


'''simple docstring'''




import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()





def 	a_					( lowerCamelCase	:     str	, lowerCamelCase	:     List[Any]	, lowerCamelCase	:     str=None				):
   # set parameter of one layer
   assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
   lowerCAmelCase     =			nn.Parameter(lowerCamelCase				)
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
      lowerCAmelCase     =			nn.Parameter(lowerCamelCase				)





def 	a_					( lowerCamelCase	:     Tuple	, lowerCamelCase	:     str	, lowerCamelCase	:     int				):
   # set torch weights for 1-to-1 comparison
   lowerCAmelCase     =			np.asarray(weights[0]				)
   lowerCAmelCase     =			np.asarray(weights[1]				)
   lowerCAmelCase     =			np.asarray(weights[2]				)

   set_param(
       torch_layer.self_attention.query_key	, torch.tensor(lowerCamelCase				).transpose(1	, 2				).contiguous().view(-1	, lowerCamelCase				)	, )
   set_param(
       torch_layer.self_attention.value	, torch.tensor(lowerCamelCase				).transpose(1	, 2				).contiguous().view(-1	, lowerCamelCase				)	, )
   set_param(
       torch_layer.output.dense	, torch.tensor(lowerCamelCase				).view(-1	, lowerCamelCase				).contiguous().transpose(0	, 1				)	, )





def 	a_					( lowerCamelCase	:     Any	, lowerCamelCase	:     Optional[Any]	, lowerCamelCase	:     Optional[int]				):
   # set torch weights for 1-to-1 comparison
   lowerCAmelCase     =			np.asarray(weights[0]				)
   lowerCAmelCase     =			np.asarray(weights[1]				)
   lowerCAmelCase     =			np.asarray(weights[2]				)
   lowerCAmelCase     =			np.asarray(weights[3]				)

   set_param(
       torch_layer.self_attention.query	, torch.tensor(lowerCamelCase				).transpose(1	, 2				).contiguous().view(-1	, lowerCamelCase				)	, )
   set_param(
       torch_layer.self_attention.key	, torch.tensor(lowerCamelCase				).transpose(1	, 2				).contiguous().view(-1	, lowerCamelCase				)	, )
   set_param(
       torch_layer.self_attention.value	, torch.tensor(lowerCamelCase				).transpose(1	, 2				).contiguous().view(-1	, lowerCamelCase				)	, )
   set_param(
       torch_layer.output.dense	, torch.tensor(lowerCamelCase				).view(-1	, lowerCamelCase				).contiguous().transpose(0	, 1				)	, )





def 	a_					( lowerCamelCase	:     Dict	, lowerCamelCase	:     Optional[int]	, lowerCamelCase	:     Dict				):
   # layernorm 1
   lowerCAmelCase     =			weights[0][0][0]
   lowerCAmelCase     =			np.asarray(layer_norm_a[0]				)
   lowerCAmelCase     =			np.asarray(layer_norm_a[1]				)
   set_param(
       torch_block.attention.layer_norm	, torch.tensor(lowerCamelCase				)	, torch.tensor(lowerCamelCase				)	, )

   # lsh weights + output
   lowerCAmelCase     =			weights[0][1]
   if len(lowerCamelCase				) < 4:
      set_layer_weights_in_torch_lsh(lowerCamelCase	, torch_block.attention	, lowerCamelCase				)
   else:
      set_layer_weights_in_torch_local(lowerCamelCase	, torch_block.attention	, lowerCamelCase				)

   # intermediate weighs
   lowerCAmelCase     =			weights[2][0][1][2]

   # Chunked Feed Forward
   if len(lowerCamelCase				) == 4:
      lowerCAmelCase     =			intermediate_weights[2]

   # layernorm 2
   lowerCAmelCase     =			np.asarray(intermediate_weights[0][0]				)
   lowerCAmelCase     =			np.asarray(intermediate_weights[0][1]				)
   set_param(
       torch_block.feed_forward.layer_norm	, torch.tensor(lowerCamelCase				)	, torch.tensor(lowerCamelCase				)	, )

   # intermediate dense
   lowerCAmelCase     =			np.asarray(intermediate_weights[1][0]				)
   lowerCAmelCase     =			np.asarray(intermediate_weights[1][1]				)
   set_param(
       torch_block.feed_forward.dense.dense	, torch.tensor(lowerCamelCase				).transpose(0	, 1				).contiguous()	, torch.tensor(lowerCamelCase				)	, )

   # intermediate out
   lowerCAmelCase     =			np.asarray(intermediate_weights[4][0]				)
   lowerCAmelCase     =			np.asarray(intermediate_weights[4][1]				)
   set_param(
       torch_block.feed_forward.output.dense	, torch.tensor(lowerCamelCase				).transpose(0	, 1				).contiguous()	, torch.tensor(lowerCamelCase				)	, )





def 	a_					( lowerCamelCase	:     Tuple	, lowerCamelCase	:     int	, lowerCamelCase	:     Dict				):
   # reformer model
   lowerCAmelCase     =			torch_model.reformer

