import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer




def 							UpperCamelCase		(			__lowercase		:					Tuple		,__lowercase		:					Optional[int]		,__lowercase		:					Any		,__lowercase		:					List[str]		,__lowercase		:					List[Any]=True		,__lowercase		:					Tuple="pt"      ):
		'''simple docstring'''




		A_						:							Dict      = {'add_prefix_space': True} if isinstance(__lowercase		,__lowercase      ) and not line.startswith(' '      ) else {}
		A_						:							int      = padding_side
		return tokenizer(
		    [line]		,max_length=__lowercase		,padding='max_length' if pad_to_max_length else None		,truncation=__lowercase		,return_tensors=__lowercase		,add_special_tokens=__lowercase		,**__lowercase		,)




def 							UpperCamelCase		(			__lowercase		:					int		,__lowercase		:					int		,__lowercase		:					Any=None		,):
		'''simple docstring'''




		A_						:							Optional[int]      = input_ids.ne(__lowercase      ).any(dim=0      )
		if attention_mask is None:
				return input_ids[:, keep_column_mask]
		else:
				return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])





class UpperCAmelCase		(   UpperCAmelCase__    ):


				'''simple docstring'''






				def __init__(		self						,      lowercase						,      lowercase						,      lowercase						,      lowercase						,      lowercase="train"						,      lowercase=None						,      lowercase=None						,      lowercase=None						,      lowercase=""						,      ):



						"""simple docstring"""





						super().__init__()
						A_						:							List[Any]      = Path(_UpperCAmelCase   ).joinpath(type_path + '.source'   )
						A_						:							Any      = Path(_UpperCAmelCase   ).joinpath(type_path + '.target'   )
						A_						:							List[Any]      = self.get_char_lens(self.src_file   )
						A_						:							Tuple      = max_source_length
						A_						:							List[Any]      = max_target_length
						assert min(self.src_lens   ) > 0, F'''found empty line in {self.src_file}'''
						A_						:							Optional[Any]      = tokenizer
						A_						:							List[str]      = prefix
						if n_obs is not None:
								A_						:							List[Any]      = self.src_lens[:n_obs]
						A_						:							List[str]      = src_lang
						A_						:							List[str]      = tgt_lang
				def __len__(		self   ):



						"""simple docstring"""





						return len(self.src_lens   )
				def __getitem__(		self						,      lowercase   ):



						"""simple docstring"""





						A_						:							int      = index + 1  # linecache starts at 1
						A_						:							Tuple      = self.prefix + linecache.getline(str(self.src_file   )						,      _UpperCAmelCase   ).rstrip('\n'   )
						A_						:							Tuple      = linecache.getline(str(self.tgt_file   )						,      _UpperCAmelCase   ).rstrip('\n'   )
						assert source_line, F'''empty source line for index {index}'''
						assert tgt_line, F'''empty tgt line for index {index}'''

						# Need to add eos token manually for T5
						if isinstance(self.tokenizer						,      _UpperCAmelCase   ):
								source_line += self.tokenizer.eos_token
								tgt_line += self.tokenizer.eos_token

						# Pad source and target to the right
						A_						:							Optional[Any]      = (
						    self.tokenizer.question_encoder if isinstance(self.tokenizer						,      _UpperCAmelCase   ) else self.tokenizer
						)
						A_						:							Optional[int]      = self.tokenizer.generator if isinstance(self.tokenizer						,      _UpperCAmelCase   ) else self.tokenizer

						A_						:							Dict      = encode_line(_UpperCAmelCase						,      _UpperCAmelCase						,      self.max_source_length						,      'right'   )
						A_						:							Tuple      = encode_line(_UpperCAmelCase						,      _UpperCAmelCase						,      self.max_target_length						,      'right'   )

						A_						:							List[Any]      = source_inputs['input_ids'].squeeze()
						A_						:							str      = target_inputs['input_ids'].squeeze()
						A_						:							Optional[int]      = source_inputs['attention_mask'].squeeze()
						return {
						    "input_ids": source_ids,
						    "attention_mask": src_mask,
						    "decoder_input_ids": target_ids,
						}
				@staticmethod
				def     lowerCAmelCase_  (		lowercase   ):



