




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


UpperCAmelCase_						='''true'''



def       UpperCAmelCase       (     _snake_case						, _snake_case=82						, _snake_case=16							):
							set_seed(42							)
							lowerCAmelCase   =		RegressionModel()
							lowerCAmelCase   =		deepcopy(_snake_case							)
							lowerCAmelCase   =		RegressionDataset(length=_snake_case							)
							lowerCAmelCase   =		DataLoader(_snake_case						, batch_size=_snake_case							)
							model.to(accelerator.device							)
							lowerCAmelCase   =		accelerator.prepare(_snake_case						, _snake_case							)
							return model, ddp_model, dataloader



def       UpperCAmelCase       (     _snake_case						, _snake_case=False							):
							lowerCAmelCase   =		AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''							)
							lowerCAmelCase   =		load_dataset('''glue'''						, '''mrpc'''						, split='''validation'''							)

							def tokenize_function(_snake_case							):
														lowerCAmelCase   =		tokenizer(examples['''sentence1''']						, examples['''sentence2''']						, truncation=_snake_case						, max_length=_snake_case							)
														return outputs

							with accelerator.main_process_first():
														lowerCAmelCase   =		dataset.map(
														    _snake_case						, batched=_snake_case						, remove_columns=['''idx''', '''sentence1''', '''sentence2''']						, )

							lowerCAmelCase   =		tokenized_datasets.rename_column('''label'''						, '''labels'''							)

							def collate_fn(_snake_case							):
														if use_longest:
																					return tokenizer.pad(_snake_case						, padding='''longest'''						, return_tensors='''pt'''							)
														return tokenizer.pad(_snake_case						, padding='''max_length'''						, max_length=128						, return_tensors='''pt'''							)

							return DataLoader(_snake_case						, shuffle=_snake_case						, collate_fn=_snake_case						, batch_size=16							)



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



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



def       UpperCAmelCase       (     _snake_case						, _snake_case=82						, _snake_case=False						, _snake_case=False						, _snake_case=16							):
							lowerCAmelCase   =		get_basic_setup(_snake_case						, _snake_case						, _snake_case							)
							lowerCAmelCase   =		generate_predictions(_snake_case						, _snake_case						, _snake_case							)
							assert (
							    len(_snake_case							) == num_samples
							), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(_snake_case							)}"""



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

							# Then do distributed
							lowerCAmelCase   =		setup['ddp']
							model.eval()
							for batch in dataloader:
														with torch.inference_mode():
																					lowerCAmelCase   =		model(**_snake_case							)
														lowerCAmelCase   =		outputs.logits.argmax(dim=-1							)
														lowerCAmelCase   =		batch['labels']
														lowerCAmelCase   =		accelerator.gather_for_metrics((preds, references)							)
														metric.add_batch(predictions=_snake_case						, references=_snake_case							)
							lowerCAmelCase   =		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       (     ):
							lowerCAmelCase   =		Accelerator(split_batches=_snake_case						, dispatch_batches=_snake_case							)
							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(_snake_case						, _snake_case							)
																												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]:
																					lowerCAmelCase   =		Accelerator(split_batches=_snake_case						, dispatch_batches=_snake_case							)
																					if accelerator.is_local_main_process:
																												print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""							)
																					test_torch_metrics(_snake_case						, 99							)
																					accelerator.state._reset_state()
							if accelerator.is_local_main_process:
														print('''**Test last batch is not dropped when perfectly divisible**'''							)
							lowerCAmelCase   =		Accelerator()
							test_torch_metrics(_snake_case						, 512							)
							accelerator.state._reset_state()



def       UpperCAmelCase       (     _snake_case							):
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
		main()








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase_						={
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=["""EfficientFormerImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=[
		    """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """EfficientFormerForImageClassification""",
		    """EfficientFormerForImageClassificationWithTeacher""",
		    """EfficientFormerModel""",
		    """EfficientFormerPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=[
		    """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFEfficientFormerForImageClassification""",
		    """TFEfficientFormerForImageClassificationWithTeacher""",
		    """TFEfficientFormerModel""",
		    """TFEfficientFormerPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_efficientformer import EfficientFormerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_efficientformer import (
				    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EfficientFormerForImageClassification,
				    EfficientFormerForImageClassificationWithTeacher,
				    EfficientFormerModel,
				    EfficientFormerPreTrainedModel,
				)
		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_efficientformer import (
				    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFEfficientFormerForImageClassification,
				    TFEfficientFormerForImageClassificationWithTeacher,
				    TFEfficientFormerModel,
				    TFEfficientFormerPreTrainedModel,
				)

else:
		import sys

		UpperCAmelCase_						=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



