





'''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

__UpperCamelCase					    =	logging.get_logger(__name__)







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Dict:



			"""simple docstring"""


			return [
			    int(1000 * (box[0] / width) ),
			    int(1000 * (box[1] / height) ),
			    int(1000 * (box[2] / width) ),
			    int(1000 * (box[3] / height) ),
			]







def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      List[str]							      =     to_pil_image(_lowerCamelCase )
			__snake_case      ,     __snake_case					:      Union[str, Any]							      =     pil_image.size
			__snake_case					:      Tuple							      =     pytesseract.image_to_data(_lowerCamelCase    ,	lang=_lowerCamelCase    ,	output_type="""dict"""    ,	config=_lowerCamelCase )
			__snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case      ,     __snake_case					:      List[str]							      =     data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

			# filter empty words and corresponding coordinates
			__snake_case					:      Dict							      =     [idx for idx, word in enumerate(_lowerCamelCase ) if not word.strip()]
			__snake_case					:      int							      =     [word for idx, word in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]
			__snake_case					:      Optional[int]							      =     [coord for idx, coord in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]
			__snake_case					:      Dict							      =     [coord for idx, coord in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]
			__snake_case					:      Optional[int]							      =     [coord for idx, coord in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]
			__snake_case					:      List[str]							      =     [coord for idx, coord in enumerate(_lowerCamelCase ) if idx not in irrelevant_indices]

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

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

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

			return words, normalized_boxes




class  _A (					__lowercase		):
		lowercase__:   Optional[int]				     =   ['''pixel_values''']

		def __init__(   self					:   List[str]  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Dict[str, int] = None  ,      __magic_name__					:   PILImageResampling = PILImageResampling.BILINEAR  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   float = 1 / 2_55  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Union[float, Iterable[float]] = None  ,      __magic_name__					:   Union[float, Iterable[float]] = None  ,      __magic_name__					:   bool = True  ,      __magic_name__					:   Optional[str] = None  ,      __magic_name__					:   Optional[str] = ""  ,      **__magic_name__					:   str  ,      )					->    None:


					"""simple docstring"""

					super().__init__(**__magic_name__			)
					__snake_case					:      int							      =     size if size is not None else {"""height""": 2_24, """width""": 2_24}
					__snake_case					:      Optional[Any]							      =     get_size_dict(__magic_name__			)

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

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


					"""simple docstring"""

					__snake_case					:      Tuple							      =     get_size_dict(__magic_name__			)
					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()}'''			)
					__snake_case					:      Any							      =     (size["""height"""], size["""width"""])
					return resize(__magic_name__  ,      size=__magic_name__  ,      resample=__magic_name__  ,      data_format=__magic_name__  ,      **__magic_name__			)

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   np.ndarray  ,      __magic_name__					:   Union[int, float]  ,      __magic_name__					:   Optional[Union[str, ChannelDimension]] = None  ,      **__magic_name__					:   Dict  ,      )					->    np.ndarray:


					"""simple docstring"""

					return rescale(__magic_name__  ,      scale=__magic_name__  ,      data_format=__magic_name__  ,      **__magic_name__			)

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   np.ndarray  ,      __magic_name__					:   Union[float, Iterable[float]]  ,      __magic_name__					:   Union[float, Iterable[float]]  ,      __magic_name__					:   Optional[Union[str, ChannelDimension]] = None  ,      **__magic_name__					:   Optional[Any]  ,      )					->    np.ndarray:


					"""simple docstring"""

					return normalize(__magic_name__  ,      mean=__magic_name__  ,      std=__magic_name__  ,      data_format=__magic_name__  ,      **__magic_name__			)

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   ImageInput  ,      __magic_name__					:   bool = None  ,      __magic_name__					:   Dict[str, int] = None  ,      __magic_name__					:   List[Any]=None  ,      __magic_name__					:   bool = None  ,      __magic_name__					:   float = None  ,      __magic_name__					:   bool = None  ,      __magic_name__					:   Union[float, Iterable[float]] = None  ,      __magic_name__					:   Union[float, Iterable[float]] = None  ,      __magic_name__					:   bool = None  ,      __magic_name__					:   Optional[str] = None  ,      __magic_name__					:   Optional[str] = None  ,      __magic_name__					:   Optional[Union[str, TensorType]] = None  ,      __magic_name__					:   ChannelDimension = ChannelDimension.FIRST  ,      **__magic_name__					:   int  ,      )					->    PIL.Image.Image:


