


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					:       Optional[Any]			    =     logging.get_logger(__name__)



def UpperCamelCase_				(				__a			,				__a			,				__a      )	->   int:
      return [
          int(1_000 * (box[0] / width)      ),
          int(1_000 * (box[1] / height)      ),
          int(1_000 * (box[2] / width)      ),
          int(1_000 * (box[3] / height)      ),
      ]



def UpperCamelCase_				(				__a			,				__a			,				__a = None      )	->   List[Any]:
      a__	:  Union[str, Any]     =   tesseract_config if tesseract_config is not None else ""

      # apply OCR
      a__	:  Any     =   to_pil_image(__a      )
      a__,	a__	:  Dict     =   pil_image.size
      a__	:  Optional[Any]     =   pytesseract.image_to_data(__a			,				lang=__a			,				output_type="dict"			,				config=__a      )
      a__,	a__,	a__,	a__,	a__	:  str     =   data["text"], data["left"], data["top"], data["width"], data["height"]

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

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

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

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

      return words, normalized_boxes



class A__     ( A__  ):



  """simple docstring"""



  _lowercase									=		['pixel_values']

  def __init__( self			:  Any				,				lowerCamelCase__			:  bool = True				,				lowerCamelCase__			:  Dict[str, int] = None				,				lowerCamelCase__			:  PILImageResampling = PILImageResampling.BILINEAR				,				lowerCamelCase__			:  bool = True				,				lowerCamelCase__			:  Optional[str] = None				,				lowerCamelCase__			:  Optional[str] = ""				,				**lowerCamelCase__			:  Optional[int]				,				):
        super().__init__(**lowerCamelCase__       )
        a__	:  Tuple     =   size if size is not None else {"height": 224, "width": 224}
        a__	:  Optional[Any]     =   get_size_dict(lowerCamelCase__       )

        a__	:  Any     =   do_resize
        a__	:  Any     =   size
        a__	:  Dict     =   resample
        a__	:  str     =   apply_ocr
        a__	:  Union[str, Any]     =   ocr_lang
        a__	:  Any     =   tesseract_config

  def    _UpperCamelCase( self			:  Dict				,				lowerCamelCase__			:  np.ndarray				,				lowerCamelCase__			:  Dict[str, int]				,				lowerCamelCase__			:  PILImageResampling = PILImageResampling.BILINEAR				,				lowerCamelCase__			:  Optional[Union[str, ChannelDimension]] = None				,				**lowerCamelCase__			:  Optional[int]				,				):
        a__	:  Dict     =   get_size_dict(lowerCamelCase__       )
        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()}'''       )
        a__	:  Optional[int]     =   (size["height"], size["width"])
        return resize(lowerCamelCase__				,				size=lowerCamelCase__				,				resample=lowerCamelCase__				,				data_format=lowerCamelCase__				,				**lowerCamelCase__       )

  def    _UpperCamelCase( self			:  int				,				lowerCamelCase__			:  ImageInput				,				lowerCamelCase__			:  bool = None				,				lowerCamelCase__			:  Dict[str, int] = None				,				lowerCamelCase__			:  PILImageResampling = None				,				lowerCamelCase__			:  bool = None				,				lowerCamelCase__			:  Optional[str] = None				,				lowerCamelCase__			:  Optional[str] = None				,				lowerCamelCase__			:  Optional[Union[str, TensorType]] = None				,				lowerCamelCase__			:  ChannelDimension = ChannelDimension.FIRST				,				**lowerCamelCase__			:  Any				,				):
        a__	:  Optional[Any]     =   do_resize if do_resize is not None else self.do_resize
        a__	:  Dict     =   size if size is not None else self.size
        a__	:  str     =   get_size_dict(lowerCamelCase__       )
        a__	:  int     =   resample if resample is not None else self.resample
        a__	:  Dict     =   apply_ocr if apply_ocr is not None else self.apply_ocr
        a__	:  Union[str, Any]     =   ocr_lang if ocr_lang is not None else self.ocr_lang
        a__	:  Optional[Any]     =   tesseract_config if tesseract_config is not None else self.tesseract_config

