



'''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

UpperCamelCase__:   str								=			logging.get_logger(__name__)


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


def  snake_case_     (					_lowerCAmelCase  :						np.ndarray   ,      _lowerCAmelCase  :						Optional[str]   ,      _lowerCAmelCase  :						Optional[str] = None  )      ->   Optional[Any]:
							UpperCAmelCase :       int 							=		tesseract_config if tesseract_config is not None else ''''''

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

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

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

							# finally, normalize the bounding boxes
							UpperCAmelCase :       int 							=		[]
							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 				SCREAMING_SNAKE_CASE(							A__			):







	"""simple docstring"""






	lowerCamelCase__			 =       ["""pixel_values"""]







	def __init__(						self			:     Optional[int]		,			__snake_case			:     bool = True		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     PILImageResampling = PILImageResampling.BILINEAR		,			__snake_case			:     bool = True		,			__snake_case			:     Optional[str] = None		,			__snake_case			:     Optional[str] = ""		,			**__snake_case			:     str		,			)			->		None:
								super().__init__(**__snake_case		)
								UpperCAmelCase :       List[str] 							=		size if size is not None else {'''height''': 224, '''width''': 224}
								UpperCAmelCase :       List[str] 							=		get_size_dict(__snake_case		)

								UpperCAmelCase :       List[Any] 							=		do_resize
								UpperCAmelCase :       Any 							=		size
								UpperCAmelCase :       List[str] 							=		resample
								UpperCAmelCase :       int 							=		apply_ocr
								UpperCAmelCase :       str 							=		ocr_lang
								UpperCAmelCase :       Optional[Any] 							=		tesseract_config







	def      A	(						self			:     List[str]		,			__snake_case			:     np.ndarray		,			__snake_case			:     Dict[str, int]		,			__snake_case			:     PILImageResampling = PILImageResampling.BILINEAR		,			__snake_case			:     Optional[Union[str, ChannelDimension]] = None		,			**__snake_case			:     List[str]		,			)			->		np.ndarray:
								UpperCAmelCase :       int 							=		get_size_dict(__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()}"""		)
								UpperCAmelCase :       Any 							=		(size['''height'''], size['''width'''])
								return resize(__snake_case		,			size=__snake_case		,			resample=__snake_case		,			data_format=__snake_case		,			**__snake_case		)







	def      A	(						self			:     Tuple		,			__snake_case			:     ImageInput		,			__snake_case			:     bool = None		,			__snake_case			:     Dict[str, int] = None		,			__snake_case			:     PILImageResampling = None		,			__snake_case			:     bool = None		,			__snake_case			:     Optional[str] = None		,			__snake_case			:     Optional[str] = None		,			__snake_case			:     Optional[Union[str, TensorType]] = None		,			__snake_case			:     ChannelDimension = ChannelDimension.FIRST		,			**__snake_case			:     List[Any]		,			)			->		PIL.Image.Image:
								UpperCAmelCase :       int 							=		do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase :       List[Any] 							=		size if size is not None else self.size
								UpperCAmelCase :       int 							=		get_size_dict(__snake_case		)
								UpperCAmelCase :       Union[str, Any] 							=		resample if resample is not None else self.resample
								UpperCAmelCase :       List[str] 							=		apply_ocr if apply_ocr is not None else self.apply_ocr
								UpperCAmelCase :       Optional[Any] 							=		ocr_lang if ocr_lang is not None else self.ocr_lang
								UpperCAmelCase :       Optional[int] 							=		tesseract_config if tesseract_config is not None else self.tesseract_config

								UpperCAmelCase :       Union[str, Any] 							=		make_list_of_images(__snake_case		)

								if not valid_images(__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.
								UpperCAmelCase :       List[Any] 							=		[to_numpy_array(__snake_case		) for image in images]

								if apply_ocr:
															requires_backends(self		,			'''pytesseract'''		)
															UpperCAmelCase :       Dict 							=		[]
															UpperCAmelCase :       Union[str, Any] 							=		[]
															for image in images:
																						UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		apply_tesseract(__snake_case		,			__snake_case		,			__snake_case		)
																						words_batch.append(__snake_case		)
																						boxes_batch.append(__snake_case		)

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

								# flip color channels from RGB to BGR (as Detectron2 requires this)
								UpperCAmelCase :       List[Any] 							=		[flip_channel_order(__snake_case		) for image in images]
								UpperCAmelCase :       Dict 							=		[to_channel_dimension_format(__snake_case		,			__snake_case		) for image in images]

