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		(			_A							,				_A							,				_A=None					):



 """simple docstring"""


 assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
 __a       =  nn.Parameter(_A					)
 if bias is not None:
  assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
  __a       =  nn.Parameter(_A					)



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


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



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


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



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __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(_A					)							,				torch.tensor(_A					)							,				)

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

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

 # Chunked Feed Forward
 if len(_A					) == 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(_A					)							,				torch.tensor(_A					)							,				)

 # 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(_A					).transpose(0							,				1					).contiguous()							,				torch.tensor(_A					)							,				)

 # 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(_A					).transpose(0							,				1					).contiguous()							,				torch.tensor(_A					)							,				)



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  torch_model.reformer

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

 if isinstance(weights[3]							,				_A					):
  __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(_A					)					)

 __a       =  weights[5]
 assert len(torch_model_reformer.encoder.layers					) * 4 == len(
     _A					), "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(_A							,				_A							,				_A					)

 # 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(_A					)							,				torch.tensor(_A					)							,				)

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



def   __A		(			_A							,				_A							,				_A					):



 """simple docstring"""


 __a       =  ReformerConfig.from_json_file(_A					)
 print(f"""Building PyTorch model from configuration: {config}"""					)
 __a       =  ReformerModelWithLMHead(_A					)

 with open(_A							,				"rb"					) as f:
  __a       =  pickle.load(_A					)["weights"]

 set_model_weights_in_torch(_A							,				_A							,				config.hidden_size					)

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


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



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE :	Dict							     =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :	List[str]							     =		{
    """facebook/xlm-roberta-xl""": """https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json""",
    """facebook/xlm-roberta-xxl""": """https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json""",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			"""xlm-roberta-xl"""

  def __init__(		self :      str     ,							__SCREAMING_SNAKE_CASE :      int=25_08_80     ,							__SCREAMING_SNAKE_CASE :      Dict=25_60     ,							__SCREAMING_SNAKE_CASE :      List[str]=36     ,							__SCREAMING_SNAKE_CASE :      int=32     ,							__SCREAMING_SNAKE_CASE :      int=1_02_40     ,							__SCREAMING_SNAKE_CASE :      Optional[int]="gelu"     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=0.1     ,							__SCREAMING_SNAKE_CASE :      int=5_14     ,							__SCREAMING_SNAKE_CASE :      List[Any]=1     ,							__SCREAMING_SNAKE_CASE :      Any=0.02     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=1E-05     ,							__SCREAMING_SNAKE_CASE :      str=1     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=0     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=2     ,							__SCREAMING_SNAKE_CASE :      Optional[int]="absolute"     ,							__SCREAMING_SNAKE_CASE :      str=True     ,							__SCREAMING_SNAKE_CASE :      str=None     ,							**__SCREAMING_SNAKE_CASE :      str     ,							):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,							bos_token_id=__SCREAMING_SNAKE_CASE     ,							eos_token_id=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )
   __a       =  vocab_size
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  hidden_act
   __a       =  intermediate_size
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  max_position_embeddings
   __a       =  type_vocab_size
   __a       =  initializer_range
   __a       =  layer_norm_eps
   __a       =  position_embedding_type
   __a       =  use_cache
   __a       =  classifier_dropout
class    A_ (       a_ ):

  @property
  def 		_UpperCAmelCase   (		self :      List[str]       ):
   if self.task == "multiple-choice":
    __a       =  {0: "batch", 1: "choice", 2: "sequence"}
   else:
    __a       =  {0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
       ]       )


