






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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Any      )		->str:
						# set torch weights for 1-to-1 comparison
						A__						:  Optional[Any]		  =  np.asarray(weights[0]      )
						A__						:  Tuple		  =  np.asarray(weights[1]      )
						A__						:  Dict		  =  np.asarray(weights[2]      )

						set_param(
						    torch_layer.self_attention.query_key,						torch.tensor(UpperCAmelCase__      ).transpose(1,						2      ).contiguous().view(-1,						UpperCAmelCase__      ),						)
						set_param(
						    torch_layer.self_attention.value,						torch.tensor(UpperCAmelCase__      ).transpose(1,						2      ).contiguous().view(-1,						UpperCAmelCase__      ),						)
						set_param(
						    torch_layer.output.dense,						torch.tensor(UpperCAmelCase__      ).view(-1,						UpperCAmelCase__      ).contiguous().transpose(0,						1      ),						)




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Any,						UpperCAmelCase__       :					Dict,						UpperCAmelCase__       :					Union[str, Any]      )		->Optional[int]:
						# set torch weights for 1-to-1 comparison
						A__						:  List[str]		  =  np.asarray(weights[0]      )
						A__						:  int		  =  np.asarray(weights[1]      )
						A__						:  Optional[int]		  =  np.asarray(weights[2]      )
						A__						:  List[str]		  =  np.asarray(weights[3]      )

						set_param(
						    torch_layer.self_attention.query,						torch.tensor(UpperCAmelCase__      ).transpose(1,						2      ).contiguous().view(-1,						UpperCAmelCase__      ),						)
						set_param(
						    torch_layer.self_attention.key,						torch.tensor(UpperCAmelCase__      ).transpose(1,						2      ).contiguous().view(-1,						UpperCAmelCase__      ),						)
						set_param(
						    torch_layer.self_attention.value,						torch.tensor(UpperCAmelCase__      ).transpose(1,						2      ).contiguous().view(-1,						UpperCAmelCase__      ),						)
						set_param(
						    torch_layer.output.dense,						torch.tensor(UpperCAmelCase__      ).view(-1,						UpperCAmelCase__      ).contiguous().transpose(0,						1      ),						)




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					Optional[Any]      )		->List[Any]:
						# layernorm 1
						A__						:  List[Any]		  =  weights[0][0][0]
						A__						:  List[str]		  =  np.asarray(layer_norm_a[0]      )
						A__						:  Dict		  =  np.asarray(layer_norm_a[1]      )
						set_param(
						    torch_block.attention.layer_norm,						torch.tensor(UpperCAmelCase__      ),						torch.tensor(UpperCAmelCase__      ),						)

						# lsh weights + output
						A__						:  Dict		  =  weights[0][1]
						if len(UpperCAmelCase__      ) < 4:
												set_layer_weights_in_torch_lsh(UpperCAmelCase__,						torch_block.attention,						UpperCAmelCase__      )
						else:
												set_layer_weights_in_torch_local(UpperCAmelCase__,						torch_block.attention,						UpperCAmelCase__      )

						# intermediate weighs
						A__						:  int		  =  weights[2][0][1][2]

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

						# layernorm 2
						A__						:  Any		  =  np.asarray(intermediate_weights[0][0]      )
						A__						:  Any		  =  np.asarray(intermediate_weights[0][1]      )
						set_param(
						    torch_block.feed_forward.layer_norm,						torch.tensor(UpperCAmelCase__      ),						torch.tensor(UpperCAmelCase__      ),						)

						# intermediate dense
						A__						:  str		  =  np.asarray(intermediate_weights[1][0]      )
						A__						:  Any		  =  np.asarray(intermediate_weights[1][1]      )
						set_param(
						    torch_block.feed_forward.dense.dense,						torch.tensor(UpperCAmelCase__      ).transpose(0,						1      ).contiguous(),						torch.tensor(UpperCAmelCase__      ),						)

