


'''simple docstring'''





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

import yaml






class     UpperCamelCase(  yaml.SafeLoader   ):


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	int:




											'''simple docstring'''

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






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Tuple=False						)			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		super().construct_mapping(SCREAMING_SNAKE_CASE     ,    deep=SCREAMING_SNAKE_CASE						)
											self._check_no_duplicates_on_constructed_node(SCREAMING_SNAKE_CASE						)
											return mapping





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


						'''simple docstring'''






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

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






class     UpperCamelCase(  _a   ):
					# class attributes
					snake_case_		:				Union[str, Any]										=	{"""train_eval_index"""}  # train-eval-index in the YAML metadata


					@classmethod
					def 							SCREAMING_SNAKE_CASE_  (						cls	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Path						)			->	"DatasetMetadata":




											'''simple docstring'''

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Path						)			->	Optional[int]:




											'''simple docstring'''

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		Optional[str] = None						)			->	str:




											'''simple docstring'''

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


					@classmethod
					def 							SCREAMING_SNAKE_CASE_  (						cls	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	"DatasetMetadata":




											'''simple docstring'''

											__snake_case				=		yaml.load(SCREAMING_SNAKE_CASE     ,    Loader=_NoDuplicateSafeLoader						) or {}

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






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	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=SCREAMING_SNAKE_CASE     ,    allow_unicode=SCREAMING_SNAKE_CASE     ,    encoding="utf-8"     ,    ).decode("utf-8"						)




A							:					Dict     						=     {
    '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

	A							:					Optional[int]     						=     ArgumentParser(usage='Validate the yaml metadata block of a README.md file.')
	ap.add_argument('readme_filepath')
	A							:					Optional[Any]     						=     ap.parse_args()

	A							:					Any     						=     Path(args.readme_filepath)
	A							:					Union[str, Any]     						=     DatasetMetadata.from_readme(readme_filepath)
	print(dataset_metadata)
	dataset_metadata.to_readme(readme_filepath)









import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A							:					int     						=     logging.get_logger(__name__)  # pylint: disable=invalid-name






class     UpperCamelCase(  _a   ):


					def __init__(						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Dict=7_6_8						)			->	int:




											'''simple docstring'''

											super().__init__(SCREAMING_SNAKE_CASE						)
											__snake_case				=		proj_size

											__snake_case				=		CLIPVisionModel(SCREAMING_SNAKE_CASE						)
											__snake_case				=		PaintByExampleMapper(SCREAMING_SNAKE_CASE						)
											__snake_case				=		nn.LayerNorm(config.hidden_size						)
											__snake_case				=		nn.Linear(config.hidden_size     ,    self.proj_size						)

											# uncondition for scaling
											__snake_case				=		nn.Parameter(torch.randn((1, 1, self.proj_size)						)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=False						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		self.model(pixel_values=SCREAMING_SNAKE_CASE						)
											__snake_case				=		clip_output.pooler_output
											__snake_case				=		self.mapper(latent_states[:, None]						)
											__snake_case				=		self.final_layer_norm(SCREAMING_SNAKE_CASE						)
											__snake_case				=		self.proj_out(SCREAMING_SNAKE_CASE						)
											if return_uncond_vector:
																	return latent_states, self.uncond_vector

											return latent_states






class     UpperCamelCase(  nn.Module   ):


					def __init__(						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		List[str]						)			->	List[Any]:




											'''simple docstring'''

											super().__init__()
											__snake_case				=		(config.num_hidden_layers + 1) // 5
											__snake_case				=		config.hidden_size
											__snake_case				=		1
											__snake_case				=		nn.ModuleList(
											    [
											        BasicTransformerBlock(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    activation_fn="gelu"     ,    attention_bias=SCREAMING_SNAKE_CASE						)
											        for _ in range(SCREAMING_SNAKE_CASE						)
											    ]						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		str						)			->	Optional[int]:




											'''simple docstring'''

											for block in self.blocks:
																	__snake_case				=		block(SCREAMING_SNAKE_CASE						)

											return hidden_states





