"""simple docstring"""





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_      (      _lowerCamelCase:		str  ,      _lowerCamelCase:		str  ,      _lowerCamelCase:		str					)       ->					Any:



							'''simple docstring'''

							def get_masked_lm_array(_lowerCamelCase:		str					):
														__lowerCamelCase    :						Optional[Any]						      =   F"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														__lowerCamelCase    :						int						      =   tf.train.load_variable(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__					)

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

														return torch.from_numpy(SCREAMING_SNAKE_CASE__					)

							def get_encoder_array(_lowerCamelCase:		str					):
														__lowerCamelCase    :						Optional[Any]						      =   F"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														__lowerCamelCase    :						int						      =   tf.train.load_variable(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__					)

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

														return torch.from_numpy(SCREAMING_SNAKE_CASE__					)

							def get_encoder_layer_array(_lowerCamelCase:		int  ,      _lowerCamelCase:		str					):
														__lowerCamelCase    :						List[Any]						      =   F"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														__lowerCamelCase    :						int						      =   tf.train.load_variable(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__					)

														if "kernel" in name:
																					__lowerCamelCase    :						Union[str, Any]						      =   array.transpose()

														return torch.from_numpy(SCREAMING_SNAKE_CASE__					)

							def get_encoder_attention_layer_array(_lowerCamelCase:		int  ,      _lowerCamelCase:		str  ,      _lowerCamelCase:		Union[str, Any]					):
														__lowerCamelCase    :						List[Any]						      =   F"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
														__lowerCamelCase    :						List[Any]						      =   tf.train.load_variable(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__					)
														__lowerCamelCase    :						Optional[Any]						      =   array.reshape(SCREAMING_SNAKE_CASE__					)

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

														return torch.from_numpy(SCREAMING_SNAKE_CASE__					)

							print(F"""Loading model based on config from {config_path}..."""					)
							__lowerCamelCase    :						Optional[int]						      =   BertConfig.from_json_file(SCREAMING_SNAKE_CASE__					)
							__lowerCamelCase    :						Dict						      =   BertForMaskedLM(SCREAMING_SNAKE_CASE__					)

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

														# Self-attention
														__lowerCamelCase    :						BertSelfAttention						      =   layer.attention.self

														__lowerCamelCase    :						Optional[int]						      =   get_encoder_attention_layer_array(
														    SCREAMING_SNAKE_CASE__  ,      "_query_dense/kernel"  ,      self_attn.query.weight.data.shape					)
														__lowerCamelCase    :						Optional[int]						      =   get_encoder_attention_layer_array(
														    SCREAMING_SNAKE_CASE__  ,      "_query_dense/bias"  ,      self_attn.query.bias.data.shape					)
														__lowerCamelCase    :						str						      =   get_encoder_attention_layer_array(
														    SCREAMING_SNAKE_CASE__  ,      "_key_dense/kernel"  ,      self_attn.key.weight.data.shape					)
														__lowerCamelCase    :						List[str]						      =   get_encoder_attention_layer_array(
														    SCREAMING_SNAKE_CASE__  ,      "_key_dense/bias"  ,      self_attn.key.bias.data.shape					)
														__lowerCamelCase    :						Union[str, Any]						      =   get_encoder_attention_layer_array(
														    SCREAMING_SNAKE_CASE__  ,      "_value_dense/kernel"  ,      self_attn.value.weight.data.shape					)
														__lowerCamelCase    :						int						      =   get_encoder_attention_layer_array(
														    SCREAMING_SNAKE_CASE__  ,      "_value_dense/bias"  ,      self_attn.value.bias.data.shape					)

														# Self-attention Output
														__lowerCamelCase    :						BertSelfOutput						      =   layer.attention.output

														__lowerCamelCase    :						Tuple						      =   get_encoder_attention_layer_array(
														    SCREAMING_SNAKE_CASE__  ,      "_output_dense/kernel"  ,      self_output.dense.weight.data.shape					)
														__lowerCamelCase    :						List[Any]						      =   get_encoder_attention_layer_array(
														    SCREAMING_SNAKE_CASE__  ,      "_output_dense/bias"  ,      self_output.dense.bias.data.shape					)

														__lowerCamelCase    :						Optional[Any]						      =   get_encoder_layer_array(SCREAMING_SNAKE_CASE__  ,      "_attention_layer_norm/gamma"					)
														__lowerCamelCase    :						List[str]						      =   get_encoder_layer_array(SCREAMING_SNAKE_CASE__  ,      "_attention_layer_norm/beta"					)

														# Intermediate
														__lowerCamelCase    :						BertIntermediate						      =   layer.intermediate

														__lowerCamelCase    :						Optional[Any]						      =   get_encoder_layer_array(SCREAMING_SNAKE_CASE__  ,      "_intermediate_dense/kernel"					)
														__lowerCamelCase    :						Optional[Any]						      =   get_encoder_layer_array(SCREAMING_SNAKE_CASE__  ,      "_intermediate_dense/bias"					)

														# Output
														__lowerCamelCase    :						BertOutput						      =   layer.output

														__lowerCamelCase    :						Optional[int]						      =   get_encoder_layer_array(SCREAMING_SNAKE_CASE__  ,      "_output_dense/kernel"					)
														__lowerCamelCase    :						Tuple						      =   get_encoder_layer_array(SCREAMING_SNAKE_CASE__  ,      "_output_dense/bias"					)

