





'''simple docstring'''




import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase            =       logging.get_logger(__name__)



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				List[Any]		=			os.path.abspath(lowercase__	)
		logger.info(f'''Converting TensorFlow checkpoint from {tf_path}'''	)
		# Load weights from TF model
		SCREAMING_SNAKE_CASE :				Optional[int]		=			tf.train.list_variables(lowercase__	)
		SCREAMING_SNAKE_CASE :				Tuple		=			[]
		SCREAMING_SNAKE_CASE :				str		=			[]
		SCREAMING_SNAKE_CASE :				Optional[Any]		=			[]
		for full_name, shape in init_vars:
				# logger.info(f"Loading TF weight {name} with shape {shape}")
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			full_name.split("""/"""	)
				if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
						logger.info(f'''Skipping non-model layer {full_name}'''	)
						continue
				if "optimizer" in full_name:
						logger.info(f'''Skipping optimization layer {full_name}'''	)
						continue
				if name[0] == "model":
						# ignore initial 'model'
						SCREAMING_SNAKE_CASE :				Dict		=			name[1:]
				# figure out how many levels deep the name is
				SCREAMING_SNAKE_CASE :				str		=			0
				for _name in name:
						if _name.startswith("""layer_with_weights"""	):
								depth += 1
						else:
								break
				layer_depth.append(lowercase__	)
				# read data
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			tf.train.load_variable(lowercase__			,			lowercase__	)
				names.append("""/""".join(lowercase__	)	)
				arrays.append(lowercase__	)
		logger.info(f'''Read a total of {len(lowercase__	):,} layers'''	)

		# Sanity check
		if len(set(lowercase__	)	) != 1:
				raise ValueError(f'''Found layer names with different depths (layer depth {list(set(lowercase__	)	)})'''	)
		SCREAMING_SNAKE_CASE :				Dict		=			list(set(lowercase__	)	)[0]
		if layer_depth != 1:
				raise ValueError(
				    """The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"""
				    """ heads."""	)

