
"""simple docstring"""



import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, 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


enable_full_determinism()

class       lowerCAmelCase__				(						unittest.TestCase	):
     __a						=					StableDiffusionLDMaDPipeline
     __a						=					TEXT_TO_IMAGE_PARAMS
     __a						=					TEXT_TO_IMAGE_BATCH_PARAMS
     __a						=					TEXT_TO_IMAGE_IMAGE_PARAMS







     def      lowercase	(							self			:			Tuple			):
       torch.manual_seed(0			)
       _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    ,      )
       _snake_case					     =       DDIMScheduler(
           beta_start=0.0_0_0_8_5    ,      beta_end=0.0_1_2    ,      beta_schedule='''scaled_linear'''    ,      clip_sample=_lowerCamelCase    ,      set_alpha_to_one=_lowerCamelCase    ,      )
       torch.manual_seed(0			)
       _snake_case					     =       AutoencoderKL(
           block_out_channels=[32, 64]    ,      in_channels=6    ,      out_channels=6    ,      down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,      up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,      latent_channels=4    ,      )
       torch.manual_seed(0			)
       _snake_case					     =       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=1000    ,      )
       _snake_case					     =       CLIPTextModel(_lowerCamelCase			)
       _snake_case					     =       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

       _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			:			Optional[Any]    ,      _lowerCamelCase			:			List[Any]    ,      _lowerCamelCase			:			int=0			):
       if str(_lowerCamelCase			).startswith('''mps'''			):
         _snake_case					     =       torch.manual_seed(_lowerCamelCase			)
       else:
         _snake_case					     =       torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)
       _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			:			Optional[Any]			):
       _snake_case					     =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator

       _snake_case					     =       self.get_dummy_components()
       _snake_case					     =       StableDiffusionLDMaDPipeline(**_lowerCamelCase			)
       _snake_case					     =       ldmad_pipe.to(_lowerCamelCase			)
       ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

       _snake_case					     =       self.get_dummy_inputs(_lowerCamelCase			)
       _snake_case					     =       ldmad_pipe(**_lowerCamelCase			)
       _snake_case     ,					_snake_case					     =       output.rgb, output.depth

       _snake_case					     =       rgb[0, -3:, -3:, -1]
       _snake_case					     =       depth[0, -3:, -1]

       assert rgb.shape == (1, 64, 64, 3)
       assert depth.shape == (1, 64, 64)

       _snake_case					     =       np.array(
           [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2]			)
       _snake_case					     =       np.array([1_0_3.4_6_7_2_7, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6]			)

       assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb			).max() < 1e-2
       assert np.abs(image_slice_depth.flatten() - expected_slice_depth			).max() < 1e-2







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       self.get_dummy_components()
       _snake_case					     =       StableDiffusionLDMaDPipeline(**_lowerCamelCase			)
       _snake_case					     =       ldmad_pipe.to(_lowerCamelCase			)
       ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

       _snake_case					     =       self.get_dummy_inputs(_lowerCamelCase			)
       _snake_case					     =       3 * [inputs['''prompt''']]

       # forward
       _snake_case					     =       ldmad_pipe(**_lowerCamelCase			)
       _snake_case     ,					_snake_case					     =       output.rgb, output.depth
       _snake_case					     =       rgb_slice_a[0, -3:, -3:, -1]
       _snake_case					     =       depth_slice_a[0, -3:, -1]

       _snake_case					     =       self.get_dummy_inputs(_lowerCamelCase			)
       _snake_case					     =       3 * [inputs.pop('''prompt'''			)]

       _snake_case					     =       ldmad_pipe.tokenizer(
           _lowerCamelCase    ,      padding='''max_length'''    ,      max_length=ldmad_pipe.tokenizer.model_max_length    ,      truncation=_lowerCamelCase    ,      return_tensors='''pt'''    ,      )
       _snake_case					     =       text_inputs['''input_ids'''].to(_lowerCamelCase			)

       _snake_case					     =       ldmad_pipe.text_encoder(_lowerCamelCase			)[0]

       _snake_case					     =       prompt_embeds

       # forward
       _snake_case					     =       ldmad_pipe(**_lowerCamelCase			)
       _snake_case     ,					_snake_case					     =       output.rgb, output.depth
       _snake_case					     =       rgb_slice_a[0, -3:, -3:, -1]
       _snake_case					     =       depth_slice_a[0, -3:, -1]

       assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten()			).max() < 1e-4
       assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten()			).max() < 1e-4







     def      lowercase	(							self			:			str			):
       _snake_case					     =       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       _snake_case					     =       self.get_dummy_components()
       _snake_case					     =       PNDMScheduler(skip_prk_steps=_lowerCamelCase			)
       _snake_case					     =       StableDiffusionLDMaDPipeline(**_lowerCamelCase			)
       _snake_case					     =       ldmad_pipe.to(_lowerCamelCase			)
       ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

