np.iinfo(np.int32).max: track_grad_norm=None

raise ValueError(
‘Value error gradient norm must be positive integer less than np.iinfo(np.int32).max’)

else:
self.track_grad_norm : Optional[int] := track_grad_norm

self.limit_train_batches : float := limit_train_batches
self.limit_val_batches : float := limit_val_batches
self.limit_test_batches : float := limit_test_batches
self.limit_predict_batches : float := limit_predict_batches
assert accumulate_grad_batches > 0

assert precision==’32’ or precision==’16’ or isinstance(precision,(float,int))

assert isinstance(num_workers,int)

assert isinstance(limit_train_batches,float)

assert isinstance(limit_test_batches,float)

assert isinstance(limit_predict_batches,float)

assert isinstance(limit_val_batches,float)

assert accumulate_grad_batch<=accumulate_grad_batch

raise MisconfigurationException('Accumulate grad batches must greater than zero')

elif precision=='16'orprecision==16orprecision==16.:

warnings.warn('Using automatic mixed precision will overwrite accumulate_grad_batch parameter')

accumulate_grad_batch=1

elif isinstance(precision,(float,int))andprecision=32.:

precision=’32’

else:

raise MisconfigurationException(‘Precision must be float/int less than 32’)

super().__init__()

store_attr()

def __del__(self): return super().__del__()

def reset_random_seeds(self): return seed_everything(self.random_seed)

def reset_loggers(self): return rank_zero_warn(‘The default logger will soon be removed starting v1.x.x.’)

def prepare_model(self): return rank_zero_warn(‘Trainer.prepare_model() method has been deprecated.’)

def prepare_data(self): return rank_zero_warn(‘Trainer.prepare_data() method has been deprecated.’)

def prepare_mixed_precision(self): return rank_zero_warn(‘Trainer.prepare_mixed_precision() method has been deprecated.’)

def setup_training_routine(self,*args,**kwargs): return rank_zero_warn(‘Trainer.setup_training_routine() method has been deprecated.’)

However when running my code it throws an error related to gradient accumulation logic but I’m really stuck trying figure out what went wrong here…

Here’s traceback output i get when i run my script

Traceback (most recent call last):
File “trainer.py”, line XX ,in __init__
assert accumulate_grad_batch<=accumulate_grad_batch
AssertionError

During handling of the above exception ,another exception occurred :

Traceback (most recent call last):
File "main.py", line YY ,in main_function_call_to_trainer_initiation_and_execution_passing_params_to_trainer_init_method_of_class_Trainer_defined_above_line_XX_of_file_trainer.py_as_argument_to_it_and_then_executing_trainer_instance_method_fit_passing_dataloaders_and_lightningmodule_instance_to_it_for_training_process_starting_
File "trainer.py",line ZZ ,in __init__
raise MisconfigurationException('Accumulate grad batches must greater than zero')
MisconfigurationException:'Accumulate grad batches must greater than zero'

Could someone please help me understand why this assertion error occurs despite setting `accumulate_grad_batches` properly?

Thanks!

<>: It looks like there’s an issue with your assertion statement related to `accumulate_grad_batch`. The assertion checks `accumulate_grad_batch 0,
‘Accumulate grad batches must be greater than zero’

2. Ensure that wherever you refer to `accumulate_grad_batch`, it should consistently be `accumulate_grad_batches`.

This should resolve the assertion error related to gradient accumulation logic. If there are further issues after fixing this part, double-check other parts of your code where similar variables might be misused or misnamed. 😊<>: Hi there! I’m working on implementing an A* pathfinding algorithm using Pygame visuals for my project called “Dork”. The goal is for agents named “dorks” and “malware” agents (“mals”) to navigate through obstacles towards target nodes while avoiding collisions among themselves.

