
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,	lowerCamelCase,	lowerCamelCase=None							):
 # set parameter of one layer
 assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
 lowerCamelCase		:	Optional[int]							=      nn.Parameter(lowerCamelCase							)
 if bias is not None:
  assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
  lowerCamelCase		:	Union[str, Any]							=      nn.Parameter(lowerCamelCase							)





def     _a					(						lowerCamelCase,	lowerCamelCase,	lowerCamelCase							):
 # set torch weights for 1-to-1 comparison
 lowerCamelCase		:	Optional[int]							=      np.asarray(weights[0]							)
 lowerCamelCase		:	List[Any]							=      np.asarray(weights[1]							)
 lowerCamelCase		:	List[str]							=      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,	lowerCamelCase,	lowerCamelCase							):
 # set torch weights for 1-to-1 comparison
 lowerCamelCase		:	Optional[int]							=      np.asarray(weights[0]							)
 lowerCamelCase		:	Any							=      np.asarray(weights[1]							)
 lowerCamelCase		:	Optional[Any]							=      np.asarray(weights[2]							)
 lowerCamelCase		:	str							=      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,	lowerCamelCase,	lowerCamelCase							):
 # layernorm 1
 lowerCamelCase		:	List[Any]							=      weights[0][0][0]
 lowerCamelCase		:	Optional[int]							=      np.asarray(layer_norm_a[0]							)
 lowerCamelCase		:	Optional[int]							=      np.asarray(layer_norm_a[1]							)
 set_param(
     torch_block.attention.layer_norm,	torch.tensor(lowerCamelCase							),	torch.tensor(lowerCamelCase							),	)

 # lsh weights + output
 lowerCamelCase		:	int							=      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		:	Optional[Any]							=      weights[2][0][1][2]

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

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

 # intermediate dense
 lowerCamelCase		:	Any							=      np.asarray(intermediate_weights[1][0]							)
 lowerCamelCase		:	Union[str, Any]							=      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		:	Dict							=      np.asarray(intermediate_weights[4][0]							)
 lowerCamelCase		:	Any							=      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,	lowerCamelCase,	lowerCamelCase							):
 # reformer model
 lowerCamelCase		:	str							=      torch_model.reformer

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

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

 lowerCamelCase		:	str							=      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		:	Dict							=      trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
  set_block_weights_in_torch(lowerCamelCase,	lowerCamelCase,	lowerCamelCase							)

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

 # output embeddings
 lowerCamelCase		:	Optional[int]							=      np.asarray(weights[9][0]							)
 lowerCamelCase		:	Optional[Any]							=      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,	lowerCamelCase,	lowerCamelCase							):
 # Initialise PyTorch model
 lowerCamelCase		:	Any							=      ReformerConfig.from_json_file(lowerCamelCase							)
 print(F'''Building PyTorch model from configuration: {config}'''							)
 lowerCamelCase		:	List[str]							=      ReformerModelWithLMHead(lowerCamelCase							)

 with open(lowerCamelCase,	"""rb"""							) as f:
  lowerCamelCase		:	Dict							=      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__":
      _lowerCamelCase					   =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."""
      )
      _lowerCamelCase					   =parser.parse_args()
      convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)






import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate





@dataclass(frozen=__SCREAMING_SNAKE_CASE)
class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    str           =			field(default="""automatic-speech-recognition"""  ,   metadata={"""include_in_asdict_even_if_is_default""": True})
      _UpperCAmelCase				:    ClassVar[Features]           =			Features({"""audio""": Audio()})
      _UpperCAmelCase				:    ClassVar[Features]           =			Features({"""transcription""": Value("""string""")})
      _UpperCAmelCase				:    str           =			"audio"
      _UpperCAmelCase				:    str           =			"transcription"
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       if self.audio_column not in features:
        raise ValueError(F'''Column {self.audio_column} is not present in features.'''      )
       if not isinstance(features[self.audio_column]    ,  __magic_name__      ):
        raise ValueError(F'''Column {self.audio_column} is not an Audio type.'''      )
       lowerCamelCase		:	Optional[Any]							=      copy.deepcopy(self      )
       lowerCamelCase		:	List[Any]							=      self.input_schema.copy()
       lowerCamelCase		:	Tuple							=      features[self.audio_column]
       lowerCamelCase		:	int							=      input_schema
       return task_template






      @property
      def 						UpperCamelCase__							(      self      ):
       return {self.audio_column: "audio", self.transcription_column: "transcription"}




