





from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml



class       A__     ( yaml.SafeLoader    ):

      '''simple docstring'''





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     [self.constructed_objects[key_node] for key_node, _ in node.value]
       UpperCamelCase			:      List[str] 			=     [tuple(A__     ) if isinstance(A__ ,							A__     ) else key for key in keys]
       UpperCamelCase			:      Any 			=     Counter(A__     )
       UpperCamelCase			:      Tuple 			=     [key for key in counter if counter[key] > 1]
       if duplicate_keys:
        raise TypeError(F"""Got duplicate yaml keys: {duplicate_keys}"""     )





      def  __UpperCamelCase(				self ,							A_ ,							A_=False     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     super().construct_mapping(A__ ,							deep=A__     )
       self._check_no_duplicates_on_constructed_node(A__     )
       return mapping



def 	A_       (  _lowerCAmelCase  )		->				Tuple[Optional[str], str]:
 UpperCamelCase			:      Optional[int] 			=     list(readme_content.splitlines()  )
 if full_content and full_content[0] == "---" and "---" in full_content[1:]:
  UpperCamelCase			:      Optional[Any] 			=     full_content[1:].index("---"  ) + 1
  UpperCamelCase			:      int 			=     "\n".join(full_content[1:sep_idx]  )
  return yamlblock, "\n".join(full_content[sep_idx + 1 :]  )

 return None, "\n".join(_lowerCAmelCase  )



class       A__     ( __a    ):

      '''simple docstring'''





      _UpperCAmelCase			:Any							=     {'train_eval_index'}  # train-eval-index in the YAML metadata





      @classmethod
      def  __UpperCamelCase(				cls ,							A_     ):
       '''simple docstring'''




       with open(A__ ,							encoding="utf-8"     ) as readme_file:
        UpperCamelCase			,					UpperCamelCase			:      Union[str, Any] 			=     _split_yaml_from_readme(readme_file.read()     )
       if yaml_string is not None:
        return cls.from_yaml_string(A__     )
       else:
        return cls()





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       if path.exists():
        with open(A__ ,							encoding="utf-8"     ) as readme_file:
         UpperCamelCase			:      int 			=     readme_file.read()
       else:
        UpperCamelCase			:      Optional[int] 			=     None
       UpperCamelCase			:      Dict 			=     self._to_readme(A__     )
       with open(A__ ,							"w" ,							encoding="utf-8"     ) as readme_file:
        readme_file.write(A__     )





      def  __UpperCamelCase(				self ,							A_ = None     ):
       '''simple docstring'''




       if readme_content is not None:
        UpperCamelCase			,					UpperCamelCase			:      int 			=     _split_yaml_from_readme(A__     )
        UpperCamelCase			:      List[str] 			=     "---\n" + self.to_yaml_string() + "---\n" + content
       else:
        UpperCamelCase			:      List[Any] 			=     "---\n" + self.to_yaml_string() + "---\n"
       return full_content





      @classmethod
      def  __UpperCamelCase(				cls ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     yaml.load(A__ ,							Loader=_NoDuplicateSafeLoader     ) or {}

       # Convert the YAML keys to DatasetMetadata fields
       UpperCamelCase			:      Union[str, Any] 			=     {
           (key.replace("-" ,							"_"     ) if key.replace("-" ,							"_"     ) in cls._FIELDS_WITH_DASHES else key): value
           for key, value in metadata_dict.items()
       }
       return cls(**A__     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return yaml.safe_dump(
           {
               (key.replace("_" ,							"-"     ) if key in self._FIELDS_WITH_DASHES else key): value
               for key, value in self.items()
           } ,							sort_keys=A__ ,							allow_unicode=A__ ,							encoding="utf-8" ,							).decode("utf-8"     )


__lowerCamelCase :  Optional[Any]										=  {
    """image-classification""": [],
    """translation""": [],
    """image-segmentation""": [],
    """fill-mask""": [],
    """automatic-speech-recognition""": [],
    """token-classification""": [],
    """sentence-similarity""": [],
    """audio-classification""": [],
    """question-answering""": [],
    """summarization""": [],
    """zero-shot-classification""": [],
    """table-to-text""": [],
    """feature-extraction""": [],
    """other""": [],
    """multiple-choice""": [],
    """text-classification""": [],
    """text-to-image""": [],
    """text2text-generation""": [],
    """zero-shot-image-classification""": [],
    """tabular-classification""": [],
    """tabular-regression""": [],
    """image-to-image""": [],
    """tabular-to-text""": [],
    """unconditional-image-generation""": [],
    """text-retrieval""": [],
    """text-to-speech""": [],
    """object-detection""": [],
    """audio-to-audio""": [],
    """text-generation""": [],
    """conversational""": [],
    """table-question-answering""": [],
    """visual-question-answering""": [],
    """image-to-text""": [],
    """reinforcement-learning""": [],
    """voice-activity-detection""": [],
    """time-series-forecasting""": [],
    """document-question-answering""": [],
}


if __name__ == "__main__":
    from argparse import ArgumentParser

    __lowerCamelCase :  str										=  ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
    ap.add_argument("""readme_filepath""")
    __lowerCamelCase :  Tuple										=  ap.parse_args()

