import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()





class 					A							(    UpperCAmelCase_		,    unittest.TestCase					):
   __UpperCAmelCase			:			Any              =      UnCLIPImageVariationPipeline
   __UpperCAmelCase			:			str              =      IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
   __UpperCAmelCase			:			Any              =      IMAGE_VARIATION_BATCH_PARAMS

   __UpperCAmelCase			:			str              =      [
       'generator',
       'return_dict',
       'decoder_num_inference_steps',
       'super_res_num_inference_steps',
   ]
   __UpperCAmelCase			:			Optional[int]              =      False


   @property
   def       lowercase_				(self					:						Tuple		)						->    Any:
         """simple docstring"""



         return 3_2


   @property
   def       lowercase_				(self					:						Dict		)						->    List[Any]:
         """simple docstring"""



         return 3_2


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



         return self.time_input_dim


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



         return self.time_input_dim * 4


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



         return 1_0_0


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



         UpperCAmelCase__								=  CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)
         return tokenizer


   @property
   def       lowercase_				(self					:						Tuple		)						->    Dict:
         """simple docstring"""



         torch.manual_seed(0		)
         UpperCAmelCase__								=  CLIPTextConfig(
             bos_token_id=0	,  eos_token_id=2	,  hidden_size=self.text_embedder_hidden_size	,  projection_dim=self.text_embedder_hidden_size	,  intermediate_size=3_7	,  layer_norm_eps=1E-05	,  num_attention_heads=4	,  num_hidden_layers=5	,  pad_token_id=1	,  vocab_size=1_0_0_0	,  )
         return CLIPTextModelWithProjection(__UpperCAmelCase		)


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



         torch.manual_seed(0		)
         UpperCAmelCase__								=  CLIPVisionConfig(
             hidden_size=self.text_embedder_hidden_size	,  projection_dim=self.text_embedder_hidden_size	,  num_hidden_layers=5	,  num_attention_heads=4	,  image_size=3_2	,  intermediate_size=3_7	,  patch_size=1	,  )
         return CLIPVisionModelWithProjection(__UpperCAmelCase		)


   @property
   def       lowercase_				(self					:						List[Any]		)						->    Dict:
         """simple docstring"""



         torch.manual_seed(0		)

         UpperCAmelCase__								=  {
             "clip_embeddings_dim": self.text_embedder_hidden_size,
             "time_embed_dim": self.time_embed_dim,
             "cross_attention_dim": self.cross_attention_dim,
         }

         UpperCAmelCase__								=  UnCLIPTextProjModel(**__UpperCAmelCase		)
         return model


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



         torch.manual_seed(0		)

         UpperCAmelCase__								=  {
             "sample_size": 3_2,
             # RGB in channels
             "in_channels": 3,
             # Out channels is double in channels because predicts mean and variance
             "out_channels": 6,
             "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,
             "cross_attention_dim": self.cross_attention_dim,
             "attention_head_dim": 4,
             "resnet_time_scale_shift": "scale_shift",
             "class_embed_type": "identity",
         }

         UpperCAmelCase__								=  UNetaDConditionModel(**__UpperCAmelCase		)
         return model


   @property
   def       lowercase_				(self					:						Tuple		)						->    List[str]:
         """simple docstring"""



         return {
             "sample_size": 6_4,
             "layers_per_block": 1,
             "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
             "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
             "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
             "in_channels": 6,
             "out_channels": 3,
         }


   @property
   def       lowercase_				(self					:						str		)						->    List[Any]:
         """simple docstring"""



         torch.manual_seed(0		)

         UpperCAmelCase__								=  UNetaDModel(**self.dummy_super_res_kwargs		)
         return model


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



         torch.manual_seed(1		)

         UpperCAmelCase__								=  UNetaDModel(**self.dummy_super_res_kwargs		)
         return model


   def       lowercase_				(self					:						List[str]		)						->    str:
         """simple docstring"""



         UpperCAmelCase__								=  self.dummy_decoder
         UpperCAmelCase__								=  self.dummy_text_proj
         UpperCAmelCase__								=  self.dummy_text_encoder
         UpperCAmelCase__								=  self.dummy_tokenizer
         UpperCAmelCase__								=  self.dummy_super_res_first
         UpperCAmelCase__								=  self.dummy_super_res_last

         UpperCAmelCase__								=  UnCLIPScheduler(
             variance_type="learned_range"	,  prediction_type="epsilon"	,  num_train_timesteps=1_0_0_0	,  )

         UpperCAmelCase__								=  UnCLIPScheduler(
             variance_type="fixed_small_log"	,  prediction_type="epsilon"	,  num_train_timesteps=1_0_0_0	,  )

         UpperCAmelCase__								=  CLIPImageProcessor(crop_size=3_2	,  size=3_2		)

