

from typing import TYPE_CHECKING

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


a		={
    """configuration_lxmert""": ["""LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LxmertConfig"""],
    """tokenization_lxmert""": ["""LxmertTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""LxmertTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """LxmertEncoder""",
					    """LxmertForPreTraining""",
					    """LxmertForQuestionAnswering""",
					    """LxmertModel""",
					    """LxmertPreTrainedModel""",
					    """LxmertVisualFeatureEncoder""",
					    """LxmertXLayer""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFLxmertForPreTraining""",
					    """TFLxmertMainLayer""",
					    """TFLxmertModel""",
					    """TFLxmertPreTrainedModel""",
					    """TFLxmertVisualFeatureEncoder""",
					]


if TYPE_CHECKING:
					from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
					from .tokenization_lxmert import LxmertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_lxmert_fast import LxmertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_lxmert import (
										    LxmertEncoder,
										    LxmertForPreTraining,
										    LxmertForQuestionAnswering,
										    LxmertModel,
										    LxmertPreTrainedModel,
										    LxmertVisualFeatureEncoder,
										    LxmertXLayer,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_lxmert import (
										    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFLxmertForPreTraining,
										    TFLxmertMainLayer,
										    TFLxmertModel,
										    TFLxmertPreTrainedModel,
										    TFLxmertVisualFeatureEncoder,
										)

else:
					import sys

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







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        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=8_2     ,			lowerCamelCase__=1_6      )  ->					List[Any]:
	set_seed(4_2      )
	__lowerCamelCase    :    Tuple 	=			RegressionModel()
	__lowerCamelCase    :    str 	=			deepcopy(lowerCamelCase__      )
	__lowerCamelCase    :    Optional[int] 	=			RegressionDataset(length=lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			DataLoader(lowerCamelCase__     ,			batch_size=lowerCamelCase__      )
	model.to(accelerator.device      )
	__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			accelerator.prepare(lowerCamelCase__     ,			lowerCamelCase__      )
	return model, ddp_model, dataloader






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=False      )  ->					List[Any]:
	__lowerCamelCase    :    Tuple 	=			AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased'      )
	__lowerCamelCase    :    Any 	=			load_dataset('glue'     ,			'mrpc'     ,			split='validation'      )

	def tokenize_function(lowerCamelCase__      ):
		__lowerCamelCase    :    Union[str, Any] 	=			tokenizer(examples['sentence1']     ,			examples['sentence2']     ,			truncation=lowerCamelCase__     ,			max_length=lowerCamelCase__      )
		return outputs

	with accelerator.main_process_first():
		__lowerCamelCase    :    Union[str, Any] 	=			dataset.map(
		    lowerCamelCase__     ,			batched=lowerCamelCase__     ,			remove_columns=['idx', 'sentence1', 'sentence2']     ,			)

	__lowerCamelCase    :    Tuple 	=			tokenized_datasets.rename_column('label'     ,			'labels'      )

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

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






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					Optional[int]:
	__lowerCamelCase    :    Optional[int] 	=			Accelerator(dispatch_batches=lowerCamelCase__     ,			split_batches=lowerCamelCase__      )
	__lowerCamelCase    :    Union[str, Any] 	=			get_dataloader(lowerCamelCase__     ,			not dispatch_batches      )
	__lowerCamelCase    :    Union[str, Any] 	=			AutoModelForSequenceClassification.from_pretrained(
	    'hf-internal-testing/mrpc-bert-base-cased'     ,			return_dict=lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Tuple 	=			accelerator.prepare(lowerCamelCase__     ,			lowerCamelCase__      )
	return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )  ->					List[Any]:
	__lowerCamelCase    :    str 	=			[]
	for batch in dataloader:
		__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			batch.values()
		with torch.no_grad():
			__lowerCamelCase    :    Tuple 	=			model(lowerCamelCase__      )
			__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			accelerator.gather_for_metrics((logit, target)      )
			logits_and_targets.append((logit, target)      )
	__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			[], []
	for logit, targ in logits_and_targets:
		logits.append(lowerCamelCase__      )
		targs.append(lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			torch.cat(lowerCamelCase__      ), torch.cat(lowerCamelCase__      )
	return logits, targs






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__=8_2     ,			lowerCamelCase__=False     ,			lowerCamelCase__=False     ,			lowerCamelCase__=1_6      )  ->					Dict:
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Optional[Any] 	=			get_basic_setup(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	__lowerCamelCase	,			__lowerCamelCase    :    Dict 	=			generate_predictions(lowerCamelCase__     ,			lowerCamelCase__     ,			lowerCamelCase__      )
	assert (
	    len(lowerCamelCase__      ) == num_samples
	), F"Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(lowerCamelCase__      )}"






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__ = False     ,			lowerCamelCase__ = False      )  ->					Dict:
	__lowerCamelCase    :    Dict 	=			evaluate.load('glue'     ,			'mrpc'      )
	__lowerCamelCase	,			__lowerCamelCase    :    Optional[int] 	=			get_mrpc_setup(lowerCamelCase__     ,			lowerCamelCase__      )
	# First do baseline
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    int 	=			setup['no']
	model.to(lowerCamelCase__      )
	model.eval()
	for batch in dataloader:
		batch.to(lowerCamelCase__      )
		with torch.inference_mode():
			__lowerCamelCase    :    Dict 	=			model(**lowerCamelCase__      )
		__lowerCamelCase    :    Any 	=			outputs.logits.argmax(dim=-1      )
		metric.add_batch(predictions=lowerCamelCase__     ,			references=batch['labels']      )
	__lowerCamelCase    :    str 	=			metric.compute()

	# Then do distributed
	__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			setup['ddp']
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			__lowerCamelCase    :    List[str] 	=			model(**lowerCamelCase__      )
		__lowerCamelCase    :    List[Any] 	=			outputs.logits.argmax(dim=-1      )
		__lowerCamelCase    :    List[str] 	=			batch['labels']
		__lowerCamelCase	,			__lowerCamelCase    :    Union[str, Any] 	=			accelerator.gather_for_metrics((preds, references)      )
		metric.add_batch(predictions=lowerCamelCase__     ,			references=lowerCamelCase__      )
	__lowerCamelCase    :    Dict 	=			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        SCREAMING_SNAKE_CASE__	(  )  ->					List[str]:
	__lowerCamelCase    :    int 	=			Accelerator(split_batches=lowerCamelCase__     ,			dispatch_batches=lowerCamelCase__      )
	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(lowerCamelCase__     ,			lowerCamelCase__      )
				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    :    Optional[Any] 	=			Accelerator(split_batches=lowerCamelCase__     ,			dispatch_batches=lowerCamelCase__      )
			if accelerator.is_local_main_process:
				print(F"With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"      )
			test_torch_metrics(lowerCamelCase__     ,			9_9      )
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('**Test last batch is not dropped when perfectly divisible**'      )
	__lowerCamelCase    :    Dict 	=			Accelerator()
	test_torch_metrics(lowerCamelCase__     ,			5_1_2      )
	accelerator.state._reset_state()






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__      )  ->					Optional[Any]:
	# For xla_spawn (TPUs)
	main()


if __name__ == "__main__":
					main()