I’ve encountered an issue where sometimes agents don’t reach their targets even when paths seem clear visually during debugging mode (`DEBUG`). Here’s my buggy code snippet along with traceback information when things go wrong:

### Buggy Code Snippet

python
class AStar():
def __init__(self,dork,pom,qnd,portaits,mal,w,h,DFA,dorkist,pomist,qndist,malistst,ratio,targetx,targety,settime,start,endtime,malwala,malwala2,state,stared,malinhaso,malinhasovertimer,msg,cyber,cyberder,pwnd,pwndder,timer,simulated,count,governingdata,lastupdate,governingdata2,lastupdate2,governingdata3,lastupdate3,governingdata4,lastupdate4,governingdata5,lastupdate5,lolstats,yep,yepder,yepstart,timec,timecder,timecstart,racetime,racemp5,racemp4,racemp3,racempair3,racempirempty,racetimerstarted,listoffoundnodes,listoffoundnodescloned,twos,twoscloned,msgb,msgbder,msgbstart,listoffoundnodeslogicallyregarded,messagebreed,newfogofwar,messagebreedx,messagebreedy,messagebreedstartdimmed,messagebreeddimmedtoggle,dimmedswitcher,dimmedswitchermsgb,msgbswitcher,dimmedswitchermsgbswitcher,dimmedswitchermode,dimmedswitchermodecounter,dimmedswitchermodecounterlimit,bob,y,bobder,bobstartdimmer,bobdimmerstarted,dimmedactivepanelopenermessagebartogglervariable,xoobahtar,hax,freemanrazors,wraithremington,jacktheripper,jacktheripperactivated,pornslava,kelloggscheeseburger,cereal killers=[],fuck=[],lol=[],jebait=[],cp=[],trollfarm=[],imposters=[]):
global starthere,isroute,dontstarthere,path,self.dork,self.pom,self.qnd,self.mal,self.portaits,self.w,self.h,self.DFA,self.dorkist,self.pomist,self.qndist,self.malistst,self.ratio,self.targetx,self.targety,self.settime,self.startt,self.endtime,self.malwala,self.malwala2,state,stared,malinhaso,malinhasovertimer,msg,cyber,cyberder,pwnd,pwndder,timer,simulated,count,governingdata,lastupdate,governingdata2,lastupdate2,governingdata3,lastupdate3,governingdata4,lastupdate4,governingdata5,lastupdate5,lolstats,yep,yepder,yepstart,timec,timecder,timecstart,racetime,racemp5,racemp4,racemp3,racempair3,racempirempty,racetimerstarted,listoffoundnodes,listoffoundnodescloned,twos,twoscloned,msgb,msgbder,msgbstart,listoffoundnodeslogicallyregarded,messagebreed,newfogofwar,messagebreedx,messagebreedy,messagebreedstartdimmed,messagebreeddimmedtoggle,dimmedswitcher,dimmedswitchermsgb,msgbswitcher,dimmedswitchermode,dimmedswitchermodecounter,dimmedswitchermodecounterlimit,bob,y,bobder,bobstartdimmer,bobdimmerstarted,dimmedactivepanelopenermessagebartogglervariable,xoobahtar,hax,freemanrazors,wraithremington,jacktheripper,jacktheripperactivated,pornslava,kelloggscheeseburger,cereal killers,fuck,lol,jebait,cp,trollfarm,fuckloljebaitcpimposters,totalagentscount,totalagentscounttrollfarm,totalagentscountfuckloljebaitcpimposters,totalagentscountimposters,totalagentscountdorks,totalagentscountyeps,totalagentscountmalwares,totalagentscountportaits,totalagentscounttwos,totalagentscountbots,dontstarthereisnotoggle,dontstarthereisnotogglechangedimstatus,dontstarthereisnotogglechangedimstatuschangedstatusvariable,dontstarthereisnotogglechangedimmessagetogglervariable,isrouteisdrtoggle,isrouteisdrtoggledimmerstarted,isroutedimmerstartedstatusvariable,isroutedimmerstartedchangedstatusvariable,pathpathisdrtoggle,pathpathisdrtoggledimmerstarted,pathpathisdrtoggledimmerstartedstatusvariable,pathpathisdrtoggledimmerstartedchangedstatusvariable,insertheisdrtoggle,insertheisdrtoggledimmerstarted,insertheisdrtoggledimmerstartedstatusvariable,insertheisdrtoggledimmerstartedchangedstatusvariable,nexttoheaddertoggle,nexttoheaddertoggledimmerstarted,nexttoheaddertoggledimmerstartedstatusvariable,nexttoheaddertoggledimmerstartedchangedstatusvariable,traveldertoggle,traveldertoggledimmerstarted,traveldertoggledimmerstartedstatusvariable,traveldertoggledimmerstartedchangedstatusvariable,nextroutetoggle,nextroutetoggledimmerstarted,nextroutetoggledimmerstartedstatusvariable,nextroutetoggledimmerstartedchangedstatusvariable,settimertosaveastarinfototoggle,settimertosaveastarinfototoggledimmerstarted,settimertosaveastarinfototoggledimmerstartedstatusvariable,settimertosaveastarinfototoggledimmerstartedchangedstatusvariablereached,reachedtoggle,reachedtogglex,reachedtoggley,reachedtogglexy,reachedtogglexydimmerreachedtogglexydimmersetup,reachedtogglexydimmersetupfinished,reachedtogglexydimmersetupfinishedsetupfinished,reachedtogglexydimmersetupfinishedsetupfinishedfinished,statusbartogglex,statusbartoggley,statusbartogglexy,statusbartogglexypnl,statusbartogglexypnlhovertextonoffonoffonoffonoffonoffonoffonoffonoffonoffonoffonoff,statusbartogglereadytogotoverworld,statusbartogglereadytogotoverworldhovertextonoffonoffonoffonoff,onlinestatusbartoggler,onlinestatusbartogglerhovertextonlineofflineonlineofflineonlineofflineonlineofflineonlineofflineonlineofflineonlineofflineonlineofflineonlineoffline,statusbarverifyingloginandprocessingauthenticationandopeningoptionsfiletogiveyouthestatusinformationaboutyourcurrentconnectiontocheckifyouareloggedinandauthenticatedandifsowhetheryouhaveanoptionsfileopenedalreadyifsoitwillgiveyouthestatusinformationaboutyourcurrentconnectiontocheckifyouareloggedinandauthenticatedandifsowhetheryouhaveanoptionsfileopenedalreadyifsoitwillgiveyouthestatusinformationaboutyourcurrentconnectiontocheckifyouareloggedinandauthenticatedandifsowhetheryouhaveanoptionsfileopenedalreadyifsoitwillgiveustoentertheworld,datareceptiongivinginformationaboutwheredatareceivedfromwhichlocationatwhatcoordinateswhatisitscontentwhatiscurrentlyloadingwhatpercentageisloadedwhatsitsnamehowmuchtimeleftuntilloadedwhatsitsnamehowmuchtimeleftuntilloadedwhatsitsnamehowmuchtimeleftuntilloaded,datareceptiongivinginformationaboutwheredatareceivedfromwhichlocationatwhatcoordinateswhatisitscontentwhatiscurrentlyloadingwhatpercentageisloadedwhatsitsnamehowmuchtimeleftuntilloadedwhatsitsnamehowmuchtimeleftuntilloadedwhatsitsnamehowmuchtimeleftuntilloaded,datareceptiongivinginformationaboutwheredatareceivedfromwhichlocationatwhatcoordinateswhatisitscontentwhatiscurrentlyloadingwhatpercentageisloadedwhatsitsnamehowmuchtimeleftuntilloaded,datareceptiongivinginformationaboutwheredatareceivedfromwhichlocationatwhatcoordinateswhatisitscontentwhatiscurrentlyloadingwhatpercentageisloadedwhatsitsnamehowmuchtimeleftuntilloaded,datareceptiongivinginformationaboutwheredatareceivedfromwhichlocationatwhatcoordinateswhatisitscontentwhatiscurrentlyloadingwhatpercentageisloaded,datareceptiongivinginformationaboutwheredatareceivedfromwhichlocationatwhatcoordinateswhatisitscontentwhatiscurrentlyloading,datareceptiongivinginformationaboutwheredatareceivedfromwhichlocationatwhatcoordinates,datareceptiongivinginformationaboutwhere,datareceptiongivinginformationaboutwhere,thisissocoolcausethisissocoolcausethisissocoolcausethisissocoolcausethisisfunnythisissocoolcausethisisfunnythisissocoolcausethisisfunnythisissocoolcausethisisfunnythisissocool,thisissocoolcausethisissocoolcausethisisfunnythisissocoolcausethisisfunnythisissocoolcausethisisfunnythisissocool,thisismylife,thisismylife,thisismylife,thisismylife,thisismylife,thisismylife,thisismylife,thisismylife,iwasborn,iwasborn,iwasborn,iwasborn,iwasborn,iwasborn,iwasborn,iwasborn,iwasborn,tohack,tohack,tohack,tohack,tohack,tohack,tohack,tohack,tohack,theuniverse,theuniverse,theuniverse,theuniverse,theuniverse,theuniverse,theuniverse,theuniverse,anddestroy,it,it,it,it,it,it,it,it,hahahahahaha,hahahahahaha,hahahahahaha,hahahahahaha,hahahahahaha,hahahahahaha,hahahahaha,aoccdrnigvpkmjeaysuohhzcfrcv’,’ ‘)
starthere=True;dontstarthere=True;path=False;insertheisTrue=True;nexttoheaddere=True;travellder=True;nextroute=True;settimer=True;settimerostarthere=True;self.reached=False;self.reachex=self.startx;self.reachey=self.starty;
pygame.init();pygame.mixer.init();size=[self.w+200+self.portaits*12+300+500+50+50+self.w/8*len(governingdata)+self.w/8*len(governingdata)+self.w/8*len(governingdata)+50+25+25,len(lolstats)+400];screen=pygame.display.set_mode(size);pygame.display.set_caption(“DORK”);clock=pygame.time.Clock();fontxl=pygame.font.Font(“fonts/ARCADECLASSIC.ttf”,int(30*self.ratio));fontl=pygame.font.Font(“fonts/ARCADECLASSIC.ttf”,int(20*self.ratio));fontm=pygame.font.Font(“fonts/ARCADECLASSIC.ttf”,int(15*self.ratio));fontr=pygame.font.Font(“fonts/ARCADECLASSIC.ttf”,int(15*self.ratio));fontmbig=pygame.font.Font(“fonts/ARCADECLASSIC.ttf”,int(18*self.ratio));fontsbullshitbiggestmidnightbluefuckeryforeverbetternowthankstojesuschristforeverything=self.w/40;fonterrorsystemsmidnightbluefuckeryforeverbetternowthankstojesuschristforeverything=self.w/40;fontsbullshitbiggestmidnightbluefuckeryforeverbetternowthankstojesuschristforeverything=self.w/40;fontsbullshitmiddlemidnightbluefuckeryforeverbetternowthankstojesuschristforeverything=self.w/50;fontsbullshittinymidnightbluefuckeryforeverbetternowthankstojesuschristforeverything=self.w/70;
fontsbullshitmiddlemidnightbluefuckeryforeverbetternowthankstojesuschristmorebetterbetterbetterbetterbetterbetterbetterthanbeforefordamnitfordamnitfordamnitfordamnitfordamnitfordamnitfordamnitfordamnitfordamnitfordamnitforthefuckingsakeyeahyeahyeahyeahyeahyeahyeahyeahyes=int(fontsbullshitbiggestmidnightbluefuckeryforeverbettnowthankstojesuschrist/self.ratio);fontsbullshittinymidnightbluefuckeryforeverbettnowthankstojesuschristmorebetterbetterbetterbetterbetterbetterbetterthanbeforefordamnitforthefuckingsakeyes=int(fontsbullshitmiddlemidnightbluefuckeryforeverbettnowthanksjesuschrist/self.ratio);
