import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


__UpperCamelCase		:       str									=     '<<<<<<< This should probably be modified because it mentions: '

__UpperCamelCase		:       Optional[Any]									=     '=======\n>>>>>>>\n'

__UpperCamelCase		:       Optional[Any]									=     [
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

__UpperCamelCase		:       str									=     [
    # (pattern, replacement)
    # Order is important here for some replacements
    (R'tfds\.core', R'datasets'),
    (R'tf\.io\.gfile\.GFile', R'open'),
    (R'tf\.([\w\d]+)', R'datasets.Value(\'\1\')'),
    (R'tfds\.features\.Text\(\)', R'datasets.Value(\'string\')'),
    (R'tfds\.features\.Text\(', R'datasets.Value(\'string\'),'),
    (R'features\s*=\s*tfds.features.FeaturesDict\(', R'features=datasets.Features('),
    (R'tfds\.features\.FeaturesDict\(', R'dict('),
    (R'The TensorFlow Datasets Authors', R'The TensorFlow Datasets Authors and the HuggingFace Datasets Authors'),
    (R'tfds\.', R'datasets.'),
    (R'dl_manager\.manual_dir', R'self.config.data_dir'),
    (R'self\.builder_config', R'self.config'),
]
def       A	(    _lowercase						):
						return ConvertCommand(args.tfds_path		,  args.datasets_directory						)





class 					lowercase__						(   UpperCamelCase_):


						@staticmethod
						def        __A       (       UpperCamelCase__	:				ArgumentParser						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Dict							=							parser.add_parser(
												    '''convert'''    ,		help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'''    ,		)
												train_parser.add_argument(
												    '''--tfds_path'''    ,		type=UpperCamelCase__    ,		required=UpperCamelCase__    ,		help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'''    ,		)
												train_parser.add_argument(
												    '''--datasets_directory'''    ,		type=UpperCamelCase__    ,		required=UpperCamelCase__    ,		help='''Path to the HuggingFace Datasets folder.'''						)
												train_parser.set_defaults(func=UpperCamelCase__						)


						def __init__(       self	:				str    ,		UpperCamelCase__	:				str    ,		UpperCamelCase__	:				str    ,		*UpperCamelCase__	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							get_logger('''datasets-cli/converting'''						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							tfds_path
												SCREAMING_SNAKE_CASE							:    List[Any]							=							datasets_directory



						def        __A       (       self	:				Tuple						):





												'''simple docstring'''





												if os.path.isdir(self._tfds_path						):
																		SCREAMING_SNAKE_CASE							:    Any							=							os.path.abspath(self._tfds_path						)
												elif os.path.isfile(self._tfds_path						):
																		SCREAMING_SNAKE_CASE							:    str							=							os.path.dirname(self._tfds_path						)
												else:
																		raise ValueError('''--tfds_path is neither a directory nor a file. Please check path.'''						)

												SCREAMING_SNAKE_CASE							:    str							=							os.path.abspath(self._datasets_directory						)

												self._logger.info(f"""Converting datasets from {abs_tfds_path} to {abs_datasets_path}"""						)

												SCREAMING_SNAKE_CASE							:    int							=							[]
												SCREAMING_SNAKE_CASE							:    str							=							[]
												SCREAMING_SNAKE_CASE							:    Tuple							=							{}

												if os.path.isdir(self._tfds_path						):
																		SCREAMING_SNAKE_CASE							:    Dict							=							os.listdir(UpperCamelCase__						)
												else:
																		SCREAMING_SNAKE_CASE							:    Dict							=							[os.path.basename(self._tfds_path						)]

												for f_name in file_names:
																		self._logger.info(f"""Looking at file {f_name}"""						)
																		SCREAMING_SNAKE_CASE							:    Any							=							os.path.join(UpperCamelCase__    ,		UpperCamelCase__						)
																		SCREAMING_SNAKE_CASE							:    Dict							=							os.path.join(UpperCamelCase__    ,		UpperCamelCase__						)

																		if not os.path.isfile(UpperCamelCase__						) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
																								self._logger.info('''Skipping file'''						)
																								continue

																		with open(UpperCamelCase__    ,		encoding='''utf-8'''						) as f:
																								SCREAMING_SNAKE_CASE							:    Dict							=							f.readlines()

