






'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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 ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class        a_	(    snake_case__			, unittest.TestCase       ):
  lowercase  	=   KandinskyVaaControlnetPipeline
  lowercase  	=   ["""image_embeds""", """negative_image_embeds""", """hint"""]
  lowercase  	=   ["""image_embeds""", """negative_image_embeds""", """hint"""]
  lowercase  	=   [
      """generator""",
      """height""",
      """width""",
      """latents""",
      """guidance_scale""",
      """num_inference_steps""",
      """return_dict""",
      """guidance_scale""",
      """num_images_per_prompt""",
      """output_type""",
      """return_dict""",
  ]
  lowercase  	=   False

  @property
  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        return 32

  @property
  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        return 32

  @property
  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        return self.time_input_dim

  @property
  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






        return self.time_input_dim * 4

  @property
  def 						A__		(   self   )						->   Optional[int]:



        """simple docstring"""






        return 100

  @property
  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        torch.manual_seed(0   )

        UpperCamelCase       						=      {
            """in_channels""": 8,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 8,
            """addition_embed_type""": """image_hint""",
            """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
            """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
            """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
            """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
            """layers_per_block""": 1,
            """encoder_hid_dim""": self.text_embedder_hidden_size,
            """encoder_hid_dim_type""": """image_proj""",
            """cross_attention_dim""": self.cross_attention_dim,
            """attention_head_dim""": 4,
            """resnet_time_scale_shift""": """scale_shift""",
            """class_embed_type""": None,
        }

        UpperCamelCase       						=      UNetaDConditionModel(**UpperCAmelCase_   )
        return model

  @property
  def 						A__		(   self   )						->   Optional[Any]:



        """simple docstring"""






        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "AttnDownEncoderBlock2D",
            ],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }

  @property
  def 						A__		(   self   )						->   int:



        """simple docstring"""






        torch.manual_seed(0   )
        UpperCamelCase       						=      VQModel(**self.dummy_movq_kwargs   )
        return model

  def 						A__		(   self   )						->   str:



        """simple docstring"""






        UpperCamelCase       						=      self.dummy_unet
        UpperCamelCase       						=      self.dummy_movq

        UpperCamelCase       						=      DDIMScheduler(
            num_train_timesteps=1000    ,	beta_schedule="""linear"""    ,	beta_start=0.0_0_0_8_5    ,	beta_end=0.0_1_2    ,	clip_sample=UpperCAmelCase_    ,	set_alpha_to_one=UpperCAmelCase_    ,	steps_offset=1    ,	prediction_type="""epsilon"""    ,	thresholding=UpperCAmelCase_    ,	)

        UpperCamelCase       						=      {
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }
        return components

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE=0   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      floats_tensor((1, self.text_embedder_hidden_size)    ,	rng=random.Random(UpperCAmelCase_   )   ).to(UpperCAmelCase_   )
        UpperCamelCase       						=      floats_tensor((1, self.text_embedder_hidden_size)    ,	rng=random.Random(seed + 1   )   ).to(
            UpperCAmelCase_   )

        # create hint
        UpperCamelCase       						=      floats_tensor((1, 3, 64, 64)    ,	rng=random.Random(UpperCAmelCase_   )   ).to(UpperCAmelCase_   )

        if str(UpperCAmelCase_   ).startswith("""mps"""   ):
              UpperCamelCase       						=      torch.manual_seed(UpperCAmelCase_   )
        else:
              UpperCamelCase       						=      torch.Generator(device=UpperCAmelCase_   ).manual_seed(UpperCAmelCase_   )
        UpperCamelCase       						=      {
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """hint""": hint,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """guidance_scale""": 4.0,
            """num_inference_steps""": 2,
            """output_type""": """np""",
        }
        return inputs


  def 						A__		(   self   )						->   List[Any]:



        """simple docstring"""






        UpperCamelCase       						=      """cpu"""

        UpperCamelCase       						=      self.get_dummy_components()

        UpperCamelCase       						=      self.pipeline_class(**UpperCAmelCase_   )
        UpperCamelCase       						=      pipe.to(UpperCAmelCase_   )

        pipe.set_progress_bar_config(disable=UpperCAmelCase_   )

        UpperCamelCase       						=      pipe(**self.get_dummy_inputs(UpperCAmelCase_   )   )
        UpperCamelCase       						=      output.images

        UpperCamelCase       						=      pipe(
            **self.get_dummy_inputs(UpperCAmelCase_   )    ,	return_dict=UpperCAmelCase_    ,	)[0]

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

        assert image.shape == (1, 64, 64, 3)

        UpperCamelCase       						=      np.array(
            [0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5]   )

        assert (
            np.abs(image_slice.flatten() - expected_slice   ).max() < 1e-2
        ), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"

        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1e-2
        ), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"






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

  def 						A__		(   self   )						->   Union[str, Any]:



        """simple docstring"""






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


  def 						A__		(   self   )						->   List[str]:



        """simple docstring"""






        UpperCamelCase       						=      load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy"""   )

        UpperCamelCase       						=      load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/hint_image_cat.png"""   )
        UpperCamelCase       						=      torch.from_numpy(np.array(UpperCAmelCase_   )   ).float() / 2_5_5.0
        UpperCamelCase       						=      hint.permute(2    ,	0    ,	1   ).unsqueeze(0   )

        UpperCamelCase       						=      KandinskyVaaPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-prior"""    ,	torch_dtype=torch.floataa   )
        pipe_prior.to(UpperCAmelCase_   )

        UpperCamelCase       						=      KandinskyVaaControlnetPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-controlnet-depth"""    ,	torch_dtype=torch.floataa   )
        UpperCamelCase       						=      pipeline.to(UpperCAmelCase_   )
        pipeline.set_progress_bar_config(disable=UpperCAmelCase_   )

        UpperCamelCase       						=      """A robot, 4k photo"""

        UpperCamelCase       						=      torch.Generator(device="""cuda"""   ).manual_seed(0   )
        UpperCamelCase						,UpperCamelCase       						=      pipe_prior(
            UpperCAmelCase_    ,	generator=UpperCAmelCase_    ,	num_inference_steps=5    ,	negative_prompt=""""""    ,	).to_tuple()

        UpperCamelCase       						=      torch.Generator(device="""cuda"""   ).manual_seed(0   )
        UpperCamelCase       						=      pipeline(
            image_embeds=UpperCAmelCase_    ,	negative_image_embeds=UpperCAmelCase_    ,	hint=UpperCAmelCase_    ,	generator=UpperCAmelCase_    ,	num_inference_steps=100    ,	output_type="""np"""    ,	)

        UpperCamelCase       						=      output.images[0]

        assert image.shape == (512, 512, 3)

        assert_mean_pixel_difference(UpperCAmelCase_    ,	UpperCAmelCase_   )








'''simple docstring'''

import fire

from utils import calculate_rouge, save_json







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		**__UpperCamelCase					)->				int:
      UpperCamelCase       						=      [x.strip() for x in open(__UpperCamelCase					).readlines()]
      UpperCamelCase       						=      [x.strip() for x in open(__UpperCamelCase					).readlines()][: len(__UpperCamelCase					)]
      UpperCamelCase       						=      calculate_rouge(__UpperCamelCase	,		__UpperCamelCase	,		**__UpperCamelCase					)
      if save_path is not None:
            save_json(__UpperCamelCase	,		__UpperCamelCase	,		indent=__UpperCamelCase					)
      return metrics  # these print nicely


if __name__ == "__main__":
     fire.Fire(calculate_rouge_path)
