


"""simple docstring"""







from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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_   :   List[Any]									=  logging.get_logger(__name__)
def _A						(						lowercase       ,    lowercase       ,    lowercase		):







    """simple docstring"""
    return [
        int(10_00 * (box[0] / width)		),
        int(10_00 * (box[1] / height)		),
        int(10_00 * (box[2] / width)		),
        int(10_00 * (box[3] / height)		),
    ]
def _A						(						lowercase       ,    lowercase       ,    lowercase = None		):







    """simple docstring"""
    a 		=tesseract_config if tesseract_config is not None else ''''''

    # apply OCR
    a 		=to_pil_image(lowercase		)
    a		,      a 		=pil_image.size
    a 		=pytesseract.image_to_data(lowercase       ,    lang=lowercase       ,    output_type='''dict'''       ,    config=lowercase		)
    a		,      a		,      a		,      a		,      a 		=data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

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

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

    return words, normalized_boxes






class       __A				(       _SCREAMING_SNAKE_CASE			):






 """simple docstring"""




 __lowerCAmelCase 					=  ["pixel_values"]


 def __init__(		self ,    __A = True ,    __A = None ,    __A = PILImageResampling.BILINEAR ,    __A = True ,    __A = None ,    __A = "" ,    **__A ,    )					->     None:
     super().__init__(**__A						)
     a 		=size if size is not None else {'''height''': 224, '''width''': 224}
     a 		=get_size_dict(__A						)

     a 		=do_resize
     a 		=size
     a 		=resample
     a 		=apply_ocr
     a 		=ocr_lang
     a 		=tesseract_config


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A = PILImageResampling.BILINEAR ,    __A = None ,    **__A ,    )					->     np.ndarray:
     a 		=get_size_dict(__A						)
     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()}'''						)
     a 		=(size['''height'''], size['''width'''])
     return resize(__A ,    size=__A ,    resample=__A ,    data_format=__A ,    **__A						)





 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = None ,    __A = ChannelDimension.FIRST ,    **__A ,    )					->     PIL.Image.Image:
     a 		=do_resize if do_resize is not None else self.do_resize
     a 		=size if size is not None else self.size
     a 		=get_size_dict(__A						)
     a 		=resample if resample is not None else self.resample
     a 		=apply_ocr if apply_ocr is not None else self.apply_ocr
     a 		=ocr_lang if ocr_lang is not None else self.ocr_lang
     a 		=tesseract_config if tesseract_config is not None else self.tesseract_config

     a 		=make_list_of_images(__A						)

     if not valid_images(__A						):
         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.'''						)

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

     if apply_ocr:
         requires_backends(self ,    '''pytesseract'''						)
         a 		=[]
         a 		=[]
         for image in images:
             a		,      a 		=apply_tesseract(__A ,    __A ,    __A						)
             words_batch.append(__A						)
             boxes_batch.append(__A						)

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

     # flip color channels from RGB to BGR (as Detectron2 requires this)
     a 		=[flip_channel_order(__A						) for image in images]
     a 		=[to_channel_dimension_format(__A ,    __A						) for image in images]

     a 		=BatchFeature(data={'''pixel_values''': images} ,    tensor_type=__A						)

     if apply_ocr:
         a 		=words_batch
         a 		=boxes_batch
     return data




'''simple docstring'''

import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__lowerCAmelCase									=   logging.get_logger(__name__)

