





import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

_lowerCamelCase 					=      logging.get_logger(__name__)


_lowerCamelCase 					=      {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

_lowerCamelCase 					=      {
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

_lowerCamelCase 					=      {'facebook/blenderbot-3B': 128}






@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   (
		    list(range(ord("""!"""     )       , ord("""~"""     ) + 1     )     ) + list(range(ord("""¡"""     )       , ord("""¬"""     ) + 1     )     ) + list(range(ord("""®"""     )       , ord("""ÿ"""     ) + 1     )     )
		)
		SCREAMING_SNAKE_CASE__								=   bs[:]
		SCREAMING_SNAKE_CASE__								=   0
		for b in range(2**8     ):
				if b not in bs:
						bs.append(UpperCamelCase__     )
						cs.append(2**8 + n     )
						n += 1
		SCREAMING_SNAKE_CASE__								=   [chr(UpperCamelCase__     ) for n in cs]
		return dict(zip(UpperCamelCase__       , UpperCamelCase__     )     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]     ):
		SCREAMING_SNAKE_CASE__								=   set()
		SCREAMING_SNAKE_CASE__								=   word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)     )
				SCREAMING_SNAKE_CASE__								=   char
		return pairs




class 	UpperCamelCase_							(  UpperCamelCase__     ):
							lowerCamelCase_							     =       VOCAB_FILES_NAMES
							lowerCamelCase_							     =       PRETRAINED_VOCAB_FILES_MAP
							lowerCamelCase_							     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowerCamelCase_							     =       ["input_ids", "attention_mask"]


							def __init__(				self     :Dict				,					__A     :Tuple				,					__A     :Union[str, Any]				,					__A     :int="replace"				,					__A     :int="<s>"				,					__A     :str="</s>"				,					__A     :Dict="</s>"				,					__A     :List[str]="<s>"				,					__A     :Dict="<unk>"				,					__A     :Optional[Any]="<pad>"				,					__A     :Tuple="<mask>"				,					__A     :List[Any]=False				,					**__A     :List[Any]				,					) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   AddedToken(__A				,					lstrip=__A				,					rstrip=__A		) if isinstance(__A				,					__A		) else bos_token
									SCREAMING_SNAKE_CASE__								=   AddedToken(__A				,					lstrip=__A				,					rstrip=__A		) if isinstance(__A				,					__A		) else eos_token
									SCREAMING_SNAKE_CASE__								=   AddedToken(__A				,					lstrip=__A				,					rstrip=__A		) if isinstance(__A				,					__A		) else sep_token
									SCREAMING_SNAKE_CASE__								=   AddedToken(__A				,					lstrip=__A				,					rstrip=__A		) if isinstance(__A				,					__A		) else cls_token
									SCREAMING_SNAKE_CASE__								=   AddedToken(__A				,					lstrip=__A				,					rstrip=__A		) if isinstance(__A				,					__A		) else unk_token
									SCREAMING_SNAKE_CASE__								=   AddedToken(__A				,					lstrip=__A				,					rstrip=__A		) if isinstance(__A				,					__A		) else pad_token

									# Mask token behave like a normal word, i.e. include the space before it
									SCREAMING_SNAKE_CASE__								=   AddedToken(__A				,					lstrip=__A				,					rstrip=__A		) if isinstance(__A				,					__A		) else mask_token

									super().__init__(
									    errors=__A				,					bos_token=__A				,					eos_token=__A				,					unk_token=__A				,					sep_token=__A				,					cls_token=__A				,					pad_token=__A				,					mask_token=__A				,					add_prefix_space=__A				,					**__A				,					)

									with open(__A				,					encoding="""utf-8"""		) as vocab_handle:
											SCREAMING_SNAKE_CASE__								=   json.load(__A		)
									SCREAMING_SNAKE_CASE__								=   {v: k for k, v in self.encoder.items()}
									SCREAMING_SNAKE_CASE__								=   errors  # how to handle errors in decoding
									SCREAMING_SNAKE_CASE__								=   bytes_to_unicode()
									SCREAMING_SNAKE_CASE__								=   {v: k for k, v in self.byte_encoder.items()}
									with open(__A				,					encoding="""utf-8"""		) as merges_handle:
											SCREAMING_SNAKE_CASE__								=   merges_handle.read().split("""\n"""		)[1:-1]
									SCREAMING_SNAKE_CASE__								=   [tuple(merge.split()		) for merge in bpe_merges]
									SCREAMING_SNAKE_CASE__								=   dict(zip(__A				,					range(len(__A		)		)		)		)
									SCREAMING_SNAKE_CASE__								=   {}
									SCREAMING_SNAKE_CASE__								=   add_prefix_space

									# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
									SCREAMING_SNAKE_CASE__								=   re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""		)


							@property
							# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
							def   _snake_case	(				self     :Dict		) -> Union[str, Any]:



									"""simple docstring"""



									return len(self.encoder		)


							def   _snake_case	(				self     :Tuple		) -> Dict:



									"""simple docstring"""



									return dict(self.encoder				,					**self.added_tokens_encoder		)


							def   _snake_case	(				self     :int				,					__A     :List[Any]		) -> Dict:



									"""simple docstring"""



									if token in self.cache:
											return self.cache[token]
									SCREAMING_SNAKE_CASE__								=   tuple(__A		)
									SCREAMING_SNAKE_CASE__								=   get_pairs(__A		)

									if not pairs:
											return token

									while True:
											SCREAMING_SNAKE_CASE__								=   min(__A				,					key=lambda __A		: self.bpe_ranks.get(__A				,					float("""inf"""		)		)		)
											if bigram not in self.bpe_ranks:
													break
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   bigram
											SCREAMING_SNAKE_CASE__								=   []
											SCREAMING_SNAKE_CASE__								=   0
											while i < len(__A		):
													try:
															SCREAMING_SNAKE_CASE__								=   word.index(__A				,					__A		)
													except ValueError:
															new_word.extend(word[i:]		)
															break
													else:
															new_word.extend(word[i:j]		)
															SCREAMING_SNAKE_CASE__								=   j

													if word[i] == first and i < len(__A		) - 1 and word[i + 1] == second:
															new_word.append(first + second		)
															i += 2
													else:
															new_word.append(word[i]		)
															i += 1
											SCREAMING_SNAKE_CASE__								=   tuple(__A		)
											SCREAMING_SNAKE_CASE__								=   new_word
											if len(__A		) == 1:
													break
											else:
													SCREAMING_SNAKE_CASE__								=   get_pairs(__A		)
									SCREAMING_SNAKE_CASE__								=   """ """.join(__A		)
									SCREAMING_SNAKE_CASE__								=   word
									return word


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :List[str]		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   []
									for token in re.findall(self.pat				,					__A		):
											SCREAMING_SNAKE_CASE__								=   """""".join(
											    self.byte_encoder[b] for b in token.encode("""utf-8"""		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
											bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__A		).split(""" """		)		)
									return bpe_tokens


							def   _snake_case	(				self     :Union[str, Any]				,					__A     :Union[str, Any]		) -> Tuple:



									"""simple docstring"""



									return self.encoder.get(__A				,					self.encoder.get(self.unk_token		)		)


							def   _snake_case	(				self     :int				,					__A     :Tuple		) -> Union[str, Any]:



									"""simple docstring"""



									return self.decoder.get(__A		)


							def   _snake_case	(				self     :Any				,					__A     :Tuple		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   """""".join(__A		)
									SCREAMING_SNAKE_CASE__								=   bytearray([self.byte_decoder[c] for c in text]		).decode("""utf-8"""				,					errors=self.errors		)
									return text


							def   _snake_case	(				self     :List[Any]				,					__A     :str				,					__A     :Optional[str] = None		) -> Tuple[str]:



									"""simple docstring"""



									if not os.path.isdir(__A		):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
											return
									SCREAMING_SNAKE_CASE__								=   os.path.join(
									    __A				,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)
									SCREAMING_SNAKE_CASE__								=   os.path.join(
									    __A				,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]		)

									with open(__A				,					"""w"""				,					encoding="""utf-8"""		) as f:
											f.write(json.dumps(self.encoder				,					indent=2				,					sort_keys=__A				,					ensure_ascii=__A		) + """\n"""		)

									SCREAMING_SNAKE_CASE__								=   0
									with open(__A				,					"""w"""				,					encoding="""utf-8"""		) as writer:
											writer.write("""#version: 0.2\n"""		)
											for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,					key=lambda __A		: kv[1]		):
													if index != token_index:
															logger.warning(
															    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
															    """ Please check that the tokenizer is not corrupted!"""		)
															SCREAMING_SNAKE_CASE__								=   token_index
													writer.write(""" """.join(__A		) + """\n"""		)
													index += 1

									return vocab_file, merge_file


							def   _snake_case	(				self     :str				,					__A     :List[int]				,					__A     :Optional[List[int]] = None				,					__A     :bool = False		) -> List[int]:



