"""simple docstring"""




import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


__lowerCamelCase							 =     logging.get_logger(__name__)

__lowerCamelCase							 =     {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__lowerCamelCase							 =     {
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
__lowerCamelCase							 =     {
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
__lowerCamelCase							 =     {
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

__lowerCamelCase							 =     {
    "facebook/dpr-ctx_encoder-single-nq-base": 5_12,
    "facebook/dpr-ctx_encoder-multiset-base": 5_12,
}
__lowerCamelCase							 =     {
    "facebook/dpr-question_encoder-single-nq-base": 5_12,
    "facebook/dpr-question_encoder-multiset-base": 5_12,
}
__lowerCamelCase							 =     {
    "facebook/dpr-reader-single-nq-base": 5_12,
    "facebook/dpr-reader-multiset-base": 5_12,
}


__lowerCamelCase							 =     {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
__lowerCamelCase							 =     {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
__lowerCamelCase							 =     {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}
class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Any  							=						VOCAB_FILES_NAMES
						lowerCAmelCase__     :  int  							=						CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase__     :  int  							=						CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase__     :  Tuple  							=						CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
						lowerCAmelCase__     :  Dict  							=						DPRContextEncoderTokenizer






class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Dict  							=						VOCAB_FILES_NAMES
						lowerCAmelCase__     :  str  							=						QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase__     :  Any  							=						QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase__     :  List[str]  							=						QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
						lowerCAmelCase__     :  int  							=						DPRQuestionEncoderTokenizer






__lowerCamelCase							 =     collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

__lowerCamelCase							 =     collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


__lowerCamelCase							 =     R"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "
@add_start_docstrings(__A       )
class 			UpperCamelCase__:





						def __call__(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = False						,__UpperCAmelCase = False						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> BatchEncoding:
													if titles is None and texts is None:
																				return super().__call__(
																				    __UpperCAmelCase						,padding=__UpperCAmelCase						,truncation=__UpperCAmelCase						,max_length=__UpperCAmelCase						,return_tensors=__UpperCAmelCase						,return_attention_mask=__UpperCAmelCase						,**__UpperCAmelCase						,)
													elif titles is None or texts is None:
																				A__		   =							titles if texts is None else texts
																				return super().__call__(
																				    __UpperCAmelCase						,__UpperCAmelCase						,padding=__UpperCAmelCase						,truncation=__UpperCAmelCase						,max_length=__UpperCAmelCase						,return_tensors=__UpperCAmelCase						,return_attention_mask=__UpperCAmelCase						,**__UpperCAmelCase						,)
													A__		   =							titles if not isinstance(__UpperCAmelCase						,__UpperCAmelCase   ) else [titles]
													A__		   =							texts if not isinstance(__UpperCAmelCase						,__UpperCAmelCase   ) else [texts]
													A__		   =							len(__UpperCAmelCase   )
													A__		   =							questions if not isinstance(__UpperCAmelCase						,__UpperCAmelCase   ) else [questions] * n_passages
													assert len(__UpperCAmelCase   ) == len(
													    __UpperCAmelCase   ), f'''There should be as many titles than texts but got {len(__UpperCAmelCase   )} titles and {len(__UpperCAmelCase   )} texts.'''
													A__		   =							super().__call__(__UpperCAmelCase						,__UpperCAmelCase						,padding=__UpperCAmelCase						,truncation=__UpperCAmelCase   )['input_ids']
													A__		   =							super().__call__(__UpperCAmelCase						,add_special_tokens=__UpperCAmelCase						,padding=__UpperCAmelCase						,truncation=__UpperCAmelCase   )['input_ids']
													A__		   =							{
													    'input_ids': [
													        (encoded_question_and_title + encoded_text)[:max_length]
													        if max_length is not None and truncation
													        else encoded_question_and_title + encoded_text
													        for encoded_question_and_title, encoded_text in zip(__UpperCAmelCase						,__UpperCAmelCase   )
													    ]
													}
													if return_attention_mask is not False:
																				A__		   =							[]
																				for input_ids in encoded_inputs["input_ids"]:
																											attention_mask.append([int(input_id != self.pad_token_id   ) for input_id in input_ids]   )
																				A__		   =							attention_mask
													return self.pad(__UpperCAmelCase						,padding=__UpperCAmelCase						,max_length=__UpperCAmelCase						,return_tensors=__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = 16						,__UpperCAmelCase = 64						,__UpperCAmelCase = 4						,)    -> List[DPRSpanPrediction]:
													A__		   =							reader_input['input_ids']
													A__   ,     A__   ,     A__		   =							reader_output[:3]
													A__		   =							len(__UpperCAmelCase   )
													A__		   =							sorted(range(__UpperCAmelCase   )						,reverse=__UpperCAmelCase						,key=relevance_logits.__getitem__   )
													A__		   =							[]
													for doc_id in sorted_docs:
																				A__		   =							list(input_ids[doc_id]   )
																				# assuming question & title information is at the beginning of the sequence
																				A__		   =							sequence_ids.index(self.sep_token_id						,2   ) + 1  # second sep id
																				if sequence_ids[-1] == self.pad_token_id:
																											A__		   =							sequence_ids.index(self.pad_token_id   )
																				else:
																											A__		   =							len(__UpperCAmelCase   )

