


'''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 						lowerCamelCase(   a__	,a__	,a__):
				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__":
							snake_case_						:			Optional[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.''',
							)
							snake_case_						:			int													=							parser.parse_args()
							convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class 						A__							(		UpperCamelCase__  ):
						UpperCAmelCase             =						["image_processor", "tokenizer"]
						UpperCAmelCase             =						"ViTImageProcessor"
						UpperCAmelCase             =						("CLIPTokenizer", "CLIPTokenizerFast")




						def __init__(       self					:      int				,    _a					:      int=None				,    _a					:      Optional[int]=None				,    **_a					:      str  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =None
										if "feature_extractor" in kwargs:
														warnings.warn(
														    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
														    ''' instead.'''				,    _a				,    )
														_SCREAMING_SNAKE_CASE			  =kwargs.pop('''feature_extractor'''  )

										_SCREAMING_SNAKE_CASE			  =image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
														raise ValueError('''You need to specify an `image_processor`.'''  )
										if tokenizer is None:
														raise ValueError('''You need to specify a `tokenizer`.'''  )

										super().__init__(_a				,    _a  )




						def __call__(       self					:      List[str]				,    _a					:      Tuple=None				,    _a					:      Optional[Any]=None				,    _a					:      List[Any]=None				,    _a					:      List[Any]=None				,    **_a					:      List[Any]  )							->     Optional[Any]:


										"""simple docstring"""





										if text is None and visual_prompt is None and images is None:
														raise ValueError('''You have to specify either text, visual prompt or images.'''  )

										if text is not None and visual_prompt is not None:
														raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.'''  )

										if text is not None:
														_SCREAMING_SNAKE_CASE			  =self.tokenizer(_a				,    return_tensors=_a				,    **_a  )

										if visual_prompt is not None:
														_SCREAMING_SNAKE_CASE			  =self.image_processor(_a				,    return_tensors=_a				,    **_a  )

										if images is not None:
														_SCREAMING_SNAKE_CASE			  =self.image_processor(_a				,    return_tensors=_a				,    **_a  )

										if visual_prompt is not None and images is not None:
														_SCREAMING_SNAKE_CASE			  ={
														    '''pixel_values''': image_features.pixel_values,
														    '''conditional_pixel_values''': prompt_features.pixel_values,
														}
														return encoding
										elif text is not None and images is not None:
														_SCREAMING_SNAKE_CASE			  =image_features.pixel_values
														return encoding
										elif text is not None:
														return encoding
										elif visual_prompt is not None:
														_SCREAMING_SNAKE_CASE			  ={
														    '''conditional_pixel_values''': prompt_features.pixel_values,
														}
														return encoding
										else:
														return BatchEncoding(data=dict(**_a  )				,    tensor_type=_a  )




						def 							__UpperCamelCase				(       self					:      Any				,    *_a					:      str				,    **_a					:      List[Any]  )							->     Tuple:


										"""simple docstring"""





										return self.tokenizer.batch_decode(*_a				,    **_a  )




						def 							__UpperCamelCase				(       self					:      Dict				,    *_a					:      str				,    **_a					:      List[Any]  )							->     List[Any]:


										"""simple docstring"""





										return self.tokenizer.decode(*_a				,    **_a  )




						@property
						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     int:


										"""simple docstring"""





										warnings.warn(
										    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''				,    _a				,    )
										return self.image_processor_class




						@property
						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Union[str, Any]:


										"""simple docstring"""





										warnings.warn(
										    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''				,    _a				,    )
										return self.image_processor