									"""simple docstring"""



									if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=__A				,					token_ids_a=__A				,					already_has_special_tokens=__A		)

									if token_ids_a is None:
											return [1] + ([0] * len(__A		)) + [1]
									return [1] + ([0] * len(__A		)) + [1, 1] + ([0] * len(__A		)) + [1]


							def   _snake_case	(				self     :Any				,					__A     :List[int]				,					__A     :Optional[List[int]] = None		) -> List[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   [self.sep_token_id]
									SCREAMING_SNAKE_CASE__								=   [self.cls_token_id]

									if token_ids_a is None:
											return len(cls + token_ids_a + sep		) * [0]
									return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]


							def   _snake_case	(				self     :Optional[int]				,					__A     :Union[str, Any]				,					__A     :int=False				,					**__A     :Optional[Any]		) -> List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   kwargs.pop("""add_prefix_space"""				,					self.add_prefix_space		)
									if (is_split_into_words or add_prefix_space) and (len(__A		) > 0 and not text[0].isspace()):
											SCREAMING_SNAKE_CASE__								=   """ """ + text
									return (text, kwargs)


							def   _snake_case	(				self     :Any				,					__A     :List[int]				,					__A     :Optional[List[int]] = None		) -> Union[str, Any]:



									"""simple docstring"""



									return token_ids_a + [self.eos_token_id]


							def   _snake_case	(				self     :Any				,					__A     :"Conversation"		) -> List[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   []
									for is_user, text in conversation.iter_texts():
											if is_user:
													# We need to space prefix as it's being done within blenderbot
													inputs.append(""" """ + text		)
											else:
													# Generated responses should contain them already.
													inputs.append(__A		)

									SCREAMING_SNAKE_CASE__								=   """  """.join(__A		)
									SCREAMING_SNAKE_CASE__								=   self.encode(__A		)
									if len(__A		) > self.model_max_length:
											SCREAMING_SNAKE_CASE__								=   input_ids[-self.model_max_length :]
											logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''		)
									return input_ids






from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[Any]       , UpperCamelCase__:				str       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Union[str, Any]     ):
		for param, grad_param in zip(model_a.parameters()       , model_b.parameters()     ):
				if not param.requires_grad:
						continue
				if not did_step:
						# Grads should not be in sync
						assert (
						    torch.allclose(param.grad       , grad_param.grad     ) is False
						), f'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
				else:
						# Grads should be in sync
						assert (
						    torch.allclose(param.grad       , grad_param.grad     ) is True
						), f'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any       , UpperCamelCase__:				Optional[Any]       , UpperCamelCase__:				Any       , UpperCamelCase__:				List[str]       , UpperCamelCase__:				Tuple=True     ):
		model.train()
		SCREAMING_SNAKE_CASE__								=   model(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   F.mse_loss(UpperCamelCase__       , target.to(output.device     )     )
		if not do_backward:
				loss /= accelerator.gradient_accumulation_steps
				loss.backward()
		else:
				accelerator.backward(UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple       , UpperCamelCase__:				List[Any]=False     ):
		set_seed(42     )
		SCREAMING_SNAKE_CASE__								=   RegressionModel()
		SCREAMING_SNAKE_CASE__								=   deepcopy(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   RegressionDataset(length=80     )
		SCREAMING_SNAKE_CASE__								=   DataLoader(UpperCamelCase__       , batch_size=16     )
		model.to(accelerator.device     )
		if sched:
				SCREAMING_SNAKE_CASE__								=   AdamW(params=model.parameters()       , lr=1e-3     )
				SCREAMING_SNAKE_CASE__								=   AdamW(params=ddp_model.parameters()       , lr=1e-3     )
				SCREAMING_SNAKE_CASE__								=   LambdaLR(UpperCamelCase__       , lr_lambda=lambda UpperCamelCase__     : epoch**0.6_5     )
				SCREAMING_SNAKE_CASE__								=   LambdaLR(UpperCamelCase__       , lr_lambda=lambda UpperCamelCase__     : epoch**0.6_5     )
		# Make a copy of `model`
		if sched:
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.prepare(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
		else:
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.prepare(UpperCamelCase__       , UpperCamelCase__     )
		if sched:
				return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
		return model, ddp_model, dataloader






