

'''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  :						Dict         =    logging.get_logger(__name__)
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     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        UpperCAmelCase_     (		_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					to_pil_image(lowercase_       )
     SCREAMING_SNAKE_CASE__					=					pil_image.size
     SCREAMING_SNAKE_CASE__					=					pytesseract.image_to_data(lowercase_   ,	lang=lowercase_   ,	output_type='''dict'''   ,	config=lowercase_       )
     SCREAMING_SNAKE_CASE__					=					data["text"], data["left"], data["top"], data["width"], data["height"]

     # filter empty words and corresponding coordinates
     SCREAMING_SNAKE_CASE__					=					[idx for idx, word in enumerate(lowercase_       ) if not word.strip()]
     SCREAMING_SNAKE_CASE__					=					[word for idx, word in enumerate(lowercase_       ) if idx not in irrelevant_indices]
     SCREAMING_SNAKE_CASE__					=					[coord for idx, coord in enumerate(lowercase_       ) if idx not in irrelevant_indices]
     SCREAMING_SNAKE_CASE__					=					[coord for idx, coord in enumerate(lowercase_       ) if idx not in irrelevant_indices]
     SCREAMING_SNAKE_CASE__					=					[coord for idx, coord in enumerate(lowercase_       ) if idx not in irrelevant_indices]
     SCREAMING_SNAKE_CASE__					=					[coord for idx, coord in enumerate(lowercase_       ) if idx not in irrelevant_indices]

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

     # finally, normalize the bounding boxes
     SCREAMING_SNAKE_CASE__					=					[]
     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  UpperCAmelCase__	( _UpperCamelCase ):






  """simple docstring"""


  a     =      ["pixel_values"]






  def __init__(							self		:	Tuple			,     __lowerCamelCase		:	bool = True			,     __lowerCamelCase		:	Dict[str, int] = None			,     __lowerCamelCase		:	PILImageResampling = PILImageResampling.BILINEAR			,     __lowerCamelCase		:	bool = True			,     __lowerCamelCase		:	float = 1 / 255			,     __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		:	Optional[Any]			,     )	->							None:
       super().__init__(**__a      )
       SCREAMING_SNAKE_CASE__					=					size if size is not None else {"height": 224, "width": 224}
       SCREAMING_SNAKE_CASE__					=					get_size_dict(__a      )

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






  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Dict[str, int]			,     __lowerCamelCase		:	PILImageResampling = PILImageResampling.BILINEAR			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	Optional[int]			,     )	->							np.ndarray:
       SCREAMING_SNAKE_CASE__					=					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()}'''      )
       SCREAMING_SNAKE_CASE__					=					(size["height"], size["width"])
       return resize(__a			,     size=__a			,     resample=__a			,     data_format=__a			,     **__a      )






  def    lowercase_	(							self		:	Tuple			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Union[int, float]			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	Optional[Any]			,     )	->							np.ndarray:
       return rescale(__a			,     scale=__a			,     data_format=__a			,     **__a      )






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	np.ndarray			,     __lowerCamelCase		:	Union[float, Iterable[float]]			,     __lowerCamelCase		:	Union[float, Iterable[float]]			,     __lowerCamelCase		:	Optional[Union[str, ChannelDimension]] = None			,     **__lowerCamelCase		:	Optional[Any]			,     )	->							np.ndarray:
       return normalize(__a			,     mean=__a			,     std=__a			,     data_format=__a			,     **__a      )







  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	ImageInput			,     __lowerCamelCase		:	bool = None			,     __lowerCamelCase		:	Dict[str, int] = None			,     __lowerCamelCase		:	str=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		:	Optional[int]			,     )	->							PIL.Image.Image:
       SCREAMING_SNAKE_CASE__					=					do_resize if do_resize is not None else self.do_resize
       SCREAMING_SNAKE_CASE__					=					size if size is not None else self.size
       SCREAMING_SNAKE_CASE__					=					get_size_dict(__a      )
       SCREAMING_SNAKE_CASE__					=					resample if resample is not None else self.resample
       SCREAMING_SNAKE_CASE__					=					do_rescale if do_rescale is not None else self.do_rescale
       SCREAMING_SNAKE_CASE__					=					rescale_factor if rescale_factor is not None else self.rescale_factor
       SCREAMING_SNAKE_CASE__					=					do_normalize if do_normalize is not None else self.do_normalize
       SCREAMING_SNAKE_CASE__					=					image_mean if image_mean is not None else self.image_mean
       SCREAMING_SNAKE_CASE__					=					image_std if image_std is not None else self.image_std
       SCREAMING_SNAKE_CASE__					=					apply_ocr if apply_ocr is not None else self.apply_ocr
       SCREAMING_SNAKE_CASE__					=					ocr_lang if ocr_lang is not None else self.ocr_lang
       SCREAMING_SNAKE_CASE__					=					tesseract_config if tesseract_config is not None else self.tesseract_config

