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

    # filter empty words and corresponding coordinates
    __UpperCamelCase  :List[str]    						=	[idx for idx, word in enumerate(SCREAMING_SNAKE_CASE			) if not word.strip()]
    __UpperCamelCase  :str    						=	[word for idx, word 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]
    __UpperCamelCase  :Optional[int]    						=	[coord for idx, coord 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  :Union[str, Any]    						=	[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  :Dict    						=	[]
    for x, y, w, h in zip(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
        __UpperCamelCase  :List[Any]    						=	[x, y, x + w, y + h]
        actual_boxes.append(SCREAMING_SNAKE_CASE			)

    # finally, normalize the bounding boxes
    __UpperCamelCase  :Any    						=	[]
    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__			:		Optional[Any]      			=   ["""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  :str    						=	size if size is not None else {'''height''': 224, '''width''': 224}
        __UpperCamelCase  :str    						=	get_size_dict(__lowercase)

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







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

        __UpperCamelCase  :Optional[Any]    						=	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  :List[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  :Union[str, Any]    						=	[]
            __UpperCamelCase  :Union[str, Any]    						=	[]
            for image in images:
                __UpperCamelCase						, __UpperCamelCase  :Dict    						=	apply_tesseract(__lowercase				,				__lowercase				,				__lowercase)
                words_batch.append(__lowercase)
                boxes_batch.append(__lowercase)

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

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

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

        __UpperCamelCase  :Union[str, Any]    						=	[to_channel_dimension_format(__lowercase				,				__lowercase) for image in images]

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

        if apply_ocr:
            __UpperCamelCase  :Tuple    						=	words_batch
            __UpperCamelCase  :List[str]    						=	boxes_batch
        return data






import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase											=      logging.get_logger(__name__)

__lowercase											=      {
    '''microsoft/unispeech-large-1500h-cv''': (
        '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}


class 			lowerCamelCase_  (     UpperCAmelCase_				):






    '''simple docstring'''






    a__			:		Dict      			=   """unispeech"""







    def __init__(			self				,				__lowercase=32				,				__lowercase=768				,				__lowercase=12				,				__lowercase=12				,				__lowercase=3_072				,				__lowercase="gelu"				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.0				,				__lowercase=0.0				,				__lowercase=0.1				,				__lowercase=0.1				,				__lowercase=0.02				,				__lowercase=1E-5				,				__lowercase="group"				,				__lowercase="gelu"				,				__lowercase=(512, 512, 512, 512, 512, 512, 512)				,				__lowercase=(5, 2, 2, 2, 2, 2, 2)				,				__lowercase=(10, 3, 3, 3, 3, 2, 2)				,				__lowercase=False				,				__lowercase=128				,				__lowercase=16				,				__lowercase=False				,				__lowercase=True				,				__lowercase=0.05				,				__lowercase=10				,				__lowercase=2				,				__lowercase=0.0				,				__lowercase=10				,				__lowercase=0				,				__lowercase=320				,				__lowercase=2				,				__lowercase=0.1				,				__lowercase=100				,				__lowercase=256				,				__lowercase=256				,				__lowercase=0.1				,				__lowercase="mean"				,				__lowercase=False				,				__lowercase=False				,				__lowercase=256				,				__lowercase=80				,				__lowercase=0				,				__lowercase=1				,				__lowercase=2				,				__lowercase=0.5				,				**__lowercase				,				) ->			Optional[int]:
        super().__init__(**__lowercase				,				pad_token_id=__lowercase				,				bos_token_id=__lowercase				,				eos_token_id=__lowercase)
        __UpperCamelCase  :str    						=	hidden_size
        __UpperCamelCase  :List[str]    						=	feat_extract_norm
        __UpperCamelCase  :str    						=	feat_extract_activation
        __UpperCamelCase  :str    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	list(__lowercase)
        __UpperCamelCase  :List[Any]    						=	list(__lowercase)
        __UpperCamelCase  :Any    						=	conv_bias
        __UpperCamelCase  :List[Any]    						=	num_conv_pos_embeddings
        __UpperCamelCase  :Tuple    						=	num_conv_pos_embedding_groups
        __UpperCamelCase  :Optional[int]    						=	len(self.conv_dim)
        __UpperCamelCase  :Optional[int]    						=	num_hidden_layers
        __UpperCamelCase  :Union[str, Any]    						=	intermediate_size
        __UpperCamelCase  :Tuple    						=	hidden_act
        __UpperCamelCase  :Optional[int]    						=	num_attention_heads
        __UpperCamelCase  :Any    						=	hidden_dropout
        __UpperCamelCase  :List[str]    						=	attention_dropout
        __UpperCamelCase  :int    						=	activation_dropout
        __UpperCamelCase  :int    						=	feat_proj_dropout
        __UpperCamelCase  :Any    						=	final_dropout
        __UpperCamelCase  :Optional[Any]    						=	layerdrop
        __UpperCamelCase  :Any    						=	layer_norm_eps
        __UpperCamelCase  :List[str]    						=	initializer_range
        __UpperCamelCase  :Tuple    						=	num_ctc_classes
        __UpperCamelCase  :Union[str, Any]    						=	vocab_size
        __UpperCamelCase  :List[Any]    						=	do_stable_layer_norm
        __UpperCamelCase  :Dict    						=	use_weighted_layer_sum
        __UpperCamelCase  :str    						=	classifier_proj_size

        if (
            (len(self.conv_stride) != self.num_feat_extract_layers)
            or (len(self.conv_kernel) != self.num_feat_extract_layers)
            or (len(self.conv_dim) != self.num_feat_extract_layers)
        ):
            raise ValueError(
                '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                f""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
                f""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")

        # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
        __UpperCamelCase  :List[Any]    						=	apply_spec_augment
        __UpperCamelCase  :Optional[int]    						=	mask_time_prob
        __UpperCamelCase  :int    						=	mask_time_length
        __UpperCamelCase  :Any    						=	mask_time_min_masks
        __UpperCamelCase  :Any    						=	mask_feature_prob
        __UpperCamelCase  :str    						=	mask_feature_length
        __UpperCamelCase  :Tuple    						=	mask_feature_min_masks

        # parameters for pretraining with codevector quantized representations
        __UpperCamelCase  :Optional[Any]    						=	num_codevectors_per_group
        __UpperCamelCase  :Dict    						=	num_codevector_groups
        __UpperCamelCase  :Optional[int]    						=	contrastive_logits_temperature
        __UpperCamelCase  :Union[str, Any]    						=	feat_quantizer_dropout
        __UpperCamelCase  :List[str]    						=	num_negatives
        __UpperCamelCase  :Union[str, Any]    						=	codevector_dim
        __UpperCamelCase  :int    						=	proj_codevector_dim
        __UpperCamelCase  :Tuple    						=	diversity_loss_weight

        # ctc loss
        __UpperCamelCase  :List[Any]    						=	ctc_loss_reduction
        __UpperCamelCase  :int    						=	ctc_zero_infinity

        # pretraining loss
        __UpperCamelCase  :Optional[Any]    						=	replace_prob







    @property
    def     UpperCamelCase__     (			self) ->			Dict:
        return functools.reduce(operator.mul				,				self.conv_stride				,				1)





