
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		['''input_features''', '''is_longer''']




							def __init__( self    ,				snake_case_=64    ,				snake_case_=48_000    ,				snake_case_=480    ,				snake_case_=10    ,				snake_case_=1_024    ,				snake_case_=0.0    ,				snake_case_=False    ,				snake_case_ = 0    ,				snake_case_ = 14_000    ,				snake_case_ = None    ,				snake_case_ = "fusion"    ,				snake_case_ = "repeatpad"    ,				**snake_case_    ,				)       ->  Dict:
													super().__init__(
													    feature_size=snake_case_    ,				sampling_rate=snake_case_    ,				padding_value=snake_case_    ,				return_attention_mask=snake_case_    ,				**snake_case_    ,				)
													__lowerCAmelCase     =  top_db
													__lowerCAmelCase     =  truncation
													__lowerCAmelCase     =  padding
													__lowerCAmelCase     =  fft_window_size
													__lowerCAmelCase     =  (fft_window_size >> 1) + 1
													__lowerCAmelCase     =  hop_length
													__lowerCAmelCase     =  max_length_s
													__lowerCAmelCase     =  max_length_s * sampling_rate
													__lowerCAmelCase     =  sampling_rate
													__lowerCAmelCase     =  frequency_min
													__lowerCAmelCase     =  frequency_max
													__lowerCAmelCase     =  mel_filter_bank(
													    num_frequency_bins=self.nb_frequency_bins    ,				num_mel_filters=snake_case_    ,				min_frequency=snake_case_    ,				max_frequency=snake_case_    ,				sampling_rate=snake_case_    ,				norm=snake_case_    ,				mel_scale="""htk"""    ,				)
													__lowerCAmelCase     =  mel_filter_bank(
													    num_frequency_bins=self.nb_frequency_bins    ,				num_mel_filters=snake_case_    ,				min_frequency=snake_case_    ,				max_frequency=snake_case_    ,				sampling_rate=snake_case_    ,				norm="""slaney"""    ,				mel_scale="""slaney"""    ,				)




							def    A__						( self				)       ->  Dict[str, Any]:
													__lowerCAmelCase     =  copy.deepcopy(self.__dict__				)
													__lowerCAmelCase     =  self.__class__.__name__
													if "mel_filters" in output:
																			del output["mel_filters"]
													if "mel_filters_slaney" in output:
																			del output["mel_filters_slaney"]
													return output




							def    A__						( self    ,				snake_case_    ,				snake_case_ = None				)       ->  np.ndarray:
													__lowerCAmelCase     =  spectrogram(
													    snake_case_    ,				window_function(self.fft_window_size    ,				"""hann"""				)    ,				frame_length=self.fft_window_size    ,				hop_length=self.hop_length    ,				power=2.0    ,				mel_filters=snake_case_    ,				log_mel="""dB"""    ,				)
													return log_mel_spectrogram.T




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  Union[str, Any]:
													__lowerCAmelCase     =  np.array_split(list(range(0    ,				total_frames - chunk_frames + 1				)				)    ,				3				)
													if len(ranges[1]				) == 0:
																			# if the audio is too short, we just use the first chunk
																			__lowerCAmelCase     =  [0]
													if len(ranges[2]				) == 0:
																			# if the audio is too short, we just use the first chunk
																			__lowerCAmelCase     =  [0]
													# randomly choose index for each part
													__lowerCAmelCase     =  np.random.choice(ranges[0]				)
													__lowerCAmelCase     =  np.random.choice(ranges[1]				)
													__lowerCAmelCase     =  np.random.choice(ranges[2]				)

													__lowerCAmelCase     =  mel[idx_front : idx_front + chunk_frames, :]
													__lowerCAmelCase     =  mel[idx_middle : idx_middle + chunk_frames, :]
													__lowerCAmelCase     =  mel[idx_back : idx_back + chunk_frames, :]

