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 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case						):
					def get_masked_lm_array(__snake_case						):
										_UpperCamelCase			     =     f"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
										_UpperCamelCase			     =     tf.train.load_variable(__snake_case  ,     __snake_case						)

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

										return torch.from_numpy(__snake_case						)

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

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

										return torch.from_numpy(__snake_case						)

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

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

										return torch.from_numpy(__snake_case						)

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

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

										return torch.from_numpy(__snake_case						)

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

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

										# Self-attention
										_UpperCamelCase			     =     layer.attention.self

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

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

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

										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_attention_layer_norm/gamma'''						)
										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_attention_layer_norm/beta'''						)

										# Intermediate
										_UpperCamelCase			     =     layer.intermediate

										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_intermediate_dense/kernel'''						)
										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_intermediate_dense/bias'''						)

										# Output
										_UpperCamelCase			     =     layer.output

										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_output_dense/kernel'''						)
										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_output_dense/bias'''						)

										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_output_layer_norm/gamma'''						)
										_UpperCamelCase			     =     get_encoder_layer_array(__snake_case  ,     '''_output_layer_norm/beta'''						)

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

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

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

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

					_UpperCamelCase			     =     get_masked_lm_array('''embedding_table'''						)

					# Pooling
					_UpperCamelCase			     =     BertPooler(config=__snake_case						)
					_UpperCamelCase			     =     get_encoder_array('''_pooler_layer/kernel'''						)
					_UpperCamelCase			     =     get_encoder_array('''_pooler_layer/bias'''						)

					# Export final model
					model.save_pretrained(__snake_case						)

					# Integration test - should load without any errors ;)
					_UpperCamelCase			     =     BertForMaskedLM.from_pretrained(__snake_case						)
					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)




'''simple docstring'''





import numpy as np

from transformers import Pipeline






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Any ):






			"""simple docstring"""




			__A=	np.max(_SCREAMING_SNAKE_CASE,axis=-1,keepdims=_SCREAMING_SNAKE_CASE )
			__A=	np.exp(outputs - maxes )
			return shifted_exp / shifted_exp.sum(axis=-1,keepdims=_SCREAMING_SNAKE_CASE )






class 						a__					(					a_   ):





			'''simple docstring'''

			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    **lowerCAmelCase_							:			List[str]	)      ->		List[str]:
						__A=	{}
						if "second_text" in kwargs:
									__A=	kwargs['second_text']
						return preprocess_kwargs, {}, {}
			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Union[str, Any]			,    lowerCAmelCase_							:			Optional[int]=None	)      ->		int:
						return self.tokenizer(lowerCAmelCase_			,    text_pair=lowerCAmelCase_			,    return_tensors=self.framework	)
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			List[str]	)      ->		Tuple:
						return self.model(**lowerCAmelCase_	)

			def 					lowerCAmelCase     (  self							:			str			,    lowerCAmelCase_							:			Dict	)      ->		Optional[int]:
						__A=	model_outputs.logits[0].numpy()
						__A=	softmax(lowerCAmelCase_	)

						__A=	np.argmax(lowerCAmelCase_	)
						__A=	self.model.config.idalabel[best_class]
						__A=	probabilities[best_class].item()
						__A=	logits.tolist()
						return {"label": label, "score": score, "logits": logits}



