


"""simple docstring"""






import json

import requests

from .fetch_github_info import AUTHENTICATED_USER_ENDPOINT, fetch_github_info



def 			A__		(					_UpperCAmelCase    :      Optional[int]	)							->	Tuple:

	'''simple docstring'''


	class 			SCREAMING_SNAKE_CASE_  :




								'''simple docstring'''





								def __init__(						self	,					lowerCamelCase__)   ->	None:







									'''simple docstring'''




									assert isinstance(lowerCamelCase__	,					(bytes, str))
									snake_case__  :						int		=      content







								def        UpperCAmelCase (						self)   ->	Union[str, Any]:







									'''simple docstring'''




									return json.loads(self.content)

	def mock_response(*_UpperCAmelCase    :      str				,    **_UpperCAmelCase    :      Optional[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(_UpperCAmelCase				,    "get"				,    _UpperCAmelCase	)
	snake_case__  :						str		=      fetch_github_info("token"	)
	assert result["login"] == "test"
	assert result["id"] == 1









"""simple docstring"""






from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     Union[str, Any]				  =  ['''image_processor''', '''tokenizer''']
							__magic_name__					:     Optional[int]				  =  '''EfficientNetImageProcessor'''
							__magic_name__					:     Optional[Any]				  =  ('''BertTokenizer''', '''BertTokenizerFast''')




							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								super().__init__(lowerCamelCase__	,					lowerCamelCase__)




							def __call__(						self	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__="max_length"	,					lowerCamelCase__=64	,					lowerCamelCase__=None	,					**lowerCamelCase__)   ->	List[str]:







								'''simple docstring'''




								if text is None and images is None:
									raise ValueError("You have to specify either text or images. Both cannot be none.")

								if text is not None:
									snake_case__  :						int		=      self.tokenizer(
									    lowerCamelCase__	,					padding=lowerCamelCase__	,					max_length=lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if images is not None:
									snake_case__  :						int		=      self.image_processor(lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if text is not None and images is not None:
									snake_case__  :						List[str]		=      image_features.pixel_values
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**lowerCamelCase__)	,					tensor_type=lowerCamelCase__)




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	int:







								'''simple docstring'''




								return self.tokenizer.batch_decode(*lowerCamelCase__	,					**lowerCamelCase__)




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	Optional[Any]:







								'''simple docstring'''




								return self.tokenizer.decode(*lowerCamelCase__	,					**lowerCamelCase__)







							@property
							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.tokenizer.model_input_names
								snake_case__  :						Optional[int]		=      self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





