





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

_SCREAMING_SNAKE_CASE 		=						logging.get_logger(__name__)


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a				):
	return [
	    int(10_00 * (box[0] / width)				),
	    int(10_00 * (box[1] / height)				),
	    int(10_00 * (box[2] / width)				),
	    int(10_00 * (box[3] / height)				),
	]


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				,	__a = None				):
	snake_case_						:     List[Any]     =		tesseract_config if tesseract_config is not None else ''

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

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

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

	# finally, normalize the bounding boxes
	snake_case_						:     Optional[Any]     =		[]
	for box in actual_boxes:
		normalized_boxes.append(normalize_box(_lowercase				,	_lowercase				,	_lowercase				)				)

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

	return words, normalized_boxes


class SCREAMING_SNAKE_CASE_  (   snake_case_   ):
						__magic_name__:     Optional[Any]  =    ["pixel_values"]






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







							"""simple docstring"""


							super().__init__(**__A		)
							snake_case_						:     Dict     =		size if size is not None else {'height': 224, 'width': 224}
							snake_case_						:     Union[str, Any]     =		get_size_dict(__A		)

							snake_case_						:     List[Any]     =		do_resize
							snake_case_						:     Any     =		size
							snake_case_						:     Union[str, Any]     =		resample
							snake_case_						:     Union[str, Any]     =		apply_ocr
							snake_case_						:     List[Any]     =		ocr_lang
							snake_case_						:     Any     =		tesseract_config






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







							"""simple docstring"""


							snake_case_						:     Dict     =		get_size_dict(__A		)
							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_						:     List[Any]     =		(size['height'], size['width'])
							return resize(__A						,				size=__A						,				resample=__A						,				data_format=__A						,				**__A		)






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







							"""simple docstring"""


							snake_case_						:     Tuple     =		do_resize if do_resize is not None else self.do_resize
							snake_case_						:     Any     =		size if size is not None else self.size
							snake_case_						:     Union[str, Any]     =		get_size_dict(__A		)
							snake_case_						:     str     =		resample if resample is not None else self.resample
							snake_case_						:     Dict     =		apply_ocr if apply_ocr is not None else self.apply_ocr
							snake_case_						:     List[str]     =		ocr_lang if ocr_lang is not None else self.ocr_lang
							snake_case_						:     str     =		tesseract_config if tesseract_config is not None else self.tesseract_config

							snake_case_						:     Optional[int]     =		make_list_of_images(__A		)

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

							if apply_ocr:
								requires_backends(self						,				'pytesseract'		)
								snake_case_						:     Dict     =		[]
								snake_case_						:     str     =		[]
								for image in images:
									snake_case_  ,snake_case_						:     Dict     =		apply_tesseract(__A						,				__A						,				__A		)
									words_batch.append(__A		)
									boxes_batch.append(__A		)

							if do_resize:
								snake_case_						:     str     =		[self.resize(image=__A						,				size=__A						,				resample=__A		) for image in images]

							# flip color channels from RGB to BGR (as Detectron2 requires this)
							snake_case_						:     List[str]     =		[flip_channel_order(__A		) for image in images]
							snake_case_						:     Optional[Any]     =		[to_channel_dimension_format(__A						,				__A		) for image in images]

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

							if apply_ocr:
								snake_case_						:     List[str]     =		words_batch
								snake_case_						:     Tuple     =		boxes_batch
							return data








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_  (   snake_case_    ,			snake_case_    ,			snake_case_    ,			unittest.TestCase   ):
						__magic_name__:     int	 =    AltDiffusionPipeline
						__magic_name__:     Any	 =    TEXT_TO_IMAGE_PARAMS
						__magic_name__:     Any	 =    TEXT_TO_IMAGE_BATCH_PARAMS
						__magic_name__:     Any	 =    TEXT_TO_IMAGE_IMAGE_PARAMS
						__magic_name__:     Union[str, Any]	 =    TEXT_TO_IMAGE_IMAGE_PARAMS






						def UpperCAmelCase_			( self : List[Any]		)							->      int:







							"""simple docstring"""


							torch.manual_seed(0		)
							snake_case_						:     List[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						,				)
							snake_case_						:     Optional[Any]     =		DDIMScheduler(
							    beta_start=0.0_0_0_8_5						,				beta_end=0.0_1_2						,				beta_schedule='scaled_linear'						,				clip_sample=_A						,				set_alpha_to_one=_A						,				)
							torch.manual_seed(0		)
							snake_case_						:     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		)
							snake_case_						:     Optional[Any]     =		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						,				)
							snake_case_						:     Any     =		CLIPTextModel(_A		)

							snake_case_						:     Any     =		XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta'		)
							snake_case_						:     Dict     =		77

							snake_case_						:     List[Any]     =		{
							    'unet': unet,
							    'scheduler': scheduler,
							    'vae': vae,
							    'text_encoder': text_encoder,
							    'tokenizer': tokenizer,
							    'safety_checker': None,
							    'feature_extractor': None,
							}
							return components






						def UpperCAmelCase_			( self : int						,				_A : Optional[int]						,				_A : int=0		)							->      Dict:







							"""simple docstring"""


							if str(_A		).startswith('mps'		):
								snake_case_						:     Union[str, Any]     =		torch.manual_seed(_A		)
							else:
								snake_case_						:     Union[str, Any]     =		torch.Generator(device=_A		).manual_seed(_A		)
							snake_case_						:     Any     =		{
							    '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 UpperCAmelCase_			( self : Optional[Any]		)							->      Any:







							"""simple docstring"""


							super().test_attention_slicing_forward_pass(expected_max_diff=3E-3		)






						def UpperCAmelCase_			( self : List[Any]		)							->      Dict:







							"""simple docstring"""


							super().test_inference_batch_single_identical(expected_max_diff=3E-3		)






						def UpperCAmelCase_			( self : Dict		)							->      Any:







							"""simple docstring"""


							snake_case_						:     Tuple     =		'cpu'  # ensure determinism for the device-dependent torch.Generator

							snake_case_						:     Any     =		self.get_dummy_components()
							torch.manual_seed(0		)
							snake_case_						:     Any     =		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
							snake_case_						:     Optional[Any]     =		RobertaSeriesModelWithTransformation(_A		)
							snake_case_						:     Optional[Any]     =		text_encoder

							snake_case_						:     Optional[Any]     =		AltDiffusionPipeline(**_A		)
							snake_case_						:     List[Any]     =		alt_pipe.to(_A		)
							alt_pipe.set_progress_bar_config(disable=_A		)

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

							assert image.shape == (1, 64, 64, 3)
							snake_case_						:     Any     =		np.array(
							    [0.5_7_4_8_1_6_2, 0.6_0_4_4_7_1_4_5, 0.4_8_8_2_1_2_1_7, 0.5_0_1_0_0_6_3_6, 0.5_4_3_1_1_8_5, 0.4_5_7_6_3_6_8_3, 0.4_9_6_5_7_6_9_6, 0.4_8_1_3_2_7_3_3, 0.4_7_5_7_3_0_9_3]		)

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






						def UpperCAmelCase_			( self : Optional[int]		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		'cpu'  # ensure determinism for the device-dependent torch.Generator

							snake_case_						:     Any     =		self.get_dummy_components()
							snake_case_						:     List[str]     =		PNDMScheduler(skip_prk_steps=_A		)
							torch.manual_seed(0		)
							snake_case_						:     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
							snake_case_						:     Tuple     =		RobertaSeriesModelWithTransformation(_A		)
							snake_case_						:     Any     =		text_encoder
							snake_case_						:     Tuple     =		AltDiffusionPipeline(**_A		)
							snake_case_						:     Dict     =		alt_pipe.to(_A		)
							alt_pipe.set_progress_bar_config(disable=_A		)

							snake_case_						:     Dict     =		self.get_dummy_inputs(_A		)
							snake_case_						:     Tuple     =		alt_pipe(**_A		)
							snake_case_						:     int     =		output.images
							snake_case_						:     List[Any]     =		image[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							snake_case_						:     Optional[int]     =		np.array(
							    [0.5_1_6_0_5_0_9_3, 0.5_7_0_7_2_4_1, 0.4_7_3_6_5_5_0_7, 0.5_0_5_7_8_8_8_6, 0.5_6_3_3_8_7_7, 0.4_6_4_2_5_0_3, 0.5_1_8_2_0_8_1, 0.4_8_7_6_3_4_8_4, 0.4_9_0_8_4_2_3_7]		)

							assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
@slow
@require_torch_gpu
class SCREAMING_SNAKE_CASE_  (   unittest.TestCase   ):






						def UpperCAmelCase_			( self : List[Any]		)							->      Tuple:







							"""simple docstring"""


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






						def UpperCAmelCase_			( self : int		)							->      List[str]:







							"""simple docstring"""


							snake_case_						:     Optional[int]     =		AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'						,				safety_checker=_A		)
							snake_case_						:     Optional[int]     =		alt_pipe.to(_A		)
							alt_pipe.set_progress_bar_config(disable=_A		)

							snake_case_						:     str     =		'A painting of a squirrel eating a burger'
							snake_case_						:     Tuple     =		torch.manual_seed(0		)
							snake_case_						:     str     =		alt_pipe([prompt]						,				generator=_A						,				guidance_scale=6.0						,				num_inference_steps=20						,				output_type='np'		)

							snake_case_						:     Any     =		output.images

							snake_case_						:     List[Any]     =		image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							snake_case_						:     Union[str, Any]     =		np.array([0.1_0_1_0, 0.0_8_0_0, 0.0_7_9_4, 0.0_8_8_5, 0.0_8_4_3, 0.0_7_6_2, 0.0_7_6_9, 0.0_7_2_9, 0.0_5_8_6]		)

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






						def UpperCAmelCase_			( self : Optional[int]		)							->      Union[str, Any]:







							"""simple docstring"""


							snake_case_						:     Optional[Any]     =		DDIMScheduler.from_pretrained('BAAI/AltDiffusion'						,				subfolder='scheduler'		)

							snake_case_						:     Union[str, Any]     =		AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'						,				scheduler=_A						,				safety_checker=_A		)
							snake_case_						:     List[str]     =		alt_pipe.to(_A		)
							alt_pipe.set_progress_bar_config(disable=_A		)

							snake_case_						:     List[Any]     =		'A painting of a squirrel eating a burger'
							snake_case_						:     int     =		torch.manual_seed(0		)

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

							snake_case_						:     Dict     =		image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							snake_case_						:     List[Any]     =		np.array([0.4_0_1_9, 0.4_0_5_2, 0.3_8_1_0, 0.4_1_1_9, 0.3_9_1_6, 0.3_9_8_2, 0.4_6_5_1, 0.4_1_9_5, 0.5_3_2_3]		)

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

