


"""simple docstring"""
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


SCREAMING_SNAKE_CASE :    Any									=				"""true"""






def 		lowercase							(     _snake_case						:      Tuple     ,   _snake_case						:      List[str]=82     ,   _snake_case						:      List[str]=16     )     ->Optional[int]:
	"""simple docstring"""





	set_seed(42     )
	__snake_case	:						Union[str, Any]						=				RegressionModel()
	__snake_case	:						Union[str, Any]						=				deepcopy(_snake_case     )
	__snake_case	:						Tuple						=				RegressionDataset(length=_snake_case     )
	__snake_case	:						Tuple						=				DataLoader(_snake_case     ,   batch_size=_snake_case     )
	model.to(accelerator.device     )
	__snake_case	,							__snake_case	:						str						=				accelerator.prepare(_snake_case     ,   _snake_case     )
	return model, ddp_model, dataloader






def 		lowercase							(     _snake_case						:      Accelerator     ,   _snake_case						:      Optional[int]=False     )     ->int:
	"""simple docstring"""





	__snake_case	:						Any						=				AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''     )
	__snake_case	:						List[str]						=				load_dataset('''glue'''     ,   '''mrpc'''     ,   split='''validation'''     )

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

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

	__snake_case	:						Optional[Any]						=				tokenized_datasets.rename_column('''label'''     ,   '''labels'''     )

	def collate_fn(_snake_case						:      List[Any]     ):
		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 		lowercase							(     _snake_case						:      List[str]     ,   _snake_case						:      Optional[int]     )     ->List[Any]:
	"""simple docstring"""





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






def 		lowercase							(     _snake_case						:      str     ,   _snake_case						:      str     ,   _snake_case						:      List[str]     )     ->int:
	"""simple docstring"""





	__snake_case	:						Optional[int]						=				[]
	for batch in dataloader:
		__snake_case	,							__snake_case	:						List[Any]						=				batch.values()
		with torch.no_grad():
			__snake_case	:						List[str]						=				model(_snake_case     )
			__snake_case	,							__snake_case	:						int						=				accelerator.gather_for_metrics((logit, target)     )
			logits_and_targets.append((logit, target)     )
	__snake_case	,							__snake_case	:						int						=				[], []
	for logit, targ in logits_and_targets:
		logits.append(_snake_case     )
		targs.append(_snake_case     )
	__snake_case	,							__snake_case	:						List[str]						=				torch.cat(_snake_case     ), torch.cat(_snake_case     )
	return logits, targs






def 		lowercase							(     _snake_case						:      Accelerator     ,   _snake_case						:      int=82     ,   _snake_case						:      List[Any]=False     ,   _snake_case						:      Optional[int]=False     ,   _snake_case						:      Dict=16     )     ->Any:
	"""simple docstring"""





	__snake_case	,							__snake_case	,							__snake_case	:						Tuple						=				get_basic_setup(_snake_case     ,   _snake_case     ,   _snake_case     )
	__snake_case	,							__snake_case	:						List[str]						=				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 		lowercase							(     _snake_case						:      bool = False     ,   _snake_case						:      bool = False     )     ->Union[str, Any]:
	"""simple docstring"""





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

	# Then do distributed
	__snake_case	,							__snake_case	,							__snake_case	:						str						=				setup['''ddp''']
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			__snake_case	:						Tuple						=				model(**_snake_case     )
		__snake_case	:						Optional[int]						=				outputs.logits.argmax(dim=-1     )
		__snake_case	:						List[Any]						=				batch['''labels''']
		__snake_case	,							__snake_case	:						Dict						=				accelerator.gather_for_metrics((preds, references)     )
		metric.add_batch(predictions=_snake_case     ,   references=_snake_case     )
	__snake_case	:						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 		lowercase							(     )     ->str:
	"""simple docstring"""





	__snake_case	:						Any						=				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]:
			__snake_case	:						Union[str, Any]						=				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**'''     )
	__snake_case	:						Any						=				Accelerator()
	test_torch_metrics(_snake_case     ,   512     )
	accelerator.state._reset_state()






def 		lowercase							(     _snake_case						:      Any     )     ->Optional[Any]:
	"""simple docstring"""





	main()


if __name__ == "__main__":
			main()









"""simple docstring"""
import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 		lowercase							(     _snake_case						:      List[Any]     ,   _snake_case						:      Tuple     ,   _snake_case						:      int     )     ->List[Any]:
	"""simple docstring"""





	if openai_config_file == "":
		__snake_case	:						Dict						=				OpenAIGPTConfig()
	else:
		__snake_case	:						int						=				OpenAIGPTConfig.from_json_file(_snake_case     )
	__snake_case	:						Tuple						=				OpenAIGPTModel(_snake_case     )

	# Load weights from numpy
	load_tf_weights_in_openai_gpt(_snake_case     ,   _snake_case     ,   _snake_case     )

	# Save pytorch-model
	__snake_case	:						str						=				pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
	__snake_case	:						Optional[Any]						=				pytorch_dump_folder_path + '''/''' + CONFIG_NAME
	print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""     )
	torch.save(model.state_dict()     ,   _snake_case     )
	print(f"""Save configuration file to {pytorch_config_dump_path}"""     )
	with open(_snake_case     ,   '''w'''     ,   encoding='''utf-8'''     ) as f:
		f.write(config.to_json_string()     )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    Any									=				argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--openai_checkpoint_folder_path""",
			    default=None,
			    type=str,
			    required=True,
			    help="""Path to the TensorFlow checkpoint path.""",
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
			)
			parser.add_argument(
			    """--openai_config_file""",
			    default="""""",
			    type=str,
			    help=(
			        """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
			        """This specifies the model architecture."""
			    ),
			)
			SCREAMING_SNAKE_CASE :    Optional[int]									=				parser.parse_args()
			convert_openai_checkpoint_to_pytorch(
			    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
			)





