


'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class A__       (						unittest.TestCase      ):






      @property
      def __UpperCAmelCase       ( self			:Dict	)       ->					Optional[Any]:




             '''simple docstring'''
             torch.manual_seed(0	)
             _a    : str     					=UNetaDModel(
                 block_out_channels=(3_2, 6_4)     ,						layers_per_block=2     ,						sample_size=3_2     ,						in_channels=3     ,						out_channels=3     ,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")     ,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")     ,						)
             return model






      def __UpperCAmelCase       ( self			:Tuple	)       ->					Union[str, Any]:




             '''simple docstring'''
             _a    : Any     					=self.dummy_uncond_unet
             _a    : Optional[Any]     					=ScoreSdeVeScheduler()

             _a    : Tuple     					=ScoreSdeVePipeline(unet=SCREAMING_SNAKE_CASE     ,						scheduler=SCREAMING_SNAKE_CASE	)
             sde_ve.to(SCREAMING_SNAKE_CASE	)
             sde_ve.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

             _a    : Union[str, Any]     					=torch.manual_seed(0	)
             _a    : Tuple     					=sde_ve(num_inference_steps=2     ,						output_type="""numpy"""     ,						generator=SCREAMING_SNAKE_CASE	).images

             _a    : Dict     					=torch.manual_seed(0	)
             _a    : Tuple     					=sde_ve(num_inference_steps=2     ,						output_type="""numpy"""     ,						generator=SCREAMING_SNAKE_CASE     ,						return_dict=SCREAMING_SNAKE_CASE	)[
                 0
             ]

             _a    : Any     					=image[0, -3:, -3:, -1]
             _a    : Tuple     					=image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 3_2, 3_2, 3)
             _a    : Any     					=np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]	)

             assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2
             assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1e-2







@slow
@require_torch
class A__       (						unittest.TestCase      ):






      def __UpperCAmelCase       ( self			:List[str]	)       ->					Tuple:




             '''simple docstring'''
             _a    : List[str]     					="""google/ncsnpp-church-256"""
             _a    : Optional[int]     					=UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE	)

             _a    : Union[str, Any]     					=ScoreSdeVeScheduler.from_pretrained(SCREAMING_SNAKE_CASE	)

             _a    : Optional[Any]     					=ScoreSdeVePipeline(unet=SCREAMING_SNAKE_CASE     ,						scheduler=SCREAMING_SNAKE_CASE	)
             sde_ve.to(SCREAMING_SNAKE_CASE	)
             sde_ve.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE	)

             _a    : int     					=torch.manual_seed(0	)
             _a    : Optional[int]     					=sde_ve(num_inference_steps=1_0     ,						output_type="""numpy"""     ,						generator=SCREAMING_SNAKE_CASE	).images

             _a    : int     					=image[0, -3:, -3:, -1]

             assert image.shape == (1, 2_5_6, 2_5_6, 3)

             _a    : str     					=np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0]	)

             assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2






'''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

A__:    Union[str, Any]					    =       logging.get_logger(__name__)


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	str    ,_UpperCAmelCase	:	Tuple    ,_UpperCAmelCase	:	List[str]  )  ->			int:
       return [
           int(1000 * (box[0] / width)  ),
           int(1000 * (box[1] / height)  ),
           int(1000 * (box[2] / width)  ),
           int(1000 * (box[3] / height)  ),
       ]


def 	SCREAMING_SNAKE_CASE_					(  _UpperCAmelCase	:	np.ndarray    ,_UpperCAmelCase	:	Optional[str]    ,_UpperCAmelCase	:	Optional[str] = None  )  ->			Optional[int]:
       _a    : Any     					=tesseract_config if tesseract_config is not None else """"""

       # apply OCR
       _a    : Optional[Any]     					=to_pil_image(_UpperCAmelCase  )
       _a					,    _a    : List[Any]     					=pil_image.size
       _a    : List[str]     					=pytesseract.image_to_data(_UpperCAmelCase    ,lang=_UpperCAmelCase    ,output_type="""dict"""    ,config=_UpperCAmelCase  )
       _a					,    _a					,    _a					,    _a					,    _a    : str     					=data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