													__lowerCAmelCase     =  torch.tensor(mel[None, None, :]				)
													__lowerCAmelCase     =  torch.nn.functional.interpolate(
													    snake_case_    ,				size=[chunk_frames, 64]    ,				mode="""bilinear"""    ,				align_corners=snake_case_				)
													__lowerCAmelCase     =  mel_shrink[0][0].numpy()
													__lowerCAmelCase     =  np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]    ,				axis=0				)
													return mel_fusion




							def    A__						( self    ,				snake_case_    ,				snake_case_    ,				snake_case_    ,				snake_case_				)       ->  np.array:
													if waveform.shape[0] > max_length:
																			if truncation == "rand_trunc":
																									__lowerCAmelCase     =  True
																									# random crop to max_length (for compatibility) -> this should be handled by self.pad
																									__lowerCAmelCase     =  len(snake_case_				) - max_length
																									__lowerCAmelCase     =  np.random.randint(0    ,				overflow + 1				)
																									__lowerCAmelCase     =  waveform[idx : idx + max_length]
																									__lowerCAmelCase     =  self._np_extract_fbank_features(snake_case_    ,				self.mel_filters_slaney				)[None, :]
																			elif truncation == "fusion":
																									__lowerCAmelCase     =  self._np_extract_fbank_features(snake_case_    ,				self.mel_filters				)
																									__lowerCAmelCase     =  max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
																									__lowerCAmelCase     =  mel.shape[0]
																									if chunk_frames == total_frames:
																															# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
																															# In this case, we just use the whole audio.
																															__lowerCAmelCase     =  np.stack([mel, mel, mel, mel]    ,				axis=0				)
																															__lowerCAmelCase     =  False
																									else:
																															__lowerCAmelCase     =  self._random_mel_fusion(snake_case_    ,				snake_case_    ,				snake_case_				)
																															__lowerCAmelCase     =  True
																			else:
																									raise NotImplementedError(f"""data_truncating {truncation} not implemented"""				)

													else:
																			__lowerCAmelCase     =  False
																			# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
																			if waveform.shape[0] < max_length:
																									if padding == "repeat":
																															__lowerCAmelCase     =  int(max_length / len(snake_case_				)				)
																															__lowerCAmelCase     =  np.stack(np.tile(snake_case_    ,				n_repeat + 1				)				)[:max_length]
																									if padding == "repeatpad":
																															__lowerCAmelCase     =  int(max_length / len(snake_case_				)				)
																															__lowerCAmelCase     =  np.stack(np.tile(snake_case_    ,				snake_case_				)				)
																									__lowerCAmelCase     =  np.pad(snake_case_    ,				(0, max_length - waveform.shape[0])    ,				mode="""constant"""    ,				constant_values=0				)

																			if truncation == "fusion":
																									__lowerCAmelCase     =  self._np_extract_fbank_features(snake_case_    ,				self.mel_filters				)
																									__lowerCAmelCase     =  np.stack([input_mel, input_mel, input_mel, input_mel]    ,				axis=0				)
																			else:
																									__lowerCAmelCase     =  self._np_extract_fbank_features(snake_case_    ,				self.mel_filters_slaney				)[None, :]

													return input_mel, longer




							def __call__( self    ,				snake_case_    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				snake_case_ = None    ,				**snake_case_    ,				)       ->  BatchFeature:
													__lowerCAmelCase     =  truncation if truncation is not None else self.truncation
													__lowerCAmelCase     =  padding if padding else self.padding

													if sampling_rate is not None:
																			if sampling_rate != self.sampling_rate:
																									raise ValueError(
																									    f"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
																									    f""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
																									    f""" was sampled with {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."""				)

													__lowerCAmelCase     =  isinstance(snake_case_    ,				np.ndarray				) and len(raw_speech.shape				) > 1
													if is_batched_numpy and len(raw_speech.shape				) > 2:
																			raise ValueError(f"""Only mono-channel audio is supported for input to {self}"""				)
													__lowerCAmelCase     =  is_batched_numpy or (
													    isinstance(snake_case_    ,				(list, tuple)				) and (isinstance(raw_speech[0]    ,				(np.ndarray, tuple, list)				))
													)

													if is_batched:
																			__lowerCAmelCase     =  [np.asarray(snake_case_    ,				dtype=np.floataa				) for speech in raw_speech]
													elif not is_batched and not isinstance(snake_case_    ,				np.ndarray				):
																			__lowerCAmelCase     =  np.asarray(snake_case_    ,				dtype=np.floataa				)
													elif isinstance(snake_case_    ,				np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
																			__lowerCAmelCase     =  raw_speech.astype(np.floataa				)

