






import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed







class  lowerCamelCase_   (		a_   ):
							def __init__(  self						:    Dict							,__lowerCamelCase						:    Dict							,__lowerCamelCase						:    Optional[Any]							,__lowerCamelCase						:    Dict=False							,__lowerCamelCase						:    List[str]=10_24							,__lowerCamelCase						:    int=10_24							,__lowerCamelCase						:    Optional[int]=3.6							,__lowerCamelCase						:    Dict=False							,):





								'''simple docstring'''



								a				=	tokenizer
								a				=	tokenizer.bos_token_id
								a				=	dataset
								a				=	seq_length
								a				=	0
								a				=	infinite
								a				=	0
								a				=	tokenized

								if self.tokenized:
									a				=	seq_length * num_of_sequences
									a				=	'''input_ids'''
								else:
									a				=	seq_length * chars_per_token * num_of_sequences
									a				=	'''content'''
							def __iter__(  self						:    Dict    ):





								'''simple docstring'''



								a				=	iter(self.dataset    )
								a				=	True
								while more_examples:
									a		,       a				=	[], 0
									while True:
										if buffer_len >= self.max_buffer_size:
											break
										try:
											buffer.append(next(lowercase__    )[self.content_field]    )
											buffer_len += len(buffer[-1]    )
										except StopIteration:
											if self.infinite:
												a				=	iter(self.dataset    )
												self.epoch += 1
												logger.info(F"""Dataset epoch: {self.epoch}"""    )
											else:
												a				=	False
												break
									if self.tokenized:
										a				=	buffer
									else:
										a				=	self.tokenizer(lowercase__							,truncation=lowercase__    )['''input_ids''']
									a				=	[]
									for tokenized_input in tokenized_inputs:
										all_token_ids.extend(tokenized_input + [self.concat_token_id]    )
									for i in range(0							,len(lowercase__    )							,self.seq_length    ):
										a				=	all_token_ids[i : i + self.seq_length]
										if len(lowercase__    ) == self.seq_length:
											self.current_size += 1
											yield torch.tensor(lowercase__    )




							def        SCREAMING_SNAKE_CASE_       (  self						:    Tuple							,__lowerCamelCase						:    Dict=10_00    ):





								'''simple docstring'''



								return ShufflerIterDataPipe(self							,buffer_size=lowercase__    )





def      SCREAMING_SNAKE_CASE__  (      snake_case_     )      ->     Any:


	"""simple docstring"""



	a				=	args.model_ckpt.split('''/'''     )[-1]
	a				=	logging.getLogger(__name__     )
	a				=	Path(args.save_dir     ) / '''log/'''
	log_dir.mkdir(exist_ok=lowerCAmelCase__     )
	a				=	f"""debug_{accelerator.process_index}.log"""
	logging.basicConfig(
	    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''',     datefmt='''%m/%d/%Y %H:%M:%S''',     level=logging.INFO,     handlers=[logging.FileHandler(log_dir / filename     ), logging.StreamHandler()],     )
	if accelerator.is_main_process:  # we only want to setup logging once
		accelerator.init_trackers(lowerCAmelCase__,     vars(lowerCAmelCase__     )     )
		a				=	accelerator.trackers[0].run.name
		logger.setLevel(logging.INFO     )
		datasets.utils.logging.set_verbosity_info()
		transformers.utils.logging.set_verbosity_info()
	else:
		a				=	''''''
		logger.setLevel(logging.ERROR     )
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	return logger, run_name





def      SCREAMING_SNAKE_CASE__  (      snake_case_     )      ->     Tuple:


	"""simple docstring"""



	a				=	{'''streaming''': True}
	a				=	load_dataset(args.dataset_name_train,     split='''train''',     **lowerCAmelCase__     )
	a				=	train_data.shuffle(buffer_size=args.shuffle_buffer,     seed=args.seed     )
	a				=	load_dataset(args.dataset_name_valid,     split='''train''',     **lowerCAmelCase__     )
	a				=	ConstantLengthDataset(
	    lowerCAmelCase__,     lowerCAmelCase__,     infinite=lowerCAmelCase__,     seq_length=args.seq_length,     tokenized=args.tokenized     )
	a				=	ConstantLengthDataset(
	    lowerCAmelCase__,     lowerCAmelCase__,     infinite=lowerCAmelCase__,     seq_length=args.seq_length,     tokenized=args.tokenized     )
	a				=	train_dataset.shuffle(buffer_size=args.shuffle_buffer     )
	a				=	DataLoader(lowerCAmelCase__,     batch_size=args.train_batch_size,     shuffle=lowerCAmelCase__     )
	a				=	DataLoader(lowerCAmelCase__,     batch_size=args.valid_batch_size     )
	return train_dataloader, eval_dataloader