       _snake_case					     =       self.get_dummy_inputs(_lowerCamelCase			)
       _snake_case					     =       '''french fries'''
       _snake_case					     =       ldmad_pipe(**_lowerCamelCase    ,      negative_prompt=_lowerCamelCase			)

       _snake_case     ,					_snake_case					     =       output.rgb, output.depth
       _snake_case					     =       rgb[0, -3:, -3:, -1]
       _snake_case					     =       depth[0, -3:, -1]

       assert rgb.shape == (1, 64, 64, 3)
       assert depth.shape == (1, 64, 64)

       _snake_case					     =       np.array(
           [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7]			)
       _snake_case					     =       np.array([1_0_7.8_4_7_3_8, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5]			)
       assert np.abs(rgb_slice.flatten() - expected_slice_rgb			).max() < 1e-2
       assert np.abs(depth_slice.flatten() - expected_slice_depth			).max() < 1e-2






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







     def      lowercase	(							self			:			int			):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			Any    ,      _lowerCamelCase			:			Optional[int]="cpu"    ,      _lowerCamelCase			:			Any=torch.floataa    ,      _lowerCamelCase			:			Tuple=0			):
       _snake_case					     =       torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)
       _snake_case					     =       np.random.RandomState(_lowerCamelCase			).standard_normal((1, 4, 64, 64)			)
       _snake_case					     =       torch.from_numpy(_lowerCamelCase			).to(device=_lowerCamelCase    ,      dtype=_lowerCamelCase			)
       _snake_case					     =       {
           '''prompt''': '''a photograph of an astronaut riding a horse''',
           '''latents''': latents,
           '''generator''': generator,
           '''num_inference_steps''': 3,
           '''guidance_scale''': 7.5,
           '''output_type''': '''numpy''',
       }
       return inputs







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''			)
       _snake_case					     =       ldmad_pipe.to(_lowerCamelCase			)
       ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

       _snake_case					     =       self.get_inputs(_lowerCamelCase			)
       _snake_case					     =       ldmad_pipe(**_lowerCamelCase			)
       _snake_case     ,					_snake_case					     =       output.rgb, output.depth
       _snake_case					     =       rgb[0, -3:, -3:, -1].flatten()
       _snake_case					     =       rgb[0, -3:, -1].flatten()

       assert rgb.shape == (1, 512, 512, 3)
       assert depth.shape == (1, 512, 512)

       _snake_case					     =       np.array(
           [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6]			)
       _snake_case					     =       np.array(
           [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6]			)
       assert np.abs(rgb_slice - expected_slice_rgb			).max() < 3e-3
       assert np.abs(depth_slice - expected_slice_depth			).max() < 3e-3






@nightly
@require_torch_gpu
class       lowerCAmelCase__				(						unittest.TestCase	):







     def      lowercase	(							self			:			int			):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			List[Any]    ,      _lowerCamelCase			:			Any="cpu"    ,      _lowerCamelCase			:			List[str]=torch.floataa    ,      _lowerCamelCase			:			str=0			):
       _snake_case					     =       torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)
       _snake_case					     =       np.random.RandomState(_lowerCamelCase			).standard_normal((1, 4, 64, 64)			)
       _snake_case					     =       torch.from_numpy(_lowerCamelCase			).to(device=_lowerCamelCase    ,      dtype=_lowerCamelCase			)
       _snake_case					     =       {
           '''prompt''': '''a photograph of an astronaut riding a horse''',
           '''latents''': latents,
           '''generator''': generator,
           '''num_inference_steps''': 50,
           '''guidance_scale''': 7.5,
           '''output_type''': '''numpy''',
       }
       return inputs







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d'''			).to(_lowerCamelCase			)
       ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

       _snake_case					     =       self.get_inputs(_lowerCamelCase			)
       _snake_case					     =       ldmad_pipe(**_lowerCamelCase			)
       _snake_case     ,					_snake_case					     =       output.rgb, output.depth