						"""simple docstring"""





						return [len(_UpperCAmelCase   ) for x in Path(_UpperCAmelCase   ).open().readlines()]







				def     lowerCAmelCase_  (		self						,      lowercase   ):



						"""simple docstring"""





						A_						:							str      = torch.stack([x['input_ids'] for x in batch]   )
						A_						:							Optional[Any]      = torch.stack([x['attention_mask'] for x in batch]   )
						A_						:							str      = torch.stack([x['decoder_input_ids'] for x in batch]   )
						A_						:							Optional[int]      = (
						    self.tokenizer.generator.pad_token_id
						    if isinstance(self.tokenizer						,      _UpperCAmelCase   )
						    else self.tokenizer.pad_token_id
						)
						A_						:							List[str]      = (
						    self.tokenizer.question_encoder.pad_token_id
						    if isinstance(self.tokenizer						,      _UpperCAmelCase   )
						    else self.tokenizer.pad_token_id
						)
						A_						:							Any      = trim_batch(_UpperCAmelCase						,      _UpperCAmelCase   )
						A_  ,     A_						:							Union[str, Any]      = trim_batch(_UpperCAmelCase						,      _UpperCAmelCase						,      attention_mask=_UpperCAmelCase   )
						A_						:							List[Any]      = {
						    'input_ids': source_ids,
						    'attention_mask': source_mask,
						    'decoder_input_ids': y,
						}
						return batch

_UpperCAmelCase          =						getLogger(__name__)




def 							UpperCamelCase		(			__lowercase		:					List[List]      ):
		'''simple docstring'''




		return list(itertools.chain.from_iterable(__lowercase      )      )




def 							UpperCamelCase		(			__lowercase		:					str      ):
		'''simple docstring'''




		A_						:							Any      = get_git_info()
		save_json(__lowercase		,os.path.join(__lowercase		,'git_log.json'      )      )




def 							UpperCamelCase		(			__lowercase		:					Optional[Any]		,__lowercase		:					Tuple		,__lowercase		:					Dict=4		,**__lowercase		:					List[str]      ):
		'''simple docstring'''




		with open(__lowercase		,'w'      ) as f:
				json.dump(__lowercase		,__lowercase		,indent=__lowercase		,**__lowercase      )




def 							UpperCamelCase		(			__lowercase		:					int      ):
		'''simple docstring'''




		with open(__lowercase      ) as f:
				return json.load(__lowercase      )




def 							UpperCamelCase		(			):
		'''simple docstring'''




		A_						:							int      = git.Repo(search_parent_directories=__lowercase      )
		A_						:							Optional[Any]      = {
		    'repo_id': str(__lowercase      ),
		    'repo_sha': str(repo.head.object.hexsha      ),
		    'repo_branch': str(repo.active_branch      ),
		    'hostname': str(socket.gethostname()      ),
		}
		return repo_infos




def 							UpperCamelCase		(			__lowercase		:					Callable		,__lowercase		:					Iterable      ):
		'''simple docstring'''




		return list(map(__lowercase		,__lowercase      )      )




def 							UpperCamelCase		(			__lowercase		:					int		,__lowercase		:					str      ):
		'''simple docstring'''




		with open(__lowercase		,'wb'      ) as f:
				return pickle.dump(__lowercase		,__lowercase      )




def 							UpperCamelCase		(			__lowercase		:					str      ):
		'''simple docstring'''




		def remove_articles(__lowercase		:					Optional[int]      ):
				return re.sub(r'\b(a|an|the)\b'		,' '		,__lowercase      )

		def white_space_fix(__lowercase		:					Optional[int]      ):
				return " ".join(text.split()      )

		def remove_punc(__lowercase		:					Tuple      ):
				A_						:							Optional[int]      = set(string.punctuation      )
				return "".join(ch for ch in text if ch not in exclude      )

		def lower(__lowercase		:					Tuple      ):
				return text.lower()

		return white_space_fix(remove_articles(remove_punc(lower(__lowercase      )      )      )      )




def 							UpperCamelCase		(			__lowercase		:					int		,__lowercase		:					List[Any]      ):
		'''simple docstring'''