         UpperCAmelCase__								=  self.dummy_image_encoder

         return {
             "decoder": decoder,
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "text_proj": text_proj,
             "feature_extractor": feature_extractor,
             "image_encoder": image_encoder,
             "super_res_first": super_res_first,
             "super_res_last": super_res_last,
             "decoder_scheduler": decoder_scheduler,
             "super_res_scheduler": super_res_scheduler,
         }


   def       lowercase_				(self					:						Union[str, Any]	,  __UpperCAmelCase					:						Optional[int]	,  __UpperCAmelCase					:						int=0	,  __UpperCAmelCase					:						Optional[Any]=True		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  floats_tensor((1, 3, 3_2, 3_2)	,  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		)

         if pil_image:
               UpperCAmelCase__								=  input_image * 0.5 + 0.5
               UpperCAmelCase__								=  input_image.clamp(0	,  1		)
               UpperCAmelCase__								=  input_image.cpu().permute(0	,  2	,  3	,  1		).float().numpy()
               UpperCAmelCase__								=  DiffusionPipeline.numpy_to_pil(__UpperCAmelCase		)[0]

         return {
             "image": input_image,
             "generator": generator,
             "decoder_num_inference_steps": 2,
             "super_res_num_inference_steps": 2,
             "output_type": "np",
         }


   def       lowercase_				(self					:						Dict		)						->    str:
         """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__								=  self.get_dummy_inputs(__UpperCAmelCase	,  pil_image=__UpperCAmelCase		)

         UpperCAmelCase__								=  pipe(**__UpperCAmelCase		)
         UpperCAmelCase__								=  output.images

         UpperCAmelCase__								=  self.get_dummy_inputs(__UpperCAmelCase	,  pil_image=__UpperCAmelCase		)

         UpperCAmelCase__								=  pipe(
             **__UpperCAmelCase	,  return_dict=__UpperCAmelCase	,  )[0]

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

         assert image.shape == (1, 6_4, 6_4, 3)

         UpperCAmelCase__								=  np.array(
             [
                 0.9997,
                 0.0002,
                 0.9997,
                 0.9997,
                 0.9969,
                 0.0023,
                 0.9997,
                 0.9969,
                 0.9970,
             ]		)

         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       lowercase_				(self					:						int		)						->    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__								=  self.get_dummy_inputs(__UpperCAmelCase	,  pil_image=__UpperCAmelCase		)

         UpperCAmelCase__								=  pipe(**__UpperCAmelCase		)
         UpperCAmelCase__								=  output.images

         UpperCAmelCase__								=  self.get_dummy_inputs(__UpperCAmelCase	,  pil_image=__UpperCAmelCase		)

         UpperCAmelCase__								=  pipe(
             **__UpperCAmelCase	,  return_dict=__UpperCAmelCase	,  )[0]

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

         assert image.shape == (1, 6_4, 6_4, 3)

         UpperCAmelCase__								=  np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]		)

         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       lowercase_				(self					:						Any		)						->    Optional[int]:
         """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__								=  self.get_dummy_inputs(__UpperCAmelCase	,  pil_image=__UpperCAmelCase		)
         UpperCAmelCase__								=  [
             pipeline_inputs["image"],
             pipeline_inputs["image"],
         ]

         UpperCAmelCase__								=  pipe(**__UpperCAmelCase		)
         UpperCAmelCase__								=  output.images

         UpperCAmelCase__								=  self.get_dummy_inputs(__UpperCAmelCase	,  pil_image=__UpperCAmelCase		)
         UpperCAmelCase__								=  [
             tuple_pipeline_inputs["image"],
             tuple_pipeline_inputs["image"],
         ]

         UpperCAmelCase__								=  pipe(
             **__UpperCAmelCase	,  return_dict=__UpperCAmelCase	,  )[0]

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

         assert image.shape == (2, 6_4, 6_4, 3)

         UpperCAmelCase__								=  np.array(
             [
                 0.9997,
                 0.9989,
                 0.0008,
                 0.0021,
                 0.9960,
                 0.0018,
                 0.0014,
                 0.0002,
                 0.9933,
             ]		)

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



         UpperCAmelCase__								=  torch.device("cpu"		)





         class 					A							:
            __UpperCAmelCase			:			Optional[int]              =      1




         UpperCAmelCase__								=  self.get_dummy_components()

         UpperCAmelCase__								=  self.pipeline_class(**__UpperCAmelCase		)
         UpperCAmelCase__								=  pipe.to(__UpperCAmelCase		)

         pipe.set_progress_bar_config(disable=__UpperCAmelCase		)

         UpperCAmelCase__								=  torch.Generator(device=__UpperCAmelCase		).manual_seed(0		)
         UpperCAmelCase__								=  pipe.decoder.dtype
         UpperCAmelCase__								=  1