        a__	:  str     =   make_list_of_images(lowerCamelCase__       )

        if not valid_images(lowerCamelCase__       ):
              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.
        a__	:  Dict     =   [to_numpy_array(lowerCamelCase__       ) for image in images]

        if apply_ocr:
              requires_backends(self				,				"pytesseract"       )
              a__	:  Dict     =   []
              a__	:  Optional[int]     =   []
              for image in images:
                    a__,	a__	:  Union[str, Any]     =   apply_tesseract(lowerCamelCase__				,				lowerCamelCase__				,				lowerCamelCase__       )
                    words_batch.append(lowerCamelCase__       )
                    boxes_batch.append(lowerCamelCase__       )

        if do_resize:
              a__	:  Tuple     =   [self.resize(image=lowerCamelCase__				,				size=lowerCamelCase__				,				resample=lowerCamelCase__       ) for image in images]

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

        a__	:  Tuple     =   BatchFeature(data={"pixel_values": images}				,				tensor_type=lowerCamelCase__       )

        if apply_ocr:
              a__	:  List[Any]     =   words_batch
              a__	:  Union[str, Any]     =   boxes_batch
        return data








'''simple docstring'''




import gc
import random
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class 				__snake_case     (					unittest.TestCase							):





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





	@property
	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   1
		snake_case__	       =   3
		snake_case__	       =   (32, 32)

		snake_case__	       =   floats_tensor((batch_size, num_channels) + sizes      ,       rng=random.Random(0		)		).to(UpperCamelCase_		)
		return image





	@property
	def   _snake_case   (		self		)      ->		Union[str, Any]:
		torch.manual_seed(0		)
		snake_case__	       =   UNetaDConditionModel(
		    block_out_channels=(32, 32, 64)      ,       layers_per_block=2      ,       sample_size=32      ,       in_channels=7      ,       out_channels=4      ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D', 'CrossAttnDownBlock2D')      ,       up_block_types=('CrossAttnUpBlock2D', 'CrossAttnUpBlock2D', 'UpBlock2D')      ,       cross_attention_dim=32      ,       attention_head_dim=8      ,       use_linear_projection=UpperCamelCase_      ,       only_cross_attention=(True, True, False)      ,       num_class_embeds=100      ,       )
		return model





	@property
	def   _snake_case   (		self		)      ->		Optional[Any]:
		torch.manual_seed(0		)
		snake_case__	       =   AutoencoderKL(
		    block_out_channels=[32, 32, 64]      ,       in_channels=3      ,       out_channels=3      ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']      ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']      ,       latent_channels=4      ,       )
		return model





	@property
	def   _snake_case   (		self		)      ->		List[str]:
		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-0_5      ,       num_attention_heads=4      ,       num_hidden_layers=5      ,       pad_token_id=1      ,       vocab_size=1000      ,       hidden_act='gelu'      ,       projection_dim=512      ,       )
		return CLIPTextModel(UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'A painting of a squirrel eating a burger'
		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       )

		snake_case__	       =   output.images

		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       return_dict=UpperCamelCase_      ,       )[0]

		snake_case__	       =   image[0, -3:, -3:, -1]
		snake_case__	       =   image_from_tuple[0, -3:, -3:, -1]

		snake_case__	       =   low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)
		snake_case__	       =   np.array([0.3_1_1_3, 0.3_9_1_0, 0.4_2_7_2, 0.4_8_5_9, 0.5_0_6_1, 0.4_6_5_2, 0.5_3_6_2, 0.5_7_1_5, 0.5_6_6_1]		)