																				A__		   =							self._get_best_spans(
																				    start_logits=start_logits[doc_id][passage_offset:sequence_len]						,end_logits=end_logits[doc_id][passage_offset:sequence_len]						,max_answer_length=__UpperCAmelCase						,top_spans=__UpperCAmelCase						,)
																				for start_index, end_index in best_spans:
																											start_index += passage_offset
																											end_index += passage_offset
																											nbest_spans_predictions.append(
																											    DPRSpanPrediction(
																											        span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]						,relevance_score=relevance_logits[doc_id]						,doc_id=__UpperCAmelCase						,start_index=__UpperCAmelCase						,end_index=__UpperCAmelCase						,text=self.decode(sequence_ids[start_index : end_index + 1]   )						,)   )
																				if len(__UpperCAmelCase   ) >= num_spans:
																											break
													return nbest_spans_predictions[:num_spans]





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,)    -> List[DPRSpanPrediction]:
													A__		   =							[]
													for start_index, start_score in enumerate(__UpperCAmelCase   ):
																				for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]   ):
																											scores.append(((start_index, start_index + answer_length), start_score + end_score)   )
													A__		   =							sorted(__UpperCAmelCase						,key=lambda __UpperCAmelCase   : x[1]						,reverse=__UpperCAmelCase   )
													A__		   =							[]
													for (start_index, end_index), score in scores:
																				assert start_index <= end_index, f'''Wrong span indices: [{start_index}:{end_index}]'''
																				A__		   =							end_index - start_index + 1
																				assert length <= max_answer_length, f'''Span is too long: {length} > {max_answer_length}'''
																				if any(
																				    start_index <= prev_start_index <= prev_end_index <= end_index
																				    or prev_start_index <= start_index <= end_index <= prev_end_index
																				    for (prev_start_index, prev_end_index) in chosen_span_intervals   ):
																											continue
																				chosen_span_intervals.append((start_index, end_index)   )

																				if len(__UpperCAmelCase   ) == top_spans:
																											break
													return chosen_span_intervals






@add_end_docstrings(__A       )
class 			UpperCamelCase__(	__A						,  __A       ):
						lowerCAmelCase__     :  Tuple  							=						VOCAB_FILES_NAMES
						lowerCAmelCase__     :  List[Any]  							=						READER_PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase__     :  str  							=						READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase__     :  List[str]  							=						READER_PRETRAINED_INIT_CONFIGURATION
						lowerCAmelCase__     :  List[Any]  							=						['input_ids', 'attention_mask']
						lowerCAmelCase__     :  Optional[int]  							=						DPRReaderTokenizer