																		SCREAMING_SNAKE_CASE							:    int							=							[]
																		SCREAMING_SNAKE_CASE							:    List[str]							=							False
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							False
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							[]
																		for line in lines:
																								SCREAMING_SNAKE_CASE							:    Optional[Any]							=							line

																								# Convert imports
																								if "import tensorflow.compat.v2 as tf" in out_line:
																														continue
																								elif "@tfds.core" in out_line:
																														continue
																								elif "builder=self" in out_line:
																														continue
																								elif "import tensorflow_datasets.public_api as tfds" in out_line:
																														SCREAMING_SNAKE_CASE							:    List[Any]							=							'''import datasets\n'''
																								elif "import tensorflow" in out_line:
																														# order is important here
																														SCREAMING_SNAKE_CASE							:    str							=							''''''
																														continue
																								elif "from absl import logging" in out_line:
																														SCREAMING_SNAKE_CASE							:    Any							=							'''from datasets import logging\n'''
																								elif "getLogger" in out_line:
																														SCREAMING_SNAKE_CASE							:    str							=							out_line.replace('''getLogger'''    ,		'''get_logger'''						)
																								elif any(expression in out_line for expression in TO_HIGHLIGHT						):
																														SCREAMING_SNAKE_CASE							:    Tuple							=							True
																														SCREAMING_SNAKE_CASE							:    int							=							list(filter(lambda UpperCamelCase__						: e in out_line    ,		UpperCamelCase__						)						)
																														out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(UpperCamelCase__						) + '''\n'''						)
																														out_lines.append(UpperCamelCase__						)
																														out_lines.append(UpperCamelCase__						)
																														continue
																								else:
																														for pattern, replacement in TO_CONVERT:
																																				SCREAMING_SNAKE_CASE							:    Optional[int]							=							re.sub(UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__						)

                # Take care of saving utilities (to later move them together with main script)
																								if "tensorflow_datasets" in out_line:
																														SCREAMING_SNAKE_CASE							:    int							=							re.match(r'''from\stensorflow_datasets.*import\s([^\.\r\n]+)'''    ,		UpperCamelCase__						)
																														tfds_imports.extend(imp.strip() for imp in match.group(1						).split(''','''						)						)
																														SCREAMING_SNAKE_CASE							:    List[Any]							=							'''from . import ''' + match.group(1						)

																								# Check we have not forget anything
																								if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
																														raise ValueError(f"""Error converting {out_line.strip()}"""						)

																								if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
																														SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							True
																								out_lines.append(UpperCamelCase__						)

																		if is_builder or "wmt" in f_name:
																								# We create a new directory for each dataset
																								SCREAMING_SNAKE_CASE							:    str							=							f_name.replace('''.py'''    ,		''''''						)
																								SCREAMING_SNAKE_CASE							:    Any							=							os.path.join(UpperCamelCase__    ,		UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    int							=							os.path.join(UpperCamelCase__    ,		UpperCamelCase__						)
																								os.makedirs(UpperCamelCase__    ,		exist_ok=UpperCamelCase__						)
																								self._logger.info(f"""Adding directory {output_dir}"""						)
																								imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}						)
																		else:
																								# Utilities will be moved at the end
																								utils_files.append(UpperCamelCase__						)

																		if needs_manual_update:
																								with_manual_update.append(UpperCamelCase__						)

																		with open(UpperCamelCase__    ,		'''w'''    ,		encoding='''utf-8'''						) as f:
																								f.writelines(UpperCamelCase__						)
																		self._logger.info(f"""Converted in {output_file}"""						)

												for utils_file in utils_files:
																		try:
																								SCREAMING_SNAKE_CASE							:    Dict							=							os.path.basename(UpperCamelCase__						)
																								SCREAMING_SNAKE_CASE							:    Dict							=							imports_to_builder_map[f_name.replace('''.py'''    ,		''''''						)]
																								self._logger.info(f"""Moving {dest_folder} to {utils_file}"""						)
																								shutil.copy(UpperCamelCase__    ,		UpperCamelCase__						)
																		except KeyError:
																								self._logger.error(f"""Cannot find destination folder for {utils_file}. Please copy manually."""						)

