



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


__snake_case       :	Optional[int]            =logging.get_logger(__name__)



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			=["""input_features""", """is_longer"""]



		def __init__(self	,__lowerCamelCase=64	,__lowerCamelCase=4_80_00	,__lowerCamelCase=4_80	,__lowerCamelCase=10	,__lowerCamelCase=10_24	,__lowerCamelCase=0.0	,__lowerCamelCase=False	,__lowerCamelCase = 0	,__lowerCamelCase = 1_40_00	,__lowerCamelCase = None	,__lowerCamelCase = "fusion"	,__lowerCamelCase = "repeatpad"	,**__lowerCamelCase	,)  ->    Dict:

						"""simple docstring"""






						super().__init__(
						    feature_size=__lowerCamelCase	,sampling_rate=__lowerCamelCase	,padding_value=__lowerCamelCase	,return_attention_mask=__lowerCamelCase	,**__lowerCamelCase	,)
						lowerCAmelCase__  :       List[Any]				=  top_db
						lowerCAmelCase__  :       Tuple				=  truncation
						lowerCAmelCase__  :       str				=  padding
						lowerCAmelCase__  :       Dict				=  fft_window_size
						lowerCAmelCase__  :       str				=  (fft_window_size >> 1) + 1
						lowerCAmelCase__  :       Tuple				=  hop_length
						lowerCAmelCase__  :       Optional[Any]				=  max_length_s
						lowerCAmelCase__  :       List[Any]				=  max_length_s * sampling_rate
						lowerCAmelCase__  :       List[Any]				=  sampling_rate
						lowerCAmelCase__  :       Dict				=  frequency_min
						lowerCAmelCase__  :       List[str]				=  frequency_max
						lowerCAmelCase__  :       List[str]				=  mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins	,num_mel_filters=__lowerCamelCase	,min_frequency=__lowerCamelCase	,max_frequency=__lowerCamelCase	,sampling_rate=__lowerCamelCase	,norm=__lowerCamelCase	,mel_scale='''htk'''	,)
						lowerCAmelCase__  :       Any				=  mel_filter_bank(
						    num_frequency_bins=self.nb_frequency_bins	,num_mel_filters=__lowerCamelCase	,min_frequency=__lowerCamelCase	,max_frequency=__lowerCamelCase	,sampling_rate=__lowerCamelCase	,norm='''slaney'''	,mel_scale='''slaney'''	,)



		def 	lowerCAmelCase__   (self				)  ->    Dict[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  copy.deepcopy(self.__dict__				)
						lowerCAmelCase__  :       Union[str, Any]				=  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 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase = None				)  ->    np.ndarray:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  spectrogram(
						    __lowerCamelCase	,window_function(self.fft_window_size	,'''hann'''				)	,frame_length=self.fft_window_size	,hop_length=self.hop_length	,power=2.0	,mel_filters=__lowerCamelCase	,log_mel='''dB'''	,)
						return log_mel_spectrogram.T



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  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__  :       Dict				=  [0]
						if len(ranges[2]				) == 0:
										# if the audio is too short, we just use the first chunk
										lowerCAmelCase__  :       Optional[Any]				=  [0]
						# randomly choose index for each part
						lowerCAmelCase__  :       int				=  np.random.choice(ranges[0]				)
						lowerCAmelCase__  :       Optional[Any]				=  np.random.choice(ranges[1]				)
						lowerCAmelCase__  :       Tuple				=  np.random.choice(ranges[2]				)

						lowerCAmelCase__  :       Union[str, Any]				=  mel[idx_front : idx_front + chunk_frames, :]
						lowerCAmelCase__  :       Dict				=  mel[idx_middle : idx_middle + chunk_frames, :]
						lowerCAmelCase__  :       Any				=  mel[idx_back : idx_back + chunk_frames, :]

						lowerCAmelCase__  :       Union[str, Any]				=  torch.tensor(mel[None, None, :]				)
						lowerCAmelCase__  :       Optional[int]				=  torch.nn.functional.interpolate(
						    __lowerCamelCase	,size=[chunk_frames, 64]	,mode='''bilinear'''	,align_corners=__lowerCamelCase				)
						lowerCAmelCase__  :       str				=  mel_shrink[0][0].numpy()
						lowerCAmelCase__  :       List[str]				=  np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]	,axis=0				)
						return mel_fusion



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    np.array:

						"""simple docstring"""






						if waveform.shape[0] > max_length:
										if truncation == "rand_trunc":
														lowerCAmelCase__  :       Union[str, Any]				=  True
														# random crop to max_length (for compatibility) -> this should be handled by self.pad
														lowerCAmelCase__  :       Union[str, Any]				=  len(__lowerCamelCase				) - max_length
														lowerCAmelCase__  :       List[str]				=  np.random.randint(0	,overflow + 1				)
														lowerCAmelCase__  :       Any				=  waveform[idx : idx + max_length]
														lowerCAmelCase__  :       List[str]				=  self._np_extract_fbank_features(__lowerCamelCase	,self.mel_filters_slaney				)[None, :]
										elif truncation == "fusion":
														lowerCAmelCase__  :       Union[str, Any]				=  self._np_extract_fbank_features(__lowerCamelCase	,self.mel_filters				)
														lowerCAmelCase__  :       Dict				=  max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
														lowerCAmelCase__  :       Dict				=  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__  :       str				=  np.stack([mel, mel, mel, mel]	,axis=0				)
																		lowerCAmelCase__  :       int				=  False
														else:
																		lowerCAmelCase__  :       List[str]				=  self._random_mel_fusion(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)
																		lowerCAmelCase__  :       Optional[Any]				=  True
										else:
														raise NotImplementedError(f"""data_truncating {truncation} not implemented"""				)

