




import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

def  UpperCAmelCase_							(				__UpperCAmelCase						:     Dict							) ->			Tuple:
      SCREAMING_SNAKE_CASE_     						=      [tensor.shape for tensor in tensor_list]
      return all(shape == shapes[0] for shape in shapes[1:]							)





class 	UpperCAmelCase_							(				_SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE	,       _SCREAMING_SNAKE_CASE	,       unittest.TestCase ):


 '''simple docstring'''







 lowercase_       =   StableDiffusionLatentUpscalePipeline
 lowercase_       =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     "height",
     "width",
     "cross_attention_kwargs",
     "negative_prompt_embeds",
     "prompt_embeds",
 }
 lowercase_       =   PipelineTesterMixin.required_optional_params - {"num_images_per_prompt"}
 lowercase_       =   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 lowercase_       =   frozenset(
     [] )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
 lowercase_       =   frozenset([] )

 lowercase_       =   True



 @property
 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       SCREAMING_SNAKE_CASE_     						=      1
       SCREAMING_SNAKE_CASE_     						=      4
       SCREAMING_SNAKE_CASE_     						=      (16, 16)

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



 def 				lowerCAmelCase_  (							self  :		Tuple						):
       torch.manual_seed(0						)
       SCREAMING_SNAKE_CASE_     						=      UNetaDConditionModel(
           act_fn='gelu'    ,    attention_head_dim=8    ,    norm_num_groups=_lowerCAmelCase    ,    block_out_channels=[32, 32, 64, 64]    ,    time_cond_proj_dim=160    ,    conv_in_kernel=1    ,    conv_out_kernel=1    ,    cross_attention_dim=32    ,    down_block_types=(
               'KDownBlock2D',
               'KCrossAttnDownBlock2D',
               'KCrossAttnDownBlock2D',
               'KCrossAttnDownBlock2D',
           )    ,    in_channels=8    ,    mid_block_type=_lowerCAmelCase    ,    only_cross_attention=_lowerCAmelCase    ,    out_channels=5    ,    resnet_time_scale_shift='scale_shift'    ,    time_embedding_type='fourier'    ,    timestep_post_act='gelu'    ,    up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')    ,    )
       SCREAMING_SNAKE_CASE_     						=      AutoencoderKL(
           block_out_channels=[32, 32, 64, 64]    ,    in_channels=3    ,    out_channels=3    ,    down_block_types=[
               'DownEncoderBlock2D',
               'DownEncoderBlock2D',
               'DownEncoderBlock2D',
               'DownEncoderBlock2D',
           ]    ,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']    ,    latent_channels=4    ,    )
       SCREAMING_SNAKE_CASE_     						=      EulerDiscreteScheduler(prediction_type='sample'						)
       SCREAMING_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=1_000    ,    hidden_act='quick_gelu'    ,    projection_dim=512    ,    )
       SCREAMING_SNAKE_CASE_     						=      CLIPTextModel(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

       SCREAMING_SNAKE_CASE_     						=      {
           'unet': model.eval(),
           'vae': vae.eval(),
           'scheduler': scheduler,
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
       }

       return components



 def 				lowerCAmelCase_  (							self  :		Optional[int]    ,    _lowerCAmelCase  :		Optional[int]    ,    _lowerCAmelCase  :		List[Any]=0						):
       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',
           'image': self.dummy_image.cpu(),
           'generator': generator,
           'num_inference_steps': 2,
           'output_type': 'numpy',
       }
       return inputs



 def 				lowerCAmelCase_  (							self  :		Optional[Any]						):
       SCREAMING_SNAKE_CASE_     						=      'cpu'

       SCREAMING_SNAKE_CASE_     						=      self.get_dummy_components()
       SCREAMING_SNAKE_CASE_     						=      self.pipeline_class(**_lowerCAmelCase						)
       pipe.to(_lowerCAmelCase						)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase						)