						# intermediate out
						A__						:  str		  =  np.asarray(intermediate_weights[4][0]      )
						A__						:  Dict		  =  np.asarray(intermediate_weights[4][1]      )
						set_param(
						    torch_block.feed_forward.output.dense,						torch.tensor(UpperCAmelCase__      ).transpose(0,						1      ).contiguous(),						torch.tensor(UpperCAmelCase__      ),						)




def      _lowerCAmelCase		(							UpperCAmelCase__       :					str,						UpperCAmelCase__       :					List[str],						UpperCAmelCase__       :					List[Any]      )		->Tuple:
						# reformer model
						A__						:  str		  =  torch_model.reformer

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

						if isinstance(weights[3],						UpperCAmelCase__      ):
												A__						:  List[str]		  =  torch_model_reformer.embeddings.position_embeddings
												for emb_idx in range(len(position_embeddings.weights      )      ):
																		A__						:  Tuple		  =  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__						:  int		  =  nn.Parameter(torch.tensor(UpperCAmelCase__      )      )

						A__						:  int		  =  weights[5]
						assert len(torch_model_reformer.encoder.layers      ) * 4 == len(
						    UpperCAmelCase__      ), "HF and trax model do not have the same number of layers"
						for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers      ):
												A__						:  Any		  =  trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
												set_block_weights_in_torch(UpperCAmelCase__,						UpperCAmelCase__,						UpperCAmelCase__      )

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

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




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[Any],						UpperCAmelCase__       :					int,						UpperCAmelCase__       :					List[str]      )		->List[str]:
						# Initialise PyTorch model
						A__						:  Union[str, Any]		  =  ReformerConfig.from_json_file(UpperCAmelCase__      )
						print(f'Building PyTorch model from configuration: {config}'      )
						A__						:  List[Any]		  =  ReformerModelWithLMHead(UpperCAmelCase__      )

						with open(UpperCAmelCase__,						"""rb"""      ) as f:
												A__						:  Union[str, Any]		  =  pickle.load(UpperCAmelCase__      )["""weights"""]

						set_model_weights_in_torch(UpperCAmelCase__,						UpperCAmelCase__,						config.hidden_size      )

						# Save pytorch-model
						print(f'Save PyTorch model to {pytorch_dump_path}'      )
						torch.save(model.state_dict(),						UpperCAmelCase__      )


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 warnings
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


A_    =							logging.get_logger(__name__)






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							['input_values', 'attention_mask']


							def __init__(			self      :						Union[str, Any]			,    snake_case      :						int = 1			,    snake_case      :						int = 1_6000			,    snake_case      :						float = 0.0			,    snake_case      :						bool = False			,    snake_case      :						int = 80			,    snake_case      :						int = 16			,    snake_case      :						int = 64			,    snake_case      :						str = "hann_window"			,    snake_case      :						float = 1.0			,    snake_case      :						float = 80			,    snake_case      :						float = 7600			,    snake_case      :						float = 1e-10			,    snake_case      :						int = 2			,    snake_case      :						bool = True			,    **snake_case      :						Dict			,    ):
													'''simple docstring'''





													super().__init__(feature_size=snake_case			,    sampling_rate=snake_case			,    padding_value=snake_case			,    **snake_case							)
													A__						:  Optional[int]		  =  do_normalize
													A__						:  List[Any]		  =  return_attention_mask

													A__						:  Tuple		  =  num_mel_bins
													A__						:  Optional[int]		  =  hop_length
													A__						:  List[str]		  =  win_length
													A__						:  List[Any]		  =  win_function
													A__						:  Tuple		  =  frame_signal_scale
													A__						:  Optional[Any]		  =  fmin
													A__						:  str		  =  fmax
													A__						:  str		  =  mel_floor
													A__						:  Dict		  =  reduction_factor

													A__						:  int		  =  win_length * sampling_rate // 1000
													A__						:  Optional[Any]		  =  hop_length * sampling_rate // 1000
													A__						:  Optional[int]		  =  optimal_fft_length(self.sample_size							)
													A__						:  List[Any]		  =  (self.n_fft // 2) + 1