def      SCREAMING_SNAKE_CASE__  (      snake_case_,     snake_case_,     snake_case_=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]     )      ->     str:


	"""simple docstring"""



	a		,       a				=	[], []
	for n, p in model.named_parameters():
		if any(nd in n for nd in no_decay     ):
			params_without_wd.append(lowerCAmelCase__     )
		else:
			params_with_wd.append(lowerCAmelCase__     )
	return [
	    {"params": params_with_wd, "weight_decay": args.weight_decay},
	    {"params": params_without_wd, "weight_decay": 0.0},
	]





def      SCREAMING_SNAKE_CASE__  (      snake_case_,     snake_case_     )      ->     Union[str, Any]:


	"""simple docstring"""



	logger.info(f"""Step {step}: {metrics}"""     )
	if accelerator.is_main_process:
		accelerator.log(lowerCAmelCase__,     lowerCAmelCase__     )





def      SCREAMING_SNAKE_CASE__  (      snake_case_,     snake_case_,     snake_case_     )      ->     List[str]:


	"""simple docstring"""



	a				=	accelerator.unwrap_model(lowerCAmelCase__     ).config
	a				=	4 if args.gradient_checkpointing else 3
	a				=	args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
	a				=	2_4 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
	a				=	factor * (
	    1.0
	    + (args.seq_length / (6.0 * config_model.n_embd))
	    + (tokenizer.vocab_size / (1_6.0 * config_model.n_layer * config_model.n_embd))
	)
	a				=	flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (1_0**1_2))
	return tflops





def      SCREAMING_SNAKE_CASE__  (      snake_case_     )      ->     List[Any]:


	"""simple docstring"""



	model.eval()
	a				=	[]
	for step, batch in enumerate(lowerCAmelCase__     ):
		with torch.no_grad():
			a				=	model(lowerCAmelCase__,     labels=lowerCAmelCase__     )
		a				=	outputs.loss.repeat(args.valid_batch_size     )
		losses.append(accelerator.gather(lowerCAmelCase__     )     )
		if args.max_eval_steps > 0 and step >= args.max_eval_steps:
			break
	a				=	torch.cat(lowerCAmelCase__     )
	a				=	losses[: eval_dataloader.dataset.current_size].mean()
	try:
		a				=	torch.exp(lowerCAmelCase__     )
	except OverflowError:
		a				=	float('''inf'''     )
	return loss.item(), perplexity.item()


# Settings
UpperCamelCase__  :					Optional[int]  						=    HfArgumentParser(TrainingArguments)
UpperCamelCase__  :					Union[str, Any]  						=    parser.parse_args()

# Accelerator
UpperCamelCase__  :					Union[str, Any]  						=    ProjectConfiguration(project_dir=args.save_dir, logging_dir="""log""")
UpperCamelCase__  :					List[str]  						=    Accelerator(log_with=["""wandb""", """tensorboard"""], project_config=config)
UpperCamelCase__  :					str  						=    {str(k): str(v) for k, v in accelerator.state.__dict__.items()}

UpperCamelCase__  :					Any  						=    Namespace(**vars(args), **acc_state)
UpperCamelCase__  :					Dict  						=    accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
				UpperCamelCase__  :					Optional[int]  						=    Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
UpperCamelCase__						,			UpperCamelCase__  :					List[Any]  						=    setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
				hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
UpperCamelCase__  :					Optional[int]  						=    AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
				model.gradient_checkpointing_enable()
UpperCamelCase__  :					Any  						=    AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
UpperCamelCase__						,			UpperCamelCase__  :					Dict  						=    create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
UpperCamelCase__  :					int  						=    AdamW(get_grouped_params(model, args), lr=args.learning_rate)
UpperCamelCase__  :					List[str]  						=    get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)





def      SCREAMING_SNAKE_CASE__  (      )      ->     List[str]:


	"""simple docstring"""



	return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__						,			UpperCamelCase__  :					int  						=    accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# load in the weights and states from a previous save
if args.resume_from_checkpoint:
				if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
								accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}")
								accelerator.load_state(args.resume_from_checkpoint)
								UpperCamelCase__  :					Any  						=    os.path.basename(args.resume_from_checkpoint)
				else:
								# Get the most recent checkpoint
								UpperCamelCase__  :					List[Any]  						=    [f.name for f in os.scandir(args.save_dir) if f.is_dir() and """step""" in str(f)]
								dirs.sort(key=os.path.getctime)
								UpperCamelCase__  :					Optional[Any]  						=    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
				# Extract the step of the checkpoint to continue from there
				UpperCamelCase__  :					Union[str, Any]  						=    os.path.splitext(path)[0]
				UpperCamelCase__  :					List[Any]  						=    int(training_difference.replace("""step_""", """"""))

# Train model
model.train()
UpperCamelCase__  :					List[str]  						=    0
UpperCamelCase__  :					Dict  						=    time.time()
UpperCamelCase__  :					str  						=    0
for step, batch in enumerate(train_dataloader, start=1):
				if args.resume_from_checkpoint and step < resume_step:
								continue  # we need to skip steps until we reach the resumed step
				UpperCamelCase__  :					List[Any]  						=    model(batch, labels=batch, use_cache=False).loss
				UpperCamelCase__  :					Union[str, Any]  						=    accelerator.gather(loss.repeat(args.train_batch_size)).mean()
				loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
				log_metrics(step, {"""samples""": step * samples_per_step, """loss_per_step/train""": loss.item()})
				UpperCamelCase__  :					Dict  						=    loss / args.gradient_accumulation_steps
				if step % args.gradient_accumulation_steps != 0:
								# Prevent backward from doing gradient all_reduce in every step
								if accelerator.distributed_type == DistributedType.MULTI_GPU:
												with model.no_sync():
																accelerator.backward(loss)
								else:
												accelerator.backward(loss)
				else:
								UpperCamelCase__  :					Union[str, Any]  						=    get_lr()
								accelerator.backward(loss)
								accelerator.clip_grad_norm_(model.parameters(), 1.0)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()
								UpperCamelCase__  :					Tuple  						=    time.time() - t_start
								UpperCamelCase__  :					Optional[int]  						=    compute_tflops(elapsed_time, accelerator, args)
								log_metrics(
								    step,
								    {
								        """steps""": completed_steps,
								        """loss/train""": loss_tracking,
								        """lr""": lr,
								        """tflops""": tflops,
								        """time_per_iteration""": elapsed_time,
								    },
								)
								UpperCamelCase__  :					Any  						=    time.time()
								UpperCamelCase__  :					Dict  						=    0
								completed_steps += 1
				if step % args.save_checkpoint_steps == 0:
								logger.info("""Evaluating and saving model checkpoint""")
								UpperCamelCase__						,			UpperCamelCase__  :					List[Any]  						=    evaluate(args)
								log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
								accelerator.wait_for_everyone()
								UpperCamelCase__  :					Tuple  						=    os.path.join(args.save_dir, F"step_{step}")
								accelerator.save_state(save_dir)
								if accelerator.is_main_process:
												hf_repo.push_to_hub(commit_message=F"step {step}")
								model.train()
				if completed_steps >= args.max_train_steps:
								break

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
UpperCamelCase__						,			UpperCamelCase__  :					Optional[Any]  						=    evaluate(args)
log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
accelerator.wait_for_everyone()
UpperCamelCase__  :					Union[str, Any]  						=    accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
UpperCamelCase__  :					Any  						=    os.path.join(args.save_dir, F"step_{step}")
accelerator.save_state(save_dir)
if accelerator.is_main_process:
				hf_repo.push_to_hub(commit_message="""final model""")






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import ast
import difflib
from collections.abc import Mapping
from typing import Any, Callable, Dict
class 				a_ (						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 pass