       SCREAMING_SNAKE_CASE_     						=      self.get_dummy_inputs(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      pipe(**_lowerCAmelCase						).images
       SCREAMING_SNAKE_CASE_     						=      image[0, -3:, -3:, -1]

       self.assertEqual(image.shape    ,    (1, 256, 256, 3)						)
       SCREAMING_SNAKE_CASE_     						=      np.array(
           [0.4722_2412, 0.4192_1633, 0.4471_7434, 0.4687_4192, 0.4258_8258, 0.4615_0726, 0.467_7534, 0.4558_3832, 0.4857_9055]						)
       SCREAMING_SNAKE_CASE_     						=      np.abs(image_slice.flatten() - expected_slice						).max()
       self.assertLessEqual(_lowerCAmelCase    ,    1E-3						)



 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       super().test_attention_slicing_forward_pass(expected_max_diff=7E-3						)



 def 				lowerCAmelCase_  (							self  :		Dict						):
       super().test_cpu_offload_forward_pass(expected_max_diff=3E-3						)



 def 				lowerCAmelCase_  (							self  :		Optional[int]						):
       super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3						)



 def 				lowerCAmelCase_  (							self  :		int						):
       super().test_inference_batch_single_identical(expected_max_diff=7E-3						)



 def 				lowerCAmelCase_  (							self  :		Dict						):
       super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3						)



 def 				lowerCAmelCase_  (							self  :		Any						):
       super().test_save_load_local(expected_max_difference=3E-3						)



 def 				lowerCAmelCase_  (							self  :		Union[str, Any]						):
       super().test_save_load_optional_components(expected_max_difference=3E-3						)




 def 				lowerCAmelCase_  (							self  :		Dict						):
       SCREAMING_SNAKE_CASE_     						=      [
           'DDIMScheduler',
           'DDPMScheduler',
           'PNDMScheduler',
           'HeunDiscreteScheduler',
           'EulerAncestralDiscreteScheduler',
           'KDPM2DiscreteScheduler',
           'KDPM2AncestralDiscreteScheduler',
           'DPMSolverSDEScheduler',
       ]
       SCREAMING_SNAKE_CASE_     						=      self.get_dummy_components()
       SCREAMING_SNAKE_CASE_     						=      self.pipeline_class(**_lowerCAmelCase						)

       # make sure that PNDM does not need warm-up
       pipe.scheduler.register_to_config(skip_prk_steps=_lowerCAmelCase						)

       pipe.to(_lowerCAmelCase						)
       pipe.set_progress_bar_config(disable=_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      self.get_dummy_inputs(_lowerCAmelCase						)
       SCREAMING_SNAKE_CASE_     						=      2

       SCREAMING_SNAKE_CASE_     						=      []
       for scheduler_enum in KarrasDiffusionSchedulers:
             if scheduler_enum.name in skip_schedulers:
                   # no sigma schedulers are not supported
                   # no schedulers
                   continue

             SCREAMING_SNAKE_CASE_     						=      getattr(_lowerCAmelCase    ,    scheduler_enum.name						)
             SCREAMING_SNAKE_CASE_     						=      scheduler_cls.from_config(pipe.scheduler.config						)
             SCREAMING_SNAKE_CASE_     						=      pipe(**_lowerCAmelCase						)[0]
             outputs.append(_lowerCAmelCase						)

       assert check_same_shape(_lowerCAmelCase						)





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


 '''simple docstring'''







 def 				lowerCAmelCase_  (							self  :		List[Any]						):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



 def 				lowerCAmelCase_  (							self  :		Any						):
       SCREAMING_SNAKE_CASE_     						=      torch.manual_seed(33						)

       SCREAMING_SNAKE_CASE_     						=      StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'    ,    torch_dtype=torch.floataa						)
       pipe.to('cuda'						)

       SCREAMING_SNAKE_CASE_     						=      StableDiffusionLatentUpscalePipeline.from_pretrained(
           'stabilityai/sd-x2-latent-upscaler'    ,    torch_dtype=torch.floataa						)
       upscaler.to('cuda'						)

