




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


__lowerCAmelCase	:       List[str]     =	'true'




def     __magic_name__       (				A	:      Dict,						A	:      Tuple=82,						A	:      List[Any]=16     ):






			'''simple docstring'''







			set_seed(42     )
			a					=    RegressionModel()
			a					=    deepcopy(A     )
			a					=    RegressionDataset(length=A     )
			a					=    DataLoader(A,						batch_size=A     )
			model.to(accelerator.device     )
			a					,	a					=    accelerator.prepare(A,						A     )
			return model, ddp_model, dataloader




def     __magic_name__       (				A	:      Accelerator,						A	:      Union[str, Any]=False     ):






			'''simple docstring'''







			a					=    AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased"     )
			a					=    load_dataset("glue",						"mrpc",						split="validation"     )

			def tokenize_function(A	:      Tuple     ):
						a					=    tokenizer(examples["sentence1"],						examples["sentence2"],						truncation=A,						max_length=A     )
						return outputs

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

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

			def collate_fn(A	:      List[Any]     ):
						if use_longest:
									return tokenizer.pad(A,						padding="longest",						return_tensors="pt"     )
						return tokenizer.pad(A,						padding="max_length",						max_length=128,						return_tensors="pt"     )

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




def     __magic_name__       (				A	:      Dict,						A	:      str     ):






			'''simple docstring'''







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




def     __magic_name__       (				A	:      List[Any],						A	:      Optional[Any],						A	:      str     ):






			'''simple docstring'''







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




def     __magic_name__       (				A	:      Accelerator,						A	:      Dict=82,						A	:      Any=False,						A	:      List[str]=False,						A	:      List[str]=16     ):






			'''simple docstring'''







			a					,	a					,	a					=    get_basic_setup(A,						A,						A     )
			a					,	a					=    generate_predictions(A,						A,						A     )
			assert (
			    len(A     ) == num_samples
			), F"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(A     )}"""




def     __magic_name__       (				A	:      bool = False,						A	:      bool = False     ):






			'''simple docstring'''







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

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






			'''simple docstring'''







			a					=    Accelerator(split_batches=A,						dispatch_batches=A     )
			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(A,						A     )
												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]:
									a					=    Accelerator(split_batches=A,						dispatch_batches=A     )
									if accelerator.is_local_main_process:
												print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""     )
									test_torch_metrics(A,						99     )
									accelerator.state._reset_state()
			if accelerator.is_local_main_process:
						print("**Test last batch is not dropped when perfectly divisible**"     )
			a					=    Accelerator()
			test_torch_metrics(A,						512     )
			accelerator.state._reset_state()




def     __magic_name__       (				A	:      Dict     ):






			'''simple docstring'''







			main()


if __name__ == "__main__":
		main()












from typing import TYPE_CHECKING

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


__lowerCAmelCase	:       Dict     =	{
    'configuration_blip': [
        'BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlipConfig',
        'BlipTextConfig',
        'BlipVisionConfig',
    ],
    'processing_blip': ['BlipProcessor'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Optional[Any]     =	['BlipImageProcessor']


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       List[Any]     =	[
		    'BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BlipModel',
		    'BlipPreTrainedModel',
		    'BlipForConditionalGeneration',
		    'BlipForQuestionAnswering',
		    'BlipVisionModel',
		    'BlipTextModel',
		    'BlipForImageTextRetrieval',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase	:       Any     =	[
		    'TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFBlipModel',
		    'TFBlipPreTrainedModel',
		    'TFBlipForConditionalGeneration',
		    'TFBlipForQuestionAnswering',
		    'TFBlipVisionModel',
		    'TFBlipTextModel',
		    'TFBlipForImageTextRetrieval',
		]

if TYPE_CHECKING:
		from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
		from .processing_blip import BlipProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_blip import BlipImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blip import (
				    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlipForConditionalGeneration,
				    BlipForImageTextRetrieval,
				    BlipForQuestionAnswering,
				    BlipModel,
				    BlipPreTrainedModel,
				    BlipTextModel,
				    BlipVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blip import (
				    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFBlipForConditionalGeneration,
				    TFBlipForImageTextRetrieval,
				    TFBlipForQuestionAnswering,
				    TFBlipModel,
				    TFBlipPreTrainedModel,
				    TFBlipTextModel,
				    TFBlipVisionModel,
				)

else:
		import sys

		__lowerCAmelCase	:       List[str]     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