													A__						:  str		  =  window_function(window_length=self.sample_size			,    name=self.win_function			,    periodic=snake_case							)

													A__						:  Optional[Any]		  =  mel_filter_bank(
													    num_frequency_bins=self.n_freqs			,    num_mel_filters=self.num_mel_bins			,    min_frequency=self.fmin			,    max_frequency=self.fmax			,    sampling_rate=self.sampling_rate			,    norm="""slaney"""			,    mel_scale="""slaney"""			,    )

													if frame_signal_scale != 1.0:
																			warnings.warn(
																			    """The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers"""			,    snake_case			,    )
													if reduction_factor != 2.0:
																			warnings.warn(
																			    """The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers"""			,    snake_case			,    )


							@staticmethod
							# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
							def 						_UpperCamelCase       (			snake_case      :						List[np.ndarray]			,    snake_case      :						List[np.ndarray]			,    snake_case      :						float = 0.0							):
													'''simple docstring'''





													if attention_mask is not None:
																			A__						:  Tuple		  =  np.array(snake_case			,    np.intaa							)
																			A__						:  List[Any]		  =  []

																			for vector, length in zip(snake_case			,    attention_mask.sum(-1							)							):
																									A__						:  str		  =  (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7							)
																									if length < normed_slice.shape[0]:
																															A__						:  int		  =  padding_value

																									normed_input_values.append(snake_case							)
													else:
																			A__						:  List[Any]		  =  [(x - x.mean()) / np.sqrt(x.var() + 1e-7							) for x in input_values]

													return normed_input_values


							def 						_UpperCamelCase       (			self      :						Optional[int]			,    snake_case      :						np.ndarray			,    ):
													'''simple docstring'''





													A__						:  List[Any]		  =  spectrogram(
													    snake_case			,    window=self.window			,    frame_length=self.sample_size			,    hop_length=self.sample_stride			,    fft_length=self.n_fft			,    mel_filters=self.mel_filters			,    mel_floor=self.mel_floor			,    log_mel="""log10"""			,    )
													return log_mel_spec.T


							def __call__(			self      :						str			,    snake_case      :						Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,    snake_case      :						Optional[Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]] = None			,    snake_case      :						Union[bool, str, PaddingStrategy] = False			,    snake_case      :						Optional[int] = None			,    snake_case      :						bool = False			,    snake_case      :						Optional[int] = None			,    snake_case      :						Optional[bool] = None			,    snake_case      :						Optional[Union[str, TensorType]] = None			,    snake_case      :						Optional[int] = None			,    **snake_case      :						Union[str, Any]			,    ):
													'''simple docstring'''





													if audio is None and audio_target is None:
																			raise ValueError("""You must provide either `audio` or `audio_target` values."""							)

													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 audio is not None:
																			A__						:  Dict		  =  self._process_audio(
																			    snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    **snake_case			,    )
													else:
																			A__						:  Dict		  =  None

													if audio_target is not None:
																			A__						:  Union[str, Any]		  =  self._process_audio(
																			    snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    snake_case			,    **snake_case			,    )

																			if inputs is None:
																									return inputs_target
																			else:
																									A__						:  Union[str, Any]		  =  inputs_target["""input_values"""]
																									A__						:  str		  =  inputs_target.get("""attention_mask"""							)
																									if decoder_attention_mask is not None:
																															A__						:  List[str]		  =  decoder_attention_mask

													return inputs


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]			,    snake_case      :						bool = False			,    snake_case      :						Union[bool, str, PaddingStrategy] = False			,    snake_case      :						Optional[int] = None			,    snake_case      :						bool = False			,    snake_case      :						Optional[int] = None			,    snake_case      :						Optional[bool] = None			,    snake_case      :						Optional[Union[str, TensorType]] = None			,    **snake_case      :						Union[str, Any]			,    ):
													'''simple docstring'''