__lowerCAmelCase									=   OrderedDict(
    [
        ('''align''', '''EfficientNetImageProcessor'''),
        ('''beit''', '''BeitImageProcessor'''),
        ('''bit''', '''BitImageProcessor'''),
        ('''blip''', '''BlipImageProcessor'''),
        ('''blip-2''', '''BlipImageProcessor'''),
        ('''bridgetower''', '''BridgeTowerImageProcessor'''),
        ('''chinese_clip''', '''ChineseCLIPImageProcessor'''),
        ('''clip''', '''CLIPImageProcessor'''),
        ('''clipseg''', '''ViTImageProcessor'''),
        ('''conditional_detr''', '''ConditionalDetrImageProcessor'''),
        ('''convnext''', '''ConvNextImageProcessor'''),
        ('''convnextv2''', '''ConvNextImageProcessor'''),
        ('''cvt''', '''ConvNextImageProcessor'''),
        ('''data2vec-vision''', '''BeitImageProcessor'''),
        ('''deformable_detr''', '''DeformableDetrImageProcessor'''),
        ('''deit''', '''DeiTImageProcessor'''),
        ('''deta''', '''DetaImageProcessor'''),
        ('''detr''', '''DetrImageProcessor'''),
        ('''dinat''', '''ViTImageProcessor'''),
        ('''donut-swin''', '''DonutImageProcessor'''),
        ('''dpt''', '''DPTImageProcessor'''),
        ('''efficientformer''', '''EfficientFormerImageProcessor'''),
        ('''efficientnet''', '''EfficientNetImageProcessor'''),
        ('''flava''', '''FlavaImageProcessor'''),
        ('''focalnet''', '''BitImageProcessor'''),
        ('''git''', '''CLIPImageProcessor'''),
        ('''glpn''', '''GLPNImageProcessor'''),
        ('''groupvit''', '''CLIPImageProcessor'''),
        ('''imagegpt''', '''ImageGPTImageProcessor'''),
        ('''instructblip''', '''BlipImageProcessor'''),
        ('''layoutlmv2''', '''LayoutLMv2ImageProcessor'''),
        ('''layoutlmv3''', '''LayoutLMv3ImageProcessor'''),
        ('''levit''', '''LevitImageProcessor'''),
        ('''mask2former''', '''Mask2FormerImageProcessor'''),
        ('''maskformer''', '''MaskFormerImageProcessor'''),
        ('''mgp-str''', '''ViTImageProcessor'''),
        ('''mobilenet_v1''', '''MobileNetV1ImageProcessor'''),
        ('''mobilenet_v2''', '''MobileNetV2ImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevit''', '''MobileViTImageProcessor'''),
        ('''mobilevitv2''', '''MobileViTImageProcessor'''),
        ('''nat''', '''ViTImageProcessor'''),
        ('''oneformer''', '''OneFormerImageProcessor'''),
        ('''owlvit''', '''OwlViTImageProcessor'''),
        ('''perceiver''', '''PerceiverImageProcessor'''),
        ('''pix2struct''', '''Pix2StructImageProcessor'''),
        ('''poolformer''', '''PoolFormerImageProcessor'''),
        ('''regnet''', '''ConvNextImageProcessor'''),
        ('''resnet''', '''ConvNextImageProcessor'''),
        ('''sam''', '''SamImageProcessor'''),
        ('''segformer''', '''SegformerImageProcessor'''),
        ('''swiftformer''', '''ViTImageProcessor'''),
        ('''swin''', '''ViTImageProcessor'''),
        ('''swin2sr''', '''Swin2SRImageProcessor'''),
        ('''swinv2''', '''ViTImageProcessor'''),
        ('''table-transformer''', '''DetrImageProcessor'''),
        ('''timesformer''', '''VideoMAEImageProcessor'''),
        ('''tvlt''', '''TvltImageProcessor'''),
        ('''upernet''', '''SegformerImageProcessor'''),
        ('''van''', '''ConvNextImageProcessor'''),
        ('''videomae''', '''VideoMAEImageProcessor'''),
        ('''vilt''', '''ViltImageProcessor'''),
        ('''vit''', '''ViTImageProcessor'''),
        ('''vit_hybrid''', '''ViTHybridImageProcessor'''),
        ('''vit_mae''', '''ViTImageProcessor'''),
        ('''vit_msn''', '''ViTImageProcessor'''),
        ('''xclip''', '''CLIPImageProcessor'''),
        ('''yolos''', '''YolosImageProcessor'''),
    ]
)

__lowerCAmelCase									=   _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)



def      __lowerCamelCase     (      lowerCAmelCase_  )			->      Optional[Any]:
		for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
				if class_name in extractors:
						_a     :      List[Any]        =							model_type_to_module_name(lowerCAmelCase_  )

						_a     :      Optional[Any]        =							importlib.import_module(f""".{module_name}"""						,			'transformers.models'  )
						try:
								return getattr(lowerCAmelCase_						,			lowerCAmelCase_  )
						except AttributeError:
								continue

		for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
				if getattr(lowerCAmelCase_						,			'__name__'						,			lowerCAmelCase_  ) == class_name:
						return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
		_a     :      Dict        =							importlib.import_module('transformers'  )
		if hasattr(lowerCAmelCase_						,			lowerCAmelCase_  ):
				return getattr(lowerCAmelCase_						,			lowerCAmelCase_  )

		return None



def      __lowerCamelCase     (      lowerCAmelCase_						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = False						,			lowerCAmelCase_ = False						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = None						,			lowerCAmelCase_ = False						,			**lowerCAmelCase_						,			)			->      Tuple:
		_a     :      List[str]        =							get_file_from_repo(
		    lowerCAmelCase_						,			lowerCAmelCase_						,			cache_dir=lowerCAmelCase_						,			force_download=lowerCAmelCase_						,			resume_download=lowerCAmelCase_						,			proxies=lowerCAmelCase_						,			use_auth_token=lowerCAmelCase_						,			revision=lowerCAmelCase_						,			local_files_only=lowerCAmelCase_						,			)
		if resolved_config_file is None:
				logger.info(
				    'Could not locate the image processor configuration file, will try to use the model config instead.'  )
				return {}

		with open(lowerCAmelCase_						,			encoding='utf-8'  ) as reader:
				return json.load(lowerCAmelCase_  )

class 	__magic_name__						:







							def __init__(							self					:       List[str]		):
									raise EnvironmentError(
									    'AutoImageProcessor is designed to be instantiated '
									    'using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method.'		)