         UpperCAmelCase__								=  (
             batch_size,
             pipe.decoder.config.in_channels,
             pipe.decoder.config.sample_size,
             pipe.decoder.config.sample_size,
         )
         UpperCAmelCase__								=  pipe.prepare_latents(
             __UpperCAmelCase	,  dtype=__UpperCAmelCase	,  device=__UpperCAmelCase	,  generator=__UpperCAmelCase	,  latents=__UpperCAmelCase	,  scheduler=DummyScheduler()		)

         UpperCAmelCase__								=  (
             batch_size,
             pipe.super_res_first.config.in_channels // 2,
             pipe.super_res_first.config.sample_size,
             pipe.super_res_first.config.sample_size,
         )
         UpperCAmelCase__								=  pipe.prepare_latents(
             __UpperCAmelCase	,  dtype=__UpperCAmelCase	,  device=__UpperCAmelCase	,  generator=__UpperCAmelCase	,  latents=__UpperCAmelCase	,  scheduler=DummyScheduler()		)

         UpperCAmelCase__								=  self.get_dummy_inputs(__UpperCAmelCase	,  pil_image=__UpperCAmelCase		)

         UpperCAmelCase__								=  pipe(
             **__UpperCAmelCase	,  decoder_latents=__UpperCAmelCase	,  super_res_latents=__UpperCAmelCase		).images

         UpperCAmelCase__								=  self.get_dummy_inputs(__UpperCAmelCase	,  pil_image=__UpperCAmelCase		)
         # Don't pass image, instead pass embedding
         UpperCAmelCase__								=  pipeline_inputs.pop("image"		)
         UpperCAmelCase__								=  pipe.image_encoder(__UpperCAmelCase		).image_embeds

         UpperCAmelCase__								=  pipe(
             **__UpperCAmelCase	,  decoder_latents=__UpperCAmelCase	,  super_res_latents=__UpperCAmelCase	,  image_embeddings=__UpperCAmelCase	,  ).images

         # make sure passing text embeddings manually is identical
         assert np.abs(img_out_a - img_out_a		).max() < 1E-4


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



         UpperCAmelCase__								=  torch_device == "cpu"

         # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
         UpperCAmelCase__								=  1E-2

         self._test_attention_slicing_forward_pass(
             test_max_difference=__UpperCAmelCase	,  expected_max_diff=__UpperCAmelCase		)


   @skip_mps
   def       lowercase_				(self					:						Dict		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  torch_device == "cpu"
         UpperCAmelCase__								=  True
         UpperCAmelCase__								=  [
             "decoder_num_inference_steps",
             "super_res_num_inference_steps",
         ]

         self._test_inference_batch_single_identical(
             test_max_difference=__UpperCAmelCase	,  relax_max_difference=__UpperCAmelCase	,  additional_params_copy_to_batched_inputs=__UpperCAmelCase	,  )


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



         UpperCAmelCase__								=  [
             "decoder_num_inference_steps",
             "super_res_num_inference_steps",
         ]

         if torch_device == "mps":
               # TODO: MPS errors with larger batch sizes
               UpperCAmelCase__								=  [2, 3]
               self._test_inference_batch_consistent(
                   batch_sizes=__UpperCAmelCase	,  additional_params_copy_to_batched_inputs=__UpperCAmelCase	,  )
         else:
               self._test_inference_batch_consistent(
                   additional_params_copy_to_batched_inputs=__UpperCAmelCase		)


   @skip_mps
   def       lowercase_				(self					:						str		)						->    int:
         """simple docstring"""



         return super().test_dict_tuple_outputs_equivalent()


   @skip_mps
   def       lowercase_				(self					:						Dict		)						->    Tuple:
         """simple docstring"""



         return super().test_save_load_local()





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



         return super().test_save_load_optional_components()





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


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



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





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



         UpperCAmelCase__								=  load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"		)
         UpperCAmelCase__								=  load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/unclip/karlo_v1_alpha_cat_variation_fp16.npy"		)

         UpperCAmelCase__								=  UnCLIPImageVariationPipeline.from_pretrained(
             "kakaobrain/karlo-v1-alpha-image-variations"	,  torch_dtype=torch.floataa		)
         UpperCAmelCase__								=  pipeline.to(__UpperCAmelCase		)
         pipeline.set_progress_bar_config(disable=__UpperCAmelCase		)

         UpperCAmelCase__								=  torch.Generator(device="cpu"		).manual_seed(0		)
         UpperCAmelCase__								=  pipeline(
             __UpperCAmelCase	,  generator=__UpperCAmelCase	,  output_type="np"	,  )

         UpperCAmelCase__								=  output.images[0]

         assert image.shape == (2_5_6, 2_5_6, 3)

         assert_mean_pixel_difference(__UpperCAmelCase	,  __UpperCAmelCase	,  1_5		)




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)



