


'''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      lowerCamelCase					(      lowerCamelCase  :      Tuple							,						lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Union[str, Any]=None):
      assert torch_layer.weight.shape == weight.shape, F'{torch_layer} layer.weight does not match'
      A_						:  Optional[Any]          =				nn.Parameter(lowercase_)
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, F'{torch_layer} layer.bias does not match'
            A_						:  Optional[Any]          =				nn.Parameter(lowercase_)




def      lowerCamelCase					(      lowerCamelCase  :      Any							,						lowerCamelCase  :      str							,						lowerCamelCase  :      Tuple):
      A_						:  List[Any]          =				np.asarray(weights[0])
      A_						:  Any          =				np.asarray(weights[1])
      A_						:  str          =				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      lowerCamelCase					(      lowerCamelCase  :      List[str]							,						lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      Any):
      A_						:  int          =				np.asarray(weights[0])
      A_						:  Optional[int]          =				np.asarray(weights[1])
      A_						:  str          =				np.asarray(weights[2])
      A_						:  List[str]          =				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      lowerCamelCase					(      lowerCamelCase  :      List[Any]							,						lowerCamelCase  :      Optional[int]							,						lowerCamelCase  :      int):
      A_						:  Union[str, Any]          =				weights[0][0][0]
      A_						:  Any          =				np.asarray(layer_norm_a[0])
      A_						:  int          =				np.asarray(layer_norm_a[1])
      set_param(
          torch_block.attention.layer_norm							,						torch.tensor(lowercase_)							,						torch.tensor(lowercase_)							,						)

      # lsh weights + output
      A_						:  List[Any]          =				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_						:  Dict          =				weights[2][0][1][2]

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

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

      # intermediate dense
      A_						:  List[Any]          =				np.asarray(intermediate_weights[1][0])
      A_						:  Tuple          =				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_						:  Union[str, Any]          =				np.asarray(intermediate_weights[4][0])
      A_						:  List[Any]          =				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      lowerCamelCase					(      lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      str							,						lowerCamelCase  :      Optional[Any]):
      A_						:  Optional[Any]          =				torch_model.reformer

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

      if isinstance(weights[3]							,						lowercase_):
            A_						:  Tuple          =				torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights)):
                  A_						:  List[str]          =				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_						:  Dict          =				nn.Parameter(torch.tensor(lowercase_))

      A_						:  Optional[Any]          =				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_						:  List[Any]          =				trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(lowercase_							,						lowercase_							,						lowercase_)

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

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




def      lowerCamelCase					(      lowerCamelCase  :      Tuple							,						lowerCamelCase  :      Union[str, Any]							,						lowerCamelCase  :      Any):
      A_						:  Dict          =				ReformerConfig.from_json_file(lowercase_)
      print(F'Building PyTorch model from configuration: {config}')
      A_						:  Dict          =				ReformerModelWithLMHead(lowercase_)

      with open(lowercase_							,						"""rb""") as f:
            A_						:  Optional[Any]          =				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__":
  __magic_name__			=      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.'
  )
  __magic_name__			=      parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)




'''simple docstring'''


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
     from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__magic_name__			=      logging.get_logger(__name__)

__magic_name__			=      {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}



class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							"""deberta-v2"""






  def __init__(							self :       Optional[Any]	,_a :       Union[str, Any]=128100	,_a :       Optional[int]=1536	,_a :       Dict=24	,_a :       int=24	,_a :       Tuple=6144	,_a :       Union[str, Any]="gelu"	,_a :       List[Any]=0.1	,_a :       Dict=0.1	,_a :       int=512	,_a :       int=0	,_a :       int=0.02	,_a :       int=1e-7	,_a :       List[str]=False	,_a :       Union[str, Any]=-1	,_a :       List[Any]=0	,_a :       Optional[Any]=True	,_a :       Tuple=None	,_a :       Any=0	,_a :       int="gelu"	,**_a :       Any	,):

        '''simple docstring'''




        super().__init__(**_a )

        A_						:  Union[str, Any]          =				hidden_size
        A_						:  Dict          =				num_hidden_layers
        A_						:  Union[str, Any]          =				num_attention_heads
        A_						:  List[Any]          =				intermediate_size
        A_						:  List[Any]          =				hidden_act
        A_						:  Optional[int]          =				hidden_dropout_prob
        A_						:  Dict          =				attention_probs_dropout_prob
        A_						:  int          =				max_position_embeddings
        A_						:  Any          =				type_vocab_size
        A_						:  List[Any]          =				initializer_range
        A_						:  int          =				relative_attention
        A_						:  Tuple          =				max_relative_positions
        A_						:  int          =				pad_token_id
        A_						:  Tuple          =				position_biased_input

        # Backwards compatibility
        if type(_a ) == str:
              A_						:  str          =				[x.strip() for x in pos_att_type.lower().split("""|""" )]

        A_						:  Any          =				pos_att_type
        A_						:  Optional[int]          =				vocab_size
        A_						:  Tuple          =				layer_norm_eps

        A_						:  Any          =				kwargs.get("""pooler_hidden_size"""	,_a )
        A_						:  Union[str, Any]          =				pooler_dropout
        A_						:  List[Any]          =				pooler_hidden_act







class 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  @property
  def        _a							(							self :       Any ):

        '''simple docstring'''




        if self.task == "multiple-choice":
              A_						:  Any          =				{0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
              A_						:  Any          =				{0: """batch""", 1: """sequence"""}
        if self._config.type_vocab_size > 0:
              return OrderedDict(
                  [("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis), ("""token_type_ids""", dynamic_axis)] )
        else:
              return OrderedDict([("""input_ids""", dynamic_axis), ("""attention_mask""", dynamic_axis)] )






  @property
  def        _a							(							self :       Optional[int] ):

        '''simple docstring'''




        return 12






  def        _a							(							self :       int	,_a :       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]	,_a :       int = -1	,_a :       int = -1	,_a :       int = -1	,_a :       bool = False	,_a :       Optional["TensorType"] = None	,_a :       int = 3	,_a :       int = 40	,_a :       int = 40	,_a :       "PreTrainedTokenizerBase" = None	,):

        '''simple docstring'''




        A_						:  Any          =				super().generate_dummy_inputs(preprocessor=_a	,framework=_a )
        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
              del dummy_inputs["token_type_ids"]
        return dummy_inputs
