




'''simple docstring'''



import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCamelCase_    	=				False






class 							_a				(			unittest.TestCase ):





       '''simple docstring'''






       pass






@nightly
@require_torch_gpu
class 							_a				(			unittest.TestCase ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion',							torch_dtype=torch.floataa  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'  )

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						torch.manual_seed(0  )
             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						pipe.dual_guided(
                 prompt='first prompt',							image=A,							text_to_image_strength=0.75,							generator=A,							guidance_scale=7.5,							num_inference_steps=2,							output_type='numpy',							).images

             with tempfile.TemporaryDirectory() as tmpdirname:
                   pipe.save_pretrained(A  )
                   SCREAMING_SNAKE_CASE   :      List[str]    					=						VersatileDiffusionPipeline.from_pretrained(A,							torch_dtype=torch.floataa  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      Dict    					=						generator.manual_seed(0  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						pipe.dual_guided(
                 prompt='first prompt',							image=A,							text_to_image_strength=0.75,							generator=A,							guidance_scale=7.5,							num_inference_steps=2,							output_type='numpy',							).images

             assert np.abs(image - new_image  ).sum() < 1E-5, "Models don't have the same forward pass"







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion',							torch_dtype=torch.floataa  )
             pipe.to(A  )
             pipe.set_progress_bar_config(disable=A  )

             SCREAMING_SNAKE_CASE   :      Tuple    					=						'cyberpunk 2077'
             SCREAMING_SNAKE_CASE   :      str    					=						load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'  )
             SCREAMING_SNAKE_CASE   :      int    					=						torch.manual_seed(0  )
             SCREAMING_SNAKE_CASE   :      Any    					=						pipe.dual_guided(
                 prompt=A,							image=A,							text_to_image_strength=0.75,							generator=A,							guidance_scale=7.5,							num_inference_steps=50,							output_type='numpy',							).images

             SCREAMING_SNAKE_CASE   :      Any    					=						image[0, 253:256, 253:256, -1]

             assert image.shape == (1, 512, 512, 3)
             SCREAMING_SNAKE_CASE   :      Any    					=						np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]  )

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

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						'A painting of a squirrel eating a burger '
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						torch.manual_seed(0  )
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						pipe.text_to_image(
                 prompt=A,							generator=A,							guidance_scale=7.5,							num_inference_steps=50,							output_type='numpy'  ).images

             SCREAMING_SNAKE_CASE   :      Dict    					=						image[0, 253:256, 253:256, -1]

             assert image.shape == (1, 512, 512, 3)
             SCREAMING_SNAKE_CASE   :      List[str]    					=						np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]  )

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

             SCREAMING_SNAKE_CASE   :      List[str]    					=						pipe.image_variation(A,							generator=A,							output_type='numpy'  ).images

             SCREAMING_SNAKE_CASE   :      Any    					=						image[0, 253:256, 253:256, -1]

             assert image.shape == (1, 512, 512, 3)
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]  )

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







'''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

snake_case_					:      List[str]    			=    logging.get_logger(__name__)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				):
						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 A__       (	UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_ = None				):
						_UpperCamelCase   :						Optional[int]												=  tesseract_config if tesseract_config is not None else ''

						# apply OCR
						_UpperCamelCase   :						int												=  to_pil_image(UpperCAmelCase_				)
						_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  pil_image.size
						_UpperCamelCase   :						Tuple												=  pytesseract.image_to_data(UpperCAmelCase_    ,    lang=UpperCAmelCase_    ,    output_type='dict'    ,    config=UpperCAmelCase_				)
						_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						int												=  data['text'], data['left'], data['top'], data['width'], data['height']

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

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

						# finally, normalize the bounding boxes
						_UpperCamelCase   :						List[Any]												=  []
						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__      (				lowercase  ):
	lowercase__    							=     ["""pixel_values"""]





	def __init__(					self   :							Dict					,lowerCamelCase__   :							bool = True					,lowerCamelCase__   :							Dict[str, int] = None					,lowerCamelCase__   :							PILImageResampling = PILImageResampling.BILINEAR					,lowerCamelCase__   :							bool = True					,lowerCamelCase__   :							Optional[str] = None					,lowerCamelCase__   :							Optional[str] = ""					,**lowerCamelCase__   :							List[Any]					,):







							'''simple docstring'''
							super().__init__(**lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  size if size is not None else {'height': 224, 'width': 224}
							_UpperCamelCase   :						Union[str, Any]												=  get_size_dict(lowerCamelCase__					)

							_UpperCamelCase   :						Optional[int]												=  do_resize
							_UpperCamelCase   :						str												=  size
							_UpperCamelCase   :						Any												=  resample
							_UpperCamelCase   :						List[str]												=  apply_ocr
							_UpperCamelCase   :						int												=  ocr_lang
							_UpperCamelCase   :						Union[str, Any]												=  tesseract_config





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







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  get_size_dict(lowerCamelCase__					)
							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   :						List[str]												=  (size['height'], size['width'])
							return resize(lowerCamelCase__					,size=lowerCamelCase__					,resample=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							ImageInput					,lowerCamelCase__   :							bool = None					,lowerCamelCase__   :							Dict[str, int] = None					,lowerCamelCase__   :							PILImageResampling = None					,lowerCamelCase__   :							bool = None					,lowerCamelCase__   :							Optional[str] = None					,lowerCamelCase__   :							Optional[str] = None					,lowerCamelCase__   :							Optional[Union[str, TensorType]] = None					,lowerCamelCase__   :							ChannelDimension = ChannelDimension.FIRST					,**lowerCamelCase__   :							List[Any]					,):







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

							_UpperCamelCase   :						Dict												=  make_list_of_images(lowerCamelCase__					)

							if not valid_images(lowerCamelCase__					):
													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.
							_UpperCamelCase   :						Union[str, Any]												=  [to_numpy_array(lowerCamelCase__					) for image in images]

							if apply_ocr:
													requires_backends(self					,'pytesseract'					)
													_UpperCamelCase   :						Tuple												=  []
													_UpperCamelCase   :						Tuple												=  []
													for image in images:
																			_UpperCamelCase							,     _UpperCamelCase   :						Optional[Any]												=  apply_tesseract(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					)
																			words_batch.append(lowerCamelCase__					)
																			boxes_batch.append(lowerCamelCase__					)

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

							# flip color channels from RGB to BGR (as Detectron2 requires this)
							_UpperCamelCase   :						Optional[int]												=  [flip_channel_order(lowerCamelCase__					) for image in images]
							_UpperCamelCase   :						Dict												=  [to_channel_dimension_format(lowerCamelCase__					,lowerCamelCase__					) for image in images]

							_UpperCamelCase   :						Union[str, Any]												=  BatchFeature(data={'pixel_values': images}					,tensor_type=lowerCamelCase__					)

							if apply_ocr:
													_UpperCamelCase   :						Dict												=  words_batch
													_UpperCamelCase   :						str												=  boxes_batch
							return data


