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


_lowerCamelCase   :				Tuple     			=						logging.get_logger(__name__)


class   lowercase      (       a       ):
							lowercase__  :    Any									=							["""input_features""", """attention_mask"""]




							def __init__(  self				:   List[str]		,							_UpperCamelCase				:   List[Any]=80		,							_UpperCamelCase				:   Optional[Any]=16_000		,							_UpperCamelCase				:   int=0.0		,							_UpperCamelCase				:   str=10		,							_UpperCamelCase				:   List[str]=25		,							_UpperCamelCase				:   Any="hamming_window"		,							_UpperCamelCase				:   Any=3_2_7_6_8.0		,							_UpperCamelCase				:   str=0.9_7		,							_UpperCamelCase				:   Optional[Any]=1.0		,							_UpperCamelCase				:   Optional[Any]=True		,							_UpperCamelCase				:   Optional[int]=True		,							_UpperCamelCase				:   List[Any]=False		,							**_UpperCamelCase				:   Dict		,							)   ->			Any:



												'''simple docstring'''

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

												SCREAMING_SNAKE_CASE           =    feature_size
												SCREAMING_SNAKE_CASE           =    sampling_rate
												SCREAMING_SNAKE_CASE           =    padding_value
												SCREAMING_SNAKE_CASE           =    hop_length
												SCREAMING_SNAKE_CASE           =    win_length
												SCREAMING_SNAKE_CASE           =    frame_signal_scale
												SCREAMING_SNAKE_CASE           =    preemphasis_coeff
												SCREAMING_SNAKE_CASE           =    mel_floor
												SCREAMING_SNAKE_CASE           =    normalize_means
												SCREAMING_SNAKE_CASE           =    normalize_vars
												SCREAMING_SNAKE_CASE           =    win_function
												SCREAMING_SNAKE_CASE           =    return_attention_mask

												SCREAMING_SNAKE_CASE           =    win_length * sampling_rate // 1_000
												SCREAMING_SNAKE_CASE           =    hop_length * sampling_rate // 1_000

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




							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   np.array )   ->			np.ndarray:



												'''simple docstring'''

												if self.win_function == "hamming_window":
																	SCREAMING_SNAKE_CASE           =    window_function(window_length=self.sample_size		,							name=self.win_function		,							periodic=_UpperCamelCase )
												else:
																	SCREAMING_SNAKE_CASE           =    window_function(window_length=self.sample_size		,							name=self.win_function )

												SCREAMING_SNAKE_CASE           =    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           =    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 		__snake_case(  self				:   List[str]		,							_UpperCamelCase				:   Tuple		,							_UpperCamelCase				:   Optional[int]		,							_UpperCamelCase				:   List[str] )   ->			Tuple:



												'''simple docstring'''

												if self.normalize_means:
																	SCREAMING_SNAKE_CASE           =    x[:input_length].mean(axis=0 )
																	SCREAMING_SNAKE_CASE           =    np.subtract(_UpperCamelCase		,							_UpperCamelCase )
												if self.normalize_vars:
																	SCREAMING_SNAKE_CASE           =    x[:input_length].std(axis=0 )
																	SCREAMING_SNAKE_CASE           =    np.divide(_UpperCamelCase		,							_UpperCamelCase )

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

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

												return x




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   List[np.ndarray]		,							_UpperCamelCase				:   Optional[np.ndarray] = None )   ->			List[np.ndarray]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    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				:   List[str]		,							_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				:   Dict		,							)   ->			BatchFeature:



												'''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           =    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           =    is_batched_numpy or (
												    isinstance(_UpperCamelCase		,							(list, tuple) ) and (isinstance(raw_speech[0]		,							(np.ndarray, tuple, list) ))
												)

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

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

												# extract fbank features
												SCREAMING_SNAKE_CASE           =    [self._extract_mfsc_features(_UpperCamelCase ) for one_waveform in raw_speech]

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

												SCREAMING_SNAKE_CASE           =    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           =    padded_inputs.get("input_features" )
												if isinstance(input_features[0]		,							_UpperCamelCase ):
																	SCREAMING_SNAKE_CASE           =    [np.asarray(_UpperCamelCase		,							dtype=np.floataa ) for feature in input_features]

