
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     (      a_						,  a_						,  a_		)						->      Any:







				"""simple docstring"""



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

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

								return torch.from_numpy(a_		)

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

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

								return torch.from_numpy(a_		)

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

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

								return torch.from_numpy(a_		)

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

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

								return torch.from_numpy(a_		)

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

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

								# Self-attention
								__A       =					layer.attention.self

								__A       =					get_encoder_attention_layer_array(
								    a_						,  "_query_dense/kernel"						,  self_attn.query.weight.data.shape		)
								__A       =					get_encoder_attention_layer_array(
								    a_						,  "_query_dense/bias"						,  self_attn.query.bias.data.shape		)
								__A       =					get_encoder_attention_layer_array(
								    a_						,  "_key_dense/kernel"						,  self_attn.key.weight.data.shape		)
								__A       =					get_encoder_attention_layer_array(
								    a_						,  "_key_dense/bias"						,  self_attn.key.bias.data.shape		)
								__A       =					get_encoder_attention_layer_array(
								    a_						,  "_value_dense/kernel"						,  self_attn.value.weight.data.shape		)
								__A       =					get_encoder_attention_layer_array(
								    a_						,  "_value_dense/bias"						,  self_attn.value.bias.data.shape		)

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

								__A       =					get_encoder_attention_layer_array(
								    a_						,  "_output_dense/kernel"						,  self_output.dense.weight.data.shape		)
								__A       =					get_encoder_attention_layer_array(
								    a_						,  "_output_dense/bias"						,  self_output.dense.bias.data.shape		)

								__A       =					get_encoder_layer_array(a_						,  "_attention_layer_norm/gamma"		)
								__A       =					get_encoder_layer_array(a_						,  "_attention_layer_norm/beta"		)

								# Intermediate
								__A       =					layer.intermediate

								__A       =					get_encoder_layer_array(a_						,  "_intermediate_dense/kernel"		)
								__A       =					get_encoder_layer_array(a_						,  "_intermediate_dense/bias"		)

								# Output
								__A       =					layer.output

								__A       =					get_encoder_layer_array(a_						,  "_output_dense/kernel"		)
								__A       =					get_encoder_layer_array(a_						,  "_output_dense/bias"		)

								__A       =					get_encoder_layer_array(a_						,  "_output_layer_norm/gamma"		)
								__A       =					get_encoder_layer_array(a_						,  "_output_layer_norm/beta"		)

				# Embeddings
				__A       =					get_encoder_array("_position_embedding_layer/embeddings"		)
				__A       =					get_encoder_array("_type_embedding_layer/embeddings"		)
				__A       =					get_encoder_array("_embedding_norm_layer/gamma"		)
				__A       =					get_encoder_array("_embedding_norm_layer/beta"		)

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

				__A       =					get_masked_lm_array("dense/kernel"		)
				__A       =					get_masked_lm_array("dense/bias"		)

				__A       =					get_masked_lm_array("layer_norm/gamma"		)
				__A       =					get_masked_lm_array("layer_norm/beta"		)

				__A       =					get_masked_lm_array("embedding_table"		)

				# Pooling
				__A       =					BertPooler(config=a_		)
				__A       =					get_encoder_array("_pooler_layer/kernel"		)
				__A       =					get_encoder_array("_pooler_layer/bias"		)

				# Export final model
				model.save_pretrained(a_		)

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

				print("Model conversion was done sucessfully!"		)


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







'''simple docstring'''




from ....configuration_utils import PretrainedConfig
from ....utils import logging


_SCREAMING_SNAKE_CASE							     =			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							     =			{
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}
class 			_lowerCAmelCase		(			A__	):






     """simple docstring"""






     snake_case_						      =   "trajectory_transformer"
     snake_case_						      =   ["past_key_values"]
     snake_case_						      =   {
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }



     def __init__(   self							:			Optional[Any]   ,       __snake_case							:			int=1_00   ,       __snake_case							:			List[str]=5   ,       __snake_case							:			str=1   ,       __snake_case							:			Union[str, Any]=1   ,       __snake_case							:			Union[str, Any]=2_49   ,       __snake_case							:			List[Any]=6   ,       __snake_case							:			Optional[int]=17   ,       __snake_case							:			Optional[Any]=25   ,       __snake_case							:			Union[str, Any]=4   ,       __snake_case							:			List[Any]=4   ,       __snake_case							:			Optional[int]=1_28   ,       __snake_case							:			Optional[Any]=0.1   ,       __snake_case							:			Any=0.1   ,       __snake_case							:			int=0.1   ,       __snake_case							:			Union[str, Any]=0.00_06   ,       __snake_case							:			Tuple=5_12   ,       __snake_case							:			int=0.02   ,       __snake_case							:			Any=1e-12   ,       __snake_case							:			Optional[Any]=1   ,       __snake_case							:			List[Any]=True   ,       __snake_case							:			Dict=1   ,       __snake_case							:			Dict=5_02_56   ,       __snake_case							:			Union[str, Any]=5_02_56   ,       **__snake_case							:			Tuple   ,       )->	Optional[int]:
        snake_case 						=   vocab_size
        snake_case 						=   action_weight
        snake_case 						=   reward_weight
        snake_case 						=   value_weight
        snake_case 						=   max_position_embeddings
        snake_case 						=   block_size
        snake_case 						=   action_dim
        snake_case 						=   observation_dim
        snake_case 						=   transition_dim
        snake_case 						=   learning_rate
        snake_case 						=   n_layer
        snake_case 						=   n_head
        snake_case 						=   n_embd
        snake_case 						=   embd_pdrop
        snake_case 						=   attn_pdrop
        snake_case 						=   resid_pdrop
        snake_case 						=   initializer_range
        snake_case 						=   layer_norm_eps
        snake_case 						=   kaiming_initializer_range
        snake_case 						=   use_cache
        super().__init__(pad_token_id=__snake_case   ,       bos_token_id=__snake_case   ,       eos_token_id=__snake_case   ,       **__snake_case						)