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple     ):
		# Test when on a single CPU or GPU that the context manager does nothing
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__     )
		# Use a single batch
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   next(iter(UpperCamelCase__     )     ).values()
		for iteration in range(3     ):
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   input.to(accelerator.device     ), target.to(accelerator.device     )
				# Perform our initial ground truth step in non "DDP"
				step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				# Do "gradient accumulation" (noop)
				if iteration % 2 == 0:
						# Accumulate grads locally
						with accelerator.no_sync(UpperCamelCase__     ):
								step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				else:
						# Sync grads
						step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )

				# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
				check_model_parameters(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				for param, ddp_param in zip(model.parameters()       , ddp_model.parameters()     ):
						if not param.requires_grad:
								continue
						assert torch.allclose(
						    param.grad       , ddp_param.grad     ), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

				# Shuffle ddp_input on each iteration
				torch.manual_seed(1_337 + iteration     )
				SCREAMING_SNAKE_CASE__								=   ddp_input[torch.randperm(len(UpperCamelCase__     )     )]






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Optional[Any]     ):
		# Test on distributed setup that context manager behaves properly
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__     )
		# Use a single batch
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   next(iter(UpperCamelCase__     )     ).values()
		for iteration in range(3     ):
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   input.to(accelerator.device     ), target.to(accelerator.device     )
				# Perform our initial ground truth step in non "DDP"
				step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				# Do "gradient accumulation" (noop)
				if iteration % 2 == 0:
						# Accumulate grads locally
						with accelerator.no_sync(UpperCamelCase__     ):
								step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				else:
						# Sync grads
						step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )

				# DDP model and model should only be in sync when not (iteration % 2 == 0)
				for param, ddp_param in zip(model.parameters()       , ddp_model.parameters()     ):
						if not param.requires_grad:
								continue
						if iteration % 2 == 0:
								# Grads should not be in sync
								assert (
								    torch.allclose(param.grad       , ddp_param.grad     ) is False
								), f'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
						else:
								# Grads should be in sync
								assert (
								    torch.allclose(param.grad       , ddp_param.grad     ) is True
								), f'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
				torch.manual_seed(1_337 + iteration     )
				SCREAMING_SNAKE_CASE__								=   ddp_input[torch.randperm(len(UpperCamelCase__     )     )]






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int=False       , UpperCamelCase__:				Union[str, Any]=False     ):
		SCREAMING_SNAKE_CASE__								=   Accelerator(
		    split_batches=UpperCamelCase__       , dispatch_batches=UpperCamelCase__       , gradient_accumulation_steps=2     )
		# Test that context manager behaves properly
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__     )
		for iteration, batch in enumerate(UpperCamelCase__     ):
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   batch.values()
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   input.to(accelerator.device     ), target.to(accelerator.device     )
				# Perform our initial ground truth step in non "DDP"
				step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				# Do "gradient accumulation" (noop)
				with accelerator.accumulate(UpperCamelCase__     ):
						step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )

				# DDP model and model should only be in sync when not (iteration % 2 == 0)
				for param, ddp_param in zip(model.parameters()       , ddp_model.parameters()     ):
						if not param.requires_grad:
								continue
						if ((iteration + 1) % 2 == 0) or (iteration == len(UpperCamelCase__     ) - 1):
								# Grads should be in sync
								assert (
								    torch.allclose(param.grad       , ddp_param.grad     ) is True
								), f'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
						else:
								# Grads should not be in sync
								assert (
								    torch.allclose(param.grad       , ddp_param.grad     ) is False
								), f'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
				torch.manual_seed(1_337 + iteration     )
				SCREAMING_SNAKE_CASE__								=   ddp_input[torch.randperm(len(UpperCamelCase__     )     )]
		GradientState._reset_state()






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Tuple=False       , UpperCamelCase__:				List[str]=False     ):
		SCREAMING_SNAKE_CASE__								=   Accelerator(
		    split_batches=UpperCamelCase__       , dispatch_batches=UpperCamelCase__       , gradient_accumulation_steps=2     )
		# Test that context manager behaves properly
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   get_training_setup(UpperCamelCase__       , UpperCamelCase__     )
		for iteration, batch in enumerate(UpperCamelCase__     ):
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   batch.values()
				# Gather the distributed inputs and targs for the base model
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.gather((ddp_input, ddp_target)     )
				SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   input.to(accelerator.device     ), target.to(accelerator.device     )
				# Perform our initial ground truth step in non "DDP"
				model.train()
				ddp_model.train()
				step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				opt.step()

				if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCamelCase__     )):
						if split_batches:
								sched.step()
						else:
								for _ in range(accelerator.num_processes     ):
										sched.step()
				opt.zero_grad()
				# Perform gradient accumulation under wrapper
				with accelerator.accumulate(UpperCamelCase__     ):
						step_model(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
						ddp_opt.step()
						ddp_sched.step()
						ddp_opt.zero_grad()