												SCREAMING_SNAKE_CASE           =    padded_inputs.get("attention_mask" )
												if attention_mask is not None:
																	SCREAMING_SNAKE_CASE           =    [np.asarray(_UpperCamelCase		,							dtype=np.intaa ) for array in attention_mask]

												if self.normalize_means or self.normalize_vars:
																	SCREAMING_SNAKE_CASE           =    (
																	    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           =    self.normalize(
																	    padded_inputs["input_features"]		,							attention_mask=_UpperCamelCase )

												if return_tensors is not None:
																	SCREAMING_SNAKE_CASE           =    padded_inputs.convert_to_tensors(_UpperCamelCase )

												return padded_inputs





from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
							import PIL

# soft dependency
if is_pytesseract_available():
							import pytesseract

_lowerCamelCase   :				str     			=						logging.get_logger(__name__)





def 		__lowerCamelCase							(UpperCAmelCase__       :							Any ,	UpperCAmelCase__       :							Optional[int] ,	UpperCAmelCase__       :							List[str]					):
					return [
					    int(1_0_0_0 * (box[0] / width)					),
					    int(1_0_0_0 * (box[1] / height)					),
					    int(1_0_0_0 * (box[2] / width)					),
					    int(1_0_0_0 * (box[3] / height)					),
					]





def 		__lowerCamelCase							(UpperCAmelCase__       :							np.ndarray ,	UpperCAmelCase__       :							Optional[str] ,	UpperCAmelCase__       :							Optional[str]					):
					SCREAMING_SNAKE_CASE           =    to_pil_image(UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    pil_image.size
					SCREAMING_SNAKE_CASE           =    pytesseract.image_to_data(UpperCAmelCase__ ,	lang=UpperCAmelCase__ ,	output_type="dict" ,	config=UpperCAmelCase__					)
					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    data["text"], data["left"], data["top"], data["width"], data["height"]

					# filter empty words and corresponding coordinates
					SCREAMING_SNAKE_CASE           =    [idx for idx, word in enumerate(UpperCAmelCase__					) if not word.strip()]
					SCREAMING_SNAKE_CASE           =    [word for idx, word in enumerate(UpperCAmelCase__					) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE           =    [coord for idx, coord in enumerate(UpperCAmelCase__					) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE           =    [coord for idx, coord in enumerate(UpperCAmelCase__					) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE           =    [coord for idx, coord in enumerate(UpperCAmelCase__					) if idx not in irrelevant_indices]
					SCREAMING_SNAKE_CASE           =    [coord for idx, coord in enumerate(UpperCAmelCase__					) if idx not in irrelevant_indices]

					# turn coordinates into (left, top, left+width, top+height) format
					SCREAMING_SNAKE_CASE           =    []
					for x, y, w, h in zip(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					):
										SCREAMING_SNAKE_CASE           =    [x, y, x + w, y + h]
										actual_boxes.append(UpperCAmelCase__					)

					# finally, normalize the bounding boxes
					SCREAMING_SNAKE_CASE           =    []
					for box in actual_boxes:
										normalized_boxes.append(normalize_box(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					)					)

					assert len(UpperCAmelCase__					) == len(UpperCAmelCase__					), "Not as many words as there are bounding boxes"

					return words, normalized_boxes


class   lowercase      (       a       ):
							lowercase__  :    Optional[int]									=							["""pixel_values"""]




							def __init__(  self				:   int		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Dict[str, int] = None		,							_UpperCamelCase				:   PILImageResampling = PILImageResampling.BILINEAR		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   float = 1 / 255		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Union[float, Iterable[float]] = None		,							_UpperCamelCase				:   Union[float, Iterable[float]] = None		,							_UpperCamelCase				:   bool = True		,							_UpperCamelCase				:   Optional[str] = None		,							_UpperCamelCase				:   Optional[str] = ""		,							**_UpperCamelCase				:   Union[str, Any]		,							)   ->			None:



												'''simple docstring'''

												super().__init__(**_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    size if size is not None else {"height": 224, "width": 224}
												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    do_resize
												SCREAMING_SNAKE_CASE           =    size
												SCREAMING_SNAKE_CASE           =    resample
												SCREAMING_SNAKE_CASE           =    do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_value
												SCREAMING_SNAKE_CASE           =    do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else IMAGENET_STANDARD_STD
												SCREAMING_SNAKE_CASE           =    apply_ocr
												SCREAMING_SNAKE_CASE           =    ocr_lang
												SCREAMING_SNAKE_CASE           =    tesseract_config




							def 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   np.ndarray		,							_UpperCamelCase				:   Dict[str, int]		,							_UpperCamelCase				:   PILImageResampling = PILImageResampling.BILINEAR		,							_UpperCamelCase				:   Optional[Union[str, ChannelDimension]] = None		,							**_UpperCamelCase				:   List[Any]		,							)   ->			np.ndarray:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase )
												if "height" not in size or "width" not in size:
																	raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}" )
												SCREAMING_SNAKE_CASE           =    (size["height"], size["width"])
												return resize(_UpperCamelCase		,							size=_UpperCamelCase		,							resample=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   Union[str, Any]		,							_UpperCamelCase				:   np.ndarray		,							_UpperCamelCase				:   Union[int, float]		,							_UpperCamelCase				:   Optional[Union[str, ChannelDimension]] = None		,							**_UpperCamelCase				:   Optional[int]		,							)   ->			np.ndarray:



												'''simple docstring'''

												return rescale(_UpperCamelCase		,							scale=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   int		,							_UpperCamelCase				:   np.ndarray		,							_UpperCamelCase				:   Union[float, Iterable[float]]		,							_UpperCamelCase				:   Union[float, Iterable[float]]		,							_UpperCamelCase				:   Optional[Union[str, ChannelDimension]] = None		,							**_UpperCamelCase				:   Optional[Any]		,							)   ->			np.ndarray:



												'''simple docstring'''

												return normalize(_UpperCamelCase		,							mean=_UpperCamelCase		,							std=_UpperCamelCase		,							data_format=_UpperCamelCase		,							**_UpperCamelCase )





							def 		__snake_case(  self				:   Tuple		,							_UpperCamelCase				:   ImageInput		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   Dict[str, int] = None		,							_UpperCamelCase				:   Optional[Any]=None		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   float = None		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   Union[float, Iterable[float]] = None		,							_UpperCamelCase				:   Union[float, Iterable[float]] = None		,							_UpperCamelCase				:   bool = None		,							_UpperCamelCase				:   Optional[str] = None		,							_UpperCamelCase				:   Optional[str] = None		,							_UpperCamelCase				:   Optional[Union[str, TensorType]] = None		,							_UpperCamelCase				:   ChannelDimension = ChannelDimension.FIRST		,							**_UpperCamelCase				:   List[Any]		,							)   ->			PIL.Image.Image:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE           =    size if size is not None else self.size
												SCREAMING_SNAKE_CASE           =    get_size_dict(_UpperCamelCase )
												SCREAMING_SNAKE_CASE           =    resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE           =    do_rescale if do_rescale is not None else self.do_rescale
												SCREAMING_SNAKE_CASE           =    rescale_factor if rescale_factor is not None else self.rescale_factor
												SCREAMING_SNAKE_CASE           =    do_normalize if do_normalize is not None else self.do_normalize
												SCREAMING_SNAKE_CASE           =    image_mean if image_mean is not None else self.image_mean
												SCREAMING_SNAKE_CASE           =    image_std if image_std is not None else self.image_std
												SCREAMING_SNAKE_CASE           =    apply_ocr if apply_ocr is not None else self.apply_ocr
												SCREAMING_SNAKE_CASE           =    ocr_lang if ocr_lang is not None else self.ocr_lang
												SCREAMING_SNAKE_CASE           =    tesseract_config if tesseract_config is not None else self.tesseract_config

												SCREAMING_SNAKE_CASE           =    make_list_of_images(_UpperCamelCase )

												if not valid_images(_UpperCamelCase ):
																	raise ValueError(
																	    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
																	    "torch.Tensor, tf.Tensor or jax.ndarray." )

												if do_resize and size is None:
																	raise ValueError("Size must be specified if do_resize is True." )

												if do_rescale and rescale_factor is None:
																	raise ValueError("Rescale factor must be specified if do_rescale is True." )

												if do_normalize and (image_mean is None or image_std is None):
																	raise ValueError("If do_normalize is True, image_mean and image_std must be specified." )

												# All transformations expect numpy arrays.
												SCREAMING_SNAKE_CASE           =    [to_numpy_array(_UpperCamelCase ) for image in images]

												# Tesseract OCR to get words + normalized bounding boxes
												if apply_ocr:
																	requires_backends(self		,							"pytesseract" )
																	SCREAMING_SNAKE_CASE           =    []
																	SCREAMING_SNAKE_CASE           =    []
																	for image in images:
																						SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    apply_tesseract(_UpperCamelCase		,							_UpperCamelCase		,							_UpperCamelCase )
																						words_batch.append(_UpperCamelCase )
																						boxes_batch.append(_UpperCamelCase )

												if do_resize:
																	SCREAMING_SNAKE_CASE           =    [self.resize(image=_UpperCamelCase		,							size=_UpperCamelCase		,							resample=_UpperCamelCase ) for image in images]

												if do_rescale:
																	SCREAMING_SNAKE_CASE           =    [self.rescale(image=_UpperCamelCase		,							scale=_UpperCamelCase ) for image in images]

												if do_normalize:
																	SCREAMING_SNAKE_CASE           =    [self.normalize(image=_UpperCamelCase		,							mean=_UpperCamelCase		,							std=_UpperCamelCase ) for image in images]

												SCREAMING_SNAKE_CASE           =    [to_channel_dimension_format(_UpperCamelCase		,							_UpperCamelCase ) for image in images]

												SCREAMING_SNAKE_CASE           =    BatchFeature(data={"pixel_values": images}		,							tensor_type=_UpperCamelCase )

												if apply_ocr:
																	SCREAMING_SNAKE_CASE           =    words_batch
																	SCREAMING_SNAKE_CASE           =    boxes_batch
												return data




