

import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							def get_masked_lm_array(UpperCamelCase__				):
														snake_case_          =   F'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
														snake_case_          =   tf.train.load_variable(UpperCamelCase__       ,							UpperCamelCase__				)

														if "kernel" in name:
																					snake_case_          =   array.transpose()

														return torch.from_numpy(UpperCamelCase__				)

							def get_encoder_array(UpperCamelCase__				):
														snake_case_          =   F'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
														snake_case_          =   tf.train.load_variable(UpperCamelCase__       ,							UpperCamelCase__				)

														if "kernel" in name:
																					snake_case_          =   array.transpose()

														return torch.from_numpy(UpperCamelCase__				)

							def get_encoder_layer_array(UpperCamelCase__       ,							UpperCamelCase__				):
														snake_case_          =   F'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
														snake_case_          =   tf.train.load_variable(UpperCamelCase__       ,							UpperCamelCase__				)

														if "kernel" in name:
																					snake_case_          =   array.transpose()

														return torch.from_numpy(UpperCamelCase__				)

							def get_encoder_attention_layer_array(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):
														snake_case_          =   F'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
														snake_case_          =   tf.train.load_variable(UpperCamelCase__       ,							UpperCamelCase__				)
														snake_case_          =   array.reshape(UpperCamelCase__				)

														if "kernel" in name:
																					snake_case_          =   array.transpose()

														return torch.from_numpy(UpperCamelCase__				)

							print(F'''Loading model based on config from {config_path}...'''				)
							snake_case_          =   BertConfig.from_json_file(UpperCamelCase__				)
							snake_case_          =   BertForMaskedLM(UpperCamelCase__				)

							# Layers
							for layer_index in range(0       ,							config.num_hidden_layers				):
														snake_case_          =   model.bert.encoder.layer[layer_index]

														# Self-attention
														snake_case_          =   layer.attention.self

														snake_case_          =   get_encoder_attention_layer_array(
														    UpperCamelCase__       ,							'_query_dense/kernel'       ,							self_attn.query.weight.data.shape				)
														snake_case_          =   get_encoder_attention_layer_array(
														    UpperCamelCase__       ,							'_query_dense/bias'       ,							self_attn.query.bias.data.shape				)
														snake_case_          =   get_encoder_attention_layer_array(
														    UpperCamelCase__       ,							'_key_dense/kernel'       ,							self_attn.key.weight.data.shape				)
														snake_case_          =   get_encoder_attention_layer_array(
														    UpperCamelCase__       ,							'_key_dense/bias'       ,							self_attn.key.bias.data.shape				)
														snake_case_          =   get_encoder_attention_layer_array(
														    UpperCamelCase__       ,							'_value_dense/kernel'       ,							self_attn.value.weight.data.shape				)
														snake_case_          =   get_encoder_attention_layer_array(
														    UpperCamelCase__       ,							'_value_dense/bias'       ,							self_attn.value.bias.data.shape				)

														# Self-attention Output
														snake_case_          =   layer.attention.output

														snake_case_          =   get_encoder_attention_layer_array(
														    UpperCamelCase__       ,							'_output_dense/kernel'       ,							self_output.dense.weight.data.shape				)
														snake_case_          =   get_encoder_attention_layer_array(
														    UpperCamelCase__       ,							'_output_dense/bias'       ,							self_output.dense.bias.data.shape				)

														snake_case_          =   get_encoder_layer_array(UpperCamelCase__       ,							'_attention_layer_norm/gamma'				)
														snake_case_          =   get_encoder_layer_array(UpperCamelCase__       ,							'_attention_layer_norm/beta'				)

														# Intermediate
														snake_case_          =   layer.intermediate

														snake_case_          =   get_encoder_layer_array(UpperCamelCase__       ,							'_intermediate_dense/kernel'				)
														snake_case_          =   get_encoder_layer_array(UpperCamelCase__       ,							'_intermediate_dense/bias'				)

														# Output
														snake_case_          =   layer.output

														snake_case_          =   get_encoder_layer_array(UpperCamelCase__       ,							'_output_dense/kernel'				)
														snake_case_          =   get_encoder_layer_array(UpperCamelCase__       ,							'_output_dense/bias'				)

														snake_case_          =   get_encoder_layer_array(UpperCamelCase__       ,							'_output_layer_norm/gamma'				)
														snake_case_          =   get_encoder_layer_array(UpperCamelCase__       ,							'_output_layer_norm/beta'				)

