
import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: Optional[Any]     )	->		str:
				class       A__  :





											def __init__(					self    :  List[Any]	,					_UpperCAmelCase    :  Union[str, Any]							)		->						None:



															"""simple docstring"""


															assert isinstance(_UpperCAmelCase	,					(bytes, str)							)
															__lowercase								=   content





											def      a__					(					self    :  Any							)		->						Tuple:



															"""simple docstring"""


															return json.loads(self.content							)

				def mock_response(*SCREAMING_SNAKE_CASE			: Dict       ,				**SCREAMING_SNAKE_CASE			: int     ):
								assert args[0] == AUTHENTICATED_USER_ENDPOINT
								assert "Authorization" in kwargs["headers"]
								assert kwargs["headers"]["Authorization"].startswith('token '     )
								assert "Accept" in kwargs["headers"]
								return FakeResponse(b'{"login":"test","id":1}'     )

				monkeypatch.setattr(SCREAMING_SNAKE_CASE       ,				'get'       ,				SCREAMING_SNAKE_CASE     )
				__lowercase								=   fetch_github_info('token'     )
				assert result["login"] == "test"
				assert result["id"] == 1







from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
							from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore
class       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		Any            =				c_float





							@property
							def      a__					(					self    :  List[str]							)		->						int:



											"""simple docstring"""


											return sizeof(self.value							)






def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: int = 0     )	->		int:
				# < 0 is possible if using a dynamic axis
				if dimension <= 0:
								__lowercase								=   fixed_dimension

				dimension -= num_token_to_add
				return dimension




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: int       ,				SCREAMING_SNAKE_CASE			: ParameterFormat     )	->		int:
				return num_parameters * dtype.size




def 				__SCREAMING_SNAKE_CASE  (						SCREAMING_SNAKE_CASE			: str     )	->		Optional[Union["AutoTokenizer", "AutoFeatureExtractor", "AutoProcessor"]]:
				from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

				try:
								return AutoProcessor.from_pretrained(SCREAMING_SNAKE_CASE     )
				except (ValueError, OSError, KeyError):
								__lowercase						,   __lowercase								=   None, None
								try:
												__lowercase								=   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE     )
								except (OSError, KeyError):
												pass
								try:
												__lowercase								=   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE     )
								except (OSError, KeyError):
												pass

								if tokenizer is not None and feature_extractor is not None:
												raise ValueError(
												    F"""Couldn't auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor."""     )
								elif tokenizer is None and feature_extractor is None:
												return None
								elif tokenizer is not None:
												return tokenizer
								else:
												return feature_extractor





