




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

			lowercase__					  =				(
			    ("layer.", "layer_"),
			    ("word_embeddings.weight", "word_embeddings"),
			    ("position_embeddings.weight", "position_embeddings"),
			    ("token_type_embeddings.weight", "token_type_embeddings"),
			    (".", "/"),
			    ("LayerNorm/weight", "LayerNorm/gamma"),
			    ("LayerNorm/bias", "LayerNorm/beta"),
			    ("weight", "kernel"),
			)

			if not os.path.isdir(SCREAMING_SNAKE_CASE_  ):
						os.makedirs(SCREAMING_SNAKE_CASE_  )

			lowercase__					  =				model.state_dict()

			def to_tf_var_name(SCREAMING_SNAKE_CASE_  ):
						for patt, repl in iter(SCREAMING_SNAKE_CASE_  ):
									lowercase__					  =				name.replace(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
						return f'''bert/{name}'''

			def create_tf_var(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
						lowercase__					  =				tf.dtypes.as_dtype(tensor.dtype  )
						lowercase__					  =				tf.get_variable(dtype=SCREAMING_SNAKE_CASE_						,  shape=tensor.shape						,  name=SCREAMING_SNAKE_CASE_						,  initializer=tf.zeros_initializer()  )
						session.run(tf.variables_initializer([tf_var]  )  )
						session.run(SCREAMING_SNAKE_CASE_  )
						return tf_var

			tf.reset_default_graph()
			with tf.Session() as session:
						for var_name in state_dict:
									lowercase__					  =				to_tf_var_name(SCREAMING_SNAKE_CASE_  )
									lowercase__					  =				state_dict[var_name].numpy()
									if any(x in var_name for x in tensors_to_transpose  ):
												lowercase__					  =				torch_tensor.T
									lowercase__					  =				create_tf_var(tensor=SCREAMING_SNAKE_CASE_						,  name=SCREAMING_SNAKE_CASE_						,  session=SCREAMING_SNAKE_CASE_  )
									tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )
									lowercase__					  =				session.run(SCREAMING_SNAKE_CASE_  )
									print(f'''Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  )}'''  )

						lowercase__					  =				tf.train.Saver(tf.trainable_variables()  )
						saver.save(SCREAMING_SNAKE_CASE_						,  os.path.join(SCREAMING_SNAKE_CASE_						,  model_name.replace("-"						,  "_"  ) + ".ckpt"  )  )



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_=None  ):
			lowercase__					  =				argparse.ArgumentParser()
			parser.add_argument("--model_name"						,  type=SCREAMING_SNAKE_CASE_						,  required=SCREAMING_SNAKE_CASE_						,  help="model name e.g. bert-base-uncased"  )
			parser.add_argument(
			    "--cache_dir"						,  type=SCREAMING_SNAKE_CASE_						,  default=SCREAMING_SNAKE_CASE_						,  required=SCREAMING_SNAKE_CASE_						,  help="Directory containing pytorch model"  )
			parser.add_argument("--pytorch_model_path"						,  type=SCREAMING_SNAKE_CASE_						,  required=SCREAMING_SNAKE_CASE_						,  help="/path/to/<pytorch-model-name>.bin"  )
			parser.add_argument("--tf_cache_dir"						,  type=SCREAMING_SNAKE_CASE_						,  required=SCREAMING_SNAKE_CASE_						,  help="Directory in which to save tensorflow model"  )
			lowercase__					  =				parser.parse_args(SCREAMING_SNAKE_CASE_  )

			lowercase__					  =				BertModel.from_pretrained(
			    pretrained_model_name_or_path=args.model_name						,  state_dict=torch.load(args.pytorch_model_path  )						,  cache_dir=args.cache_dir						,  )

			convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_						,  ckpt_dir=args.tf_cache_dir						,  model_name=args.model_name  )


if __name__ == "__main__":
							main()









import re
from pathlib import Path
from unittest import TestCase

import pytest
@pytest.mark.integration
class 				_snake_case			(					lowercase__):




				def 							A__      (						self					:						Optional[Any],					__lowercase					:						str						):

							with open(__lowercase,					encoding="utf-8"						) as input_file:
										lowercase__					  =				re.compile(R"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)"						)
										lowercase__					  =				input_file.read()
										lowercase__					  =				regexp.search(__lowercase						)

							return match




				def 							A__      (						self					:						str,					__lowercase					:						str						):

							with open(__lowercase,					encoding="utf-8"						) as input_file:
										lowercase__					  =				re.compile(R"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()",					re.DOTALL						)
										lowercase__					  =				input_file.read()
										# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
										lowercase__					  =				regexp.finditer(__lowercase						)

							lowercase__					  =				[match for match in matches if match is not None and match.group(1						) is not None]
							return matches[0] if matches else None




				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				Path("./datasets"						)
							lowercase__					  =				list(dataset_paths.absolute().glob("**/*.py"						)						)

							for dataset in dataset_files:
										if self._no_encoding_on_file_open(str(__lowercase						)						):
													raise AssertionError(F'''open(...) must use utf-8 encoding in {dataset}'''						)




				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				Path("./datasets"						)
							lowercase__					  =				list(dataset_paths.absolute().glob("**/*.py"						)						)

							for dataset in dataset_files:
										if self._no_print_statements(str(__lowercase						)						):
													raise AssertionError(F'''print statement found in {dataset}. Use datasets.logger/logging instead.'''						)




