





"""simple docstring"""
from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowerCamelCase_								=		logging.get_logger(__name__)






class 					_SCREAMING_SNAKE_CASE(   A  ):
       SCREAMING_SNAKE_CASE_   :      int							=  ['''input_values''', '''padding_mask''']




       def __init__(						self		,SCREAMING_SNAKE_CASE__ = 1		,SCREAMING_SNAKE_CASE__ = 2_40_00		,SCREAMING_SNAKE_CASE__ = 0.0		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,**SCREAMING_SNAKE_CASE__		,)			->      Optional[Any]:





             """simple docstring"""






             super().__init__(feature_size=SCREAMING_SNAKE_CASE__		,sampling_rate=SCREAMING_SNAKE_CASE__		,padding_value=SCREAMING_SNAKE_CASE__		,**SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE   :List[Any]						=  chunk_length_s
             __SCREAMING_SNAKE_CASE   :List[str]						=  overlap




       @property
       def     _UpperCamelCase     (						self							)			->      Optional[int]:





             """simple docstring"""






             if self.chunk_length_s is None:
                   return None
             else:
                   return int(self.chunk_length_s * self.sampling_rate							)




       @property
       def     _UpperCamelCase     (						self							)			->      Optional[int]:





             """simple docstring"""






             if self.chunk_length_s is None or self.overlap is None:
                   return None
             else:
                   return max(1		,int((1.0 - self.overlap) * self.chunk_length							)							)




       def __call__(						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = False		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,SCREAMING_SNAKE_CASE__ = None		,)			->      BatchFeature:





             """simple docstring"""






             if sampling_rate is not None:
                   if sampling_rate != self.sampling_rate:
                         raise ValueError(
                             f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
                             f''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
                             f''' {self.sampling_rate} and not {sampling_rate}.'''							)
             else:
                   logger.warning(
                       '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
                       '''Failing to do so can result in silent errors that might be hard to debug.'''							)

             if padding and truncation:
                   raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''							)
             elif padding is None:
                   # by default let's pad the inputs
                   __SCREAMING_SNAKE_CASE   :List[str]						=  True

             __SCREAMING_SNAKE_CASE   :str						=  bool(
                 isinstance(SCREAMING_SNAKE_CASE__		,(list, tuple)							) and (isinstance(raw_audio[0]		,(np.ndarray, tuple, list)							))							)

             if is_batched:
                   __SCREAMING_SNAKE_CASE   :List[Any]						=  [np.asarray(SCREAMING_SNAKE_CASE__		,dtype=np.floataa							).T for audio in raw_audio]
             elif not is_batched and not isinstance(SCREAMING_SNAKE_CASE__		,np.ndarray							):
                   __SCREAMING_SNAKE_CASE   :str						=  np.asarray(SCREAMING_SNAKE_CASE__		,dtype=np.floataa							)
             elif isinstance(SCREAMING_SNAKE_CASE__		,np.ndarray							) and raw_audio.dtype is np.dtype(np.floataa							):
                   __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  raw_audio.astype(np.floataa							)

             # always return batch
             if not is_batched:
                   __SCREAMING_SNAKE_CASE   :Tuple						=  [np.asarray(SCREAMING_SNAKE_CASE__							).T]

             # verify inputs are valid
             for idx, example in enumerate(SCREAMING_SNAKE_CASE__							):
                   if example.ndim > 2:
                         raise ValueError(f'''Expected input shape (channels, length) but got shape {example.shape}'''							)
                   if self.feature_size == 1 and example.ndim != 1:
                         raise ValueError(f'''Expected mono audio but example has {example.shape[-1]} channels'''							)
                   if self.feature_size == 2 and example.shape[-1] != 2:
                         raise ValueError(f'''Expected stereo audio but example has {example.shape[-1]} channels'''							)

             __SCREAMING_SNAKE_CASE   :Tuple						=  None
             __SCREAMING_SNAKE_CASE   :List[Any]						=  BatchFeature({'''input_values''': raw_audio}							)
             if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
                   if truncation:
                         __SCREAMING_SNAKE_CASE   :Dict						=  min(array.shape[0] for array in raw_audio							)
                         __SCREAMING_SNAKE_CASE   :str						=  int(np.floor(max_length / self.chunk_stride							)							)
                         __SCREAMING_SNAKE_CASE   :List[str]						=  (nb_step - 1) * self.chunk_stride + self.chunk_length
                   elif padding:
                         __SCREAMING_SNAKE_CASE   :List[Any]						=  max(array.shape[0] for array in raw_audio							)
                         __SCREAMING_SNAKE_CASE   :Tuple						=  int(np.ceil(max_length / self.chunk_stride							)							)
                         __SCREAMING_SNAKE_CASE   :Any						=  (nb_step - 1) * self.chunk_stride + self.chunk_length
                         __SCREAMING_SNAKE_CASE   :Dict						=  '''max_length'''
                   else:
                         __SCREAMING_SNAKE_CASE   :Optional[Any]						=  input_values

