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

__lowercase							 =					logging.get_logger(__name__)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    return [
        int(1_000 * (box[0] / width)	),
        int(1_000 * (box[1] / height)	),
        int(1_000 * (box[2] / width)	),
        int(1_000 * (box[3] / height)	),
    ]



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		to_pil_image(SCREAMING_SNAKE_CASE	)
    __UpperCamelCase  ,       __UpperCamelCase     :str			  =		pil_image.size
    __UpperCamelCase     :str			  =		pytesseract.image_to_data(SCREAMING_SNAKE_CASE  ,			lang=SCREAMING_SNAKE_CASE  ,			output_type='''dict'''  ,			config=SCREAMING_SNAKE_CASE	)
    __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase     :List[str]			  =		data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

    # filter empty words and corresponding coordinates
    __UpperCamelCase     :Dict			  =		[idx for idx, word in enumerate(SCREAMING_SNAKE_CASE	) if not word.strip()]
    __UpperCamelCase     :Union[str, Any]			  =		[word for idx, word in enumerate(SCREAMING_SNAKE_CASE	) if idx not in irrelevant_indices]
    __UpperCamelCase     :List[Any]			  =		[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE	) if idx not in irrelevant_indices]
    __UpperCamelCase     :Dict			  =		[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE	) if idx not in irrelevant_indices]
    __UpperCamelCase     :List[str]			  =		[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE	) if idx not in irrelevant_indices]
    __UpperCamelCase     :str			  =		[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE	) if idx not in irrelevant_indices]

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

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

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

    return words, normalized_boxes


class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						int						=      ["""pixel_values"""]







    def __init__(	self , __lowercase = True , __lowercase = None , __lowercase = PILImageResampling.BILINEAR , __lowercase = True , __lowercase = 1 / 255 , __lowercase = True , __lowercase = None , __lowercase = None , __lowercase = True , __lowercase = None , __lowercase = "" , **__lowercase , )       ->	None:
        super().__init__(**__lowercase)
        __UpperCamelCase     :Any			  =		size if size is not None else {'''height''': 224, '''width''': 224}
        __UpperCamelCase     :str			  =		get_size_dict(__lowercase)

        __UpperCamelCase     :Tuple			  =		do_resize
        __UpperCamelCase     :Tuple			  =		size
        __UpperCamelCase     :Union[str, Any]			  =		resample
        __UpperCamelCase     :str			  =		do_rescale
        __UpperCamelCase     :Optional[int]			  =		rescale_value
        __UpperCamelCase     :List[str]			  =		do_normalize
        __UpperCamelCase     :Optional[Any]			  =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        __UpperCamelCase     :str			  =		image_std if image_std is not None else IMAGENET_STANDARD_STD
        __UpperCamelCase     :str			  =		apply_ocr
        __UpperCamelCase     :Optional[Any]			  =		ocr_lang
        __UpperCamelCase     :Optional[Any]			  =		tesseract_config







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase = PILImageResampling.BILINEAR , __lowercase = None , **__lowercase , )       ->	np.ndarray:
        __UpperCamelCase     :List[str]			  =		get_size_dict(__lowercase)
        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()}""")
        __UpperCamelCase     :Any			  =		(size['''height'''], size['''width'''])
        return resize(__lowercase , size=__lowercase , resample=__lowercase , data_format=__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase = None , **__lowercase , )       ->	np.ndarray:
        return rescale(__lowercase , scale=__lowercase , data_format=__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase , __lowercase = None , **__lowercase , )       ->	np.ndarray:
        return normalize(__lowercase , mean=__lowercase , std=__lowercase , data_format=__lowercase , **__lowercase)







