



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       __lowercase  (					a__				,  a__				,  a__  )							->					List[Any]:


							def get_masked_lm_array(a__  ):
														__SCREAMING_SNAKE_CASE										=   f"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														__SCREAMING_SNAKE_CASE										=   tf.train.load_variable(UpperCamelCase__				,  UpperCamelCase__  )

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

														return torch.from_numpy(UpperCamelCase__  )

							def get_encoder_array(a__  ):
														__SCREAMING_SNAKE_CASE										=   f"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														__SCREAMING_SNAKE_CASE										=   tf.train.load_variable(UpperCamelCase__				,  UpperCamelCase__  )

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

														return torch.from_numpy(UpperCamelCase__  )

							def get_encoder_layer_array(a__				,  a__  ):
														__SCREAMING_SNAKE_CASE										=   f"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														__SCREAMING_SNAKE_CASE										=   tf.train.load_variable(UpperCamelCase__				,  UpperCamelCase__  )

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

														return torch.from_numpy(UpperCamelCase__  )

							def get_encoder_attention_layer_array(a__				,  a__				,  a__  ):
														__SCREAMING_SNAKE_CASE										=   f"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														__SCREAMING_SNAKE_CASE										=   tf.train.load_variable(UpperCamelCase__				,  UpperCamelCase__  )
														__SCREAMING_SNAKE_CASE										=   array.reshape(UpperCamelCase__  )

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

														return torch.from_numpy(UpperCamelCase__  )

							print(f"""Loading model based on config from {config_path}..."""  )
							__SCREAMING_SNAKE_CASE										=   BertConfig.from_json_file(UpperCamelCase__  )
							__SCREAMING_SNAKE_CASE										=   BertForMaskedLM(UpperCamelCase__  )

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

														# Self-attention
														__SCREAMING_SNAKE_CASE										=   layer.attention.self

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

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

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

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

														# Intermediate
														__SCREAMING_SNAKE_CASE										=   layer.intermediate

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

														# Output
														__SCREAMING_SNAKE_CASE										=   layer.output

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

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

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

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

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

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

							__SCREAMING_SNAKE_CASE										=   get_masked_lm_array('embedding_table'  )

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

							# Export final model
							model.save_pretrained(UpperCamelCase__  )

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

							print('Model conversion was done sucessfully!'  )


if __name__ == "__main__":
			lowerCAmelCase__						:	str					=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.''',
			)
			lowerCAmelCase__						:	int					=parser.parse_args()
			convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)












# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser







def      lowerCamelCase_						(			)	->			Union[str, Any]:





				"""simple docstring"""


				__lowerCamelCase									=       ArgumentParser('Accelerate CLI tool'					,	usage='accelerate <command> [<args>]'					,	allow_abbrev=UpperCamelCase__ )
				__lowerCamelCase									=       parser.add_subparsers(help='accelerate command helpers' )

				# Register commands
				get_config_parser(subparsers=UpperCamelCase__ )
				env_command_parser(subparsers=UpperCamelCase__ )
				launch_command_parser(subparsers=UpperCamelCase__ )
				tpu_command_parser(subparsers=UpperCamelCase__ )
				test_command_parser(subparsers=UpperCamelCase__ )

				# Let's go
				__lowerCamelCase									=       parser.parse_args()

				if not hasattr(UpperCamelCase__					,	'func' ):
								parser.print_help()
								exit(1 )

				# Run
				args.func(UpperCamelCase__ )


if __name__ == "__main__":
					main()