		# convert layers
		logger.info("""Converting weights..."""	)
		for full_name, array in zip(lowercase__			,			lowercase__	):
				SCREAMING_SNAKE_CASE :				Any		=			full_name.split("""/"""	)
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			model
				SCREAMING_SNAKE_CASE :				Any		=			[]
				for i, m_name in enumerate(lowercase__	):
						if m_name == ".ATTRIBUTES":
								# variable names end with .ATTRIBUTES/VARIABLE_VALUE
								break
						if m_name.startswith("""layer_with_weights"""	):
								SCREAMING_SNAKE_CASE :				int		=			int(m_name.split("""-"""	)[-1]	)
								if layer_num <= 2:
										# embedding layers
										# layer_num 0: word_embeddings
										# layer_num 1: position_embeddings
										# layer_num 2: token_type_embeddings
										continue
								elif layer_num == 3:
										# embedding LayerNorm
										trace.extend(["""embeddings""", """LayerNorm"""]	)
										SCREAMING_SNAKE_CASE :				List[str]		=			getattr(lowercase__			,			"""embeddings"""	)
										SCREAMING_SNAKE_CASE :				str		=			getattr(lowercase__			,			"""LayerNorm"""	)
								elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
										# encoder layers
										trace.extend(["""encoder""", """layer""", str(layer_num - 4	)]	)
										SCREAMING_SNAKE_CASE :				List[str]		=			getattr(lowercase__			,			"""encoder"""	)
										SCREAMING_SNAKE_CASE :				Any		=			getattr(lowercase__			,			"""layer"""	)
										SCREAMING_SNAKE_CASE :				str		=			pointer[layer_num - 4]
								elif layer_num == config.num_hidden_layers + 4:
										# pooler layer
										trace.extend(["""pooler""", """dense"""]	)
										SCREAMING_SNAKE_CASE :				Tuple		=			getattr(lowercase__			,			"""pooler"""	)
										SCREAMING_SNAKE_CASE :				Union[str, Any]		=			getattr(lowercase__			,			"""dense"""	)
						elif m_name == "embeddings":
								trace.append("""embeddings"""	)
								SCREAMING_SNAKE_CASE :				Union[str, Any]		=			getattr(lowercase__			,			"""embeddings"""	)
								if layer_num == 0:
										trace.append("""word_embeddings"""	)
										SCREAMING_SNAKE_CASE :				Tuple		=			getattr(lowercase__			,			"""word_embeddings"""	)
								elif layer_num == 1:
										trace.append("""position_embeddings"""	)
										SCREAMING_SNAKE_CASE :				Any		=			getattr(lowercase__			,			"""position_embeddings"""	)
								elif layer_num == 2:
										trace.append("""token_type_embeddings"""	)
										SCREAMING_SNAKE_CASE :				List[Any]		=			getattr(lowercase__			,			"""token_type_embeddings"""	)
								else:
										raise ValueError(f'''Unknown embedding layer with name {full_name}'''	)
								trace.append("""weight"""	)
								SCREAMING_SNAKE_CASE :				Dict		=			getattr(lowercase__			,			"""weight"""	)
						elif m_name == "_attention_layer":
								# self-attention layer
								trace.extend(["""attention""", """self"""]	)
								SCREAMING_SNAKE_CASE :				List[Any]		=			getattr(lowercase__			,			"""attention"""	)
								SCREAMING_SNAKE_CASE :				Optional[Any]		=			getattr(lowercase__			,			"""self"""	)
						elif m_name == "_attention_layer_norm":
								# output attention norm
								trace.extend(["""attention""", """output""", """LayerNorm"""]	)
								SCREAMING_SNAKE_CASE :				Dict		=			getattr(lowercase__			,			"""attention"""	)
								SCREAMING_SNAKE_CASE :				Any		=			getattr(lowercase__			,			"""output"""	)
								SCREAMING_SNAKE_CASE :				Optional[int]		=			getattr(lowercase__			,			"""LayerNorm"""	)
						elif m_name == "_attention_output_dense":
								# output attention dense
								trace.extend(["""attention""", """output""", """dense"""]	)
								SCREAMING_SNAKE_CASE :				str		=			getattr(lowercase__			,			"""attention"""	)
								SCREAMING_SNAKE_CASE :				List[str]		=			getattr(lowercase__			,			"""output"""	)
								SCREAMING_SNAKE_CASE :				Optional[int]		=			getattr(lowercase__			,			"""dense"""	)
						elif m_name == "_output_dense":
								# output dense
								trace.extend(["""output""", """dense"""]	)
								SCREAMING_SNAKE_CASE :				List[Any]		=			getattr(lowercase__			,			"""output"""	)
								SCREAMING_SNAKE_CASE :				Optional[int]		=			getattr(lowercase__			,			"""dense"""	)
						elif m_name == "_output_layer_norm":
								# output dense
								trace.extend(["""output""", """LayerNorm"""]	)
								SCREAMING_SNAKE_CASE :				List[str]		=			getattr(lowercase__			,			"""output"""	)
								SCREAMING_SNAKE_CASE :				List[str]		=			getattr(lowercase__			,			"""LayerNorm"""	)
						elif m_name == "_key_dense":
								# attention key
								trace.append("""key"""	)
								SCREAMING_SNAKE_CASE :				Dict		=			getattr(lowercase__			,			"""key"""	)
						elif m_name == "_query_dense":
								# attention query
								trace.append("""query"""	)
								SCREAMING_SNAKE_CASE :				int		=			getattr(lowercase__			,			"""query"""	)
						elif m_name == "_value_dense":
								# attention value
								trace.append("""value"""	)
								SCREAMING_SNAKE_CASE :				List[str]		=			getattr(lowercase__			,			"""value"""	)
						elif m_name == "_intermediate_dense":
								# attention intermediate dense
								trace.extend(["""intermediate""", """dense"""]	)
								SCREAMING_SNAKE_CASE :				Tuple		=			getattr(lowercase__			,			"""intermediate"""	)
								SCREAMING_SNAKE_CASE :				str		=			getattr(lowercase__			,			"""dense"""	)
						elif m_name == "_output_layer_norm":
								# output layer norm
								trace.append("""output"""	)
								SCREAMING_SNAKE_CASE :				str		=			getattr(lowercase__			,			"""output"""	)
						# weights & biases
						elif m_name in ["bias", "beta"]:
								trace.append("""bias"""	)
								SCREAMING_SNAKE_CASE :				Optional[Any]		=			getattr(lowercase__			,			"""bias"""	)
						elif m_name in ["kernel", "gamma"]:
								trace.append("""weight"""	)
								SCREAMING_SNAKE_CASE :				Dict		=			getattr(lowercase__			,			"""weight"""	)
						else:
								logger.warning(f'''Ignored {m_name}'''	)
								# for certain layers reshape is necessary
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			".".join(lowercase__	)
				if re.match(R"""(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"""			,			lowercase__	) or re.match(
				    R"""(\S+)\.attention\.output\.dense\.weight"""			,			lowercase__	):
						SCREAMING_SNAKE_CASE :				Tuple		=			array.reshape(pointer.data.shape	)
				if "kernel" in full_name:
						SCREAMING_SNAKE_CASE :				List[Any]		=			array.transpose()
				if pointer.shape == array.shape:
						SCREAMING_SNAKE_CASE :				Tuple		=			torch.from_numpy(lowercase__	)
				else:
						raise ValueError(
						    f'''Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'''
						    f''' {array.shape}'''	)
				logger.info(f'''Successfully set variable {full_name} to PyTorch layer {trace}'''	)
		return model