												if with_manual_update:
																		for file_path in with_manual_update:
																								self._logger.warning(
																								    f"""You need to manually update file {file_path} to remove configurations using 'TextEncoderConfig'."""						)






from typing import List, Optional, Union

import numpy as np

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


__UpperCamelCase		:       Optional[Any]									=     logging.get_logger(__name__)





class 					lowercase__						(   UpperCamelCase_):
						UpperCamelCase_     =		["""input_features""", """attention_mask"""]


						def __init__(       self	:				Any    ,		UpperCamelCase__	:				List[str]=80    ,		UpperCamelCase__	:				Tuple=1_6000    ,		UpperCamelCase__	:				Dict=0.0    ,		UpperCamelCase__	:				Tuple=10    ,		UpperCamelCase__	:				int=25    ,		UpperCamelCase__	:				Optional[Any]="hamming_window"    ,		UpperCamelCase__	:				Tuple=3_2768.0    ,		UpperCamelCase__	:				str=0.97    ,		UpperCamelCase__	:				List[str]=1.0    ,		UpperCamelCase__	:				Optional[int]=True    ,		UpperCamelCase__	:				Dict=True    ,		UpperCamelCase__	:				List[str]=False    ,		**UpperCamelCase__	:				List[str]    ,		):





												'''simple docstring'''





												super().__init__(feature_size=UpperCamelCase__    ,		sampling_rate=UpperCamelCase__    ,		padding_value=UpperCamelCase__    ,		**UpperCamelCase__						)

												SCREAMING_SNAKE_CASE							:    Dict							=							feature_size
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							sampling_rate
												SCREAMING_SNAKE_CASE							:    int							=							padding_value
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							hop_length
												SCREAMING_SNAKE_CASE							:    Tuple							=							win_length
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							frame_signal_scale
												SCREAMING_SNAKE_CASE							:    int							=							preemphasis_coeff
												SCREAMING_SNAKE_CASE							:    List[Any]							=							mel_floor
												SCREAMING_SNAKE_CASE							:    int							=							normalize_means
												SCREAMING_SNAKE_CASE							:    List[str]							=							normalize_vars
												SCREAMING_SNAKE_CASE							:    Any							=							win_function
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							return_attention_mask

												SCREAMING_SNAKE_CASE							:    int							=							win_length * sampling_rate // 1000
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							hop_length * sampling_rate // 1000

												SCREAMING_SNAKE_CASE							:    int							=							optimal_fft_length(self.sample_size						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							(self.n_fft // 2) + 1


						def        __A       (       self	:				str    ,		UpperCamelCase__	:				np.array						):





												'''simple docstring'''





												if self.win_function == "hamming_window":
																		SCREAMING_SNAKE_CASE							:    List[str]							=							window_function(window_length=self.sample_size    ,		name=self.win_function    ,		periodic=UpperCamelCase__						)
												else:
																		SCREAMING_SNAKE_CASE							:    Tuple							=							window_function(window_length=self.sample_size    ,		name=self.win_function						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							mel_filter_bank(
												    num_frequency_bins=self.n_freqs    ,		num_mel_filters=self.feature_size    ,		min_frequency=0.0    ,		max_frequency=self.sampling_rate / 2.0    ,		sampling_rate=self.sampling_rate    ,		)

												SCREAMING_SNAKE_CASE							:    Tuple							=							spectrogram(
												    one_waveform * self.frame_signal_scale    ,		window=UpperCamelCase__    ,		frame_length=self.sample_size    ,		hop_length=self.sample_stride    ,		fft_length=self.n_fft    ,		center=UpperCamelCase__    ,		preemphasis=self.preemphasis_coeff    ,		mel_filters=UpperCamelCase__    ,		mel_floor=self.mel_floor    ,		log_mel='''log'''    ,		)
												return msfc_features.T


						def        __A       (       self	:				int    ,		UpperCamelCase__	:				Optional[Any]    ,		UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				Optional[int]						):





