


'''simple docstring'''
import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 					__lowerCAmelCase   (   lowercase				,			unittest.TestCase						):

						"""simple docstring"""






						_UpperCAmelCase		:       List[str]   						=DDIMPipeline
						_UpperCAmelCase		:       Optional[Any]   						=UNCONDITIONAL_IMAGE_GENERATION_PARAMS
						_UpperCAmelCase		:       List[str]   						=PipelineTesterMixin.required_optional_params - {
						    "num_images_per_prompt",
						    "latents",
						    "callback",
						    "callback_steps",
						}
						_UpperCAmelCase		:       str   						=UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
						_UpperCAmelCase		:       Optional[int]   						=False






						def 				_UpperCAmelCase       (  self							:      Dict	):
													torch.manual_seed(0	)
													A_			    =		UNetaDModel(
													    block_out_channels=(32, 64) ,  layers_per_block=2 ,  sample_size=32 ,  in_channels=3 ,  out_channels=3 ,  down_block_types=("DownBlock2D", "AttnDownBlock2D") ,  up_block_types=("AttnUpBlock2D", "UpBlock2D") ,  )
													A_			    =		DDIMScheduler()
													A_			    =		{"unet": unet, "scheduler": scheduler}
													return components






						def 				_UpperCAmelCase       (  self							:      List[str] ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      List[str]=0	):
													if str(lowerCAmelCase	).startswith("mps"	):
																				A_			    =		torch.manual_seed(lowerCAmelCase	)
													else:
																				A_			    =		torch.Generator(device=lowerCAmelCase	).manual_seed(lowerCAmelCase	)
													A_			    =		{
													    "batch_size": 1,
													    "generator": generator,
													    "num_inference_steps": 2,
													    "output_type": "numpy",
													}
													return inputs






						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													A_			    =		"cpu"

													A_			    =		self.get_dummy_components()
													A_			    =		self.pipeline_class(**lowerCAmelCase	)
													pipe.to(lowerCAmelCase	)
													pipe.set_progress_bar_config(disable=lowerCAmelCase	)

													A_			    =		self.get_dummy_inputs(lowerCAmelCase	)
													A_			    =		pipe(**lowerCAmelCase	).images
													A_			    =		image[0, -3:, -3:, -1]

													self.assertEqual(image.shape ,  (1, 32, 32, 3)	)
													A_			    =		np.array(
													    [1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]	)
													A_			    =		np.abs(image_slice.flatten() - expected_slice	).max()
													self.assertLessEqual(lowerCAmelCase ,  1e-3	)






						def 				_UpperCAmelCase       (  self							:      List[str]	):
													super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3	)






						def 				_UpperCAmelCase       (  self							:      Optional[int]	):
													super().test_save_load_local(expected_max_difference=3e-3	)






						def 				_UpperCAmelCase       (  self							:      Optional[int]	):
													super().test_save_load_optional_components(expected_max_difference=3e-3	)






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													super().test_inference_batch_single_identical(expected_max_diff=3e-3	)






@slow
@require_torch_gpu
class 					__lowerCAmelCase   (   unittest.TestCase						):

						"""simple docstring"""






						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													A_			    =		"google/ddpm-cifar10-32"

													A_			    =		UNetaDModel.from_pretrained(lowerCAmelCase	)
													A_			    =		DDIMScheduler()

													A_			    =		DDIMPipeline(unet=lowerCAmelCase ,  scheduler=lowerCAmelCase	)
													ddim.to(lowerCAmelCase	)
													ddim.set_progress_bar_config(disable=lowerCAmelCase	)

													A_			    =		torch.manual_seed(0	)
													A_			    =		ddim(generator=lowerCAmelCase ,  eta=0.0 ,  output_type="numpy"	).images

													A_			    =		image[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													A_			    =		np.array([0.1_7_2_3, 0.1_6_1_7, 0.1_6_0_0, 0.1_6_2_6, 0.1_4_9_7, 0.1_5_1_3, 0.1_5_0_5, 0.1_4_4_2, 0.1_4_5_3]	)

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






						def 				_UpperCAmelCase       (  self							:      List[Any]	):
													A_			    =		"google/ddpm-ema-bedroom-256"

													A_			    =		UNetaDModel.from_pretrained(lowerCAmelCase	)
													A_			    =		DDIMScheduler.from_pretrained(lowerCAmelCase	)

													A_			    =		DDIMPipeline(unet=lowerCAmelCase ,  scheduler=lowerCAmelCase	)
													ddpm.to(lowerCAmelCase	)
													ddpm.set_progress_bar_config(disable=lowerCAmelCase	)