       SCREAMING_SNAKE_CASE_     						=      'a photo of an astronaut high resolution, unreal engine, ultra realistic'

       SCREAMING_SNAKE_CASE_     						=      pipe(_lowerCAmelCase    ,    generator=_lowerCAmelCase    ,    output_type='latent'						).images

       SCREAMING_SNAKE_CASE_     						=      upscaler(
           prompt=_lowerCAmelCase    ,    image=_lowerCAmelCase    ,    num_inference_steps=20    ,    guidance_scale=0    ,    generator=_lowerCAmelCase    ,    output_type='np'    ,    ).images[0]

       SCREAMING_SNAKE_CASE_     						=      load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'						)
       assert np.abs((expected_image - image).mean()						) < 5E-2




 def 				lowerCAmelCase_  (							self  :		Tuple						):
       SCREAMING_SNAKE_CASE_     						=      torch.manual_seed(33						)

       SCREAMING_SNAKE_CASE_     						=      StableDiffusionLatentUpscalePipeline.from_pretrained(
           'stabilityai/sd-x2-latent-upscaler'    ,    torch_dtype=torch.floataa						)
       upscaler.to('cuda'						)

       SCREAMING_SNAKE_CASE_     						=      'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

       SCREAMING_SNAKE_CASE_     						=      load_image(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'						)

       SCREAMING_SNAKE_CASE_     						=      upscaler(
           prompt=_lowerCAmelCase    ,    image=_lowerCAmelCase    ,    num_inference_steps=20    ,    guidance_scale=0    ,    generator=_lowerCAmelCase    ,    output_type='np'    ,    ).images[0]

       SCREAMING_SNAKE_CASE_     						=      load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'						)
       assert np.abs((expected_image - image).max()						) < 5E-2





from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests

def  UpperCAmelCase_							(				__UpperCAmelCase						:     str = "isbn/0140328726"							) ->			dict:
      SCREAMING_SNAKE_CASE_     						=      olid.strip().strip('/'							)  # Remove leading/trailing whitespace & slashes
      if new_olid.count('/'							) != 1:
            SCREAMING_SNAKE_CASE_     						=      f"{olid} is not a valid Open Library olid"
            raise ValueError(__UpperCAmelCase							)
      return requests.get(f"https://openlibrary.org/{new_olid}.json"							).json()

def  UpperCAmelCase_							(				__UpperCAmelCase						:     dict							) ->			dict:
      SCREAMING_SNAKE_CASE_     						=      {
          'title': 'Title',
          'publish_date': 'Publish date',
          'authors': 'Authors',
          'number_of_pages': 'Number of pages:',
          'first_sentence': 'First sentence',
          'isbn_10': 'ISBN (10)',
          'isbn_13': 'ISBN (13)',
      }
      SCREAMING_SNAKE_CASE_     						=      {better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
      SCREAMING_SNAKE_CASE_     						=      [
          get_openlibrary_data(author['key']							)['name'] for author in data['Authors']
      ]
      SCREAMING_SNAKE_CASE_     						=      data['First sentence']['value']
      for key, value in data.items():
            if isinstance(__UpperCAmelCase      ,      __UpperCAmelCase							):
                  SCREAMING_SNAKE_CASE_     						=      ', '.join(__UpperCAmelCase							)
      return data


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   while True:
      lowerCamelCase__			:       Optional[Any]         =							input('\nEnter the ISBN code to search (or \'quit\' to stop): ').strip()
      if isbn.lower() in ("", "q", "quit", "exit", "stop"):
         break

      if len(isbn) not in (10, 13) or not isbn.isdigit():
         print(f'''Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.''')
         continue

      print(f'''\nSearching Open Library for ISBN: {isbn}...\n''')

      try:
         lowerCamelCase__			:       Union[str, Any]         =							summarize_book(get_openlibrary_data(f'''isbn/{isbn}'''))
         print('\n'.join(f'''{key}: {value}''' for key, value in book_summary.items()))
      except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
         print(f'''Sorry, there are no results for ISBN: {isbn}.''')