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

import yaml

class        _UpperCamelCase				(					yaml.SafeLoader  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       int						,	snake_case_ :       List[str]      ):
							UpperCamelCase_:							Any	=						[self.constructed_objects[key_node] for key_node, _ in node.value]
							UpperCamelCase_:							Any	=						[tuple(snake_case_      ) if isinstance(snake_case_						,	snake_case_      ) else key for key in keys]
							UpperCamelCase_:							Dict	=						Counter(snake_case_      )
							UpperCamelCase_:							List[str]	=						[key for key in counter if counter[key] > 1]
							if duplicate_keys:
											raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''      )




			def       lowerCAmelCase__	(       self :       Tuple						,	snake_case_ :       str						,	snake_case_ :       Optional[int]=False      ):
							UpperCamelCase_:							Tuple	=						super().construct_mapping(snake_case_						,	deep=snake_case_      )
							self._check_no_duplicates_on_constructed_node(snake_case_      )
							return mapping







def        A__					(						lowerCamelCase						)  ->	List[str]:
				UpperCamelCase_:							Dict	=						list(readme_content.splitlines()						)
				if full_content and full_content[0] == "---" and "---" in full_content[1:]:
								UpperCamelCase_:							Union[str, Any]	=						full_content[1:].index("""---"""						) + 1
								UpperCamelCase_:							List[str]	=						"""\n""".join(full_content[1:sep_idx]						)
								return yamlblock, "\n".join(full_content[sep_idx + 1 :]						)

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

class        _UpperCamelCase				(					UpperCAmelCase__  ):






			'''simple docstring'''

			__UpperCamelCase      :						Tuple					      = {'train_eval_index'}  # train-eval-index in the YAML metadata

			@classmethod
			def       lowerCAmelCase__	(       cls :       List[Any]						,	snake_case_ :       Path      ):




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

			def       lowerCAmelCase__	(       self :       Any						,	snake_case_ :       Path      ):




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

			def       lowerCAmelCase__	(       self :       Optional[Any]						,	snake_case_ :       Optional[str] = None      ):




							if readme_content is not None:
											UpperCamelCase_:							List[str]	=						_split_yaml_from_readme(snake_case_      )
											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       lowerCAmelCase__	(       cls :       Optional[int]						,	snake_case_ :       str      ):
							UpperCamelCase_:							int	=						yaml.load(snake_case_						,	Loader=_NoDuplicateSafeLoader      ) or {}

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




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




lowerCamelCase_						: List[str]         =  {
    """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_						: Dict         =  ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
							ap.add_argument("""readme_filepath""")
							lowerCamelCase_						: Union[str, Any]         =  ap.parse_args()

							lowerCamelCase_						: Dict         =  Path(args.readme_filepath)
							lowerCamelCase_						: Any         =  DatasetMetadata.from_readme(readme_filepath)
							print(dataset_metadata)
							dataset_metadata.to_readme(readme_filepath)



import unittest

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

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()

class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			@property
			def       lowerCAmelCase__	(       self :       int      ):
							torch.manual_seed(0      )
							UpperCamelCase_:							Any	=						UNetaDModel(
							    block_out_channels=(32, 64)						,	layers_per_block=2						,	sample_size=32						,	in_channels=3						,	out_channels=3						,	down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")						,	up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")						,	)
							return model

			@property
			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							torch.manual_seed(0      )
							UpperCamelCase_:							Union[str, Any]	=						VQModel(
							    block_out_channels=[32, 64]						,	in_channels=3						,	out_channels=3						,	down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]						,	up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]						,	latent_channels=3						,	)
							return model

			@property
			def       lowerCAmelCase__	(       self :       Any      ):
							torch.manual_seed(0      )
							UpperCamelCase_:							List[str]	=						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=1000						,	)
							return CLIPTextModel(snake_case_      )




			def       lowerCAmelCase__	(       self :       Dict      ):
							UpperCamelCase_:							Union[str, Any]	=						self.dummy_uncond_unet
							UpperCamelCase_:							Optional[Any]	=						DDIMScheduler()
							UpperCamelCase_:							List[str]	=						self.dummy_vq_model

							UpperCamelCase_:							List[Any]	=						LDMPipeline(unet=snake_case_						,	vqvae=snake_case_						,	scheduler=snake_case_      )
							ldm.to(snake_case_      )
							ldm.set_progress_bar_config(disable=snake_case_      )

							UpperCamelCase_:							str	=						torch.manual_seed(0      )
							UpperCamelCase_:							int	=						ldm(generator=snake_case_						,	num_inference_steps=2						,	output_type="""numpy"""      ).images

							UpperCamelCase_:							Dict	=						torch.manual_seed(0      )
							UpperCamelCase_:							str	=						ldm(generator=snake_case_						,	num_inference_steps=2						,	output_type="""numpy"""						,	return_dict=snake_case_      )[0]

							UpperCamelCase_:							List[Any]	=						image[0, -3:, -3:, -1]
							UpperCamelCase_:							Optional[int]	=						image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)
							UpperCamelCase_:							str	=						np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172]      )
							UpperCamelCase_:							Optional[Any]	=						1e-2 if torch_device != """mps""" else 3e-2

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < tolerance
							assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < tolerance




@slow
@require_torch
class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							Dict	=						LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""      )
							ldm.to(snake_case_      )
							ldm.set_progress_bar_config(disable=snake_case_      )

							UpperCamelCase_:							List[str]	=						torch.manual_seed(0      )
							UpperCamelCase_:							Optional[int]	=						ldm(generator=snake_case_						,	num_inference_steps=5						,	output_type="""numpy"""      ).images

							UpperCamelCase_:							List[Any]	=						image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)
							UpperCamelCase_:							List[str]	=						np.array([0.4399, 0.4_4975, 0.4_6825, 0.474, 0.4359, 0.4581, 0.4_5095, 0.4341, 0.4447]      )
							UpperCamelCase_:							Dict	=						1e-2 if torch_device != """mps""" else 3e-2

							assert np.abs(image_slice.flatten() - expected_slice      ).max() < tolerance






