




"""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 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase=None				)						->    int:
      assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
      lowerCAmelCase__      :			List[Any]   =	nn.Parameter(__a				)
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
            lowerCAmelCase__      :			Tuple   =	nn.Parameter(__a				)



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    int:
      lowerCAmelCase__      :			int   =	np.asarray(weights[0]				)
      lowerCAmelCase__      :			Optional[Any]   =	np.asarray(weights[1]				)
      lowerCAmelCase__      :			Tuple   =	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 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Union[str, Any]:
      lowerCAmelCase__      :			List[Any]   =	np.asarray(weights[0]				)
      lowerCAmelCase__      :			Optional[Any]   =	np.asarray(weights[1]				)
      lowerCAmelCase__      :			List[str]   =	np.asarray(weights[2]				)
      lowerCAmelCase__      :			List[Any]   =	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 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Optional[Any]:
      lowerCAmelCase__      :			Dict   =	weights[0][0][0]
      lowerCAmelCase__      :			Tuple   =	np.asarray(layer_norm_a[0]				)
      lowerCAmelCase__      :			Dict   =	np.asarray(layer_norm_a[1]				)
      set_param(
          torch_block.attention.layer_norm    ,		torch.tensor(__a				)    ,		torch.tensor(__a				)    ,		)

      # lsh weights + output
      lowerCAmelCase__      :			List[str]   =	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
      lowerCAmelCase__      :			str   =	weights[2][0][1][2]

      # Chunked Feed Forward
      if len(__a				) == 4:
            lowerCAmelCase__      :			Optional[Any]   =	intermediate_weights[2]

      # layernorm 2
      lowerCAmelCase__      :			str   =	np.asarray(intermediate_weights[0][0]				)
      lowerCAmelCase__      :			List[str]   =	np.asarray(intermediate_weights[0][1]				)
      set_param(
          torch_block.feed_forward.layer_norm    ,		torch.tensor(__a				)    ,		torch.tensor(__a				)    ,		)

      # intermediate dense
      lowerCAmelCase__      :			Tuple   =	np.asarray(intermediate_weights[1][0]				)
      lowerCAmelCase__      :			Dict   =	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
      lowerCAmelCase__      :			Union[str, Any]   =	np.asarray(intermediate_weights[4][0]				)
      lowerCAmelCase__      :			int   =	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 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Dict:
      lowerCAmelCase__      :			Union[str, Any]   =	torch_model.reformer

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

      if isinstance(weights[3]    ,		__a				):
            lowerCAmelCase__      :			str   =	torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights				)				):
                  lowerCAmelCase__      :			Union[str, Any]   =	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__      :			int   =	nn.Parameter(torch.tensor(__a				)				)

      lowerCAmelCase__      :			Optional[Any]   =	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				):
            lowerCAmelCase__      :			Dict   =	trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(__a    ,		__a    ,		__a				)

      # output layer norm
      lowerCAmelCase__      :			str   =	np.asarray(weights[7][0]				)
      lowerCAmelCase__      :			Any   =	np.asarray(weights[7][1]				)
      set_param(
          torch_model_reformer.encoder.layer_norm    ,		torch.tensor(__a				)    ,		torch.tensor(__a				)    ,		)

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



def 	lowercase_				(			__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase				)						->    Union[str, Any]:
      lowerCAmelCase__      :			Tuple   =	ReformerConfig.from_json_file(__a				)
      print(f"""Building PyTorch model from configuration: {config}"""				)
      lowerCAmelCase__      :			int   =	ReformerModelWithLMHead(__a				)

      with open(__a    ,		"""rb"""				) as f:
            lowerCAmelCase__      :			List[str]   =	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__":
 _A													=      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."""
 )
 _A													=      parser.parse_args()
 convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)











"""simple docstring"""


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

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


@dataclass(frozen=a_						)
class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :str     				=     field(default="audio-classification"       ,	metadata={"include_in_asdict_even_if_is_default": True}						)
   _lowerCamelCase    :ClassVar[Features]     				=     Features({"audio": Audio()}						)
   _lowerCamelCase    :ClassVar[Features]     				=     Features({"labels": ClassLabel}						)
   _lowerCamelCase    :str     				=     "audio"
   _lowerCamelCase    :str     				=     "labels"






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		Optional[int]   )			->						Union[str, Any]:

         """simple docstring"""
         if self.label_column not in features:
               raise ValueError(f"""Column {self.label_column} is not present in features."""   )
         if not isinstance(features[self.label_column]   ,     UpperCamelCase   ):
               raise ValueError(f"""Column {self.label_column} is not a ClassLabel."""   )
         lowerCAmelCase__      :			str   =	copy.deepcopy(self   )
         lowerCAmelCase__      :			Optional[int]   =	self.label_schema.copy()
         lowerCAmelCase__      :			List[Any]   =	features[self.label_column]
         lowerCAmelCase__      :			Optional[int]   =	label_schema
         return task_template






   @property
   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Dict[str, str]:

         """simple docstring"""
         return {
             self.audio_column: "audio",
             self.label_column: "labels",
         }





