




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   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_=None						)						->		Any:
 # set parameter of one layer
 assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
 A__	=    nn.Parameter(lowercase_						)
 if bias is not None:
  assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
  A__	=    nn.Parameter(lowercase_						)







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		List[Any]:
 # set torch weights for 1-to-1 comparison
 A__	=    np.asarray(weights[0]						)
 A__	=    np.asarray(weights[1]						)
 A__	=    np.asarray(weights[2]						)

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







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		List[Any]:
 # set torch weights for 1-to-1 comparison
 A__	=    np.asarray(weights[0]						)
 A__	=    np.asarray(weights[1]						)
 A__	=    np.asarray(weights[2]						)
 A__	=    np.asarray(weights[3]						)

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







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		List[str]:
 # layernorm 1
 A__	=    weights[0][0][0]
 A__	=    np.asarray(layer_norm_a[0]						)
 A__	=    np.asarray(layer_norm_a[1]						)
 set_param(
     torch_block.attention.layer_norm      ,			torch.tensor(lowercase_						)      ,			torch.tensor(lowercase_						)      ,			)

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

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

 # Chunked Feed Forward
 if len(lowercase_						) == 4:
  A__	=    intermediate_weights[2]

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

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

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







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		str:
 # reformer model
 A__	=    torch_model.reformer

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

 if isinstance(weights[3]      ,			lowercase_						):
  A__	=    torch_model_reformer.embeddings.position_embeddings
  for emb_idx in range(len(position_embeddings.weights						)						):
   A__	=    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"""
   A__	=    nn.Parameter(torch.tensor(lowercase_						)						)

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

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

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







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_						)						->		List[str]:
 # Initialise PyTorch model
 A__	=    ReformerConfig.from_json_file(lowercase_						)
 print(f"""Building PyTorch model from configuration: {config}"""						)
 A__	=    ReformerModelWithLMHead(lowercase_						)

 with open(lowercase_      ,			"rb"						) as f:
  A__	=    pickle.load(lowercase_						)['''weights''']

 set_model_weights_in_torch(lowercase_      ,			lowercase_      ,			config.hidden_size						)

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


if __name__ == "__main__":
      SCREAMING_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."
      )
      SCREAMING_SNAKE_CASE    =	parser.parse_args()
      convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)











"""simple docstring"""



from argparse import ArgumentParser

from .env import EnvironmentCommand







def   _SCREAMING_SNAKE_CASE       (							)						->		List[Any]:
 A__	=    ArgumentParser("Diffusers CLI tool"      ,			usage="diffusers-cli <command> [<args>]"						)
 A__	=    parser.add_subparsers(help="diffusers-cli command helpers"						)

 # Register commands
 EnvironmentCommand.register_subcommand(lowercase_						)

 # Let's go
 A__	=    parser.parse_args()

 if not hasattr(lowercase_      ,			"func"						):
  parser.print_help()
  exit(1						)

 # Run
 A__	=    args.func(lowercase_						)
 service.run()


if __name__ == "__main__":
      main()