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase = None , __lowercase = None , __lowercase=None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = None , __lowercase = ChannelDimension.FIRST , **__lowercase , )       ->	PIL.Image.Image:
        __UpperCamelCase     :Optional[Any]			  =		do_resize if do_resize is not None else self.do_resize
        __UpperCamelCase     :Dict			  =		size if size is not None else self.size
        __UpperCamelCase     :Union[str, Any]			  =		get_size_dict(__lowercase)
        __UpperCamelCase     :List[Any]			  =		resample if resample is not None else self.resample
        __UpperCamelCase     :Optional[int]			  =		do_rescale if do_rescale is not None else self.do_rescale
        __UpperCamelCase     :List[Any]			  =		rescale_factor if rescale_factor is not None else self.rescale_factor
        __UpperCamelCase     :str			  =		do_normalize if do_normalize is not None else self.do_normalize
        __UpperCamelCase     :Any			  =		image_mean if image_mean is not None else self.image_mean
        __UpperCamelCase     :List[str]			  =		image_std if image_std is not None else self.image_std
        __UpperCamelCase     :List[Any]			  =		apply_ocr if apply_ocr is not None else self.apply_ocr
        __UpperCamelCase     :int			  =		ocr_lang if ocr_lang is not None else self.ocr_lang
        __UpperCamelCase     :Dict			  =		tesseract_config if tesseract_config is not None else self.tesseract_config

        __UpperCamelCase     :Optional[int]			  =		make_list_of_images(__lowercase)

        if not valid_images(__lowercase):
            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.
        __UpperCamelCase     :Any			  =		[to_numpy_array(__lowercase) for image in images]

        # Tesseract OCR to get words + normalized bounding boxes
        if apply_ocr:
            requires_backends(self , '''pytesseract''')
            __UpperCamelCase     :str			  =		[]
            __UpperCamelCase     :Optional[Any]			  =		[]
            for image in images:
                __UpperCamelCase  ,       __UpperCamelCase     :Optional[Any]			  =		apply_tesseract(__lowercase , __lowercase , __lowercase)
                words_batch.append(__lowercase)
                boxes_batch.append(__lowercase)

        if do_resize:
            __UpperCamelCase     :Dict			  =		[self.resize(image=__lowercase , size=__lowercase , resample=__lowercase) for image in images]

        if do_rescale:
            __UpperCamelCase     :Tuple			  =		[self.rescale(image=__lowercase , scale=__lowercase) for image in images]

        if do_normalize:
            __UpperCamelCase     :List[str]			  =		[self.normalize(image=__lowercase , mean=__lowercase , std=__lowercase) for image in images]

        __UpperCamelCase     :Dict			  =		[to_channel_dimension_format(__lowercase , __lowercase) for image in images]

        __UpperCamelCase     :Any			  =		BatchFeature(data={'''pixel_values''': images} , tensor_type=__lowercase)

        if apply_ocr:
            __UpperCamelCase     :List[Any]			  =		words_batch
            __UpperCamelCase     :int			  =		boxes_batch
        return data







import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer




class 						lowercase__  (						_UpperCAmelCase  ):
	A__    :   Union[str, Any]				       ="""Wav2Vec2FeatureExtractor"""
	A__    :   Any				       ="""AutoTokenizer"""
	def __init__(			self							:   List[Any]       ,   UpperCAmelCase_							:   List[str]       ,   UpperCAmelCase_							:   str							):
						super().__init__(UpperCAmelCase_       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= self.feature_extractor
						SCREAMING_SNAKE_CASE__													= False
	@classmethod
	def      A_				(			cls							:   Union[str, Any]       ,   UpperCAmelCase_							:   Any       ,   **UpperCAmelCase_							:   Optional[Any]							):
						try:
											return super().from_pretrained(UpperCAmelCase_       ,   **UpperCAmelCase_							)
						except OSError:
											warnings.warn(
											    F'Loading a tokenizer inside {cls.__name__} from a config that does not'
											    ' include a `tokenizer_class` attribute is deprecated and will be '
											    'removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'
											    ' attribute to either your `config.json` or `tokenizer_config.json` '
											    'file to suppress this warning: '       ,   UpperCAmelCase_       ,   )

