


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 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case=None						)     ->			Tuple:







 """simple docstring"""


 assert torch_layer.weight.shape == weight.shape, F"{torch_layer} layer.weight does not match"
 _lowercase							      =nn.Parameter(__snake_case						)
 if bias is not None:
  assert torch_layer.bias.shape == bias.shape, F"{torch_layer} layer.bias does not match"
  _lowercase							      =nn.Parameter(__snake_case						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Union[str, Any]:







 """simple docstring"""


 _lowercase							      =np.asarray(weights[0]						)
 _lowercase							      =np.asarray(weights[1]						)
 _lowercase							      =np.asarray(weights[2]						)

 set_param(
     torch_layer.self_attention.query_key     ,						torch.tensor(__snake_case						).transpose(1     ,						2						).contiguous().view(-1     ,						__snake_case						)     ,						)
 set_param(
     torch_layer.self_attention.value     ,						torch.tensor(__snake_case						).transpose(1     ,						2						).contiguous().view(-1     ,						__snake_case						)     ,						)
 set_param(
     torch_layer.output.dense     ,						torch.tensor(__snake_case						).view(-1     ,						__snake_case						).contiguous().transpose(0     ,						1						)     ,						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			int:







 """simple docstring"""


 _lowercase							      =np.asarray(weights[0]						)
 _lowercase							      =np.asarray(weights[1]						)
 _lowercase							      =np.asarray(weights[2]						)
 _lowercase							      =np.asarray(weights[3]						)

 set_param(
     torch_layer.self_attention.query     ,						torch.tensor(__snake_case						).transpose(1     ,						2						).contiguous().view(-1     ,						__snake_case						)     ,						)
 set_param(
     torch_layer.self_attention.key     ,						torch.tensor(__snake_case						).transpose(1     ,						2						).contiguous().view(-1     ,						__snake_case						)     ,						)
 set_param(
     torch_layer.self_attention.value     ,						torch.tensor(__snake_case						).transpose(1     ,						2						).contiguous().view(-1     ,						__snake_case						)     ,						)
 set_param(
     torch_layer.output.dense     ,						torch.tensor(__snake_case						).view(-1     ,						__snake_case						).contiguous().transpose(0     ,						1						)     ,						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Optional[Any]:







 """simple docstring"""


 _lowercase							      =weights[0][0][0]
 _lowercase							      =np.asarray(layer_norm_a[0]						)
 _lowercase							      =np.asarray(layer_norm_a[1]						)
 set_param(
     torch_block.attention.layer_norm     ,						torch.tensor(__snake_case						)     ,						torch.tensor(__snake_case						)     ,						)

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

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

 # Chunked Feed Forward
 if len(__snake_case						) == 4:
  _lowercase							      =intermediate_weights[2]

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

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

 # intermediate out
 _lowercase							      =np.asarray(intermediate_weights[4][0]						)
 _lowercase							      =np.asarray(intermediate_weights[4][1]						)
 set_param(
     torch_block.feed_forward.output.dense     ,						torch.tensor(__snake_case						).transpose(0     ,						1						).contiguous()     ,						torch.tensor(__snake_case						)     ,						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			List[Any]:







 """simple docstring"""


 _lowercase							      =torch_model.reformer

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

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

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

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

 # output embeddings
 _lowercase							      =np.asarray(weights[9][0]						)
 _lowercase							      =np.asarray(weights[9][1]						)
 set_param(
     torch_model.lm_head.decoder     ,						torch.tensor(__snake_case						).transpose(0     ,						1						).contiguous()     ,						torch.tensor(__snake_case						)     ,						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Any:







 """simple docstring"""


 _lowercase							      =ReformerConfig.from_json_file(__snake_case						)
 print(F"Building PyTorch model from configuration: {config}"						)
 _lowercase							      =ReformerModelWithLMHead(__snake_case						)

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

 set_model_weights_in_torch(__snake_case     ,						__snake_case     ,						config.hidden_size						)

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


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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


UpperCamelCase  							=						{}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase  							=						['''GPTSw3Tokenizer''']


if TYPE_CHECKING:
      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_gpt_swa import GPTSwaTokenizer

else:
      import sys

      UpperCamelCase  							=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