		A_						:							Tuple      = normalize_answer(__lowercase      ).split()
		A_						:							Optional[int]      = normalize_answer(__lowercase      ).split()
		A_						:							Optional[Any]      = Counter(__lowercase      ) & Counter(__lowercase      )
		A_						:							Any      = sum(common.values()      )
		if num_same == 0:
				return 0
		A_						:							Dict      = 1.0 * num_same / len(__lowercase      )
		A_						:							Union[str, Any]      = 1.0 * num_same / len(__lowercase      )
		A_						:							Dict      = (2 * precision * recall) / (precision + recall)
		return fa




def 							UpperCamelCase		(			__lowercase		:					int		,__lowercase		:					str      ):
		'''simple docstring'''




		return normalize_answer(__lowercase      ) == normalize_answer(__lowercase      )




def 							UpperCamelCase		(			__lowercase		:					List[str]		,__lowercase		:					List[str]      ):
		'''simple docstring'''




		assert len(__lowercase      ) == len(__lowercase      )
		A_						:							List[str]      = 0
		for hypo, pred in zip(__lowercase		,__lowercase      ):
				em += exact_match_score(__lowercase		,__lowercase      )
		if len(__lowercase      ) > 0:
				em /= len(__lowercase      )
		return {"em": em}




def 							UpperCamelCase		(			__lowercase		:					List[str]      ):
		'''simple docstring'''




		return model_prefix.startswith('rag'      )




def 							UpperCamelCase		(			__lowercase		:					Union[str, Any]		,__lowercase		:					int		,__lowercase		:					int      ):
		'''simple docstring'''




		A_						:							Optional[int]      = {p: p for p in extra_params}
		# T5 models don't have `dropout` param, they have `dropout_rate` instead
		A_						:							Optional[Any]      = 'dropout_rate'
		for p in extra_params:
				if getattr(__lowercase		,__lowercase		,__lowercase      ):
						if not hasattr(__lowercase		,__lowercase      ) and not hasattr(__lowercase		,equivalent_param[p]      ):
								logger.info('config doesn\'t have a `{}` attribute'.format(__lowercase      )      )
								delattr(__lowercase		,__lowercase      )
								continue
						A_						:							List[Any]      = p if hasattr(__lowercase		,__lowercase      ) else equivalent_param[p]
						setattr(__lowercase		,__lowercase		,getattr(__lowercase		,__lowercase      )      )
						delattr(__lowercase		,__lowercase      )
		return hparams, config










import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


A			:						Optional[Any]        =  1_6
A			:						str        =  3_2






def 					UpperCamelCase   (      __magic_name__	:				Accelerator       ,       __magic_name__	:				int = 16	)  ->    List[str]:
		"""simple docstring"""




		lowercase__	     =						AutoTokenizer.from_pretrained("""bert-base-cased"""	)
		lowercase__	     =						load_dataset("""glue"""       ,       """mrpc"""	)

		def tokenize_function(__magic_name__	:				int	):
				# max_length=None => use the model max length (it's actually the default)
				lowercase__	     =						tokenizer(examples["""sentence1"""]       ,       examples["""sentence2"""]       ,       truncation=__magic_name__       ,       max_length=__magic_name__	)
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		# starting with the main process first:
		with accelerator.main_process_first():
				lowercase__	     =						datasets.map(
				    __magic_name__       ,       batched=__magic_name__       ,       remove_columns=["""idx""", """sentence1""", """sentence2"""]       ,       )

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		lowercase__	     =						tokenized_datasets.rename_column("""label"""       ,       """labels"""	)

		def collate_fn(__magic_name__	:				Optional[int]	):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				lowercase__	     =						128 if accelerator.distributed_type == DistributedType.TPU else None
				# When using mixed precision we want round multiples of 8/16
				if accelerator.mixed_precision == "fp8":
						lowercase__	     =						16
				elif accelerator.mixed_precision != "no":
						lowercase__	     =						8
				else:
						lowercase__	     =						None

				return tokenizer.pad(
				    __magic_name__       ,       padding="""longest"""       ,       max_length=__magic_name__       ,       pad_to_multiple_of=__magic_name__       ,       return_tensors="""pt"""       ,       )