								UpperCAmelCase :       List[str] 							=		BatchFeature(data={'''pixel_values''': images}		,			tensor_type=__snake_case		)

								if apply_ocr:
															UpperCAmelCase :       Dict 							=		words_batch
															UpperCAmelCase :       List[str] 							=		boxes_batch
								return data










'''simple docstring'''






import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, 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


enable_full_determinism()

class 				SCREAMING_SNAKE_CASE(							A__							,		A__							,		A__							,		unittest.TestCase			):







	"""simple docstring"""






	lowerCamelCase__			 =       AltDiffusionPipeline
	lowerCamelCase__			 =       TEXT_TO_IMAGE_PARAMS
	lowerCamelCase__			 =       TEXT_TO_IMAGE_BATCH_PARAMS
	lowerCamelCase__			 =       TEXT_TO_IMAGE_IMAGE_PARAMS
	lowerCamelCase__			 =       TEXT_TO_IMAGE_IMAGE_PARAMS







	def      A	(						self			:     Dict		)			->		int:
								torch.manual_seed(0		)
								UpperCAmelCase :       str 							=		UNetaDConditionModel(
								    block_out_channels=(32, 64)		,			layers_per_block=2		,			sample_size=32		,			in_channels=4		,			out_channels=4		,			down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')		,			up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')		,			cross_attention_dim=32		,			)
								UpperCAmelCase :       Dict 							=		DDIMScheduler(
								    beta_start=0.0_00_85		,			beta_end=0.0_12		,			beta_schedule='''scaled_linear'''		,			clip_sample=__snake_case		,			set_alpha_to_one=__snake_case		,			)
								torch.manual_seed(0		)
								UpperCAmelCase :       Union[str, Any] 							=		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		,			)

								# TODO: address the non-deterministic text encoder (fails for save-load tests)
								# torch.manual_seed(0)
								# text_encoder_config = RobertaSeriesConfig(
								#     hidden_size=32,
								#     project_dim=32,
								#     intermediate_size=37,
								#     layer_norm_eps=1e-05,
								#     num_attention_heads=4,
								#     num_hidden_layers=5,
								#     vocab_size=5002,
								# )
								# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

								torch.manual_seed(0		)
								UpperCAmelCase :       Dict 							=		CLIPTextConfig(
								    bos_token_id=0		,			eos_token_id=2		,			hidden_size=32		,			projection_dim=32		,			intermediate_size=37		,			layer_norm_eps=1E-05		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=5002		,			)
								UpperCAmelCase :       List[Any] 							=		CLIPTextModel(__snake_case		)

								UpperCAmelCase :       Optional[Any] 							=		XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''		)
								UpperCAmelCase :       Optional[int] 							=		77

								UpperCAmelCase :       Optional[int] 							=		{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components







	def      A	(						self			:     Optional[Any]		,			__snake_case			:     Dict		,			__snake_case			:     List[str]=0		)			->		Union[str, Any]:
								if str(__snake_case		).startswith('''mps'''		):
															UpperCAmelCase :       str 							=		torch.manual_seed(__snake_case		)
								else:
															UpperCAmelCase :       Tuple 							=		torch.Generator(device=__snake_case		).manual_seed(__snake_case		)
								UpperCAmelCase :       Dict 							=		{
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''output_type''': '''numpy''',
								}
								return inputs







	def      A	(						self			:     Union[str, Any]		)			->		List[str]:
								super().test_attention_slicing_forward_pass(expected_max_diff=3E-3		)







	def      A	(						self			:     Tuple		)			->		List[str]:
								super().test_inference_batch_single_identical(expected_max_diff=3E-3		)







	def      A	(						self			:     Tuple		)			->		Optional[int]:
								UpperCAmelCase :       List[str] 							=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator

								UpperCAmelCase :       Any 							=		self.get_dummy_components()
								torch.manual_seed(0		)
								UpperCAmelCase :       Optional[int] 							=		RobertaSeriesConfig(
								    hidden_size=32		,			project_dim=32		,			intermediate_size=37		,			layer_norm_eps=1E-05		,			num_attention_heads=4		,			num_hidden_layers=5		,			vocab_size=5002		,			)
								# TODO: remove after fixing the non-deterministic text encoder
								UpperCAmelCase :       List[str] 							=		RobertaSeriesModelWithTransformation(__snake_case		)
								UpperCAmelCase :       str 							=		text_encoder