							@classmethod
							@replace_list_option_in_docstrings(_UpperCAmelCase		)
							def 							__lowercase			(							cls					:       Dict	,_UpperCAmelCase					:       Union[str, Any]	,**_UpperCAmelCase					:       Optional[Any]		):
									_a     :      Any        =							kwargs.pop('config'	,_UpperCAmelCase		)
									_a     :      Dict        =							kwargs.pop('trust_remote_code'	,_UpperCAmelCase		)
									_a     :      Any        =							True

									_a       ,					_a     :      Tuple        =							ImageProcessingMixin.get_image_processor_dict(_UpperCAmelCase	,**_UpperCAmelCase		)
									_a     :      List[Any]        =							config_dict.get('image_processor_type'	,_UpperCAmelCase		)
									_a     :      int        =							None
									if "AutoImageProcessor" in config_dict.get('auto_map'	,{}		):
											_a     :      Any        =							config_dict['auto_map']['AutoImageProcessor']

									# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
									# and if so, infer the image processor class from there.
									if image_processor_class is None and image_processor_auto_map is None:
											_a     :      List[Any]        =							config_dict.pop('feature_extractor_type'	,_UpperCAmelCase		)
											if feature_extractor_class is not None:
													logger.warning(
													    'Could not find image processor class in the image processor config or the model config. Loading'
													    ' based on pattern matching with the model\'s feature extractor configuration.'		)
													_a     :      Optional[int]        =							feature_extractor_class.replace('FeatureExtractor'	,'ImageProcessor'		)
											if "AutoFeatureExtractor" in config_dict.get('auto_map'	,{}		):
													_a     :      List[Any]        =							config_dict['auto_map']['AutoFeatureExtractor']
													_a     :      List[str]        =							feature_extractor_auto_map.replace('FeatureExtractor'	,'ImageProcessor'		)
													logger.warning(
													    'Could not find image processor auto map in the image processor config or the model config.'
													    ' Loading based on pattern matching with the model\'s feature extractor configuration.'		)

        # If we don't find the image processor class in the image processor config, let's try the model config.
									if image_processor_class is None and image_processor_auto_map is None:
											if not isinstance(_UpperCAmelCase	,_UpperCAmelCase		):
													_a     :      Dict        =							AutoConfig.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)
											# It could be in `config.image_processor_type``
											_a     :      Optional[int]        =							getattr(_UpperCAmelCase	,'image_processor_type'	,_UpperCAmelCase		)
											if hasattr(_UpperCAmelCase	,'auto_map'		) and "AutoImageProcessor" in config.auto_map:
													_a     :      Union[str, Any]        =							config.auto_map['AutoImageProcessor']

									if image_processor_class is not None:
											_a     :      Optional[int]        =							image_processor_class_from_name(_UpperCAmelCase		)

									_a     :      List[str]        =							image_processor_auto_map is not None
									_a     :      Optional[int]        =							image_processor_class is not None or type(_UpperCAmelCase		) in IMAGE_PROCESSOR_MAPPING
									_a     :      Optional[int]        =							resolve_trust_remote_code(
									    _UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase	,_UpperCAmelCase		)

									if has_remote_code and trust_remote_code:
											_a     :      Dict        =							get_class_from_dynamic_module(
											    _UpperCAmelCase	,_UpperCAmelCase	,**_UpperCAmelCase		)
											_a     :      int        =							kwargs.pop('code_revision'	,_UpperCAmelCase		)
											if os.path.isdir(_UpperCAmelCase		):
													image_processor_class.register_for_auto_class()
											return image_processor_class.from_dict(_UpperCAmelCase	,**_UpperCAmelCase		)
									elif image_processor_class is not None:
											return image_processor_class.from_dict(_UpperCAmelCase	,**_UpperCAmelCase		)
									# Last try: we use the IMAGE_PROCESSOR_MAPPING.
									elif type(_UpperCAmelCase		) in IMAGE_PROCESSOR_MAPPING:
											_a     :      Dict        =							IMAGE_PROCESSOR_MAPPING[type(_UpperCAmelCase		)]
											return image_processor_class.from_dict(_UpperCAmelCase	,**_UpperCAmelCase		)

									raise ValueError(
									    F"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
									    F"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
									    F"""`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()		)}"""		)







							@staticmethod
							def 							__lowercase			(							_UpperCAmelCase					:       Optional[Any]	,_UpperCAmelCase					:       Dict		):
									IMAGE_PROCESSOR_MAPPING.register(_UpperCAmelCase	,_UpperCAmelCase		)