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__=None	,  lowerCAmelCase__=False	):
       try:
              lowerCAmelCase__     	=					ast.parse(lowerCAmelCase__	)
       except SyntaxError as e:
              print('The code generated by the agent is not valid.\n'	,  lowerCAmelCase__	)
              return
       if state is None:
              lowerCAmelCase__     	=					{}
       lowerCAmelCase__     	=					None
       for idx, node in enumerate(expression.body	):
              try:
                     lowerCAmelCase__     	=					evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
              except InterpretorError as e:
                     lowerCAmelCase__     	=					F"""Evaluation of the code stopped at line {idx} before the end because of the following error"""
                     if chat_mode:
                            msg += (
                                F""". Copy paste the following error message and send it back to the agent:\nI get an error: '{e}'"""
                            )
                     else:
                            msg += F""":\n{e}"""
                     print(lowerCAmelCase__	)
                     break
              if line_result is not None:
                     lowerCAmelCase__     	=					line_result

       return result






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       if isinstance(lowerCAmelCase__	,  ast.Assign	):
              # Assignement -> we evaluate the assignement which should update the state
              # We return the variable assigned as it may be used to determine the final result.
              return evaluate_assign(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       elif isinstance(lowerCAmelCase__	,  ast.Call	):
              # Function call -> we return the value of the function call
              return evaluate_call(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       elif isinstance(lowerCAmelCase__	,  ast.Constant	):
              # Constant -> just return the value
              return expression.value
       elif isinstance(lowerCAmelCase__	,  ast.Dict	):
              # Dict -> evaluate all keys and values
              lowerCAmelCase__     	=					[evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	) for k in expression.keys]
              lowerCAmelCase__     	=					[evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	) for v in expression.values]
              return dict(zip(lowerCAmelCase__	,  lowerCAmelCase__	)	)
       elif isinstance(lowerCAmelCase__	,  ast.Expr	):
              # Expression -> evaluate the content
              return evaluate_ast(expression.value	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       elif isinstance(lowerCAmelCase__	,  ast.For	):
              # For loop -> execute the loop
              return evaluate_for(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       elif isinstance(lowerCAmelCase__	,  ast.FormattedValue	):
              # Formatted value (part of f-string) -> evaluate the content and return
              return evaluate_ast(expression.value	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       elif isinstance(lowerCAmelCase__	,  ast.If	):
              # If -> execute the right branch
              return evaluate_if(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       elif hasattr(lowerCAmelCase__	,  'Index'	) and isinstance(lowerCAmelCase__	,  ast.Index	):
              return evaluate_ast(expression.value	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       elif isinstance(lowerCAmelCase__	,  ast.JoinedStr	):
              return "".join([str(evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)	) for v in expression.values]	)
       elif isinstance(lowerCAmelCase__	,  ast.List	):
              # List -> evaluate all elements
              return [evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	) for elt in expression.elts]
       elif isinstance(lowerCAmelCase__	,  ast.Name	):
              # Name -> pick up the value in the state
              return evaluate_name(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       elif isinstance(lowerCAmelCase__	,  ast.Subscript	):
              # Subscript -> return the value of the indexing
              return evaluate_subscript(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       else:
              # For now we refuse anything else. Let's add things as we need them.
              raise InterpretorError(F"""{expression.__class__.__name__} is not supported."""	)






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       lowerCAmelCase__     	=					assign.targets
       lowerCAmelCase__     	=					evaluate_ast(assign.value	,  lowerCAmelCase__	,  lowerCAmelCase__	)

       if len(lowerCAmelCase__	) == 1:
              lowerCAmelCase__     	=					result
       else:
              if len(lowerCAmelCase__	) != len(lowerCAmelCase__	):
                     raise InterpretorError(F"""Expected {len(lowerCAmelCase__	)} values but got {len(lowerCAmelCase__	)}."""	)
              for var_name, r in zip(lowerCAmelCase__	,  lowerCAmelCase__	):
                     lowerCAmelCase__     	=					r
       return result






