
import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


__lowerCamelCase							=       """
Hugging 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.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

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.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""






class   UpperCAmelCase		(						unittest.TestCase       ,A_ ):



   def 				_SCREAMING_SNAKE_CASE   (self		:					Dict )		->	int:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      load_tool("text-question-answering" )
          self.tool.setup()
          snake_case       :   str							=      load_tool("text-question-answering"    ,						remote=snake_case__ )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	int:


          '''simple docstring'''


          snake_case       :   Tuple							=      self.tool(snake_case__    ,						"What did Hugging Face do in April 2021?" )
          self.assertEqual(snake_case__    ,						"launched the BigScience Research Workshop" )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   Optional[int]							=      self.remote_tool(snake_case__    ,						"What did Hugging Face do in April 2021?" )
          self.assertEqual(snake_case__    ,						"launched the BigScience Research Workshop" )



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	int:


          '''simple docstring'''


          snake_case       :   Dict							=      self.tool(text=snake_case__    ,						question="What did Hugging Face do in April 2021?" )
          self.assertEqual(snake_case__    ,						"launched the BigScience Research Workshop" )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	int:


          '''simple docstring'''


          snake_case       :   List[Any]							=      self.remote_tool(text=snake_case__    ,						question="What did Hugging Face do in April 2021?" )
          self.assertEqual(snake_case__    ,						"launched the BigScience Research Workshop" )








from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a__				=		logging.get_logger(__name__)

a__				=		{
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}

class 				UpperCAmelCase_		(			__lowercase				,       __lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    "bit"
							UpperCAmelCase__       :						Optional[int]	    =    ["preactivation", "bottleneck"]
							UpperCAmelCase__       :						Optional[Any]	    =    ["SAME", "VALID"]


							def __init__(			self       ,							_a=3       ,							_a=6_4       ,							_a=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]       ,							_a=[3, 4, 6, 3]       ,							_a="preactivation"       ,							_a="relu"       ,							_a=None       ,							_a=3_2       ,							_a=0.0       ,							_a=False       ,							_a=3_2       ,							_a=1       ,							_a=None       ,							_a=None       ,							**_a       ,							)    ->    Union[str, Any]:
												super().__init__(**_a       )
												if layer_type not in self.layer_types:
																	raise ValueError(F"""layer_type={layer_type} is not one of {','.join(self.layer_types       )}"""       )
												if global_padding is not None:
																	if global_padding.upper() in self.supported_padding:
																						_a	:    Any         =		global_padding.upper()
																	else:
																						raise ValueError(F"""Padding strategy {global_padding} not supported"""       )
												_a	:    Optional[int]         =		num_channels
												_a	:    List[Any]         =		embedding_size
												_a	:    Any         =		hidden_sizes
												_a	:    int         =		depths
												_a	:    Dict         =		layer_type
												_a	:    int         =		hidden_act
												_a	:    Optional[Any]         =		global_padding
												_a	:    Optional[Any]         =		num_groups
												_a	:    Union[str, Any]         =		drop_path_rate
												_a	:    Tuple         =		embedding_dynamic_padding
												_a	:    Union[str, Any]         =		output_stride
												_a	:    Any         =		width_factor

												_a	:    Any         =		['''stem'''] + [F"""stage{idx}""" for idx in range(1       ,							len(_a       ) + 1       )]
												_a      ,					_a	:    List[str]         =		get_aligned_output_features_output_indices(
												    out_features=_a       ,							out_indices=_a       ,							stage_names=self.stage_names       )



