import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
      import jax
      import jax.numpy as jnp
      from flax.jax_utils import replicate
      from flax.training.common_utils import shard





@slow
@require_flax
class 					A							(    unittest.TestCase					):


   def       lowercase_				(self					:						Any		)						->    int:
         """simple docstring"""



         super().tearDown()
         gc.collect()


   def       lowercase_				(self					:						Optional[Any]		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__		,				UpperCAmelCase__								=  FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-canny"	,  from_pt=__UpperCAmelCase	,  dtype=jnp.bfloataa		)
         UpperCAmelCase__		,				UpperCAmelCase__								=  FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"	,  controlnet=__UpperCAmelCase	,  from_pt=__UpperCAmelCase	,  dtype=jnp.bfloataa		)
         UpperCAmelCase__								=  controlnet_params

         UpperCAmelCase__								=  "bird"
         UpperCAmelCase__								=  jax.device_count()
         UpperCAmelCase__								=  pipe.prepare_text_inputs([prompts] * num_samples		)

         UpperCAmelCase__								=  load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"		)
         UpperCAmelCase__								=  pipe.prepare_image_inputs([canny_image] * num_samples		)

         UpperCAmelCase__								=  jax.random.PRNGKey(0		)
         UpperCAmelCase__								=  jax.random.split(__UpperCAmelCase	,  jax.device_count()		)

         UpperCAmelCase__								=  replicate(__UpperCAmelCase		)
         UpperCAmelCase__								=  shard(__UpperCAmelCase		)
         UpperCAmelCase__								=  shard(__UpperCAmelCase		)

         UpperCAmelCase__								=  pipe(
             prompt_ids=__UpperCAmelCase	,  image=__UpperCAmelCase	,  params=__UpperCAmelCase	,  prng_seed=__UpperCAmelCase	,  num_inference_steps=5_0	,  jit=__UpperCAmelCase	,  ).images
         assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

         UpperCAmelCase__								=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
         UpperCAmelCase__								=  images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         UpperCAmelCase__								=  jnp.asarray(jax.device_get(image_slice.flatten()		)		)
         UpperCAmelCase__								=  jnp.array(
             [0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078]		)
         print(f"""output_slice: {output_slice}"""		)
         assert jnp.abs(output_slice - expected_slice		).max() < 1E-2





   def       lowercase_				(self					:						Any		)						->    str:
         """simple docstring"""



         UpperCAmelCase__		,				UpperCAmelCase__								=  FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-openpose"	,  from_pt=__UpperCAmelCase	,  dtype=jnp.bfloataa		)
         UpperCAmelCase__		,				UpperCAmelCase__								=  FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"	,  controlnet=__UpperCAmelCase	,  from_pt=__UpperCAmelCase	,  dtype=jnp.bfloataa		)
         UpperCAmelCase__								=  controlnet_params

         UpperCAmelCase__								=  "Chef in the kitchen"
         UpperCAmelCase__								=  jax.device_count()
         UpperCAmelCase__								=  pipe.prepare_text_inputs([prompts] * num_samples		)

         UpperCAmelCase__								=  load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"		)
         UpperCAmelCase__								=  pipe.prepare_image_inputs([pose_image] * num_samples		)

         UpperCAmelCase__								=  jax.random.PRNGKey(0		)
         UpperCAmelCase__								=  jax.random.split(__UpperCAmelCase	,  jax.device_count()		)

         UpperCAmelCase__								=  replicate(__UpperCAmelCase		)
         UpperCAmelCase__								=  shard(__UpperCAmelCase		)
         UpperCAmelCase__								=  shard(__UpperCAmelCase		)

         UpperCAmelCase__								=  pipe(
             prompt_ids=__UpperCAmelCase	,  image=__UpperCAmelCase	,  params=__UpperCAmelCase	,  prng_seed=__UpperCAmelCase	,  num_inference_steps=5_0	,  jit=__UpperCAmelCase	,  ).images
         assert images.shape == (jax.device_count(), 1, 7_6_8, 5_1_2, 3)

         UpperCAmelCase__								=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
         UpperCAmelCase__								=  images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         UpperCAmelCase__								=  jnp.asarray(jax.device_get(image_slice.flatten()		)		)
         UpperCAmelCase__								=  jnp.array(
             [[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]]		)
         print(f"""output_slice: {output_slice}"""		)
         assert jnp.abs(output_slice - expected_slice		).max() < 1E-2




