
import argparse
import os
import re


A__			:    Optional[int]         =				'src/transformers'

# Pattern that looks at the indentation in a line.
A__			:    Union[str, Any]         =				re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
A__			:    List[str]         =				re.compile(r'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
A__			:    List[Any]         =				re.compile(r'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
A__			:    int         =				re.compile(r'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
A__			:    Tuple         =				re.compile(r'\[([^\]]+)\]')






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    _re_indent.search(lowerCamelCase_       )
				return "" if search is None else search.groups()[0]






def 						a    (		lowerCamelCase_	,     lowerCamelCase_=""	,     lowerCamelCase_=None	,     lowerCamelCase_=None       ):






				'''simple docstring'''
				lowercase__     				=    0
				lowercase__     				=    code.split('''\n'''       )
				if start_prompt is not None:
								while not lines[index].startswith(lowerCamelCase_       ):
												index += 1
								lowercase__     				=    ['''\n'''.join(lines[:index]       )]
				else:
								lowercase__     				=    []

				# We split into blocks until we get to the `end_prompt` (or the end of the block).
				lowercase__     				=    [lines[index]]
				index += 1
				while index < len(lowerCamelCase_       ) and (end_prompt is None or not lines[index].startswith(lowerCamelCase_       )):
								if len(lines[index]       ) > 0 and get_indent(lines[index]       ) == indent_level:
												if len(lowerCamelCase_       ) > 0 and get_indent(current_block[-1]       ).startswith(indent_level + ''' '''       ):
																current_block.append(lines[index]       )
																blocks.append('''\n'''.join(lowerCamelCase_       )       )
																if index < len(lowerCamelCase_       ) - 1:
																				lowercase__     				=    [lines[index + 1]]
																				index += 1
																else:
																				lowercase__     				=    []
												else:
																blocks.append('''\n'''.join(lowerCamelCase_       )       )
																lowercase__     				=    [lines[index]]
								else:
												current_block.append(lines[index]       )
								index += 1

				# Adds current block if it's nonempty.
				if len(lowerCamelCase_       ) > 0:
								blocks.append('''\n'''.join(lowerCamelCase_       )       )

				# Add final block after end_prompt if provided.
				if end_prompt is not None and index < len(lowerCamelCase_       ):
								blocks.append('''\n'''.join(lines[index:]       )       )

				return blocks






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''

				def _inner(lowerCamelCase_       ):
								return key(lowerCamelCase_       ).lower().replace('''_'''	,     ''''''       )

				return _inner






def 						a    (		lowerCamelCase_	,     lowerCamelCase_=None       ):






				'''simple docstring'''

				# If no key is provided, we use a noop.
				def noop(lowerCamelCase_       ):
								return x

				if key is None:
								lowercase__     				=    noop
				# Constants are all uppercase, they go first.
				lowercase__     				=    [obj for obj in objects if key(lowerCamelCase_       ).isupper()]
				# Classes are not all uppercase but start with a capital, they go second.
				lowercase__     				=    [obj for obj in objects if key(lowerCamelCase_       )[0].isupper() and not key(lowerCamelCase_       ).isupper()]
				# Functions begin with a lowercase, they go last.
				lowercase__     				=    [obj for obj in objects if not key(lowerCamelCase_       )[0].isupper()]

				lowercase__     				=    ignore_underscore(lowerCamelCase_       )
				return sorted(lowerCamelCase_	,     key=lowerCamelCase_       ) + sorted(lowerCamelCase_	,     key=lowerCamelCase_       ) + sorted(lowerCamelCase_	,     key=lowerCamelCase_       )






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''

				# This inner function sort imports between [ ].
				def _replace(lowerCamelCase_       ):
								lowercase__     				=    match.groups()[0]
								if "," not in imports:
												return F"""[{imports}]"""
								lowercase__     				=    [part.strip().replace('''"'''	,     ''''''       ) for part in imports.split(''','''       )]
								# We will have a final empty element if the line finished with a comma.
								if len(keys[-1]       ) == 0:
												lowercase__     				=    keys[:-1]
								return "[" + ", ".join([F"""\"{k}\"""" for k in sort_objects(lowerCamelCase_       )]       ) + "]"

				lowercase__     				=    import_statement.split('''\n'''       )
				if len(lowerCamelCase_       ) > 3:
								# Here we have to sort internal imports that are on several lines (one per name):
								# key: [
								#     "object1",
								#     "object2",
								#     ...
								# ]