        # normal padding on batch
             if padded_inputs is None:
                   __SCREAMING_SNAKE_CASE   :Optional[Any]						=  self.pad(
                       SCREAMING_SNAKE_CASE__		,max_length=SCREAMING_SNAKE_CASE__		,truncation=SCREAMING_SNAKE_CASE__		,padding=SCREAMING_SNAKE_CASE__		,return_attention_mask=SCREAMING_SNAKE_CASE__		,)
                   if padding:
                         __SCREAMING_SNAKE_CASE   :Optional[int]						=  padded_inputs.pop('''attention_mask'''							)

             __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  []
             for example in padded_inputs.pop('''input_values'''							):
                   if self.feature_size == 1:
                         __SCREAMING_SNAKE_CASE   :int						=  example[..., None]
                   input_values.append(example.T							)

             __SCREAMING_SNAKE_CASE   :Optional[Any]						=  input_values
             if return_tensors is not None:
                   __SCREAMING_SNAKE_CASE   :Tuple						=  padded_inputs.convert_to_tensors(SCREAMING_SNAKE_CASE__							)

             return padded_inputs






"""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   (							a_     :		Union[str, Any] ,   a_     :		Tuple ,   a_     :		str=None							)			->      Union[str, Any]:
      # set parameter of one layer
      assert torch_layer.weight.shape == weight.shape, f'''{torch_layer} layer.weight does not match'''
      __SCREAMING_SNAKE_CASE   :Dict						=  nn.Parameter(a_							)
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, f'''{torch_layer} layer.bias does not match'''
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  nn.Parameter(a_							)



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

      # lsh weights + output
      __SCREAMING_SNAKE_CASE   :List[Any]						=  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
      __SCREAMING_SNAKE_CASE   :List[Any]						=  weights[2][0][1][2]

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

      # layernorm 2
      __SCREAMING_SNAKE_CASE   :Tuple						=  np.asarray(intermediate_weights[0][0]							)
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  np.asarray(intermediate_weights[0][1]							)
      set_param(
          torch_block.feed_forward.layer_norm ,   torch.tensor(a_							) ,   torch.tensor(a_							) ,   )

      # intermediate dense
      __SCREAMING_SNAKE_CASE   :int						=  np.asarray(intermediate_weights[1][0]							)
      __SCREAMING_SNAKE_CASE   :int						=  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
      __SCREAMING_SNAKE_CASE   :str						=  np.asarray(intermediate_weights[4][0]							)
      __SCREAMING_SNAKE_CASE   :str						=  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      __lowerCamelCase   (							a_     :		List[str] ,   a_     :		str ,   a_     :		List[Any]							)			->      Optional[Any]:
      # reformer model
      __SCREAMING_SNAKE_CASE   :Dict						=  torch_model.reformer

      # word embeds
      __SCREAMING_SNAKE_CASE   :List[Any]						=  np.asarray(weights[1]							)
      set_param(
          torch_model_reformer.embeddings.word_embeddings ,   torch.tensor(a_							) ,   )

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

      __SCREAMING_SNAKE_CASE   :Optional[int]						=  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							):
            __SCREAMING_SNAKE_CASE   :Dict						=  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(a_ ,   a_ ,   a_							)

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

      # output embeddings
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  np.asarray(weights[9][0]							)
      __SCREAMING_SNAKE_CASE   :str						=  np.asarray(weights[9][1]							)
      set_param(
          torch_model.lm_head.decoder ,   torch.tensor(a_							).transpose(0 ,   1							).contiguous() ,   torch.tensor(a_							) ,   )



def      __lowerCamelCase   (							a_     :		Any ,   a_     :		Dict ,   a_     :		Dict							)			->      Tuple:
      # Initialise PyTorch model
      __SCREAMING_SNAKE_CASE   :List[str]						=  ReformerConfig.from_json_file(a_							)
      print(f'''Building PyTorch model from configuration: {config}'''							)
      __SCREAMING_SNAKE_CASE   :List[Any]						=  ReformerModelWithLMHead(a_							)

      with open(a_ ,   '''rb'''							) as f:
            __SCREAMING_SNAKE_CASE   :Any						=  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__":
  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)