											SCREAMING_SNAKE_CASE__													= WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase_       ,   **UpperCAmelCase_							)
											SCREAMING_SNAKE_CASE__													= WavaVecaCTCTokenizer.from_pretrained(UpperCAmelCase_       ,   **UpperCAmelCase_							)

											return cls(feature_extractor=UpperCAmelCase_       ,   tokenizer=UpperCAmelCase_							)
	def __call__(			self							:   Any       ,   *UpperCAmelCase_							:   Dict       ,   **UpperCAmelCase_							:   Any							):
						# For backward compatibility
						if self._in_target_context_manager:
											return self.current_processor(*UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if "raw_speech" in kwargs:
											warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'							)
											SCREAMING_SNAKE_CASE__													= kwargs.pop('raw_speech'							)
						else:
											SCREAMING_SNAKE_CASE__													= kwargs.pop('audio'       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= kwargs.pop('sampling_rate'       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= kwargs.pop('text'       ,   UpperCAmelCase_							)
						if len(UpperCAmelCase_							) > 0:
											SCREAMING_SNAKE_CASE__													= args[0]
											SCREAMING_SNAKE_CASE__													= args[1:]

						if audio is None and text is None:
											raise ValueError('You need to specify either an `audio` or `text` input to process.'							)

						if audio is not None:
											SCREAMING_SNAKE_CASE__													= self.feature_extractor(UpperCAmelCase_       ,   *UpperCAmelCase_       ,   sampling_rate=UpperCAmelCase_       ,   **UpperCAmelCase_							)
						if text is not None:
											SCREAMING_SNAKE_CASE__													= self.tokenizer(UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if text is None:
											return inputs
						elif audio is None:
											return encodings
						else:
											SCREAMING_SNAKE_CASE__													= encodings['input_ids']
											return inputs
	def      A_				(			self							:   List[str]       ,   *UpperCAmelCase_							:   List[Any]       ,   **UpperCAmelCase_							:   Optional[int]							):
						# For backward compatibility
						if self._in_target_context_manager:
											return self.current_processor.pad(*UpperCAmelCase_       ,   **UpperCAmelCase_							)

						SCREAMING_SNAKE_CASE__													= kwargs.pop('input_features'       ,   UpperCAmelCase_							)
						SCREAMING_SNAKE_CASE__													= kwargs.pop('labels'       ,   UpperCAmelCase_							)
						if len(UpperCAmelCase_							) > 0:
											SCREAMING_SNAKE_CASE__													= args[0]
											SCREAMING_SNAKE_CASE__													= args[1:]

						if input_features is not None:
											SCREAMING_SNAKE_CASE__													= self.feature_extractor.pad(UpperCAmelCase_       ,   *UpperCAmelCase_       ,   **UpperCAmelCase_							)
						if labels is not None:
											SCREAMING_SNAKE_CASE__													= self.tokenizer.pad(UpperCAmelCase_       ,   **UpperCAmelCase_							)

						if labels is None:
											return input_features
						elif input_features is None:
											return labels
						else:
											SCREAMING_SNAKE_CASE__													= labels['input_ids']
											return input_features
	def      A_				(			self							:   List[Any]       ,   *UpperCAmelCase_							:   List[str]       ,   **UpperCAmelCase_							:   List[str]							):
						return self.tokenizer.batch_decode(*UpperCAmelCase_       ,   **UpperCAmelCase_							)
	def      A_				(			self							:   Tuple       ,   *UpperCAmelCase_							:   int       ,   **UpperCAmelCase_							:   List[str]							):
						return self.tokenizer.decode(*UpperCAmelCase_       ,   **UpperCAmelCase_							)





	@contextmanager
	def      A_				(			self							:   str							):
						warnings.warn(
						    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
						    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
						    'your audio inputs, or in a separate call.'							)
						SCREAMING_SNAKE_CASE__													= True
						SCREAMING_SNAKE_CASE__													= self.tokenizer
						yield
						SCREAMING_SNAKE_CASE__													= self.feature_extractor
						SCREAMING_SNAKE_CASE__													= False