				# Learning rates should be the same
				assert (
				    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
				), f'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n'''
				SCREAMING_SNAKE_CASE__								=   (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCamelCase__     ))
				if accelerator.num_processes > 1:
						check_model_parameters(UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__       , UpperCamelCase__     )
				# Shuffle ddp_input on each iteration
				torch.manual_seed(1_337 + iteration     )
		GradientState._reset_state()






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   Accelerator()

		SCREAMING_SNAKE_CASE__								=   RegressionDataset(length=80     )
		SCREAMING_SNAKE_CASE__								=   DataLoader(UpperCamelCase__       , batch_size=16     )
		SCREAMING_SNAKE_CASE__								=   RegressionDataset(length=96     )
		SCREAMING_SNAKE_CASE__								=   DataLoader(UpperCamelCase__       , batch_size=16     )
		SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   accelerator.prepare(UpperCamelCase__       , UpperCamelCase__     )
		assert accelerator.gradient_state.active_dataloader is None
		for iteration, _ in enumerate(UpperCamelCase__     ):
				assert id(accelerator.gradient_state.active_dataloader     ) == id(UpperCamelCase__     )
				if iteration < len(UpperCamelCase__     ) - 1:
						assert not accelerator.gradient_state.end_of_dataloader
						if iteration == 1:
								for batch_num, _ in enumerate(UpperCamelCase__     ):
										assert id(accelerator.gradient_state.active_dataloader     ) == id(UpperCamelCase__     )
										if batch_num < len(UpperCamelCase__     ) - 1:
												assert not accelerator.gradient_state.end_of_dataloader
										else:
												assert accelerator.gradient_state.end_of_dataloader
				else:
						assert accelerator.gradient_state.end_of_dataloader
		assert accelerator.gradient_state.active_dataloader is None






def 					SCREAMING_SNAKE_CASE__				(							):
		SCREAMING_SNAKE_CASE__								=   Accelerator()
		SCREAMING_SNAKE_CASE__								=   accelerator.state
		if state.local_process_index == 0:
				print("""**Test `accumulate` gradient accumulation with dataloader break**"""     )
		test_dataloader_break()
		if state.distributed_type == DistributedType.NO:
				if state.local_process_index == 0:
						print("""**Test NOOP `no_sync` context manager**"""     )
				test_noop_sync(UpperCamelCase__     )
		if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
				if state.local_process_index == 0:
						print("""**Test Distributed `no_sync` context manager**"""     )
				test_distributed_sync(UpperCamelCase__     )
		if state.distributed_type == DistributedType.MULTI_GPU:
				for split_batch in [True, False]:
						for dispatch_batches in [True, False]:
								if state.local_process_index == 0:
										print(
										    """**Test `accumulate` gradient accumulation, """       , f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''       , )
								test_gradient_accumulation(UpperCamelCase__       , UpperCamelCase__     )

    # Currently will break on torch 2.0 +, need to investigate why
		if is_torch_version("""<"""       , """2.0"""     ) or state.distributed_type == DistributedType.NO:
				if state.local_process_index == 0:
						print(
						    """**Test `accumulate` gradient accumulation with optimizer and scheduler, """       , """`split_batches=False`, `dispatch_batches=False`**"""       , )
				test_gradient_accumulation_with_opt_and_scheduler()
				if state.distributed_type == DistributedType.MULTI_GPU:
						for split_batch in [True, False]:
								for dispatch_batches in [True, False]:
										if not split_batch and not dispatch_batches:
												continue
										if state.local_process_index == 0:
												print(
												    """**Test `accumulate` gradient accumulation with optimizer and scheduler, """       , f'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''       , )
										test_gradient_accumulation_with_opt_and_scheduler(UpperCamelCase__       , UpperCamelCase__     )






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Union[str, Any]     ):
		# For xla_spawn (TPUs)
		main()


if __name__ == "__main__":
	main()