


'''simple docstring'''



import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
		from transformers.models.rag.configuration_rag import RagConfig
		from transformers.models.rag.tokenization_rag import RagTokenizer


@require_faiss
@require_torch
class 		_snake_case					(     a__	):




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Tuple        =     tempfile.mkdtemp()
								UpperCAmelCase__  :					int        =     8

								# DPR tok
								UpperCAmelCase__  :					Any        =     [
								    """[UNK]""",
								    """[CLS]""",
								    """[SEP]""",
								    """[PAD]""",
								    """[MASK]""",
								    """want""",
								    """##want""",
								    """##ed""",
								    """wa""",
								    """un""",
								    """runn""",
								    """##ing""",
								    """,""",
								    """low""",
								    """lowest""",
								]
								UpperCAmelCase__  :					Dict        =     os.path.join(self.tmpdirname							,							"""dpr_tokenizer""")
								os.makedirs(_lowerCamelCase							,							exist_ok=_lowerCamelCase)
								UpperCAmelCase__  :					Dict        =     os.path.join(_lowerCamelCase							,							DPR_VOCAB_FILES_NAMES["""vocab_file"""])
								with open(self.vocab_file							,							"""w"""							,							encoding="""utf-8""") as vocab_writer:
											vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]))

								# BART tok
								UpperCAmelCase__  :					List[Any]        =     [
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """\u0120""",
								    """\u0120l""",
								    """\u0120n""",
								    """\u0120lo""",
								    """\u0120low""",
								    """er""",
								    """\u0120lowest""",
								    """\u0120newer""",
								    """\u0120wider""",
								    """<unk>""",
								]
								UpperCAmelCase__  :					List[Any]        =     dict(zip(_lowerCamelCase							,							range(len(_lowerCamelCase))))
								UpperCAmelCase__  :					int        =     ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
								UpperCAmelCase__  :					Optional[int]        =     {"""unk_token""": """<unk>"""}

								UpperCAmelCase__  :					List[str]        =     os.path.join(self.tmpdirname							,							"""bart_tokenizer""")
								os.makedirs(_lowerCamelCase							,							exist_ok=_lowerCamelCase)
								UpperCAmelCase__  :					List[str]        =     os.path.join(_lowerCamelCase							,							BART_VOCAB_FILES_NAMES["""vocab_file"""])
								UpperCAmelCase__  :					List[Any]        =     os.path.join(_lowerCamelCase							,							BART_VOCAB_FILES_NAMES["""merges_file"""])
								with open(self.vocab_file							,							"""w"""							,							encoding="""utf-8""") as fp:
											fp.write(json.dumps(_lowerCamelCase) + """\n""")
								with open(self.merges_file							,							"""w"""							,							encoding="""utf-8""") as fp:
											fp.write("""\n""".join(_lowerCamelCase))




					def 				snake_case__						(      self):
								return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,							"""dpr_tokenizer"""))




					def 				snake_case__						(      self):
								return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname							,							"""bart_tokenizer"""))




					def 				snake_case__						(      self):
								shutil.rmtree(self.tmpdirname)




					@require_tokenizers
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					str        =     os.path.join(self.tmpdirname							,							"""rag_tokenizer""")
								UpperCAmelCase__  :					Tuple        =     RagConfig(question_encoder=DPRConfig().to_dict()							,							generator=BartConfig().to_dict())
								UpperCAmelCase__  :					Dict        =     RagTokenizer(question_encoder=self.get_dpr_tokenizer()							,							generator=self.get_bart_tokenizer())
								rag_config.save_pretrained(_lowerCamelCase)
								rag_tokenizer.save_pretrained(_lowerCamelCase)
								UpperCAmelCase__  :					str        =     RagTokenizer.from_pretrained(_lowerCamelCase							,							config=_lowerCamelCase)
								self.assertIsInstance(new_rag_tokenizer.question_encoder							,							_lowerCamelCase)
								self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()							,							rag_tokenizer.question_encoder.get_vocab())
								self.assertIsInstance(new_rag_tokenizer.generator							,							_lowerCamelCase)
								self.assertEqual(new_rag_tokenizer.generator.get_vocab()							,							rag_tokenizer.generator.get_vocab())




					@slow
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Any        =     RagTokenizer.from_pretrained("""facebook/rag-token-nq""")
								UpperCAmelCase__  :					Optional[int]        =     [
								    """who got the first nobel prize in physics""",
								    """when is the next deadpool movie being released""",
								    """which mode is used for short wave broadcast service""",
								    """who is the owner of reading football club""",
								    """when is the next scandal episode coming out""",
								    """when is the last time the philadelphia won the superbowl""",
								    """what is the most current adobe flash player version""",
								    """how many episodes are there in dragon ball z""",
								    """what is the first step in the evolution of the eye""",
								    """where is gall bladder situated in human body""",
								    """what is the main mineral in lithium batteries""",
								    """who is the president of usa right now""",
								    """where do the greasers live in the outsiders""",
								    """panda is a national animal of which country""",
								    """what is the name of manchester united stadium""",
								]
								UpperCAmelCase__  :					Optional[Any]        =     tokenizer(_lowerCamelCase)
								self.assertIsNotNone(_lowerCamelCase)