"""simple docstring"""




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      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							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      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__=True ):






							"""simple docstring"""





							model.train()
							A__		   =							model(UpperCamelCase__ )
							A__		   =							F.mse_loss(UpperCamelCase__				,			target.to(output.device ) )
							if not do_backward:
														loss /= accelerator.gradient_accumulation_steps
														loss.backward()
							else:
														accelerator.backward(UpperCamelCase__ )






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__=False ):






							"""simple docstring"""





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






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							A__   ,     A__   ,     A__		   =							get_training_setup(UpperCamelCase__ )
							# Use a single batch
							A__   ,     A__		   =							next(iter(UpperCamelCase__ ) ).values()
							for iteration in range(3 ):
														# Gather the distributed inputs and targs for the base model
														A__   ,     A__		   =							accelerator.gather((ddp_input, ddp_target) )
														A__   ,     A__		   =							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 )
														A__		   =							ddp_input[torch.randperm(len(UpperCamelCase__ ) )]






def      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							A__   ,     A__   ,     A__		   =							get_training_setup(UpperCamelCase__ )
							# Use a single batch
							A__   ,     A__		   =							next(iter(UpperCamelCase__ ) ).values()
							for iteration in range(3 ):
														# Gather the distributed inputs and targs for the base model
														A__   ,     A__		   =							accelerator.gather((ddp_input, ddp_target) )
														A__   ,     A__		   =							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 )
														A__		   =							ddp_input[torch.randperm(len(UpperCamelCase__ ) )]






def      UpperCAmelCase    (		UpperCamelCase__=False				,			UpperCamelCase__=False ):






							"""simple docstring"""





							A__		   =							Accelerator(
							    split_batches=UpperCamelCase__				,			dispatch_batches=UpperCamelCase__				,			gradient_accumulation_steps=2 )
							# Test that context manager behaves properly
							A__   ,     A__   ,     A__		   =							get_training_setup(UpperCamelCase__ )
							for iteration, batch in enumerate(UpperCamelCase__ ):
														A__   ,     A__		   =							batch.values()
														# Gather the distributed inputs and targs for the base model
														A__   ,     A__		   =							accelerator.gather((ddp_input, ddp_target) )
														A__   ,     A__		   =							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 )
														A__		   =							ddp_input[torch.randperm(len(UpperCamelCase__ ) )]
							GradientState._reset_state()






def      UpperCAmelCase    (		UpperCamelCase__=False				,			UpperCamelCase__=False ):






							"""simple docstring"""





							A__		   =							Accelerator(
							    split_batches=UpperCamelCase__				,			dispatch_batches=UpperCamelCase__				,			gradient_accumulation_steps=2 )
							# Test that context manager behaves properly
							A__   ,     A__   ,     A__   ,     A__   ,     A__   ,     A__   ,     A__		   =							get_training_setup(UpperCamelCase__				,			UpperCamelCase__ )
							for iteration, batch in enumerate(UpperCamelCase__ ):
														A__   ,     A__		   =							batch.values()
														# Gather the distributed inputs and targs for the base model
														A__   ,     A__		   =							accelerator.gather((ddp_input, ddp_target) )
														A__   ,     A__		   =							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'''
														A__		   =							(((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      UpperCAmelCase    (		):






							"""simple docstring"""





							A__		   =							Accelerator()

							A__		   =							RegressionDataset(length=80 )
							A__		   =							DataLoader(UpperCamelCase__				,			batch_size=16 )
							A__		   =							RegressionDataset(length=96 )
							A__		   =							DataLoader(UpperCamelCase__				,			batch_size=16 )
							A__   ,     A__		   =							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      UpperCAmelCase    (		):






							"""simple docstring"""





							A__		   =							Accelerator()
							A__		   =							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      UpperCAmelCase    (		UpperCamelCase__ ):






							"""simple docstring"""





							main()


if __name__ == "__main__":
				main()