					"""simple docstring"""

					__snake_case					:      Dict							      =     do_resize if do_resize is not None else self.do_resize
					__snake_case					:      List[str]							      =     size if size is not None else self.size
					__snake_case					:      Dict							      =     get_size_dict(__magic_name__			)
					__snake_case					:      List[Any]							      =     resample if resample is not None else self.resample
					__snake_case					:      Union[str, Any]							      =     do_rescale if do_rescale is not None else self.do_rescale
					__snake_case					:      int							      =     rescale_factor if rescale_factor is not None else self.rescale_factor
					__snake_case					:      Optional[int]							      =     do_normalize if do_normalize is not None else self.do_normalize
					__snake_case					:      int							      =     image_mean if image_mean is not None else self.image_mean
					__snake_case					:      Dict							      =     image_std if image_std is not None else self.image_std
					__snake_case					:      Dict							      =     apply_ocr if apply_ocr is not None else self.apply_ocr
					__snake_case					:      Optional[Any]							      =     ocr_lang if ocr_lang is not None else self.ocr_lang
					__snake_case					:      List[str]							      =     tesseract_config if tesseract_config is not None else self.tesseract_config

					__snake_case					:      str							      =     make_list_of_images(__magic_name__			)

					if not valid_images(__magic_name__			):
								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.
					__snake_case					:      Optional[Any]							      =     [to_numpy_array(__magic_name__			) for image in images]

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

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

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

					if do_normalize:
								__snake_case					:      Optional[int]							      =     [self.normalize(image=__magic_name__  ,      mean=__magic_name__  ,      std=__magic_name__			) for image in images]

					__snake_case					:      int							      =     [to_channel_dimension_format(__magic_name__  ,      __magic_name__			) for image in images]

					__snake_case					:      Union[str, Any]							      =     BatchFeature(data={"""pixel_values""": images}  ,      tensor_type=__magic_name__			)

					if apply_ocr:
								__snake_case					:      str							      =     words_batch
								__snake_case					:      List[Any]							      =     boxes_batch
					return data









import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


_lowercase		:  Any			       =False






@skip_mps
class 						UpperCamelCase_  (  snake_case__  ,     snake_case__  ,     snake_case__  ,     unittest.TestCase							):
					_a      :						Optional[int]						    =					StableDiffusionAttendAndExcitePipeline
					_a      :						Union[str, Any]						    =					False
					_a      :						Dict						    =					TEXT_TO_IMAGE_PARAMS
					_a      :						List[str]						    =					TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'}							)
					_a      :						Optional[int]						    =					TEXT_TO_IMAGE_IMAGE_PARAMS
					_a      :						List[Any]						    =					TEXT_TO_IMAGE_IMAGE_PARAMS



					@classmethod
					def   __a     (		cls     :     Tuple						):
							super().setUpClass()
							torch.use_deterministic_algorithms(lowerCamelCase						)



					@classmethod
					def   __a     (		cls     :     Tuple						):
							super().tearDownClass()
							torch.use_deterministic_algorithms(lowerCamelCase						)



					def   __a     (		self     :     Dict						):
							torch.manual_seed(0						)
							lowerCamelCase_	:     int 						=      UNetaDConditionModel(
							    block_out_channels=(32, 64)       ,      layers_per_block=1       ,      sample_size=32       ,      in_channels=4       ,      out_channels=4       ,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,      cross_attention_dim=32       ,      attention_head_dim=(2, 4)       ,      use_linear_projection=lowerCamelCase       ,      )
							lowerCamelCase_	:     Dict 						=      DDIMScheduler(
							    beta_start=0.00_085       ,      beta_end=0.012       ,      beta_schedule='scaled_linear'       ,      clip_sample=lowerCamelCase       ,      set_alpha_to_one=lowerCamelCase       ,      )
							torch.manual_seed(0						)
							lowerCamelCase_	:     int 						=      AutoencoderKL(
							    block_out_channels=[32, 64]       ,      in_channels=3       ,      out_channels=3       ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,      latent_channels=4       ,      sample_size=1_28       ,      )
							torch.manual_seed(0						)
							lowerCamelCase_	:     List[Any] 						=      CLIPTextConfig(
							    bos_token_id=0       ,      eos_token_id=2       ,      hidden_size=32       ,      intermediate_size=37       ,      layer_norm_eps=1E-05       ,      num_attention_heads=4       ,      num_hidden_layers=5       ,      pad_token_id=1       ,      vocab_size=10_00       ,      hidden_act='gelu'       ,      projection_dim=5_12       ,      )
							lowerCamelCase_	:     Any 						=      CLIPTextModel(lowerCamelCase						)
							lowerCamelCase_	:     int 						=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