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





	def   _snake_case   (		self		)      ->		List[str]:
		snake_case__	       =   'cpu'  # ensure determinism for the device-dependent torch.Generator
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'A painting of a squirrel eating a burger'
		snake_case__	       =   sd_pipe(
		    2 * [prompt]      ,       image=2 * [low_res_image]      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       )
		snake_case__	       =   output.images
		assert image.shape[0] == 2

		snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_images_per_prompt=2      ,       guidance_scale=6.0      ,       noise_level=20      ,       num_inference_steps=2      ,       output_type='np'      ,       )
		snake_case__	       =   output.images
		assert image.shape[0] == 2





	@unittest.skipIf(torch_device != 'cuda'      ,       'This test requires a GPU'		)
	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   self.dummy_cond_unet_upscale
		snake_case__	       =   DDPMScheduler()
		snake_case__	       =   DDIMScheduler(prediction_type='v_prediction'		)
		snake_case__	       =   self.dummy_vae
		snake_case__	       =   self.dummy_text_encoder
		snake_case__	       =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'		)

		snake_case__	       =   self.dummy_image.cpu().permute(0      ,       2      ,       3      ,       1		)[0]
		snake_case__	       =   Image.fromarray(np.uinta(UpperCamelCase_		)		).convert('RGB'		).resize((64, 64)		)

		# put models in fp16, except vae as it overflows in fp16
		snake_case__	       =   unet.half()
		snake_case__	       =   text_encoder.half()

		# make sure here that pndm scheduler skips prk
		snake_case__	       =   StableDiffusionUpscalePipeline(
		    unet=UpperCamelCase_      ,       low_res_scheduler=UpperCamelCase_      ,       scheduler=UpperCamelCase_      ,       vae=UpperCamelCase_      ,       text_encoder=UpperCamelCase_      ,       tokenizer=UpperCamelCase_      ,       max_noise_level=350      ,       )
		snake_case__	       =   sd_pipe.to(UpperCamelCase_		)
		sd_pipe.set_progress_bar_config(disable=UpperCamelCase_		)

		snake_case__	       =   'A painting of a squirrel eating a burger'
		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   sd_pipe(
		    [prompt]      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_inference_steps=2      ,       output_type='np'      ,       ).images

		snake_case__	       =   low_res_image.size[0] * 4
		assert image.shape == (1, expected_height_width, expected_height_width, 3)






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





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





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)
		snake_case__	       =   load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
		    '/upsampled_cat.npy'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(UpperCamelCase_		)
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       output_type='np'      ,       )
		snake_case__	       =   output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image		).max() < 1E-3





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)
		snake_case__	       =   load_numpy(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'
		    '/upsampled_cat_fp16.npy'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(
		    UpperCamelCase_      ,       torch_dtype=torch.floataa      ,       )
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       output_type='np'      ,       )
		snake_case__	       =   output.images[0]

		assert image.shape == (512, 512, 3)
		assert np.abs(expected_image - image		).max() < 5E-1





	def   _snake_case   (		self		)      ->		int:
		torch.cuda.empty_cache()
		torch.cuda.reset_max_memory_allocated()
		torch.cuda.reset_peak_memory_stats()

		snake_case__	       =   load_image(
		    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
		    '/sd2-upscale/low_res_cat.png'		)

		snake_case__	       =   'stabilityai/stable-diffusion-x4-upscaler'
		snake_case__	       =   StableDiffusionUpscalePipeline.from_pretrained(
		    UpperCamelCase_      ,       torch_dtype=torch.floataa      ,       )
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		pipe.enable_attention_slicing(1		)
		pipe.enable_sequential_cpu_offload()

		snake_case__	       =   'a cat sitting on a park bench'

		snake_case__	       =   torch.manual_seed(0		)
		snake_case__	       =   pipe(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       generator=UpperCamelCase_      ,       num_inference_steps=5      ,       output_type='np'      ,       )

		snake_case__	       =   torch.cuda.max_memory_allocated()
		# make sure that less than 2.9 GB is allocated
		assert mem_bytes < 2.9 * 10**9