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       if not isinstance(call.func	,  ast.Name	):
              raise InterpretorError(
                  F"""It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func} of """
                  F"""type {type(call.func	)}."""	)
       lowerCAmelCase__     	=					call.func.id
       if func_name not in tools:
              raise InterpretorError(
                  F"""It is not permitted to evaluate other functions than the provided tools (tried to execute {call.func.id})."""	)

       lowerCAmelCase__     	=					tools[func_name]
       # Todo deal with args
       lowerCAmelCase__     	=					[evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	) for arg in call.args]
       lowerCAmelCase__     	=					{keyword.arg: evaluate_ast(keyword.value	,  lowerCAmelCase__	,  lowerCAmelCase__	) for keyword in call.keywords}
       return func(*lowerCAmelCase__	,  **lowerCAmelCase__	)






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       lowerCAmelCase__     	=					evaluate_ast(subscript.slice	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       lowerCAmelCase__     	=					evaluate_ast(subscript.value	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       if isinstance(lowerCAmelCase__	,  (list, tuple)	):
              return value[int(lowerCAmelCase__	)]
       if index in value:
              return value[index]
       if isinstance(lowerCAmelCase__	,  lowerCAmelCase__	) and isinstance(lowerCAmelCase__	,  lowerCAmelCase__	):
              lowerCAmelCase__     	=					difflib.get_close_matches(lowerCAmelCase__	,  list(value.keys()	)	)
              if len(lowerCAmelCase__	) > 0:
                     return value[close_matches[0]]

       raise InterpretorError(F"""Could not index {value} with '{index}'."""	)






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       if name.id in state:
              return state[name.id]
       lowerCAmelCase__     	=					difflib.get_close_matches(name.id	,  list(state.keys()	)	)
       if len(lowerCAmelCase__	) > 0:
              return state[close_matches[0]]
       raise InterpretorError(F"""The variable `{name.id}` is not defined."""	)






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       if len(condition.ops	) > 1:
              raise InterpretorError('Cannot evaluate conditions with multiple operators'	)

       lowerCAmelCase__     	=					evaluate_ast(condition.left	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       lowerCAmelCase__     	=					condition.ops[0]
       lowerCAmelCase__     	=					evaluate_ast(condition.comparators[0]	,  lowerCAmelCase__	,  lowerCAmelCase__	)

       if isinstance(lowerCAmelCase__	,  ast.Eq	):
              return left == right
       elif isinstance(lowerCAmelCase__	,  ast.NotEq	):
              return left != right
       elif isinstance(lowerCAmelCase__	,  ast.Lt	):
              return left < right
       elif isinstance(lowerCAmelCase__	,  ast.LtE	):
              return left <= right
       elif isinstance(lowerCAmelCase__	,  ast.Gt	):
              return left > right
       elif isinstance(lowerCAmelCase__	,  ast.GtE	):
              return left >= right
       elif isinstance(lowerCAmelCase__	,  ast.Is	):
              return left is right
       elif isinstance(lowerCAmelCase__	,  ast.IsNot	):
              return left is not right
       elif isinstance(lowerCAmelCase__	,  ast.In	):
              return left in right
       elif isinstance(lowerCAmelCase__	,  ast.NotIn	):
              return left not in right
       else:
              raise InterpretorError(F"""Operator not supported: {comparator}"""	)






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       lowerCAmelCase__     	=					None
       if evaluate_condition(if_statement.test	,  lowerCAmelCase__	,  lowerCAmelCase__	):
              for line in if_statement.body:
                     lowerCAmelCase__     	=					evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
                     if line_result is not None:
                            lowerCAmelCase__     	=					line_result
       else:
              for line in if_statement.orelse:
                     lowerCAmelCase__     	=					evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
                     if line_result is not None:
                            lowerCAmelCase__     	=					line_result
       return result






def       __lowerCamelCase		(						lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	):
       lowerCAmelCase__     	=					None
       lowerCAmelCase__     	=					evaluate_ast(for_loop.iter	,  lowerCAmelCase__	,  lowerCAmelCase__	)
       for counter in iterator:
              lowerCAmelCase__     	=					counter
              for expression in for_loop.body:
                     lowerCAmelCase__     	=					evaluate_ast(lowerCAmelCase__	,  lowerCAmelCase__	,  lowerCAmelCase__	)
                     if line_result is not None:
                            lowerCAmelCase__     	=					line_result
       return result