							lowerCamelCase_	:     Optional[Any] 						=      {
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}

							return components



					def   __a     (		self     :     List[str]       ,      lowerCamelCase     :     Tuple       ,      lowerCamelCase     :     List[Any]=0						):
							if str(lowerCamelCase						).startswith('mps'						):
									lowerCamelCase_	:     Union[str, Any] 						=      torch.manual_seed(lowerCamelCase						)
							else:
									lowerCamelCase_	:     Optional[Any] 						=      torch.Generator(device=lowerCamelCase						).manual_seed(lowerCamelCase						)
							lowerCamelCase_	:     Dict 						=      {
							    'prompt': 'a cat and a frog',
							    'token_indices': [2, 5],
							    'generator': generator,
							    'num_inference_steps': 1,
							    'guidance_scale': 6.0,
							    'output_type': 'numpy',
							    'max_iter_to_alter': 2,
							    'thresholds': {0: 0.7},
							}
							return inputs



					def   __a     (		self     :     Union[str, Any]						):
							lowerCamelCase_	:     List[Any] 						=      'cpu'

							lowerCamelCase_	:     List[Any] 						=      self.get_dummy_components()
							lowerCamelCase_	:     List[str] 						=      self.pipeline_class(**lowerCamelCase						)
							pipe.to(lowerCamelCase						)
							pipe.set_progress_bar_config(disable=lowerCamelCase						)

							lowerCamelCase_	:     List[Any] 						=      self.get_dummy_inputs(lowerCamelCase						)
							lowerCamelCase_	:     List[str] 						=      pipe(**lowerCamelCase						).images
							lowerCamelCase_	:     Optional[Any] 						=      image[0, -3:, -3:, -1]

							self.assertEqual(image.shape       ,      (1, 64, 64, 3)						)
							lowerCamelCase_	:     Dict 						=      np.array(
							    [0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496]						)
							lowerCamelCase_	:     Any 						=      np.abs(image_slice.flatten() - expected_slice						).max()
							self.assertLessEqual(lowerCamelCase       ,      1E-3						)



					def   __a     (		self     :     Tuple						):
							super().test_cpu_offload_forward_pass(expected_max_diff=5E-4						)



					def   __a     (		self     :     Dict						):
							# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
							self._test_inference_batch_consistent(batch_sizes=[1, 2]						)



					def   __a     (		self     :     List[str]						):
							self._test_inference_batch_single_identical(batch_size=2       ,      expected_max_diff=7E-4						)



					def   __a     (		self     :     str						):
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3						)



					def   __a     (		self     :     Optional[Any]						):
							super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4						)



					def   __a     (		self     :     Tuple						):
							super().test_save_load_local(expected_max_difference=5E-4						)





					def   __a     (		self     :     List[Any]						):
							super().test_save_load_optional_components(expected_max_difference=4E-4						)






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



					@classmethod
					def   __a     (		cls     :     Union[str, Any]						):
							super().setUpClass()
							torch.use_deterministic_algorithms(lowerCamelCase						)



					@classmethod
					def   __a     (		cls     :     List[Any]						):
							super().tearDownClass()
							torch.use_deterministic_algorithms(lowerCamelCase						)



					def   __a     (		self     :     List[str]						):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





					def   __a     (		self     :     str						):
							lowerCamelCase_	:     Optional[Any] 						=      torch.manual_seed(51						)

							lowerCamelCase_	:     Any 						=      StableDiffusionAttendAndExcitePipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4'       ,      safety_checker=lowerCamelCase       ,      torch_dtype=torch.floataa						)
							pipe.to('cuda'						)

							lowerCamelCase_	:     List[Any] 						=      'a painting of an elephant with glasses'
							lowerCamelCase_	:     Tuple 						=      [5, 7]

							lowerCamelCase_	:     List[str] 						=      pipe(
							    prompt=lowerCamelCase       ,      token_indices=lowerCamelCase       ,      guidance_scale=7.5       ,      generator=lowerCamelCase       ,      num_inference_steps=5       ,      max_iter_to_alter=5       ,      output_type='numpy'       ,      ).images[0]

							lowerCamelCase_	:     Optional[Any] 						=      load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy'						)
							assert np.abs((expected_image - image).max()						) < 5E-1