													A_			    =		torch.manual_seed(0	)
													A_			    =		ddpm(generator=lowerCAmelCase ,  output_type="numpy"	).images

													A_			    =		image[0, -3:, -3:, -1]

													assert image.shape == (1, 2_56, 2_56, 3)
													A_			    =		np.array([0.0_0_6_0, 0.0_2_0_1, 0.0_3_4_4, 0.0_0_2_4, 0.0_0_1_8, 0.0_0_0_2, 0.0_0_2_2, 0.0_0_0_0, 0.0_0_6_9]	)

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









'''simple docstring'''
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

__SCREAMING_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_ ):
							A_			    =		to_pil_image(UpperCamelCase_ )
							A_    ,					A_			    =		pil_image.size
							A_			    =		pytesseract.image_to_data(UpperCamelCase_						,  lang=UpperCamelCase_						,  output_type="dict"						,  config=UpperCamelCase_ )
							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(UpperCamelCase_ ) if not word.strip()]
							A_			    =		[word for idx, word in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]
							A_			    =		[coord for idx, coord in enumerate(UpperCamelCase_ ) if idx not in irrelevant_indices]

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

							# finally, normalize the bounding boxes
							A_			    =		[]
							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 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Any   						=["pixel_values"]






						def __init__(  self							:      List[Any] ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BILINEAR ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      float = 1 / 2_55 ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Optional[str] = None ,  lowerCAmelCase							:      Optional[str] = "" ,  **lowerCAmelCase							:      int ,  ):
													super().__init__(**lowerCAmelCase	)
													A_			    =		size if size is not None else {"height": 2_24, "width": 2_24}
													A_			    =		get_size_dict(lowerCAmelCase	)

													A_			    =		do_resize
													A_			    =		size
													A_			    =		resample
													A_			    =		do_rescale
													A_			    =		rescale_value
													A_			    =		do_normalize
													A_			    =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
													A_			    =		image_std if image_std is not None else IMAGENET_STANDARD_STD
													A_			    =		apply_ocr
													A_			    =		ocr_lang
													A_			    =		tesseract_config






						def 				_UpperCAmelCase       (  self							:      List[str] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Dict[str, int] ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BILINEAR ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      List[str] ,  ):
													A_			    =		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()}"	)
													A_			    =		(size["height"], size["width"])
													return resize(lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[int, float] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Tuple ,  ):
													return rescale(lowerCAmelCase ,  scale=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      int ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[float, Iterable[float]] ,  lowerCAmelCase							:      Union[float, Iterable[float]] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      int ,  ):
													return normalize(lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      ImageInput ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      Any=None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      float = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      Optional[str] = None ,  lowerCAmelCase							:      Optional[str] = None ,  lowerCAmelCase							:      Optional[Union[str, TensorType]] = None ,  lowerCAmelCase							:      ChannelDimension = ChannelDimension.FIRST ,  **lowerCAmelCase							:      int ,  ):
													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(lowerCAmelCase	)
													A_			    =		resample if resample is not None else self.resample
													A_			    =		do_rescale if do_rescale is not None else self.do_rescale
													A_			    =		rescale_factor if rescale_factor is not None else self.rescale_factor
													A_			    =		do_normalize if do_normalize is not None else self.do_normalize
													A_			    =		image_mean if image_mean is not None else self.image_mean
													A_			    =		image_std if image_std is not None else self.image_std
													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(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."	)

													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.
													A_			    =		[to_numpy_array(lowerCAmelCase	) for image in images]

													# Tesseract OCR to get words + normalized bounding boxes
													if apply_ocr:
																				requires_backends(self ,  "pytesseract"	)
																				A_			    =		[]
																				A_			    =		[]
																				for image in images:
																											A_    ,					A_			    =		apply_tesseract(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase	)
																											words_batch.append(lowerCAmelCase	)
																											boxes_batch.append(lowerCAmelCase	)

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

													if do_rescale:
																				A_			    =		[self.rescale(image=lowerCAmelCase ,  scale=lowerCAmelCase	) for image in images]

													if do_normalize:
																				A_			    =		[self.normalize(image=lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase	) for image in images]

													A_			    =		[to_channel_dimension_format(lowerCAmelCase ,  lowerCAmelCase	) for image in images]

													A_			    =		BatchFeature(data={"pixel_values": images} ,  tensor_type=lowerCAmelCase	)

													if apply_ocr:
																				A_			    =		words_batch
																				A_			    =		boxes_batch
													return data