												'''simple docstring'''





												if self.normalize_means:
																		SCREAMING_SNAKE_CASE							:    str							=							x[:input_length].mean(axis=0						)
																		SCREAMING_SNAKE_CASE							:    List[str]							=							np.subtract(UpperCamelCase__    ,		UpperCamelCase__						)
												if self.normalize_vars:
																		SCREAMING_SNAKE_CASE							:    str							=							x[:input_length].std(axis=0						)
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							np.divide(UpperCamelCase__    ,		UpperCamelCase__						)

												if input_length < x.shape[0]:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							padding_value

												# make sure array is in float32
												SCREAMING_SNAKE_CASE							:    str							=							x.astype(np.floataa						)

												return x


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				List[np.ndarray]    ,		UpperCamelCase__	:				Optional[np.ndarray] = None						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							attention_mask.sum(-1						) if attention_mask is not None else [x.shape[0] for x in input_features]
												return [self._normalize_one(UpperCamelCase__    ,		UpperCamelCase__    ,		self.padding_value						) for x, n in zip(UpperCamelCase__    ,		UpperCamelCase__						)]



						def __call__(       self	:				Dict    ,		UpperCamelCase__	:				Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]    ,		UpperCamelCase__	:				Union[bool, str, PaddingStrategy] = False    ,		UpperCamelCase__	:				Optional[int] = None    ,		UpperCamelCase__	:				bool = False    ,		UpperCamelCase__	:				Optional[int] = None    ,		UpperCamelCase__	:				Optional[bool] = None    ,		UpperCamelCase__	:				Optional[Union[str, TensorType]] = None    ,		UpperCamelCase__	:				Optional[int] = None    ,		**UpperCamelCase__	:				Optional[int]    ,		):





												'''simple docstring'''





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

												SCREAMING_SNAKE_CASE							:    Any							=							isinstance(UpperCamelCase__    ,		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}"""						)
												SCREAMING_SNAKE_CASE							:    str							=							is_batched_numpy or (
												    isinstance(UpperCamelCase__    ,		(list, tuple)						) and (isinstance(raw_speech[0]    ,		(np.ndarray, tuple, list)						))
												)

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

												# always return batch
												if not is_batched:
																		SCREAMING_SNAKE_CASE							:    List[str]							=							[raw_speech]

												# extract fbank features
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							[self._extract_mfsc_features(UpperCamelCase__						) for one_waveform in raw_speech]

												# convert into correct format for padding
												SCREAMING_SNAKE_CASE							:    Tuple							=							BatchFeature({'''input_features''': features}						)

												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							self.pad(
												    UpperCamelCase__    ,		padding=UpperCamelCase__    ,		max_length=UpperCamelCase__    ,		truncation=UpperCamelCase__    ,		pad_to_multiple_of=UpperCamelCase__    ,		return_attention_mask=UpperCamelCase__    ,		**UpperCamelCase__    ,		)
												# make sure list is in array format
												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							padded_inputs.get('''input_features'''						)
												if isinstance(input_features[0]    ,		UpperCamelCase__						):
																		SCREAMING_SNAKE_CASE							:    Any							=							[np.asarray(UpperCamelCase__    ,		dtype=np.floataa						) for feature in input_features]

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							padded_inputs.get('''attention_mask'''						)
												if attention_mask is not None:
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							[np.asarray(UpperCamelCase__    ,		dtype=np.intaa						) for array in attention_mask]

												if self.normalize_means or self.normalize_vars:
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							(
																		    np.array(UpperCamelCase__    ,		dtype=np.intaa						)
																		    if self._get_padding_strategies(UpperCamelCase__    ,		max_length=UpperCamelCase__						) is not PaddingStrategy.DO_NOT_PAD
																		    and padding
																		    else None
																		)
																		SCREAMING_SNAKE_CASE							:    List[Any]							=							self.normalize(
																		    padded_inputs['''input_features''']    ,		attention_mask=UpperCamelCase__						)

												if return_tensors is not None:
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							padded_inputs.convert_to_tensors(UpperCamelCase__						)

												return padded_inputs