													# always return batch
													if not is_batched:
																			__lowerCAmelCase     =  [np.asarray(snake_case_				)]

													# convert to mel spectrogram, truncate and pad if needed.
													__lowerCAmelCase     =  [
													    self._get_input_mel(snake_case_    ,				max_length if max_length else self.nb_max_samples    ,				snake_case_    ,				snake_case_				)
													    for waveform in raw_speech
													]

													__lowerCAmelCase     =  []
													__lowerCAmelCase     =  []
													for mel, longer in padded_inputs:
																			input_mel.append(snake_case_				)
																			is_longer.append(snake_case_				)

													if truncation == "fusion" and sum(snake_case_				) == 0:
																			# if no audio is longer than 10s, then randomly select one audio to be longer
																			__lowerCAmelCase     =  np.random.randint(0    ,				len(snake_case_				)				)
																			__lowerCAmelCase     =  True

													if isinstance(input_mel[0]    ,				snake_case_				):
																			__lowerCAmelCase     =  [np.asarray(snake_case_    ,				dtype=np.floataa				) for feature in input_mel]

													# is_longer is a list of bool
													__lowerCAmelCase     =  [[longer] for longer in is_longer]

													__lowerCAmelCase     =  {"""input_features""": input_mel, """is_longer""": is_longer}
													__lowerCAmelCase     =  BatchFeature(snake_case_				)

													if return_tensors is not None:
																			__lowerCAmelCase     =  input_features.convert_to_tensors(snake_case_				)

													return input_features


"""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     (_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     =  nn.Parameter(_lowerCAmelCase						)
						if bias is not None:
												assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
												__lowerCAmelCase     =  nn.Parameter(_lowerCAmelCase						)

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# set torch weights for 1-to-1 comparison
						__lowerCAmelCase     =  np.asarray(weights[0]						)
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						__lowerCAmelCase     =  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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# set torch weights for 1-to-1 comparison
						__lowerCAmelCase     =  np.asarray(weights[0]						)
						__lowerCAmelCase     =  np.asarray(weights[1]						)
						__lowerCAmelCase     =  np.asarray(weights[2]						)
						__lowerCAmelCase     =  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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# layernorm 1
						__lowerCAmelCase     =  weights[0][0][0]
						__lowerCAmelCase     =  np.asarray(layer_norm_a[0]						)
						__lowerCAmelCase     =  np.asarray(layer_norm_a[1]						)
						set_param(
						    torch_block.attention.layer_norm ,    torch.tensor(_lowerCAmelCase						) ,    torch.tensor(_lowerCAmelCase						) ,    )

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

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

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

						# intermediate dense
						__lowerCAmelCase     =  np.asarray(intermediate_weights[1][0]						)
						__lowerCAmelCase     =  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     =  np.asarray(intermediate_weights[4][0]						)
						__lowerCAmelCase     =  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  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# reformer model
						__lowerCAmelCase     =  torch_model.reformer

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

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

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

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

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

def  lowercase     (_lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase						):
						# Initialise PyTorch model
						__lowerCAmelCase     =  ReformerConfig.from_json_file(_lowerCAmelCase						)
						print(f"""Building PyTorch model from configuration: {config}"""						)
						__lowerCAmelCase     =  ReformerModelWithLMHead(_lowerCAmelCase						)

						with open(_lowerCAmelCase ,    """rb"""						) as f:
												__lowerCAmelCase     =  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__":
			SCREAMING_SNAKE_CASE_					     =	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.'''
			)
			SCREAMING_SNAKE_CASE_					     =	parser.parse_args()
			convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)