					@slow
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[Any]        =     RagTokenizer.from_pretrained("""facebook/rag-sequence-nq""")
								UpperCAmelCase__  :					Any        =     [
								    """who got the first nobel prize in physics""",
								    """when is the next deadpool movie being released""",
								    """which mode is used for short wave broadcast service""",
								    """who is the owner of reading football club""",
								    """when is the next scandal episode coming out""",
								    """when is the last time the philadelphia won the superbowl""",
								    """what is the most current adobe flash player version""",
								    """how many episodes are there in dragon ball z""",
								    """what is the first step in the evolution of the eye""",
								    """where is gall bladder situated in human body""",
								    """what is the main mineral in lithium batteries""",
								    """who is the president of usa right now""",
								    """where do the greasers live in the outsiders""",
								    """panda is a national animal of which country""",
								    """what is the name of manchester united stadium""",
								]
								UpperCAmelCase__  :					Dict        =     tokenizer(_lowerCamelCase)
								self.assertIsNotNone(_lowerCamelCase)



'''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


__A	=logging.get_logger(__name__)


class 		_snake_case					(     a__	):
					lowerCAmelCase      :int   =					['''input_features''', '''is_longer''']




					def __init__(      self							,							_lowerCamelCase=64							,							_lowerCamelCase=4_8000							,							_lowerCamelCase=480							,							_lowerCamelCase=10							,							_lowerCamelCase=1024							,							_lowerCamelCase=0.0							,							_lowerCamelCase=False							,							_lowerCamelCase = 0							,							_lowerCamelCase = 1_4000							,							_lowerCamelCase = None							,							_lowerCamelCase = "fusion"							,							_lowerCamelCase = "repeatpad"							,							**_lowerCamelCase							,							):
								super().__init__(
								    feature_size=_lowerCamelCase							,							sampling_rate=_lowerCamelCase							,							padding_value=_lowerCamelCase							,							return_attention_mask=_lowerCamelCase							,							**_lowerCamelCase							,							)
								UpperCAmelCase__  :					Dict        =     top_db
								UpperCAmelCase__  :					int        =     truncation
								UpperCAmelCase__  :					Optional[Any]        =     padding
								UpperCAmelCase__  :					Union[str, Any]        =     fft_window_size
								UpperCAmelCase__  :					List[Any]        =     (fft_window_size >> 1) + 1
								UpperCAmelCase__  :					Optional[int]        =     hop_length
								UpperCAmelCase__  :					int        =     max_length_s
								UpperCAmelCase__  :					Tuple        =     max_length_s * sampling_rate
								UpperCAmelCase__  :					List[Any]        =     sampling_rate
								UpperCAmelCase__  :					Tuple        =     frequency_min
								UpperCAmelCase__  :					Dict        =     frequency_max
								UpperCAmelCase__  :					Union[str, 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=_lowerCamelCase							,							mel_scale="""htk"""							,							)
								UpperCAmelCase__  :					Optional[int]        =     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 				snake_case__						(      self):
								UpperCAmelCase__  :					List[Any]        =     copy.deepcopy(self.__dict__)
								UpperCAmelCase__  :					Optional[int]        =     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 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__  :					str        =     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 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					List[str]        =     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
											UpperCAmelCase__  :					List[Any]        =     [0]
								if len(ranges[2]) == 0:
											# if the audio is too short, we just use the first chunk
											UpperCAmelCase__  :					Union[str, Any]        =     [0]
								# randomly choose index for each part
								UpperCAmelCase__  :					List[str]        =     np.random.choice(ranges[0])
								UpperCAmelCase__  :					Optional[int]        =     np.random.choice(ranges[1])
								UpperCAmelCase__  :					List[str]        =     np.random.choice(ranges[2])

								UpperCAmelCase__  :					str        =     mel[idx_front : idx_front + chunk_frames, :]
								UpperCAmelCase__  :					Optional[int]        =     mel[idx_middle : idx_middle + chunk_frames, :]
								UpperCAmelCase__  :					Tuple        =     mel[idx_back : idx_back + chunk_frames, :]

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




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

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

											if truncation == "fusion":
														UpperCAmelCase__  :					List[Any]        =     self._np_extract_fbank_features(_lowerCamelCase							,							self.mel_filters)
														UpperCAmelCase__  :					Dict        =     np.stack([input_mel, input_mel, input_mel, input_mel]							,							axis=0)
											else:
														UpperCAmelCase__  :					int        =     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							,							):
								UpperCAmelCase__  :					Dict        =     truncation if truncation is not None else self.truncation
								UpperCAmelCase__  :					List[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.""")

								UpperCAmelCase__  :					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}''')
								UpperCAmelCase__  :					List[str]        =     is_batched_numpy or (
								    isinstance(_lowerCamelCase							,							(list, tuple)) and (isinstance(raw_speech[0]							,							(np.ndarray, tuple, list)))
								)

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

								# always return batch
								if not is_batched:
											UpperCAmelCase__  :					int        =     [np.asarray(_lowerCamelCase)]

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

								UpperCAmelCase__  :					int        =     []
								UpperCAmelCase__  :					List[str]        =     []
								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
											UpperCAmelCase__  :					Optional[int]        =     np.random.randint(0							,							len(_lowerCamelCase))
											UpperCAmelCase__  :					Tuple        =     True

								if isinstance(input_mel[0]							,							_lowerCamelCase):
											UpperCAmelCase__  :					str        =     [np.asarray(_lowerCamelCase							,							dtype=np.floataa) for feature in input_mel]

								# is_longer is a list of bool
								UpperCAmelCase__  :					str        =     [[longer] for longer in is_longer]

								UpperCAmelCase__  :					Optional[int]        =     {"""input_features""": input_mel, """is_longer""": is_longer}
								UpperCAmelCase__  :					Optional[int]        =     BatchFeature(_lowerCamelCase)

								if return_tensors is not None:
											UpperCAmelCase__  :					str        =     input_features.convert_to_tensors(_lowerCamelCase)

								return input_features