


import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


a					  =     'true'


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__=8_2						,							UpperCAmelCase__=1_6  ):
					set_seed(4_2  )
					lowercase_  							=			RegressionModel()
					lowercase_  							=			deepcopy(UpperCAmelCase__  )
					lowercase_  							=			RegressionDataset(length=UpperCAmelCase__  )
					lowercase_  							=			DataLoader(UpperCAmelCase__						,							batch_size=UpperCAmelCase__  )
					model.to(accelerator.device  )
					lowercase_							,					lowercase_  							=			accelerator.prepare(UpperCAmelCase__						,							UpperCAmelCase__  )
					return model, ddp_model, dataloader


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__=False  ):
					lowercase_  							=			AutoTokenizer.from_pretrained("""hf-internal-testing/mrpc-bert-base-cased"""  )
					lowercase_  							=			load_dataset("""glue"""						,							"""mrpc"""						,							split="""validation"""  )

					def tokenize_function(UpperCAmelCase__  ):
										lowercase_  							=			tokenizer(examples["""sentence1"""]						,							examples["""sentence2"""]						,							truncation=UpperCAmelCase__						,							max_length=UpperCAmelCase__  )
										return outputs

					with accelerator.main_process_first():
										lowercase_  							=			dataset.map(
										    UpperCAmelCase__						,							batched=UpperCAmelCase__						,							remove_columns=["""idx""", """sentence1""", """sentence2"""]						,							)

					lowercase_  							=			tokenized_datasets.rename_column("""label"""						,							"""labels"""  )

					def collate_fn(UpperCAmelCase__  ):
										if use_longest:
															return tokenizer.pad(UpperCAmelCase__						,							padding="""longest"""						,							return_tensors="""pt"""  )
										return tokenizer.pad(UpperCAmelCase__						,							padding="""max_length"""						,							max_length=1_2_8						,							return_tensors="""pt"""  )

					return DataLoader(UpperCAmelCase__						,							shuffle=UpperCAmelCase__						,							collate_fn=UpperCAmelCase__						,							batch_size=1_6  )


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			Accelerator(dispatch_batches=UpperCAmelCase__						,							split_batches=UpperCAmelCase__  )
					lowercase_  							=			get_dataloader(UpperCAmelCase__						,							not dispatch_batches  )
					lowercase_  							=			AutoModelForSequenceClassification.from_pretrained(
					    """hf-internal-testing/mrpc-bert-base-cased"""						,							return_dict=UpperCAmelCase__  )
					lowercase_							,					lowercase_  							=			accelerator.prepare(UpperCAmelCase__						,							UpperCAmelCase__  )
					return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			[]
					for batch in dataloader:
										lowercase_							,					lowercase_  							=			batch.values()
										with torch.no_grad():
															lowercase_  							=			model(UpperCAmelCase__  )
															lowercase_							,					lowercase_  							=			accelerator.gather_for_metrics((logit, target)  )
															logits_and_targets.append((logit, target)  )
					lowercase_							,					lowercase_  							=			[], []
					for logit, targ in logits_and_targets:
										logits.append(UpperCAmelCase__  )
										targs.append(UpperCAmelCase__  )
					lowercase_							,					lowercase_  							=			torch.cat(UpperCAmelCase__  ), torch.cat(UpperCAmelCase__  )
					return logits, targs


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__=8_2						,							UpperCAmelCase__=False						,							UpperCAmelCase__=False						,							UpperCAmelCase__=1_6  ):
					lowercase_							,					lowercase_							,					lowercase_  							=			get_basic_setup(UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  )
					lowercase_							,					lowercase_  							=			generate_predictions(UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  )
					assert (
					    len(UpperCAmelCase__  ) == num_samples
					), F'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(UpperCAmelCase__  )}'''


def 			UpperCAmelCase_				(      UpperCAmelCase__ = False						,							UpperCAmelCase__ = False  ):
					lowercase_  							=			evaluate.load("""glue"""						,							"""mrpc"""  )
					lowercase_							,					lowercase_  							=			get_mrpc_setup(UpperCAmelCase__						,							UpperCAmelCase__  )
					# First do baseline
					lowercase_							,					lowercase_							,					lowercase_  							=			setup["""no"""]
					model.to(UpperCAmelCase__  )
					model.eval()
					for batch in dataloader:
										batch.to(UpperCAmelCase__  )
										with torch.inference_mode():
															lowercase_  							=			model(**UpperCAmelCase__  )
										lowercase_  							=			outputs.logits.argmax(dim=-1  )
										metric.add_batch(predictions=UpperCAmelCase__						,							references=batch["""labels"""]  )
					lowercase_  							=			metric.compute()

					# Then do distributed
					lowercase_							,					lowercase_							,					lowercase_  							=			setup["""ddp"""]
					model.eval()
					for batch in dataloader:
										with torch.inference_mode():
															lowercase_  							=			model(**UpperCAmelCase__  )
										lowercase_  							=			outputs.logits.argmax(dim=-1  )
										lowercase_  							=			batch["""labels"""]
										lowercase_							,					lowercase_  							=			accelerator.gather_for_metrics((preds, references)  )
										metric.add_batch(predictions=UpperCAmelCase__						,							references=UpperCAmelCase__  )
					lowercase_  							=			metric.compute()

					for key in "accuracy f1".split():
										assert math.isclose(
										    baseline[key]						,							distributed[key]  ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''


def 			UpperCAmelCase_				(      ):
					lowercase_  							=			Accelerator(split_batches=UpperCAmelCase__						,							dispatch_batches=UpperCAmelCase__  )
					if accelerator.is_local_main_process:
										datasets.utils.logging.set_verbosity_warning()
										transformers.utils.logging.set_verbosity_warning()
					else:
										datasets.utils.logging.set_verbosity_error()
										transformers.utils.logging.set_verbosity_error()
					# These are a bit slower so they should only be ran on the GPU or TPU
					if torch.cuda.is_available() or is_tpu_available():
										if accelerator.is_local_main_process:
															print("""**Testing gather_for_metrics**"""  )
										for split_batches in [True, False]:
															for dispatch_batches in [True, False]:
																				if accelerator.is_local_main_process:
																									print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''  )
																				test_mrpc(UpperCAmelCase__						,							UpperCAmelCase__  )
																				accelerator.state._reset_state()
					if accelerator.is_local_main_process:
										print("""**Test torch metrics**"""  )
					for split_batches in [True, False]:
										for dispatch_batches in [True, False]:
															lowercase_  							=			Accelerator(split_batches=UpperCAmelCase__						,							dispatch_batches=UpperCAmelCase__  )
															if accelerator.is_local_main_process:
																				print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''  )
															test_torch_metrics(UpperCAmelCase__						,							9_9  )
															accelerator.state._reset_state()
					if accelerator.is_local_main_process:
										print("""**Test last batch is not dropped when perfectly divisible**"""  )
					lowercase_  							=			Accelerator()
					test_torch_metrics(UpperCAmelCase__						,							5_1_2  )
					accelerator.state._reset_state()


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					# For xla_spawn (TPUs)
					main()


if __name__ == "__main__":
							main()






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


a					  =     {
    'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
    'processing_trocr': ['TrOCRProcessor'],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a					  =     [
							    'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TrOCRForCausalLM',
							    'TrOCRPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
							from .processing_trocr import TrOCRProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
							import sys

							a					  =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