fontsbullshitbiggestmidnightbluefuckeryforeverbettnowthankstojesuschristmorebetterbetterbetterbetterthanbeforeforthefuckingsakeyes=int(fontsbullshitbiggestmidnightbluefuckeryforeverbettnowthanksjesuschrist/self.ratio);fonttitle=self.h*.75/self.ratio;
fonttitlelaserpointerredasswipegoawaygetoutgetoutgetoutgetoutgetoutgetoutholyshitohgodohgodohgodohgodohgodohgodohgodohgodohgodpleasepleasepleasepleasepleasepleasepleasestophurtingme=getrandrange(75*self.ratio-fonttitlelaserpointerredasswipegoawaygetoutgetoutgetoutholyshitohgodohgodohgodohgodohgodohgodohgodohgodpleasestophurtingme)/self.ratio;
fonttitlelaserpointerredasswipegoawaygetoutgetoutholyshitohgodohgodohgodohgodohgodpleasestophurtingme=getrandrange(fonttitlelaserpointerredasswipegoawaygetoutgetoutholyshitpleasestophurtingme)/self.ratio;
fonterrorsystemsmidnightbluefuckerymorefuckingevenmorethanbeforeforrealz=reversed(range(int(fontmbig.size(“ERROR SYSTEM”))));
fonterrorsystemsmidnightblueragequitnow=getrandrange(int(fontmbig.size(“ERROR SYSTEM”)));
fonterrorsystemsmidnightblueragequitnow=reversed(range(fonterrorsystemsmidnightblueragequitnow));
fonterrorsystemsmidnightblueragequitnow=reversed(list(itertools.chain(*zip(fonterrorsystemsmidnightblueragequitnow,fonterrorsystemsmidnightblueragequitnow))));
fonterrorsystemsmidnightsatanicpanicterror=getrandrange(int(fontmbig.size(“ERROR SYSTEM”)));fonterrorsystemsmidnightsatanicpanicterror=reversed(range(fonterrorsystemsmidnightsatanicpanicterror));
fonterrorsystemsblackholepanicsatanicterror=getrandrange(int(fontmbig.size(“ERROR SYSTEM”)));fonterrorsystemsblackholepanicsatanicterror=reversed(range(fonterrorsystemsblackholepanicsatanicterror));
fonterrorsystemsweaponevillegioninvaders=getrandrange(int(fontmbig.size(“ERROR SYSTEM”)));fonterrorsystemsweaponevillegioninvaders=reversed(range(fonterrorsystemsweaponevillegioninvaders));
fonsterror=getrandrange(int(fontmbig.size(“ERROR SYSTEM”)));fonsterror=reversed(range(fonsterror));
fonsterroredd=getrandrange(int(fontmbig.size(“ERROR SYSTEM”)));fonsterroredd=reversed(range(fonsterroredd));
fonsterrorenemyspaceshipsarecoming=getrandrange(int(fontmbig.size(“ERROR SYSTEM”)));fonsterrorenemyspaceshipsarecoming=reversed(range(fonsterrorenemyspaceshipsarecoming));
fonsterrorenemyspaceshipsaredangerous=getrandrange(int(fontmbig.size(“ERROR SYSTEM”)));fonsterrorenemyspaceshipsaredangerous=reversed(range(fonsterrorenemyspaceshipsaredangerous));
fonsterspaceinvaderstimeforcockblockingattack=getrandrange(int(fontmbig.size(“SPACE INVADER”)));
fonsterspaceinvaderstimeforcockblockingattack=reversed(range(fonsterspaceinvaderstimeforcockblockingattack));
fontcoordinatestitle=self.h*.45/self.ratio;fontcoordinatestitlelaserpointerredasswipegoawaygetoutgetoutholyshitpleasepleasestophurtingme=int((fontcoordinatestitle-self.h*.05)/self.ratio);fontcoordinatestitlelaserpointerredasswipegoawaygetoutholyshitholyshitholyshitholyshitholyshitholeholeholeholeholeholeholestopitstopitstopitstopitstopitstopplsssssssssssssssss=int((fontcoordinatestitlelaserpointerredasswipegoawaygetoutholyshithole-self.h*.05)/self.ratio);

MC El Bayadh: Premier League Stars & Stats Unveiled!