       SCREAMING_SNAKE_CASE__					=					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.'''      )

       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.
       SCREAMING_SNAKE_CASE__					=					[to_numpy_array(__a      ) for image in images]

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

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

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

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

       SCREAMING_SNAKE_CASE__					=					[to_channel_dimension_format(__a			,     __a      ) for image in images]

       SCREAMING_SNAKE_CASE__					=					BatchFeature(data={'''pixel_values''': images}			,     tensor_type=__a      )

       if apply_ocr:
            SCREAMING_SNAKE_CASE__					=					words_batch
            SCREAMING_SNAKE_CASE__					=					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  UpperCAmelCase__	( A__		,  A__		,  A__		,  unittest.TestCase ):






  """simple docstring"""


  a     =      AltDiffusionPipeline
  a     =      TEXT_TO_IMAGE_PARAMS
  a     =      TEXT_TO_IMAGE_BATCH_PARAMS
  a     =      TEXT_TO_IMAGE_IMAGE_PARAMS
  a     =      TEXT_TO_IMAGE_IMAGE_PARAMS






  def    lowercase_	(							self		:	Tuple      )	->							Tuple:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					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			,     )
       SCREAMING_SNAKE_CASE__					=					DDIMScheduler(
           beta_start=0.00085			,     beta_end=0.012			,     beta_schedule='''scaled_linear'''			,     clip_sample=__lowerCamelCase			,     set_alpha_to_one=__lowerCamelCase			,     )
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					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      )
       SCREAMING_SNAKE_CASE__					=					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			,     )
       SCREAMING_SNAKE_CASE__					=					CLIPTextModel(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''      )
       SCREAMING_SNAKE_CASE__					=					77

       SCREAMING_SNAKE_CASE__					=					{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	Optional[int]=0      )	->							Union[str, Any]:
       if str(__lowerCamelCase      ).startswith('''mps'''      ):
            SCREAMING_SNAKE_CASE__					=					torch.manual_seed(__lowerCamelCase      )
       else:
            SCREAMING_SNAKE_CASE__					=					torch.Generator(device=__lowerCamelCase      ).manual_seed(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					{
           '''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    lowercase_	(							self		:	List[Any]      )	->							str:
       super().test_attention_slicing_forward_pass(expected_max_diff=3e-3      )






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






  def    lowercase_	(							self		:	Dict      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					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
       SCREAMING_SNAKE_CASE__					=					RobertaSeriesModelWithTransformation(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					text_encoder

       SCREAMING_SNAKE_CASE__					=					AltDiffusionPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					alt_pipe.to(__lowerCamelCase      )
       alt_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					'''A photo of an astronaut'''
       SCREAMING_SNAKE_CASE__					=					alt_pipe(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					output.images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]      )

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







  def    lowercase_	(							self		:	List[Any]      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					'''cpu'''  # ensure determinism for the device-dependent torch.Generator

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_components()
       SCREAMING_SNAKE_CASE__					=					PNDMScheduler(skip_prk_steps=__lowerCamelCase      )
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					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
       SCREAMING_SNAKE_CASE__					=					RobertaSeriesModelWithTransformation(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					text_encoder
       SCREAMING_SNAKE_CASE__					=					AltDiffusionPipeline(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					alt_pipe.to(__lowerCamelCase      )
       alt_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					self.get_dummy_inputs(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					alt_pipe(**__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					output.images
       SCREAMING_SNAKE_CASE__					=					image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)
       SCREAMING_SNAKE_CASE__					=					np.array(
           [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]      )

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






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






  """simple docstring"""


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






  def    lowercase_	(							self		:	str      )	->							Any:
       # make sure here that pndm scheduler skips prk
       SCREAMING_SNAKE_CASE__					=					AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''			,     safety_checker=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					alt_pipe.to(__lowerCamelCase      )
       alt_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					'''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__					=					torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__					=					alt_pipe([prompt]			,     generator=__lowerCamelCase			,     guidance_scale=6.0			,     num_inference_steps=20			,     output_type='''np'''      )

       SCREAMING_SNAKE_CASE__					=					output.images

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

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]      )

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







  def    lowercase_	(							self		:	Optional[int]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					DDIMScheduler.from_pretrained('''BAAI/AltDiffusion'''			,     subfolder='''scheduler'''      )

       SCREAMING_SNAKE_CASE__					=					AltDiffusionPipeline.from_pretrained('''BAAI/AltDiffusion'''			,     scheduler=__lowerCamelCase			,     safety_checker=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					alt_pipe.to(__lowerCamelCase      )
       alt_pipe.set_progress_bar_config(disable=__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					'''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__					=					torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__					=					alt_pipe([prompt]			,     generator=__lowerCamelCase			,     num_inference_steps=2			,     output_type='''numpy'''      )
       SCREAMING_SNAKE_CASE__					=					output.images

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

       assert image.shape == (1, 512, 512, 3)
       SCREAMING_SNAKE_CASE__					=					np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]      )

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