													A__						:  List[str]		  =  isinstance(snake_case			,    np.ndarray							) and len(speech.shape							) > 1
													if is_batched_numpy and len(speech.shape							) > 2:
																			raise ValueError(F'Only mono-channel audio is supported for input to {self}'							)
													A__						:  Tuple		  =  is_batched_numpy or (
													    isinstance(snake_case			,    (list, tuple)							) and (isinstance(speech[0]			,    (np.ndarray, tuple, list)							))
													)

													if is_batched:
																			A__						:  Tuple		  =  [np.asarray(snake_case			,    dtype=np.floataa							) for speech in speech]
													elif not is_batched and not isinstance(snake_case			,    np.ndarray							):
																			A__						:  Tuple		  =  np.asarray(snake_case			,    dtype=np.floataa							)
													elif isinstance(snake_case			,    np.ndarray							) and speech.dtype is np.dtype(np.floataa							):
																			A__						:  str		  =  speech.astype(np.floataa							)

													# always return batch
													if not is_batched:
																			A__						:  Optional[Any]		  =  [speech]

													# needed to make pad() work on spectrogram inputs
													A__						:  str		  =  self.feature_size

													# convert into correct format for padding
													if is_target:
																			A__						:  str		  =  [self._extract_mel_features(snake_case							) for waveform in speech]
																			A__						:  List[str]		  =  BatchFeature({"""input_values""": features}							)
																			A__						:  Dict		  =  self.num_mel_bins
													else:
																			A__						:  Tuple		  =  BatchFeature({"""input_values""": speech}							)

													A__						:  List[str]		  =  self.pad(
													    snake_case			,    padding=snake_case			,    max_length=snake_case			,    truncation=snake_case			,    pad_to_multiple_of=snake_case			,    return_attention_mask=snake_case			,    **snake_case			,    )

													A__						:  List[str]		  =  feature_size_hack

													# convert input values to correct format
													A__						:  str		  =  padded_inputs["""input_values"""]
													if not isinstance(input_values[0]			,    np.ndarray							):
																			A__						:  Tuple		  =  [np.asarray(snake_case			,    dtype=np.floataa							) for array in input_values]
													elif (
													    not isinstance(snake_case			,    np.ndarray							)
													    and isinstance(input_values[0]			,    np.ndarray							)
													    and input_values[0].dtype is np.dtype(np.floataa							)
													):
																			A__						:  Dict		  =  [array.astype(np.floataa							) for array in input_values]
													elif isinstance(snake_case			,    np.ndarray							) and input_values.dtype is np.dtype(np.floataa							):
																			A__						:  Any		  =  input_values.astype(np.floataa							)

													# convert attention_mask to correct format
													A__						:  List[Any]		  =  padded_inputs.get("""attention_mask"""							)
													if attention_mask is not None:
																			A__						:  Any		  =  [np.asarray(snake_case			,    dtype=np.intaa							) for array in attention_mask]

													# zero-mean and unit-variance normalization
													if not is_target and self.do_normalize:
																			A__						:  Optional[int]		  =  (
																			    attention_mask
																			    if self._get_padding_strategies(snake_case			,    max_length=snake_case							) is not PaddingStrategy.DO_NOT_PAD
																			    else None
																			)
																			A__						:  Optional[int]		  =  self.zero_mean_unit_var_norm(
																			    padded_inputs["""input_values"""]			,    attention_mask=snake_case			,    padding_value=self.padding_value							)

													if return_tensors is not None:
																			A__						:  Any		  =  padded_inputs.convert_to_tensors(snake_case							)

													return padded_inputs


							def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  List[str]		  =  super().to_dict()

													# Don't serialize these as they are derived from the other properties.
													A__						:  Dict		  =  ["""window""", """mel_filters""", """sample_size""", """sample_stride""", """n_fft""", """n_freqs"""]
													for name in names:
																			if name in output:
																									del output[name]

													return output