														__lowerCamelCase    :						Union[str, Any]						      =   get_encoder_layer_array(SCREAMING_SNAKE_CASE__  ,      "_output_layer_norm/gamma"					)
														__lowerCamelCase    :						Optional[int]						      =   get_encoder_layer_array(SCREAMING_SNAKE_CASE__  ,      "_output_layer_norm/beta"					)

							# Embeddings
							__lowerCamelCase    :						Optional[int]						      =   get_encoder_array("_position_embedding_layer/embeddings"					)
							__lowerCamelCase    :						str						      =   get_encoder_array("_type_embedding_layer/embeddings"					)
							__lowerCamelCase    :						Optional[int]						      =   get_encoder_array("_embedding_norm_layer/gamma"					)
							__lowerCamelCase    :						List[Any]						      =   get_encoder_array("_embedding_norm_layer/beta"					)

							# LM Head
							__lowerCamelCase    :						str						      =   model.cls.predictions.transform

							__lowerCamelCase    :						Any						      =   get_masked_lm_array("dense/kernel"					)
							__lowerCamelCase    :						Tuple						      =   get_masked_lm_array("dense/bias"					)

							__lowerCamelCase    :						List[str]						      =   get_masked_lm_array("layer_norm/gamma"					)
							__lowerCamelCase    :						Tuple						      =   get_masked_lm_array("layer_norm/beta"					)

							__lowerCamelCase    :						Optional[Any]						      =   get_masked_lm_array("embedding_table"					)

							# Pooling
							__lowerCamelCase    :						Tuple						      =   BertPooler(config=SCREAMING_SNAKE_CASE__					)
							__lowerCamelCase    :						BertPooler						      =   get_encoder_array("_pooler_layer/kernel"					)
							__lowerCamelCase    :						BertPooler						      =   get_encoder_array("_pooler_layer/bias"					)

							# Export final model
							model.save_pretrained(SCREAMING_SNAKE_CASE__					)

							# Integration test - should load without any errors ;)
							__lowerCamelCase    :						List[str]						      =   BertForMaskedLM.from_pretrained(SCREAMING_SNAKE_CASE__					)
							print(new_model.eval()					)

							print("Model conversion was done sucessfully!"					)


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



import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


lowerCamelCase											=       logging.getLogger()




@unittest.skip('Temporarily disable the doc tests.'     )
@require_torch
@require_tf
@slow
class A  (   unittest.TestCase     ):






      def        lowerCamelCase       (					self						:     Dict			,   lowercase_						:     Path			,   lowercase_						:     Union[str, None] = None			,   lowercase_						:     Union[List[str], None] = None			,   lowercase_						:     Union[str, List[str], None] = None			,   lowercase_						:     bool = True			,   )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=[file for file in os.listdir(lowercase_ ) if os.path.isfile(os.path.join(lowercase_			,   lowercase_ ) )]

             if identifier is not None:
                    _lowerCamelCase       :		Any		=[file for file in files if identifier in file]

             if n_identifier is not None:
                    if isinstance(lowercase_			,   lowercase_ ):
                           for n_ in n_identifier:
                                  _lowerCamelCase       :		Any		=[file for file in files if n_ not in file]
                    else:
                           _lowerCamelCase       :		List[Any]		=[file for file in files if n_identifier not in file]

             _lowerCamelCase       :		List[Any]		=ignore_files or []
             ignore_files.append('__init__.py' )
             _lowerCamelCase       :		List[Any]		=[file for file in files if file not in ignore_files]

             for file in files:
                    # Open all files
                    print('Testing'			,   lowercase_ )

                    if only_modules:
                           _lowerCamelCase       :		Optional[int]		=file.split('.' )[0]
                           try:
                                  _lowerCamelCase       :		Any		=getattr(lowercase_			,   lowercase_ )
                                  _lowerCamelCase       :		Optional[int]		=doctest.DocTestSuite(lowercase_ )
                                  _lowerCamelCase       :		List[Any]		=unittest.TextTestRunner().run(lowercase_ )
                                  self.assertIs(len(result.failures )			,   0 )
                           except AttributeError:
                                  logger.info(F'''{module_identifier} is not a module.''' )
                    else:
                           _lowerCamelCase       :		int		=doctest.testfile(str('..' / directory / file )			,   optionflags=doctest.ELLIPSIS )
                           self.assertIs(result.failed			,   0 )






      def        lowerCamelCase       (					self						:     Any )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		int		=Path('src/transformers' )
             _lowerCamelCase       :		Optional[Any]		='modeling'
             _lowerCamelCase       :		List[str]		=[
                 'modeling_ctrl.py',
                 'modeling_tf_ctrl.py',
             ]
             self.analyze_directory(lowercase_			,   identifier=lowercase_			,   ignore_files=lowercase_ )






      def        lowerCamelCase       (					self						:     List[str] )					->						Tuple:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=Path('src/transformers' )
             _lowerCamelCase       :		Union[str, Any]		='tokenization'
             self.analyze_directory(lowercase_			,   identifier=lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[int] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		str		=Path('src/transformers' )
             _lowerCamelCase       :		Any		='configuration'
             self.analyze_directory(lowercase_			,   identifier=lowercase_ )






      def        lowerCamelCase       (					self						:     List[str] )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		=Path('src/transformers' )
             _lowerCamelCase       :		int		=['configuration', 'modeling', 'tokenization']
             self.analyze_directory(lowercase_			,   n_identifier=lowercase_ )






      def        lowerCamelCase       (					self						:     Optional[Any] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		str		=Path('docs/source' )
             _lowerCamelCase       :		Dict		=['favicon.ico']
             self.analyze_directory(lowercase_			,   ignore_files=lowercase_			,   only_modules=lowercase_ )