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

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

       # finally, normalize the bounding boxes
       _a    : str     					=[]
       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 A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  List[Any]	       =							["pixel_values"]






      def __init__( self			:Tuple     ,						SCREAMING_SNAKE_CASE			:bool = True     ,						SCREAMING_SNAKE_CASE			:Dict[str, int] = None     ,						SCREAMING_SNAKE_CASE			:PILImageResampling = PILImageResampling.BILINEAR     ,						SCREAMING_SNAKE_CASE			:bool = True     ,						SCREAMING_SNAKE_CASE			:Optional[str] = None     ,						SCREAMING_SNAKE_CASE			:Optional[str] = ""     ,						**SCREAMING_SNAKE_CASE			:Tuple     ,						)       ->					None:




             '''simple docstring'''
             super().__init__(**SCREAMING_SNAKE_CASE	)
             _a    : List[Any]     					=size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
             _a    : Tuple     					=get_size_dict(SCREAMING_SNAKE_CASE	)

             _a    : Dict     					=do_resize
             _a    : Tuple     					=size
             _a    : str     					=resample
             _a    : Dict     					=apply_ocr
             _a    : Union[str, Any]     					=ocr_lang
             _a    : Dict     					=tesseract_config






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




             '''simple docstring'''
             _a    : int     					=get_size_dict(SCREAMING_SNAKE_CASE	)
             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    : Any     					=(size["""height"""], size["""width"""])
             return resize(SCREAMING_SNAKE_CASE     ,						size=SCREAMING_SNAKE_CASE     ,						resample=SCREAMING_SNAKE_CASE     ,						data_format=SCREAMING_SNAKE_CASE     ,						**SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:Dict     ,						SCREAMING_SNAKE_CASE			:ImageInput     ,						SCREAMING_SNAKE_CASE			:bool = None     ,						SCREAMING_SNAKE_CASE			:Dict[str, int] = None     ,						SCREAMING_SNAKE_CASE			:PILImageResampling = None     ,						SCREAMING_SNAKE_CASE			:bool = None     ,						SCREAMING_SNAKE_CASE			:Optional[str] = None     ,						SCREAMING_SNAKE_CASE			:Optional[str] = None     ,						SCREAMING_SNAKE_CASE			:Optional[Union[str, TensorType]] = None     ,						SCREAMING_SNAKE_CASE			:ChannelDimension = ChannelDimension.FIRST     ,						**SCREAMING_SNAKE_CASE			:Optional[Any]     ,						)       ->					PIL.Image.Image:




             '''simple docstring'''
             _a    : Optional[int]     					=do_resize if do_resize is not None else self.do_resize
             _a    : Optional[int]     					=size if size is not None else self.size
             _a    : str     					=get_size_dict(SCREAMING_SNAKE_CASE	)
             _a    : List[str]     					=resample if resample is not None else self.resample
             _a    : int     					=apply_ocr if apply_ocr is not None else self.apply_ocr
             _a    : str     					=ocr_lang if ocr_lang is not None else self.ocr_lang
             _a    : Union[str, Any]     					=tesseract_config if tesseract_config is not None else self.tesseract_config

             _a    : List[str]     					=make_list_of_images(SCREAMING_SNAKE_CASE	)

             if not valid_images(SCREAMING_SNAKE_CASE	):
                    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    : List[Any]     					=[to_numpy_array(SCREAMING_SNAKE_CASE	) for image in images]

             if apply_ocr:
                    requires_backends(self     ,						"""pytesseract"""	)
                    _a    : Any     					=[]
                    _a    : Any     					=[]
                    for image in images:
                           _a					,    _a    : int     					=apply_tesseract(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)
                           words_batch.append(SCREAMING_SNAKE_CASE	)
                           boxes_batch.append(SCREAMING_SNAKE_CASE	)

             if do_resize:
                    _a    : Union[str, Any]     					=[self.resize(image=SCREAMING_SNAKE_CASE     ,						size=SCREAMING_SNAKE_CASE     ,						resample=SCREAMING_SNAKE_CASE	) for image in images]

             # flip color channels from RGB to BGR (as Detectron2 requires this)
             _a    : Dict     					=[flip_channel_order(SCREAMING_SNAKE_CASE	) for image in images]
             _a    : str     					=[to_channel_dimension_format(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	) for image in images]

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

             if apply_ocr:
                    _a    : List[Any]     					=words_batch
                    _a    : Dict     					=boxes_batch
             return data


