
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    a      ( a		,						a		,						a	)     ->List[str]:




			'''simple docstring'''


			def get_masked_lm_array(a	):
						SCREAMING_SNAKE_CASE     						= F"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
						SCREAMING_SNAKE_CASE     						= tf.train.load_variable(a		,						a	)

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

						return torch.from_numpy(a	)

			def get_encoder_array(a	):
						SCREAMING_SNAKE_CASE     						= F"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
						SCREAMING_SNAKE_CASE     						= tf.train.load_variable(a		,						a	)

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

						return torch.from_numpy(a	)

			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(a		,						a	)

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

						return torch.from_numpy(a	)

			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(a		,						a	)
						SCREAMING_SNAKE_CASE     						= array.reshape(a	)

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

						return torch.from_numpy(a	)

			print(F"""Loading model based on config from {config_path}..."""	)
			SCREAMING_SNAKE_CASE     						= BertConfig.from_json_file(a	)
			SCREAMING_SNAKE_CASE     						= BertForMaskedLM(a	)

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

						SCREAMING_SNAKE_CASE     						= get_encoder_layer_array(a		,						'''_attention_layer_norm/gamma'''	)
						SCREAMING_SNAKE_CASE     						= get_encoder_layer_array(a		,						'''_attention_layer_norm/beta'''	)

						# Intermediate
						SCREAMING_SNAKE_CASE     						= layer.intermediate

						SCREAMING_SNAKE_CASE     						= get_encoder_layer_array(a		,						'''_intermediate_dense/kernel'''	)
						SCREAMING_SNAKE_CASE     						= get_encoder_layer_array(a		,						'''_intermediate_dense/bias'''	)

						# Output
						SCREAMING_SNAKE_CASE     						= layer.output

						SCREAMING_SNAKE_CASE     						= get_encoder_layer_array(a		,						'''_output_dense/kernel'''	)
						SCREAMING_SNAKE_CASE     						= get_encoder_layer_array(a		,						'''_output_dense/bias'''	)

						SCREAMING_SNAKE_CASE     						= get_encoder_layer_array(a		,						'''_output_layer_norm/gamma'''	)
						SCREAMING_SNAKE_CASE     						= get_encoder_layer_array(a		,						'''_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=a	)
			SCREAMING_SNAKE_CASE     						= get_encoder_array('''_pooler_layer/kernel'''	)
			SCREAMING_SNAKE_CASE     						= get_encoder_array('''_pooler_layer/bias'''	)

			# Export final model
			model.save_pretrained(a	)

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

			print('''Model conversion was done sucessfully!'''	)


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

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase					   =       {
    'configuration_bridgetower': [
        'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BridgeTowerConfig',
        'BridgeTowerTextConfig',
        'BridgeTowerVisionConfig',
    ],
    'processing_bridgetower': ['BridgeTowerProcessor'],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       ['BridgeTowerImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BridgeTowerForContrastiveLearning',
							    'BridgeTowerForImageAndTextRetrieval',
							    'BridgeTowerForMaskedLM',
							    'BridgeTowerModel',
							    'BridgeTowerPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_bridgetower import (
							    BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BridgeTowerConfig,
							    BridgeTowerTextConfig,
							    BridgeTowerVisionConfig,
							)
							from .processing_bridgetower import BridgeTowerProcessor

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_bridgetower import BridgeTowerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bridgetower import (
														    BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BridgeTowerForContrastiveLearning,
														    BridgeTowerForImageAndTextRetrieval,
														    BridgeTowerForMaskedLM,
														    BridgeTowerModel,
														    BridgeTowerPreTrainedModel,
														)


else:
							import sys

							__lowerCAmelCase					   =       _LazyModule(__name__, globals()['__file__'], _import_structure)