   # word embeds
   lowerCAmelCase     =			np.asarray(weights[1]				)
   set_param(
       torch_model_reformer.embeddings.word_embeddings	, torch.tensor(lowerCamelCase				)	, )

   if isinstance(weights[3]	, lowerCamelCase				):
      lowerCAmelCase     =			torch_model_reformer.embeddings.position_embeddings
      for emb_idx in range(len(position_embeddings.weights				)				):
         lowerCAmelCase     =			np.asarray(weights[3][emb_idx][0]				)
         assert (
             position_embeddings.weights[emb_idx].shape == emb_weights.shape
         ), f'''{position_embeddings[emb_idx]} emb does not match'''
         lowerCAmelCase     =			nn.Parameter(torch.tensor(lowerCamelCase				)				)

   lowerCAmelCase     =			weights[5]
   assert len(torch_model_reformer.encoder.layers				) * 4 == len(
       lowerCamelCase				), "HF and trax model do not have the same number of layers"
   for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers				):
      lowerCAmelCase     =			trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
      set_block_weights_in_torch(lowerCamelCase	, lowerCamelCase	, lowerCamelCase				)

   # output layer norm
   lowerCAmelCase     =			np.asarray(weights[7][0]				)
   lowerCAmelCase     =			np.asarray(weights[7][1]				)
   set_param(
       torch_model_reformer.encoder.layer_norm	, torch.tensor(lowerCamelCase				)	, torch.tensor(lowerCamelCase				)	, )

   # output embeddings
   lowerCAmelCase     =			np.asarray(weights[9][0]				)
   lowerCAmelCase     =			np.asarray(weights[9][1]				)
   set_param(
       torch_model.lm_head.decoder	, torch.tensor(lowerCamelCase				).transpose(0	, 1				).contiguous()	, torch.tensor(lowerCamelCase				)	, )





def 	a_					( lowerCamelCase	:     Any	, lowerCamelCase	:     List[str]	, lowerCamelCase	:     str				):
   # Initialise PyTorch model
   lowerCAmelCase     =			ReformerConfig.from_json_file(lowerCamelCase				)
   print(f'''Building PyTorch model from configuration: {config}'''				)
   lowerCAmelCase     =			ReformerModelWithLMHead(lowerCamelCase				)

   with open(lowerCamelCase	, 'rb'				) as f:
      lowerCAmelCase     =			pickle.load(lowerCamelCase				)['weights']

   set_model_weights_in_torch(lowerCamelCase	, lowerCamelCase	, config.hidden_size				)

   # Save pytorch-model
   print(f'''Save PyTorch model to {pytorch_dump_path}'''				)
   torch.save(model.state_dict()	, lowerCamelCase				)


if __name__ == "__main__":
       __snake_case    						=argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
       )
       parser.add_argument(
           """--config_file""",
           default=None,
           type=str,
           required=True,
           help=(
               """The config json file corresponding to the pre-trained Reformer 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."""
       )
       __snake_case    						=parser.parse_args()
       convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)









"""simple docstring"""
import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 		lowercase							(     _snake_case						:      List[Any]     ,   _snake_case						:      Tuple     ,   _snake_case						:      int     )     ->List[Any]:
	"""simple docstring"""





	if openai_config_file == "":
		__snake_case	:						Dict						=				OpenAIGPTConfig()
	else:
		__snake_case	:						int						=				OpenAIGPTConfig.from_json_file(_snake_case     )
	__snake_case	:						Tuple						=				OpenAIGPTModel(_snake_case     )

	# Load weights from numpy
	load_tf_weights_in_openai_gpt(_snake_case     ,   _snake_case     ,   _snake_case     )

	# Save pytorch-model
	__snake_case	:						str						=				pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
	__snake_case	:						Optional[Any]						=				pytorch_dump_folder_path + '''/''' + CONFIG_NAME
	print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""     )
	torch.save(model.state_dict()     ,   _snake_case     )
	print(f"""Save configuration file to {pytorch_config_dump_path}"""     )
	with open(_snake_case     ,   '''w'''     ,   encoding='''utf-8'''     ) as f:
		f.write(config.to_json_string()     )


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE :    Any									=				argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--openai_checkpoint_folder_path""",
			    default=None,
			    type=str,
			    required=True,
			    help="""Path to the TensorFlow checkpoint path.""",
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
			)
			parser.add_argument(
			    """--openai_config_file""",
			    default="""""",
			    type=str,
			    help=(
			        """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
			        """This specifies the model architecture."""
			    ),
			)
			SCREAMING_SNAKE_CASE :    Optional[int]									=				parser.parse_args()
			convert_openai_checkpoint_to_pytorch(
			    args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
			)