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		logger.info(f'''Loading model based on config from {config_path}...'''	)
		SCREAMING_SNAKE_CASE :				int		=			BertConfig.from_json_file(lowercase__	)
		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			BertModel(lowercase__	)

		# Load weights from checkpoint
		logger.info(f'''Loading weights from checkpoint {tf_checkpoint_path}...'''	)
		load_tfa_weights_in_bert(lowercase__			,			lowercase__			,			lowercase__	)

		# Save pytorch-model
		logger.info(f'''Saving PyTorch model to {pytorch_dump_path}...'''	)
		torch.save(model.state_dict()			,			lowercase__	)


if __name__ == "__main__":
							__UpperCAmelCase            =       argparse.ArgumentParser()
							parser.add_argument(
							    """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow 2.x checkpoint path."""
							)
							parser.add_argument(
							    """--bert_config_file""",
							    type=str,
							    required=True,
							    help="""The config json file corresponding to the BERT model. This specifies the model architecture.""",
							)
							parser.add_argument(
							    """--pytorch_dump_path""",
							    type=str,
							    required=True,
							    help="""Path to the output PyTorch model (must include filename).""",
							)
							__UpperCAmelCase            =       parser.parse_args()
							convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





'''simple docstring'''





def snake_case_     (					lowercase__ = "The quick brown fox jumps over the lazy dog"   ,       ):
  UpperCAmelCase__      :					Dict				       =							set()

  # Replace all the whitespace in our sentence
  UpperCAmelCase__      :					str				       =							input_str.replace(" "   ,       ""		)
  for alpha in input_str:
    if "a" <= alpha.lower() <= "z":
      frequency.add(alpha.lower()		)
  return len(lowercase__		) == 2_6







def snake_case_     (					lowercase__ = "The quick brown fox jumps over the lazy dog"   ,       ):
  UpperCAmelCase__      :					str				       =							[False] * 2_6
  for char in input_str:
    if char.islower():
      UpperCAmelCase__      :					List[Any]				       =							True
    elif char.isupper():
      UpperCAmelCase__      :					List[Any]				       =							True
  return all(lowercase__		)







def snake_case_     (					lowercase__ = "The quick brown fox jumps over the lazy dog"   ,       ):
  return len({char for char in input_str.lower() if char.isalpha()}		) == 2_6







def snake_case_     (					):
  from timeit import timeit

  UpperCAmelCase__      :					Union[str, Any]				       =							"from __main__ import is_pangram, is_pangram_faster, is_pangram_fastest"
  print(timeit("is_pangram()"   ,       setup=lowercase__		)		)
  print(timeit("is_pangram_faster()"   ,       setup=lowercase__		)		)
  print(timeit("is_pangram_fastest()"   ,       setup=lowercase__		)		)
  # 5.348480500048026, 2.6477354579837993, 1.8470395830227062
  # 5.036091582966037, 2.644472333951853,  1.8869528750656173


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      benchmark()