						else:
										lowerCAmelCase__  :       str				=  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__  :       str				=  int(max_length / len(__lowerCamelCase				)				)
																		lowerCAmelCase__  :       int				=  np.stack(np.tile(__lowerCamelCase	,n_repeat + 1				)				)[:max_length]
														if padding == "repeatpad":
																		lowerCAmelCase__  :       Any				=  int(max_length / len(__lowerCamelCase				)				)
																		lowerCAmelCase__  :       Any				=  np.stack(np.tile(__lowerCamelCase	,__lowerCamelCase				)				)
														lowerCAmelCase__  :       List[Any]				=  np.pad(__lowerCamelCase	,(0, max_length - waveform.shape[0])	,mode='''constant'''	,constant_values=0				)

										if truncation == "fusion":
														lowerCAmelCase__  :       Tuple				=  self._np_extract_fbank_features(__lowerCamelCase	,self.mel_filters				)
														lowerCAmelCase__  :       List[Any]				=  np.stack([input_mel, input_mel, input_mel, input_mel]	,axis=0				)
										else:
														lowerCAmelCase__  :       Any				=  self._np_extract_fbank_features(__lowerCamelCase	,self.mel_filters_slaney				)[None, :]

						return input_mel, longer



		def __call__(self	,__lowerCamelCase	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,__lowerCamelCase = None	,**__lowerCamelCase	,)  ->    BatchFeature:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  truncation if truncation is not None else self.truncation
						lowerCAmelCase__  :       Union[str, Any]				=  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__  :       int				=  isinstance(__lowerCamelCase	,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__  :       Optional[int]				=  is_batched_numpy or (
						    isinstance(__lowerCamelCase	,(list, tuple)				) and (isinstance(raw_speech[0]	,(np.ndarray, tuple, list)				))
						)

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

						# always return batch
						if not is_batched:
										lowerCAmelCase__  :       Tuple				=  [np.asarray(__lowerCamelCase				)]

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

						lowerCAmelCase__  :       Optional[int]				=  []
						lowerCAmelCase__  :       Optional[int]				=  []
						for mel, longer in padded_inputs:
										input_mel.append(__lowerCamelCase				)
										is_longer.append(__lowerCamelCase				)

						if truncation == "fusion" and sum(__lowerCamelCase				) == 0:
										# if no audio is longer than 10s, then randomly select one audio to be longer
										lowerCAmelCase__  :       Any				=  np.random.randint(0	,len(__lowerCamelCase				)				)
										lowerCAmelCase__  :       Union[str, Any]				=  True

						if isinstance(input_mel[0]	,__lowerCamelCase				):
										lowerCAmelCase__  :       Any				=  [np.asarray(__lowerCamelCase	,dtype=np.floataa				) for feature in input_mel]

						# is_longer is a list of bool
						lowerCAmelCase__  :       Any				=  [[longer] for longer in is_longer]

						lowerCAmelCase__  :       Optional[int]				=  {'''input_features''': input_mel, '''is_longer''': is_longer}
						lowerCAmelCase__  :       Dict				=  BatchFeature(__lowerCamelCase				)

						if return_tensors is not None:
										lowerCAmelCase__  :       Any				=  input_features.convert_to_tensors(__lowerCamelCase				)

						return input_features






import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__snake_case       :	Optional[int]            =get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
							__snake_case       :	Optional[Any]            =json.load(f)



@require_torch
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						return FSMTTokenizer.from_pretrained(__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  FSMTForConditionalGeneration.from_pretrained(__lowerCamelCase				).to(__lowerCamelCase				)
						if torch_device == "cuda":
										model.half()
						return model



		@parameterized.expand(
		    [
		        ['''en-ru''', 26.0],
		        ['''ru-en''', 22.0],
		        ['''en-de''', 22.0],
		        ['''de-en''', 29.0],
		    ]				)
		@slow
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  f"""facebook/wmt19-{pair}"""
						lowerCAmelCase__  :       int				=  self.get_tokenizer(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  self.get_model(__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''src''']
						lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''tgt''']

						lowerCAmelCase__  :       List[str]				=  tokenizer(__lowerCamelCase	,return_tensors='''pt'''	,truncation=__lowerCamelCase	,padding='''longest'''				).to(__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  model.generate(
						    input_ids=batch.input_ids	,num_beams=8	,)
						lowerCAmelCase__  :       Tuple				=  tokenizer.batch_decode(
						    __lowerCamelCase	,skip_special_tokens=__lowerCamelCase	,clean_up_tokenization_spaces=__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  calculate_bleu(__lowerCamelCase	,__lowerCamelCase				)
						print(__lowerCamelCase				)
						self.assertGreaterEqual(scores['''bleu''']	,__lowerCamelCase				)