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

import yaml





class 					A							(    yaml.SafeLoader					):


   def       lowercase_				(self					:						Tuple	,  __UpperCAmelCase					:						str		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  [self.constructed_objects[key_node] for key_node, _ in node.value]
         UpperCAmelCase__								=  [tuple(__UpperCAmelCase		) if isinstance(__UpperCAmelCase	,  __UpperCAmelCase		) else key for key in keys]
         UpperCAmelCase__								=  Counter(__UpperCAmelCase		)
         UpperCAmelCase__								=  [key for key in counter if counter[key] > 1]
         if duplicate_keys:
               raise TypeError(f"""Got duplicate yaml keys: {duplicate_keys}"""		)





   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						Any=False		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  super().construct_mapping(__UpperCAmelCase	,  deep=__UpperCAmelCase		)
         self._check_no_duplicates_on_constructed_node(__UpperCAmelCase		)
         return mapping




def        lowerCAmelCase_			(					__A							)					->    Tuple[Optional[str], str]:







      '''simple docstring'''


      UpperCAmelCase__								=  list(readme_content.splitlines()							)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            UpperCAmelCase__								=  full_content[1:].index("---"							) + 1
            UpperCAmelCase__								=  "\n".join(full_content[1:sep_idx]							)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]							)

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





class 					A							(    UpperCAmelCase_					):
   # class attributes
   __UpperCAmelCase			:			Optional[Any]              =      {'train_eval_index'}  # train-eval-index in the YAML metadata


   @classmethod
   def       lowercase_				(cls					:						List[str]	,  __UpperCAmelCase					:						Path		)						->    "DatasetMetadata":
         """simple docstring"""



         with open(__UpperCAmelCase	,  encoding="utf-8"		) as readme_file:
               UpperCAmelCase__		,				UpperCAmelCase__								=  _split_yaml_from_readme(readme_file.read()		)
         if yaml_string is not None:
               return cls.from_yaml_string(__UpperCAmelCase		)
         else:
               return cls()


   def       lowercase_				(self					:						int	,  __UpperCAmelCase					:						Path		)						->    str:
         """simple docstring"""



         if path.exists():
               with open(__UpperCAmelCase	,  encoding="utf-8"		) as readme_file:
                     UpperCAmelCase__								=  readme_file.read()
         else:
               UpperCAmelCase__								=  None
         UpperCAmelCase__								=  self._to_readme(__UpperCAmelCase		)
         with open(__UpperCAmelCase	,  "w"	,  encoding="utf-8"		) as readme_file:
               readme_file.write(__UpperCAmelCase		)


   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						Optional[str] = None		)						->    str:
         """simple docstring"""



         if readme_content is not None:
               UpperCAmelCase__		,				UpperCAmelCase__								=  _split_yaml_from_readme(__UpperCAmelCase		)
               UpperCAmelCase__								=  "---\n" + self.to_yaml_string() + "---\n" + content
         else:
               UpperCAmelCase__								=  "---\n" + self.to_yaml_string() + "---\n"
         return full_content


   @classmethod
   def       lowercase_				(cls					:						Optional[int]	,  __UpperCAmelCase					:						str		)						->    "DatasetMetadata":
         """simple docstring"""



         UpperCAmelCase__								=  yaml.load(__UpperCAmelCase	,  Loader=_NoDuplicateSafeLoader		) or {}

         # Convert the YAML keys to DatasetMetadata fields
         UpperCAmelCase__								=  {
             (key.replace("-"	,  "_"		) if key.replace("-"	,  "_"		) in cls._FIELDS_WITH_DASHES else key): value
             for key, value in metadata_dict.items()
         }
         return cls(**__UpperCAmelCase		)





   def       lowercase_				(self					:						List[str]		)						->    str:
         """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=__UpperCAmelCase	,  allow_unicode=__UpperCAmelCase	,  encoding="utf-8"	,  ).decode("utf-8"		)




UpperCamelCase__    							= {
    '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

      UpperCamelCase__    							= ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
      ap.add_argument('readme_filepath')
      UpperCamelCase__    							= ap.parse_args()

      UpperCamelCase__    							= Path(args.readme_filepath)
      UpperCamelCase__    							= DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)



