

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     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							def get_masked_lm_array(UpperCamelCase__				):
														snake_case_          =   F'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
														snake_case_          =   tf.train.load_variable(UpperCamelCase__       ,							UpperCamelCase__				)

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

														return torch.from_numpy(UpperCamelCase__				)

							def get_encoder_array(UpperCamelCase__				):
														snake_case_          =   F'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
														snake_case_          =   tf.train.load_variable(UpperCamelCase__       ,							UpperCamelCase__				)

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

														return torch.from_numpy(UpperCamelCase__				)

							def get_encoder_layer_array(UpperCamelCase__       ,							UpperCamelCase__				):
														snake_case_          =   F'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
														snake_case_          =   tf.train.load_variable(UpperCamelCase__       ,							UpperCamelCase__				)

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

														return torch.from_numpy(UpperCamelCase__				)

							def get_encoder_attention_layer_array(UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):
														snake_case_          =   F'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
														snake_case_          =   tf.train.load_variable(UpperCamelCase__       ,							UpperCamelCase__				)
														snake_case_          =   array.reshape(UpperCamelCase__				)

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

														return torch.from_numpy(UpperCamelCase__				)

							print(F'''Loading model based on config from {config_path}...'''				)
							snake_case_          =   BertConfig.from_json_file(UpperCamelCase__				)
							snake_case_          =   BertForMaskedLM(UpperCamelCase__				)

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

														# Self-attention
														snake_case_          =   layer.attention.self

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

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

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

														snake_case_          =   get_encoder_layer_array(UpperCamelCase__       ,							'_attention_layer_norm/gamma'				)
														snake_case_          =   get_encoder_layer_array(UpperCamelCase__       ,							'_attention_layer_norm/beta'				)

														# Intermediate
														snake_case_          =   layer.intermediate

														snake_case_          =   get_encoder_layer_array(UpperCamelCase__       ,							'_intermediate_dense/kernel'				)
														snake_case_          =   get_encoder_layer_array(UpperCamelCase__       ,							'_intermediate_dense/bias'				)

														# Output
														snake_case_          =   layer.output

														snake_case_          =   get_encoder_layer_array(UpperCamelCase__       ,							'_output_dense/kernel'				)
														snake_case_          =   get_encoder_layer_array(UpperCamelCase__       ,							'_output_dense/bias'				)

														snake_case_          =   get_encoder_layer_array(UpperCamelCase__       ,							'_output_layer_norm/gamma'				)
														snake_case_          =   get_encoder_layer_array(UpperCamelCase__       ,							'_output_layer_norm/beta'				)

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

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

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

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

							snake_case_          =   get_masked_lm_array('embedding_table'				)

							# Pooling
							snake_case_          =   BertPooler(config=UpperCamelCase__				)
							snake_case_          =   get_encoder_array('_pooler_layer/kernel'				)
							snake_case_          =   get_encoder_array('_pooler_layer/bias'				)

							# Export final model
							model.save_pretrained(UpperCamelCase__				)

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

							print('Model conversion was done sucessfully!'				)


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








import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__       ,							UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   BigBirdConfig.from_json_file(UpperCamelCase__				)
							print(F'''Building PyTorch model from configuration: {config}'''				)

							if is_trivia_qa:
														snake_case_          =   BigBirdForQuestionAnswering(UpperCamelCase__				)
							else:
														snake_case_          =   BigBirdForPreTraining(UpperCamelCase__				)

							# Load weights from tf checkpoint
							load_tf_weights_in_big_bird(UpperCamelCase__       ,							UpperCamelCase__       ,							is_trivia_qa=UpperCamelCase__				)

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}'''				)
							model.save_pretrained(UpperCamelCase__				)


if __name__ == "__main__":
					_UpperCAmelCase		:       Dict						    =	argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--big_bird_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained BERT model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
					)
					parser.add_argument(
					    """--is_trivia_qa""", action="""store_true""", help="""Whether to convert a model with a trivia_qa head."""
					)
					_UpperCAmelCase		:       str						    =	parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
					)