    __lowerCamelCase :  List[str]										=  Path(args.readme_filepath)
    __lowerCamelCase :  Dict										=  DatasetMetadata.from_readme(readme_filepath)
    print(dataset_metadata)
    dataset_metadata.to_readme(readme_filepath)







import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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    ):
      _UpperCAmelCase			:str							=     KandinskyVaaPipeline
      _UpperCAmelCase			:str							=     [
          'image_embeds',
          'negative_image_embeds',
      ]
      _UpperCAmelCase			:str							=     ['image_embeds', 'negative_image_embeds']
      _UpperCAmelCase			:List[str]							=     [
          'generator',
          'height',
          'width',
          'latents',
          'guidance_scale',
          'num_inference_steps',
          'return_dict',
          'guidance_scale',
          'num_images_per_prompt',
          'output_type',
          'return_dict',
      ]
      _UpperCAmelCase			:List[str]							=     False





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 32





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 32





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.time_input_dim





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.time_input_dim * 4





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 100





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       torch.manual_seed(0     )

       UpperCamelCase			:      List[str] 			=     {
           "in_channels": 4,
           # Out channels is double in channels because predicts mean and variance
           "out_channels": 8,
           "addition_embed_type": "image",
           "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			:      Dict 			=     UNetaDConditionModel(**A_     )
       return model





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return {
           "block_out_channels": [32, 64],
           "down_block_types": ["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",
           ],
           "vq_embed_dim": 4,
       }





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       torch.manual_seed(0     )
       UpperCamelCase			:      Optional[Any] 			=     VQModel(**self.dummy_movq_kwargs     )
       return model





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     self.dummy_unet
       UpperCamelCase			:      Optional[Any] 			=     self.dummy_movq

       UpperCamelCase			:      Dict 			=     DDIMScheduler(
           num_train_timesteps=1000 ,							beta_schedule="linear" ,							beta_start=0.0_00_85 ,							beta_end=0.0_12 ,							clip_sample=A_ ,							set_alpha_to_one=A_ ,							steps_offset=1 ,							prediction_type="epsilon" ,							thresholding=A_ ,							)

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





      def  __UpperCamelCase(				self ,							A_ ,							A_=0     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     floats_tensor((1, self.text_embedder_hidden_size) ,							rng=random.Random(A_     )     ).to(A_     )
       UpperCamelCase			:      Optional[Any] 			=     floats_tensor((1, self.text_embedder_hidden_size) ,							rng=random.Random(seed + 1     )     ).to(
           A_     )
       if str(A_     ).startswith("mps"     ):
        UpperCamelCase			:      Optional[Any] 			=     torch.manual_seed(A_     )
       else:
        UpperCamelCase			:      List[Any] 			=     torch.Generator(device=A_     ).manual_seed(A_     )
       UpperCamelCase			:      Optional[int] 			=     {
           "image_embeds": image_embeds,
           "negative_image_embeds": negative_image_embeds,
           "generator": generator,
           "height": 64,
           "width": 64,
           "guidance_scale": 4.0,
           "num_inference_steps": 2,
           "output_type": "np",
       }
       return inputs





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     "cpu"

       UpperCamelCase			:      List[str] 			=     self.get_dummy_components()

       UpperCamelCase			:      Tuple 			=     self.pipeline_class(**A_     )
       UpperCamelCase			:      List[str] 			=     pipe.to(A_     )

       pipe.set_progress_bar_config(disable=A_     )

       UpperCamelCase			:      Dict 			=     pipe(**self.get_dummy_inputs(A_     )     )
       UpperCamelCase			:      Optional[int] 			=     output.images

       UpperCamelCase			:      int 			=     pipe(
           **self.get_dummy_inputs(A_     ) ,							return_dict=A_ ,							)[0]

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

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

       UpperCamelCase			:      int 			=     np.array(
           [0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46]     )

       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  __UpperCamelCase(				self     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     load_numpy(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
           "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"     )

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

       UpperCamelCase			:      Dict 			=     KandinskyVaaPipeline.from_pretrained(
           "kandinsky-community/kandinsky-2-2-decoder" ,							torch_dtype=torch.floataa     )
       UpperCamelCase			:      Tuple 			=     pipeline.to(A_     )
       pipeline.set_progress_bar_config(disable=A_     )

       UpperCamelCase			:      str 			=     "red cat, 4k photo"

       UpperCamelCase			:      str 			=     torch.Generator(device="cuda"     ).manual_seed(0     )
       UpperCamelCase			,					UpperCamelCase			:      Tuple 			=     pipe_prior(
           A_ ,							generator=A_ ,							num_inference_steps=5 ,							negative_prompt="" ,							).to_tuple()

       UpperCamelCase			:      int 			=     torch.Generator(device="cuda"     ).manual_seed(0     )
       UpperCamelCase			:      Tuple 			=     pipeline(
           image_embeds=A_ ,							negative_image_embeds=A_ ,							generator=A_ ,							num_inference_steps=100 ,							output_type="np" ,							)

       UpperCamelCase			:      Union[str, Any] 			=     output.images[0]

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

       assert_mean_pixel_difference(A_ ,							A_     )