								UpperCAmelCase :       Optional[int] 							=		AltDiffusionPipeline(**__snake_case		)
								UpperCAmelCase :       str 							=		alt_pipe.to(__snake_case		)
								alt_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		self.get_dummy_inputs(__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		'''A photo of an astronaut'''
								UpperCAmelCase :       List[Any] 							=		alt_pipe(**__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		output.images
								UpperCAmelCase :       str 							=		image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								UpperCAmelCase :       List[str] 							=		np.array(
								    [0.5_74_81_62, 0.60_44_71_45, 0.48_82_12_17, 0.50_10_06_36, 0.5_43_11_85, 0.45_76_36_83, 0.49_65_76_96, 0.48_13_27_33, 0.47_57_30_93]		)

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







	def      A	(						self			:     int		)			->		Union[str, Any]:
								UpperCAmelCase :       Optional[int] 							=		'''cpu'''  # ensure determinism for the device-dependent torch.Generator

								UpperCAmelCase :       int 							=		self.get_dummy_components()
								UpperCAmelCase :       int 							=		PNDMScheduler(skip_prk_steps=__snake_case		)
								torch.manual_seed(0		)
								UpperCAmelCase :       int 							=		RobertaSeriesConfig(
								    hidden_size=32		,			project_dim=32		,			intermediate_size=37		,			layer_norm_eps=1E-05		,			num_attention_heads=4		,			num_hidden_layers=5		,			vocab_size=5002		,			)
								# TODO: remove after fixing the non-deterministic text encoder
								UpperCAmelCase :       Union[str, Any] 							=		RobertaSeriesModelWithTransformation(__snake_case		)
								UpperCAmelCase :       Union[str, Any] 							=		text_encoder
								UpperCAmelCase :       Optional[int] 							=		AltDiffusionPipeline(**__snake_case		)
								UpperCAmelCase :       Dict 							=		alt_pipe.to(__snake_case		)
								alt_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase :       int 							=		self.get_dummy_inputs(__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		alt_pipe(**__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		output.images
								UpperCAmelCase :       int 							=		image[0, -3:, -3:, -1]

								assert image.shape == (1, 64, 64, 3)
								UpperCAmelCase :       Optional[int] 							=		np.array(
								    [0.51_60_50_93, 0.5_70_72_41, 0.47_36_55_07, 0.50_57_88_86, 0.5_63_38_77, 0.4_64_25_03, 0.5_18_20_81, 0.48_76_34_84, 0.49_08_42_37]		)

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

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







	"""simple docstring"""






	def      A	(						self			:     str		)			->		Any:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()







	def      A	(						self			:     List[Any]		)			->		Any:
								# make sure here that pndm scheduler skips prk
								UpperCAmelCase :       List[Any] 							=		AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''		,			safety_checker=__snake_case		)
								UpperCAmelCase :       Tuple 							=		alt_pipe.to(__snake_case		)
								alt_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase :       List[Any] 							=		'''A painting of a squirrel eating a burger'''
								UpperCAmelCase :       Any 							=		torch.manual_seed(0		)
								UpperCAmelCase :       Optional[int] 							=		alt_pipe([prompt]		,			generator=__snake_case		,			guidance_scale=6.0		,			num_inference_steps=20		,			output_type='''np'''		)

								UpperCAmelCase :       Dict 							=		output.images

								UpperCAmelCase :       Optional[Any] 							=		image[0, -3:, -3:, -1]

								assert image.shape == (1, 512, 512, 3)
								UpperCAmelCase :       List[str] 							=		np.array([0.10_10, 0.08_00, 0.07_94, 0.08_85, 0.08_43, 0.07_62, 0.07_69, 0.07_29, 0.05_86]		)

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







	def      A	(						self			:     Tuple		)			->		int:
								UpperCAmelCase :       int 							=		DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''		,			subfolder='''scheduler'''		)

								UpperCAmelCase :       Tuple 							=		AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''		,			scheduler=__snake_case		,			safety_checker=__snake_case		)
								UpperCAmelCase :       Dict 							=		alt_pipe.to(__snake_case		)
								alt_pipe.set_progress_bar_config(disable=__snake_case		)

								UpperCAmelCase :       Tuple 							=		'''A painting of a squirrel eating a burger'''
								UpperCAmelCase :       Optional[int] 							=		torch.manual_seed(0		)

								UpperCAmelCase :       List[Any] 							=		alt_pipe([prompt]		,			generator=__snake_case		,			num_inference_steps=2		,			output_type='''numpy'''		)
								UpperCAmelCase :       Dict 							=		output.images

								UpperCAmelCase :       Optional[Any] 							=		image[0, -3:, -3:, -1]

								assert image.shape == (1, 512, 512, 3)
								UpperCAmelCase :       Union[str, Any] 							=		np.array([0.40_19, 0.40_52, 0.38_10, 0.41_19, 0.39_16, 0.39_82, 0.46_51, 0.41_95, 0.53_23]		)

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