								# We may have to ignore one or two lines on each side.
								lowercase__     				=    2 if lines[1].strip() == '''[''' else 1
								lowercase__     				=    [(i, _re_strip_line.search(lowerCamelCase_       ).groups()[0]) for i, line in enumerate(lines[idx:-idx]       )]
								lowercase__     				=    sort_objects(lowerCamelCase_	,     key=lambda lowerCamelCase_       : x[1]       )
								lowercase__     				=    [lines[x[0] + idx] for x in sorted_indices]
								return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]       )
				elif len(lowerCamelCase_       ) == 3:
								# Here we have to sort internal imports that are on one separate line:
								# key: [
								#     "object1", "object2", ...
								# ]
								if _re_bracket_content.search(lines[1]       ) is not None:
												lowercase__     				=    _re_bracket_content.sub(_replace	,     lines[1]       )
								else:
												lowercase__     				=    [part.strip().replace('''"'''	,     ''''''       ) for part in lines[1].split(''','''       )]
												# We will have a final empty element if the line finished with a comma.
												if len(keys[-1]       ) == 0:
																lowercase__     				=    keys[:-1]
												lowercase__     				=    get_indent(lines[1]       ) + ''', '''.join([F"""\"{k}\"""" for k in sort_objects(lowerCamelCase_       )]       )
								return "\n".join(lowerCamelCase_       )
				else:
								# Finally we have to deal with imports fitting on one line
								lowercase__     				=    _re_bracket_content.sub(_replace	,     lowerCamelCase_       )
								return import_statement






def 						a    (		lowerCamelCase_	,     lowerCamelCase_=True       ):






				'''simple docstring'''
				with open(lowerCamelCase_	,     encoding='''utf-8'''       ) as f:
								lowercase__     				=    f.read()

				if "_import_structure" not in code:
								return

				# Blocks of indent level 0
				lowercase__     				=    split_code_in_indented_blocks(
				    lowerCamelCase_	,     start_prompt='''_import_structure = {'''	,     end_prompt='''if TYPE_CHECKING:'''       )

				# We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
				for block_idx in range(1	,     len(lowerCamelCase_       ) - 1       ):
								# Check if the block contains some `_import_structure`s thingy to sort.
								lowercase__     				=    main_blocks[block_idx]
								lowercase__     				=    block.split('''\n'''       )

								# Get to the start of the imports.
								lowercase__     				=    0
								while line_idx < len(lowerCamelCase_       ) and "_import_structure" not in block_lines[line_idx]:
												# Skip dummy import blocks
												if "import dummy" in block_lines[line_idx]:
																lowercase__     				=    len(lowerCamelCase_       )
												else:
																line_idx += 1
								if line_idx >= len(lowerCamelCase_       ):
												continue

								# Ignore beginning and last line: they don't contain anything.
								lowercase__     				=    '''\n'''.join(block_lines[line_idx:-1]       )
								lowercase__     				=    get_indent(block_lines[1]       )
								# Slit the internal block into blocks of indent level 1.
								lowercase__     				=    split_code_in_indented_blocks(lowerCamelCase_	,     indent_level=lowerCamelCase_       )
								# We have two categories of import key: list or _import_structure[key].append/extend
								lowercase__     				=    _re_direct_key if '''_import_structure = {''' in block_lines[0] else _re_indirect_key
								# Grab the keys, but there is a trap: some lines are empty or just comments.
								lowercase__     				=    [(pattern.search(lowerCamelCase_       ).groups()[0] if pattern.search(lowerCamelCase_       ) is not None else None) for b in internal_blocks]
								# We only sort the lines with a key.
								lowercase__     				=    [(i, key) for i, key in enumerate(lowerCamelCase_       ) if key is not None]
								lowercase__     				=    [x[0] for x in sorted(lowerCamelCase_	,     key=lambda lowerCamelCase_       : x[1]       )]

								# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
								lowercase__     				=    0
								lowercase__     				=    []
								for i in range(len(lowerCamelCase_       )       ):
												if keys[i] is None:
																reorderded_blocks.append(internal_blocks[i]       )
												else:
																lowercase__     				=    sort_objects_in_import(internal_blocks[sorted_indices[count]]       )
																reorderded_blocks.append(lowerCamelCase_       )
																count += 1

        # And we put our main block back together with its first and last line.
								lowercase__     				=    '''\n'''.join(block_lines[:line_idx] + reorderded_blocks + [block_lines[-1]]       )

				if code != "\n".join(lowerCamelCase_       ):
								if check_only:
												return True
								else:
												print(F"""Overwriting {file}."""       )
												with open(lowerCamelCase_	,     '''w'''	,     encoding='''utf-8'''       ) as f:
																f.write('''\n'''.join(lowerCamelCase_       )       )






def 						a    (		lowerCamelCase_=True       ):






