
import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


__SCREAMING_SNAKE_CASE			:   Tuple         =     "\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n"


class 		__magic_name__   (						unittest.TestCase,				SCREAMING_SNAKE_CASE__    ):





  def       _A							(							self    :	Any				):
       lowerCAmelCase		:					Tuple  	=     load_tool('''summarization'''				)
       self.tool.setup()
       lowerCAmelCase		:					int  	=     load_tool('''summarization'''						,  remote=lowerCamelCase__				)





  def       _A							(							self    :	List[str]				):
       lowerCAmelCase		:					Union[str, Any]  	=     self.tool(lowerCamelCase__				)
       self.assertEqual(
           lowerCamelCase__						,  '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''						,  )





  def       _A							(							self    :	str				):
       lowerCAmelCase		:					int  	=     self.remote_tool(lowerCamelCase__				)
       self.assertEqual(
           lowerCamelCase__						,  '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''						,  )





  def       _A							(							self    :	Optional[int]				):
       lowerCAmelCase		:					List[str]  	=     self.tool(text=lowerCamelCase__				)
       self.assertEqual(
           lowerCamelCase__						,  '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''						,  )





  def       _A							(							self    :	Union[str, Any]				):
       lowerCAmelCase		:					int  	=     self.remote_tool(text=lowerCamelCase__				)
       self.assertEqual(
           lowerCamelCase__						,  '''Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM.'''						,  )


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 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	c_float





		@property
		def       _A							(							self    :	int				):
							return sizeof(self.value				)



def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int							,  __magic_name__						:     int = 0  ):


					'''simple docstring'''

					if dimension <= 0:
										lowerCAmelCase		:					Tuple  	=     fixed_dimension

					dimension -= num_token_to_add
					return dimension
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     ParameterFormat  ):


					'''simple docstring'''

					return num_parameters * dtype.size
def     UpperCAmelCase__							(       __magic_name__						:     str  ):


					'''simple docstring'''

					from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

					try:
										return AutoProcessor.from_pretrained(__magic_name__  )
					except (ValueError, OSError, KeyError):
										lowerCAmelCase	,							lowerCAmelCase		:					Any  	=     None, None
										try:
															lowerCAmelCase		:					Union[str, Any]  	=     AutoTokenizer.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass
										try:
															lowerCAmelCase		:					List[Any]  	=     AutoFeatureExtractor.from_pretrained(__magic_name__  )
										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
