






import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType


class     __SCREAMING_SNAKE_CASE					:






						def __init__(	self     ,						_UpperCamelCase=2     ,						_UpperCamelCase=3     ,						_UpperCamelCase=64     ,						_UpperCamelCase=None   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						np.random.default_rng(_UpperCamelCase   )
												lowerCAmelCase__    						=						length
												lowerCAmelCase__    						=						rng.normal(size=(length,)   ).astype(np.floataa   )
												lowerCAmelCase__    						=						a * self.x + b + rng.normal(scale=0.1     ,						size=(length,)   ).astype(np.floataa   )






						def __len__(	self   ):






												"""simple docstring"""
												return self.length







						def __getitem__(	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												return {"x": self.x[i], "y": self.y[i]}




class     __SCREAMING_SNAKE_CASE					(   torch.nn.Module):






						def __init__(	self     ,						_UpperCamelCase=0     ,						_UpperCamelCase=0     ,						_UpperCamelCase=False   ):






												"""simple docstring"""
												super().__init__()
												lowerCAmelCase__    						=						torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
												lowerCAmelCase__    						=						torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
												lowerCAmelCase__    						=						True







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase=None   ):






												"""simple docstring"""
												if self.first_batch:
																		print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"   )
																		lowerCAmelCase__    						=						False
												return x * self.a[0] + self.b[0]




class     __SCREAMING_SNAKE_CASE					(   torch.nn.Module):






						def __init__(	self     ,						_UpperCamelCase=0     ,						_UpperCamelCase=0     ,						_UpperCamelCase=False   ):






												"""simple docstring"""
												super().__init__()
												lowerCAmelCase__    						=						torch.nn.Parameter(torch.tensor(_UpperCamelCase   ).float()   )
												lowerCAmelCase__    						=						torch.nn.Parameter(torch.tensor(_UpperCamelCase   ).float()   )
												lowerCAmelCase__    						=						True







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase=None   ):






												"""simple docstring"""
												if self.first_batch:
																		print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"   )
																		lowerCAmelCase__    						=						False
												return x * self.a + self.b







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					int = 16		)  ->   Optional[Any]:

						"""simple docstring"""



						from datasets import load_dataset
						from transformers import AutoTokenizer

						lowerCAmelCase__    						=						AutoTokenizer.from_pretrained('bert-base-cased'		)
						lowerCAmelCase__    						=						{'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
						lowerCAmelCase__    						=						load_dataset('csv'    ,       data_files=UpperCamelCase_		)
						lowerCAmelCase__    						=						datasets['train'].unique('label'		)

						lowerCAmelCase__    						=						{v: i for i, v in enumerate(UpperCamelCase_		)}

						def tokenize_function(UpperCamelCase_       :					List[Any]		):
												# max_length=None => use the model max length (it's actually the default)
												lowerCAmelCase__    						=						tokenizer(
												    examples['sentence1']    ,       examples['sentence2']    ,       truncation=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       padding='max_length'		)
												if "label" in examples:
																		lowerCAmelCase__    						=						[label_to_id[l] for l in examples['label']]
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						lowerCAmelCase__    						=						datasets.map(
						    UpperCamelCase_    ,       batched=UpperCamelCase_    ,       remove_columns=['sentence1', 'sentence2', 'label']    ,       )

						def collate_fn(UpperCamelCase_       :					List[str]		):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												if accelerator.distributed_type == DistributedType.TPU:
																		return tokenizer.pad(UpperCamelCase_    ,       padding='max_length'    ,       max_length=128    ,       return_tensors='pt'		)
												return tokenizer.pad(UpperCamelCase_    ,       padding='longest'    ,       return_tensors='pt'		)

						# Instantiate dataloaders.
						lowerCAmelCase__    						=						DataLoader(tokenized_datasets['train']    ,       shuffle=UpperCamelCase_    ,       collate_fn=UpperCamelCase_    ,       batch_size=2		)
						lowerCAmelCase__    						=						DataLoader(tokenized_datasets['validation']    ,       shuffle=UpperCamelCase_    ,       collate_fn=UpperCamelCase_    ,       batch_size=1		)

						return train_dataloader, eval_dataloader













import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()







def 	_UpperCamelCase   (    UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					str    ,       UpperCamelCase_       :					Optional[Any]		)  ->   Any:

						"""simple docstring"""



						lowerCAmelCase__    						=						BertConfig.from_json_file(UpperCamelCase_		)
						print(F"Building PyTorch model from configuration: {config}"		)
						lowerCAmelCase__    						=						BertForPreTraining(UpperCamelCase_		)

						# Load weights from tf checkpoint
						load_tf_weights_in_bert(UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)

						# Save pytorch-model
						print(F"Save PyTorch model to {pytorch_dump_path}"		)
						torch.save(model.state_dict()    ,       UpperCamelCase_		)


if __name__ == "__main__":
					__snake_case    :    Union[str, Any]         =     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--bert_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					__snake_case    :    List[str]         =     parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