				'''simple docstring'''
				lowercase__     				=    []
				for root, _, files in os.walk(lowerCamelCase_       ):
								if "__init__.py" in files:
												lowercase__     				=    sort_imports(os.path.join(lowerCamelCase_	,     '''__init__.py'''       )	,     check_only=lowerCamelCase_       )
												if result:
																lowercase__     				=    [os.path.join(lowerCamelCase_	,     '''__init__.py'''       )]
				if len(lowerCamelCase_       ) > 0:
								raise ValueError(F"""Would overwrite {len(lowerCamelCase_       )} files, run `make style`."""       )


if __name__ == "__main__":
							A__			:    str         =				argparse.ArgumentParser()
							parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
							A__			:    int         =				parser.parse_args()

							sort_imports_in_all_inits(check_only=args.check_only)












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__			:    Tuple         =				logging.get_logger(__name__)
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= ["""input_features""", """is_longer"""]




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					int=64,						lowerCamelCase		:					Union[str, Any]=48_000,						lowerCamelCase		:					str=480,						lowerCamelCase		:					Tuple=10,						lowerCamelCase		:					List[Any]=1_024,						lowerCamelCase		:					Optional[int]=0.0,						lowerCamelCase		:					Optional[Any]=False,						lowerCamelCase		:					float = 0,						lowerCamelCase		:					float = 14_000,						lowerCamelCase		:					int = None,						lowerCamelCase		:					str = "fusion",						lowerCamelCase		:					str = "repeatpad",						**lowerCamelCase		:					Dict,						):

								'''simple docstring'''
								super().__init__(
								    feature_size=lowerCamelCase,						sampling_rate=lowerCamelCase,						padding_value=lowerCamelCase,						return_attention_mask=lowerCamelCase,						**lowerCamelCase,						)
								lowercase__     				=    top_db
								lowercase__     				=    truncation
								lowercase__     				=    padding
								lowercase__     				=    fft_window_size
								lowercase__     				=    (fft_window_size >> 1) + 1
								lowercase__     				=    hop_length
								lowercase__     				=    max_length_s
								lowercase__     				=    max_length_s * sampling_rate
								lowercase__     				=    sampling_rate
								lowercase__     				=    frequency_min
								lowercase__     				=    frequency_max
								lowercase__     				=    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''',						)
								lowercase__     				=    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        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    copy.deepcopy(self.__dict__						)
								lowercase__     				=    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        lowercase__     (  self		:					Optional[int],						lowerCamelCase		:					np.array,						lowerCamelCase		:					Optional[np.array] = None						):

								'''simple docstring'''
								lowercase__     				=    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        lowercase__     (  self		:					int,						lowerCamelCase		:					str,						lowerCamelCase		:					List[str],						lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								lowercase__     				=    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
												lowercase__     				=    [0]
								if len(ranges[2]						) == 0:
												# if the audio is too short, we just use the first chunk
												lowercase__     				=    [0]
								# randomly choose index for each part
								lowercase__     				=    np.random.choice(ranges[0]						)
								lowercase__     				=    np.random.choice(ranges[1]						)
								lowercase__     				=    np.random.choice(ranges[2]						)

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

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




				def        lowercase__     (  self		:					List[str],						lowerCamelCase		:					np.array,						lowerCamelCase		:					int,						lowerCamelCase		:					Dict,						lowerCamelCase		:					Union[str, Any]						):

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

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

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

								return input_mel, longer




				def __call__(  self		:					Union[str, Any],						lowerCamelCase		:					Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],						lowerCamelCase		:					str = None,						lowerCamelCase		:					Optional[str] = None,						lowerCamelCase		:					Optional[int] = None,						lowerCamelCase		:					Optional[int] = None,						lowerCamelCase		:					Optional[Union[str, TensorType]] = None,						**lowerCamelCase		:					List[str],						):

								'''simple docstring'''
								lowercase__     				=    truncation if truncation is not None else self.truncation
								lowercase__     				=    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.'''						)

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

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

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

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

								lowercase__     				=    []
								lowercase__     				=    []
								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
												lowercase__     				=    np.random.randint(0,						len(lowerCamelCase						)						)
												lowercase__     				=    True

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

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

								lowercase__     				=    {'''input_features''': input_mel, '''is_longer''': is_longer}
								lowercase__     				=    BatchFeature(lowerCamelCase						)

								if return_tensors is not None:
												lowercase__     				=    input_features.convert_to_tensors(lowerCamelCase						)

								return input_features