							# Embeddings
							snake_case_          =   get_encoder_array('_position_embedding_layer/embeddings'				)
							snake_case_          =   get_encoder_array('_type_embedding_layer/embeddings'				)
							snake_case_          =   get_encoder_array('_embedding_norm_layer/gamma'				)
							snake_case_          =   get_encoder_array('_embedding_norm_layer/beta'				)

							# LM Head
							snake_case_          =   model.cls.predictions.transform

							snake_case_          =   get_masked_lm_array('dense/kernel'				)
							snake_case_          =   get_masked_lm_array('dense/bias'				)

							snake_case_          =   get_masked_lm_array('layer_norm/gamma'				)
							snake_case_          =   get_masked_lm_array('layer_norm/beta'				)

							snake_case_          =   get_masked_lm_array('embedding_table'				)

							# Pooling
							snake_case_          =   BertPooler(config=UpperCamelCase__				)
							snake_case_          =   get_encoder_array('_pooler_layer/kernel'				)
							snake_case_          =   get_encoder_array('_pooler_layer/bias'				)

							# Export final model
							model.save_pretrained(UpperCamelCase__				)

							# Integration test - should load without any errors ;)
							snake_case_          =   BertForMaskedLM.from_pretrained(UpperCamelCase__				)
							print(new_model.eval()				)

							print('Model conversion was done sucessfully!'				)


if __name__ == "__main__":
					_UpperCAmelCase		:       int						    =	argparse.ArgumentParser()
					parser.add_argument(
					    """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow Token Dropping 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.""",
					)
					_UpperCAmelCase		:       int						    =	parser.parse_args()
					convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







# Lint as: python3
import itertools
import os
import re


_UpperCAmelCase		:       str						    =	re.compile(R"""([A-Z]+)([A-Z][a-z])""")
_UpperCAmelCase		:       Dict						    =	re.compile(R"""([a-z\d])([A-Z])""")

_UpperCAmelCase		:       Dict						    =	re.compile(R"""(?<!_)_(?!_)""")
_UpperCAmelCase		:       Tuple						    =	re.compile(R"""(_{2,})""")

_UpperCAmelCase		:       Any						    =	R"""^\w+(\.\w+)*$"""

_UpperCAmelCase		:       List[str]						    =	R"""<>:/\|?*"""





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   _uppercase_uppercase_re.sub(r'\1_\2'       ,							UpperCamelCase__				)
							snake_case_          =   _lowercase_uppercase_re.sub(r'\1_\2'       ,							UpperCamelCase__				)
							return name.lower()





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   _single_underscore_re.split(UpperCamelCase__				)
							snake_case_          =   [_multiple_underscores_re.split(UpperCamelCase__				) for n in name]
							return "".join(n.capitalize() for n in itertools.chain.from_iterable(UpperCamelCase__				) if n != ''				)





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							if os.path.basename(UpperCamelCase__				) != name:
														raise ValueError(F'''Should be a dataset name, not a path: {name}'''				)
							return camelcase_to_snakecase(UpperCamelCase__				)





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							if os.path.basename(UpperCamelCase__				) != name:
														raise ValueError(F'''Should be a dataset name, not a path: {name}'''				)
							if not re.match(_split_re       ,							UpperCamelCase__				):
														raise ValueError(F'''Split name should match \'{_split_re}\'\' but got \'{split}\'.'''				)
							return F'''{filename_prefix_for_name(UpperCamelCase__				)}-{split}'''





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__=None				):







							'''simple docstring'''






							snake_case_          =   filename_prefix_for_split(UpperCamelCase__       ,							UpperCamelCase__				)
							if filetype_suffix:
														prefix += F'''.{filetype_suffix}'''
							snake_case_          =   os.path.join(UpperCamelCase__       ,							UpperCamelCase__				)
							return F'''{filepath}*'''





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__=None       ,							UpperCamelCase__=None				):







							'''simple docstring'''






							snake_case_          =   filename_prefix_for_split(UpperCamelCase__       ,							UpperCamelCase__				)
							snake_case_          =   os.path.join(UpperCamelCase__       ,							UpperCamelCase__				)

							if shard_lengths:
														snake_case_          =   len(UpperCamelCase__				)
														snake_case_          =   [F'''{prefix}-{shard_id:05d}-of-{num_shards:05d}''' for shard_id in range(UpperCamelCase__				)]
														if filetype_suffix:
																					snake_case_          =   [filename + F'''.{filetype_suffix}''' for filename in filenames]
														return filenames
							else:
														snake_case_          =   prefix
														if filetype_suffix:
																					filename += F'''.{filetype_suffix}'''
														return [filename]




