





"""simple docstring"""





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

import numpy as np

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


lowerCAmelCase__     			=			logging.get_logger(__name__)





class        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			["input_features"]


						def __init__(self   ,							__a=80   ,							__a=1_60_00   ,							__a=1_60   ,							__a=30   ,							__a=4_00   ,							__a=0.0   ,							__a=False   ,							**__a   ,							) ->   Tuple:
								super().__init__(
								    feature_size=__a   ,							sampling_rate=__a   ,							padding_value=__a   ,							return_attention_mask=__a   ,							**__a   ,							)
								UpperCamelCase   =	n_fft
								UpperCamelCase   =	hop_length
								UpperCamelCase   =	chunk_length
								UpperCamelCase   =	chunk_length * sampling_rate
								UpperCamelCase   =	self.n_samples // hop_length
								UpperCamelCase   =	sampling_rate
								UpperCamelCase   =	mel_filter_bank(
								    num_frequency_bins=1 + n_fft // 2   ,							num_mel_filters=__a   ,							min_frequency=0.0   ,							max_frequency=8000.0   ,							sampling_rate=__a   ,							norm="slaney"   ,							mel_scale="slaney"   ,							)


						def     snake_case_   (self   ,							__a		) ->   np.ndarray:
								UpperCamelCase   =	spectrogram(
								    __a   ,							window_function(self.n_fft   ,							"hann"		)   ,							frame_length=self.n_fft   ,							hop_length=self.hop_length   ,							power=2.0   ,							mel_filters=self.mel_filters   ,							log_mel="log10"   ,							)
								UpperCamelCase   =	log_spec[:, :-1]
								UpperCamelCase   =	np.maximum(__a   ,							log_spec.max() - 8.0		)
								UpperCamelCase   =	(log_spec + 4.0) / 4.0
								return log_spec


						@staticmethod
						# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
						def     snake_case_   (__a   ,							__a   ,							__a = 0.0		) ->   List[np.ndarray]:
								if attention_mask is not None:
										UpperCamelCase   =	np.array(__a   ,							np.intaa		)
										UpperCamelCase   =	[]

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

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

								return normed_input_values


						def __call__(self   ,							__a   ,							__a = True   ,							__a = None   ,							__a = None   ,							__a = None   ,							__a = "max_length"   ,							__a = None   ,							__a = None   ,							__a = None   ,							**__a   ,							) ->   BatchFeature:

								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."		)

								UpperCamelCase   =	isinstance(__a   ,							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}"		)
								UpperCamelCase   =	is_batched_numpy or (
								    isinstance(__a   ,							(list, tuple)		) and (isinstance(raw_speech[0]   ,							(np.ndarray, tuple, list)		))
								)

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

								# always return batch
								if not is_batched:
										UpperCamelCase   =	[np.asarray([raw_speech]		).T]

								UpperCamelCase   =	BatchFeature({"input_features": raw_speech}		)

								# convert into correct format for padding

								UpperCamelCase   =	self.pad(
								    __a   ,							padding=__a   ,							max_length=max_length if max_length else self.n_samples   ,							truncation=__a   ,							pad_to_multiple_of=__a   ,							return_attention_mask=return_attention_mask or do_normalize   ,							)

								# zero-mean and unit-variance normalization
								if do_normalize:
										UpperCamelCase   =	self.zero_mean_unit_var_norm(
										    padded_inputs["input_features"]   ,							attention_mask=padded_inputs["attention_mask"]   ,							padding_value=self.padding_value   ,							)
										UpperCamelCase   =	np.stack(padded_inputs["input_features"]   ,							axis=0		)

								# make sure list is in array format
								UpperCamelCase   =	padded_inputs.get("input_features"		).transpose(2   ,							0   ,							1		)

								UpperCamelCase   =	[self._np_extract_fbank_features(__a		) for waveform in input_features[0]]

								if isinstance(input_features[0]   ,							__a		):
										UpperCamelCase   =	[np.asarray(__a   ,							dtype=np.floataa		) for feature in input_features]
								else:
										UpperCamelCase   =	input_features

								if return_attention_mask:
										# rescale from sample (48000) to feature (3000)
										UpperCamelCase   =	padded_inputs["attention_mask"][:, :: self.hop_length]

								if return_tensors is not None:
										UpperCamelCase   =	padded_inputs.convert_to_tensors(__a		)

								return padded_inputs




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













"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[
		    "encoder.version",
		    "decoder.version",
		    "model.encoder.version",
		    "model.decoder.version",
		    "decoder.output_projection.weight",
		    "_float_tensor",
		    "encoder.embed_positions._float_tensor",
		    "decoder.embed_positions._float_tensor",
		]
		for k in ignore_keys:
				state_dict.pop(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   ,				UpperCamelCase   =	emb.weight.shape
		UpperCamelCase   =	nn.Linear(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	bias=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	emb.weight.data
		return lin_layer


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	torch.load(_SCREAMING_SNAKE_CASE  ,	map_location="cpu"    )
		UpperCamelCase   =	mam_aaa["args"] or mam_aaa["cfg"]["model"]
		UpperCamelCase   =	mam_aaa["model"]
		remove_ignore_keys_(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	state_dict["encoder.embed_tokens.weight"].shape[0]

		UpperCamelCase   =	MaMaaaConfig(
		    vocab_size=_SCREAMING_SNAKE_CASE  ,	max_position_embeddings=1_024  ,	encoder_layers=args.encoder_layers  ,	decoder_layers=args.decoder_layers  ,	encoder_attention_heads=args.encoder_attention_heads  ,	decoder_attention_heads=args.decoder_attention_heads  ,	encoder_ffn_dim=args.encoder_ffn_embed_dim  ,	decoder_ffn_dim=args.decoder_ffn_embed_dim  ,	d_model=args.encoder_embed_dim  ,	encoder_layerdrop=args.encoder_layerdrop  ,	decoder_layerdrop=args.decoder_layerdrop  ,	dropout=args.dropout  ,	attention_dropout=args.attention_dropout  ,	activation_dropout=args.activation_dropout  ,	activation_function="relu"  ,	)

		UpperCamelCase   =	state_dict["decoder.embed_tokens.weight"]
		UpperCamelCase   =	MaMaaaForConditionalGeneration(_SCREAMING_SNAKE_CASE    )
		model.model.load_state_dict(_SCREAMING_SNAKE_CASE  ,	strict=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	make_linear_from_emb(model.model.shared    )

		return model


if __name__ == "__main__":
						lowerCAmelCase__     			=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
						parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						lowerCAmelCase__     			=			parser.parse_args()
						lowerCAmelCase__     			=			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
						model.save_pretrained(args.pytorch_dump_folder_path)






