


"""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      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




					'''simple docstring'''


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

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

										return torch.from_numpy(lowerCamelCase__     )

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

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

										return torch.from_numpy(lowerCamelCase__     )

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

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

										return torch.from_numpy(lowerCamelCase__     )

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

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

										return torch.from_numpy(lowerCamelCase__     )

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

					# 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(
										    lowerCamelCase__						,		"_query_dense/kernel"						,		self_attn.query.weight.data.shape     )
										__SCREAMING_SNAKE_CASE		     =       get_encoder_attention_layer_array(
										    lowerCamelCase__						,		"_query_dense/bias"						,		self_attn.query.bias.data.shape     )
										__SCREAMING_SNAKE_CASE		     =       get_encoder_attention_layer_array(
										    lowerCamelCase__						,		"_key_dense/kernel"						,		self_attn.key.weight.data.shape     )
										__SCREAMING_SNAKE_CASE		     =       get_encoder_attention_layer_array(
										    lowerCamelCase__						,		"_key_dense/bias"						,		self_attn.key.bias.data.shape     )
										__SCREAMING_SNAKE_CASE		     =       get_encoder_attention_layer_array(
										    lowerCamelCase__						,		"_value_dense/kernel"						,		self_attn.value.weight.data.shape     )
										__SCREAMING_SNAKE_CASE		     =       get_encoder_attention_layer_array(
										    lowerCamelCase__						,		"_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(
										    lowerCamelCase__						,		"_output_dense/kernel"						,		self_output.dense.weight.data.shape     )
										__SCREAMING_SNAKE_CASE		     =       get_encoder_attention_layer_array(
										    lowerCamelCase__						,		"_output_dense/bias"						,		self_output.dense.bias.data.shape     )

										__SCREAMING_SNAKE_CASE		     =       get_encoder_layer_array(lowerCamelCase__						,		"_attention_layer_norm/gamma"     )
										__SCREAMING_SNAKE_CASE		     =       get_encoder_layer_array(lowerCamelCase__						,		"_attention_layer_norm/beta"     )

										# Intermediate
										__SCREAMING_SNAKE_CASE		     =       layer.intermediate

										__SCREAMING_SNAKE_CASE		     =       get_encoder_layer_array(lowerCamelCase__						,		"_intermediate_dense/kernel"     )
										__SCREAMING_SNAKE_CASE		     =       get_encoder_layer_array(lowerCamelCase__						,		"_intermediate_dense/bias"     )

										# Output
										__SCREAMING_SNAKE_CASE		     =       layer.output

										__SCREAMING_SNAKE_CASE		     =       get_encoder_layer_array(lowerCamelCase__						,		"_output_dense/kernel"     )
										__SCREAMING_SNAKE_CASE		     =       get_encoder_layer_array(lowerCamelCase__						,		"_output_dense/bias"     )

										__SCREAMING_SNAKE_CASE		     =       get_encoder_layer_array(lowerCamelCase__						,		"_output_layer_norm/gamma"     )
										__SCREAMING_SNAKE_CASE		     =       get_encoder_layer_array(lowerCamelCase__						,		"_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=lowerCamelCase__     )
					__SCREAMING_SNAKE_CASE		     =       get_encoder_array("_pooler_layer/kernel"     )
					__SCREAMING_SNAKE_CASE		     =       get_encoder_array("_pooler_layer/bias"     )

					# Export final model
					model.save_pretrained(lowerCamelCase__     )

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

					print("Model conversion was done sucessfully!"     )


if __name__ == "__main__":
	a__       :							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.''',
	)
	a__       :							Optional[Any]        =  parser.parse_args()
	convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor


a__       :							Dict        =  logging.get_logger(__name__)




class 				UpperCamelCase_			(				UpperCamelCase):

	"""simple docstring"""





	def __init__(				self    :       str    ,    *UpperCAmelCase__    :       List[Any]    ,    **UpperCAmelCase__    :       Tuple     ) ->			None:
						warnings.warn(
						    "The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
						    " Please use VideoMAEImageProcessor instead."    ,    UpperCAmelCase__    ,    )
						super().__init__(*UpperCAmelCase__    ,    **UpperCAmelCase__     )