       _snake_case					     =       0.4_9_5_5_8_6
       _snake_case					     =       0.3_3_7_9_5_5_1_5
       _snake_case					     =       1_1_2.4_8_5_1_8
       _snake_case					     =       9_8.4_8_9_7_4_6
       assert np.abs(expected_rgb_mean - rgb.mean()			) < 1e-3
       assert np.abs(expected_rgb_std - rgb.std()			) < 1e-3
       assert np.abs(expected_depth_mean - depth.mean()			) < 1e-3
       assert np.abs(expected_depth_std - depth.std()			) < 1e-3







     def      lowercase	(							self			:			str			):
       _snake_case					     =       StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c'''			).to(_lowerCamelCase			)
       ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase			)

       _snake_case					     =       self.get_inputs(_lowerCamelCase			)
       _snake_case					     =       ldmad_pipe(**_lowerCamelCase			)
       _snake_case     ,					_snake_case					     =       output.rgb, output.depth

       _snake_case					     =       0.4_1_9_4_1_2_7
       _snake_case					     =       0.3_5_3_7_5_5_8_6
       _snake_case					     =       0.5_6_3_8_5_0_2
       _snake_case					     =       0.3_4_6_8_6_1_0_3

       assert rgb.shape == (1, 512, 512, 3)
       assert depth.shape == (1, 512, 512, 1)
       assert np.abs(expected_rgb_mean - rgb.mean()			) < 1e-3
       assert np.abs(expected_rgb_std - rgb.std()			) < 1e-3
       assert np.abs(expected_depth_mean - depth.mean()			) < 1e-3
       assert np.abs(expected_depth_std - depth.std()			) < 1e-3





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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    to_pil_image(__SCREAMING_SNAKE_CASE							)
	lowercase  ,					lowercase       =    pil_image.size
	lowercase       =    pytesseract.image_to_data(__SCREAMING_SNAKE_CASE ,							lang=__SCREAMING_SNAKE_CASE ,							output_type='dict' ,							config=__SCREAMING_SNAKE_CASE							)
	lowercase  ,					lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    data['text'], data['left'], data['top'], data['width'], data['height']

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

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

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

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

	return words, normalized_boxes







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Dict							 =       ["""pixel_values"""]





	def __init__(       self		,    snake_case = True		,    snake_case = None		,    snake_case = PILImageResampling.BILINEAR		,    snake_case = True		,    snake_case = 1 / 255		,    snake_case = True		,    snake_case = None		,    snake_case = None		,    snake_case = True		,    snake_case = None		,    snake_case = ""		,    **snake_case		,    ):
		super().__init__(**snake_case       )
		lowercase       =    size if size is not None else {'height': 224, 'width': 224}
		lowercase       =    get_size_dict(snake_case       )

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





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = PILImageResampling.BILINEAR		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    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()}'''       )
		lowercase       =    (size['height'], size['width'])
		return resize(snake_case		,    size=snake_case		,    resample=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return rescale(snake_case		,    scale=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case		,    snake_case = None		,    **snake_case		,    ):
		return normalize(snake_case		,    mean=snake_case		,    std=snake_case		,    data_format=snake_case		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case = None		,    snake_case = None		,    snake_case=None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = None		,    snake_case = ChannelDimension.FIRST		,    **snake_case		,    ):
		lowercase       =    do_resize if do_resize is not None else self.do_resize
		lowercase       =    size if size is not None else self.size
		lowercase       =    get_size_dict(snake_case       )
		lowercase       =    resample if resample is not None else self.resample
		lowercase       =    do_rescale if do_rescale is not None else self.do_rescale
		lowercase       =    rescale_factor if rescale_factor is not None else self.rescale_factor
		lowercase       =    do_normalize if do_normalize is not None else self.do_normalize
		lowercase       =    image_mean if image_mean is not None else self.image_mean
		lowercase       =    image_std if image_std is not None else self.image_std
		lowercase       =    apply_ocr if apply_ocr is not None else self.apply_ocr
		lowercase       =    ocr_lang if ocr_lang is not None else self.ocr_lang
		lowercase       =    tesseract_config if tesseract_config is not None else self.tesseract_config

		lowercase       =    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.'       )

		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.
		lowercase       =    [to_numpy_array(snake_case       ) for image in images]

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

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

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

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

		lowercase       =    [to_channel_dimension_format(snake_case		,    snake_case       ) for image in images]

		lowercase       =    BatchFeature(data={'pixel_values': images}		,    tensor_type=snake_case       )

		if apply_ocr:
			lowercase       =    words_batch
			lowercase       =    boxes_batch
		return data

