



"""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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase=None							) ->						Optional[int]:







  '''simple docstring'''


  assert torch_layer.weight.shape == weight.shape, F'''{torch_layer} layer.weight does not match'''
  lowercase_								= nn.Parameter(__lowerCAmelCase							)
  if bias is not None:
    assert torch_layer.bias.shape == bias.shape, F'''{torch_layer} layer.bias does not match'''
    lowercase_								= nn.Parameter(__lowerCAmelCase							)


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						Optional[int]:







  '''simple docstring'''


  lowercase_								= np.asarray(weights[0]							)
  lowercase_								= np.asarray(weights[1]							)
  lowercase_								= 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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						Any:







  '''simple docstring'''


  lowercase_								= np.asarray(weights[0]							)
  lowercase_								= np.asarray(weights[1]							)
  lowercase_								= np.asarray(weights[2]							)
  lowercase_								= 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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						Dict:







  '''simple docstring'''


  lowercase_								= weights[0][0][0]
  lowercase_								= np.asarray(layer_norm_a[0]							)
  lowercase_								= np.asarray(layer_norm_a[1]							)
  set_param(
      torch_block.attention.layer_norm ,  torch.tensor(__lowerCAmelCase							) ,  torch.tensor(__lowerCAmelCase							) ,  )

  # lsh weights + output
  lowercase_								= 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
  lowercase_								= weights[2][0][1][2]

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

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

  # intermediate dense
  lowercase_								= np.asarray(intermediate_weights[1][0]							)
  lowercase_								= 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
  lowercase_								= np.asarray(intermediate_weights[4][0]							)
  lowercase_								= 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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						List[str]:







  '''simple docstring'''


  lowercase_								= torch_model.reformer

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

  if isinstance(weights[3] ,  __lowerCAmelCase							):
    lowercase_								= torch_model_reformer.embeddings.position_embeddings
    for emb_idx in range(len(position_embeddings.weights							)							):
      lowercase_								= 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'''
      lowercase_								= nn.Parameter(torch.tensor(__lowerCAmelCase							)							)

  lowercase_								= 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							):
    lowercase_								= trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
    set_block_weights_in_torch(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							)

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

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


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						Optional[Any]:







  '''simple docstring'''


  lowercase_								= ReformerConfig.from_json_file(__lowerCAmelCase							)
  print(F'''Building PyTorch model from configuration: {config}'''							)
  lowercase_								= ReformerModelWithLMHead(__lowerCAmelCase							)

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






"""simple docstring"""
from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


UpperCAmelCase						:		Union[str, Any]			= datasets.utils.logging.get_logger(__name__)


class    SCREAMING_SNAKE_CASE__ (							folder_based_builder.FolderBasedBuilderConfig   ):
    lowercase__      =   None
    lowercase__      =   None







class    SCREAMING_SNAKE_CASE__ (							folder_based_builder.FolderBasedBuilder   ):
    lowercase__      =   datasets.Audio()
    lowercase__      =   "audio"
    lowercase__      =   AudioFolderConfig
    lowercase__      =   42  # definition at the bottom of the script
    lowercase__      =   AudioClassification(audio_column="audio"      ,  label_column="label"   )







UpperCAmelCase						:		Tuple			= [
    ".aiff",
    ".au",
    ".avr",
    ".caf",
    ".flac",
    ".htk",
    ".svx",
    ".mat4",
    ".mat5",
    ".mpc2k",
    ".ogg",
    ".paf",
    ".pvf",
    ".raw",
    ".rf64",
    ".sd2",
    ".sds",
    ".ircam",
    ".voc",
    ".w64",
    ".wav",
    ".nist",
    ".wavex",
    ".wve",
    ".xi",
    ".mp3",
    ".opus",
]
UpperCAmelCase						:		Any			= AUDIO_EXTENSIONS