		# Instantiate dataloaders.
		lowercase__	     =						DataLoader(
		    tokenized_datasets["""train"""]       ,       shuffle=__magic_name__       ,       collate_fn=__magic_name__       ,       batch_size=__magic_name__	)
		lowercase__	     =						DataLoader(
		    tokenized_datasets["""validation"""]       ,       shuffle=__magic_name__       ,       collate_fn=__magic_name__       ,       batch_size=__magic_name__	)

		return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
	from accelerate.test_utils.training import mocked_dataloaders

	A			:						Union[str, Any]        =  mocked_dataloaders  # noqa: F811






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]       ,       __magic_name__	:				List[Any]	)  ->    Dict:
		"""simple docstring"""




		if os.environ.get("""TESTING_MOCKED_DATALOADERS"""       ,       __magic_name__	) == "1":
				lowercase__	     =						2
		# New Code #
		lowercase__	     =						int(args.gradient_accumulation_steps	)
		# Initialize accelerator
		lowercase__	     =						Accelerator(
		    cpu=args.cpu       ,       mixed_precision=args.mixed_precision       ,       gradient_accumulation_steps=__magic_name__	)
		if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
				raise NotImplementedError(
				    """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""	)
		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowercase__	     =						config["""lr"""]
		lowercase__	     =						int(config["""num_epochs"""]	)
		lowercase__	     =						int(config["""seed"""]	)
		lowercase__	     =						int(config["""batch_size"""]	)

		lowercase__	     =						evaluate.load("""glue"""       ,       """mrpc"""	)

		set_seed(__magic_name__	)
		lowercase__     ,      lowercase__	     =						get_dataloaders(__magic_name__       ,       __magic_name__	)
		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowercase__	     =						AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""       ,       return_dict=__magic_name__	)

		# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
		# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
		# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
		lowercase__	     =						model.to(accelerator.device	)

		# Instantiate optimizer
		lowercase__	     =						AdamW(params=model.parameters()       ,       lr=__magic_name__	)

		# Instantiate scheduler
		lowercase__	     =						get_linear_schedule_with_warmup(
		    optimizer=__magic_name__       ,       num_warmup_steps=100       ,       num_training_steps=(len(__magic_name__	) * num_epochs)       ,       )

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__     ,      lowercase__	     =						accelerator.prepare(
		    __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__       ,       __magic_name__	)

		# Now we train the model
		for epoch in range(__magic_name__	):
				model.train()
				for step, batch in enumerate(__magic_name__	):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device	)
						# New code #
						# We use the new `accumulate` context manager to perform gradient accumulation
						# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
						with accelerator.accumulate(__magic_name__	):
								lowercase__	     =						model(**__magic_name__	)
								lowercase__	     =						output.loss
								accelerator.backward(__magic_name__	)
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				for step, batch in enumerate(__magic_name__	):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device	)
						with torch.no_grad():
								lowercase__	     =						model(**__magic_name__	)
						lowercase__	     =						outputs.logits.argmax(dim=-1	)
						lowercase__     ,      lowercase__	     =						accelerator.gather_for_metrics((predictions, batch["""labels"""])	)
						metric.add_batch(
						    predictions=__magic_name__       ,       references=__magic_name__       ,       )

				lowercase__	     =						metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}:'''       ,       __magic_name__	)






def 					UpperCamelCase   (      )  ->    Optional[Any]:
		"""simple docstring"""




		lowercase__	     =						argparse.ArgumentParser(description="""Simple example of training script."""	)
		parser.add_argument(
		    """--mixed_precision"""       ,       type=__magic_name__       ,       default=__magic_name__       ,       choices=["""no""", """fp16""", """bf16""", """fp8"""]       ,       help="""Whether to use mixed precision. Choose"""
		    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
		    """and an Nvidia Ampere GPU."""       ,       )
		# New Code #
		parser.add_argument(
		    """--gradient_accumulation_steps"""       ,       type=__magic_name__       ,       default=1       ,       help="""The number of minibatches to be ran before gradients are accumulated."""       ,       )
		parser.add_argument("""--cpu"""       ,       action="""store_true"""       ,       help="""If passed, will train on the CPU."""	)
		lowercase__	     =						parser.parse_args()
		lowercase__	     =						{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
		training_function(__magic_name__       ,       __magic_name__	)


if __name__ == "__main__":
	main()

