


"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Dict   ="roberta"

       def __init__(						self		,     snake_case__=50_265		,     snake_case__=768		,     snake_case__=12		,     snake_case__=12		,     snake_case__=3_072		,     snake_case__="gelu"		,     snake_case__=0.1		,     snake_case__=0.1		,     snake_case__=512		,     snake_case__=2		,     snake_case__=0.02		,     snake_case__=1e-12		,     snake_case__=1		,     snake_case__=0		,     snake_case__=2		,     snake_case__="absolute"		,     snake_case__=True		,     snake_case__=None		,     **snake_case__		,     ):






         """simple docstring"""





         super().__init__(pad_token_id=snake_case__		,     bos_token_id=snake_case__		,     eos_token_id=snake_case__		,     **snake_case__						)

         lowerCAmelCase     :     Dict								=							vocab_size
         lowerCAmelCase     :     str								=							hidden_size
         lowerCAmelCase     :     Union[str, Any]								=							num_hidden_layers
         lowerCAmelCase     :     Dict								=							num_attention_heads
         lowerCAmelCase     :     Union[str, Any]								=							hidden_act
         lowerCAmelCase     :     Tuple								=							intermediate_size
         lowerCAmelCase     :     Union[str, Any]								=							hidden_dropout_prob
         lowerCAmelCase     :     Optional[Any]								=							attention_probs_dropout_prob
         lowerCAmelCase     :     Dict								=							max_position_embeddings
         lowerCAmelCase     :     Any								=							type_vocab_size
         lowerCAmelCase     :     Any								=							initializer_range
         lowerCAmelCase     :     List[str]								=							layer_norm_eps
         lowerCAmelCase     :     List[Any]								=							position_embedding_type
         lowerCAmelCase     :     Any								=							use_cache
         lowerCAmelCase     :     Union[str, Any]								=							classifier_dropout


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         if self.task == "multiple-choice":
           lowerCAmelCase     :     str								=							{0: "batch", 1: "choice", 2: "sequence"}
         else:
           lowerCAmelCase     :     str								=							{0: "batch", 1: "sequence"}
         return OrderedDict(
             [
                 ("input_ids", dynamic_axis),
                 ("attention_mask", dynamic_axis),
             ]						)







"""simple docstring"""




import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


_A			:       int   		=       """
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 		a__						(      unittest.TestCase,			a_						):
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							load_tool("text-question-answering"				)
          self.tool.setup()
          lowercase    :				Dict       		=							load_tool("text-question-answering"	,    remote=_a				)
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							self.tool(_a	,    "What did Hugging Face do in April 2021?"				)
          self.assertEqual(_a	,    "launched the BigScience Research Workshop"				)
   def 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							self.remote_tool(_a	,    "What did Hugging Face do in April 2021?"				)
          self.assertEqual(_a	,    "launched the BigScience Research Workshop"				)
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.tool(text=_a	,    question="What did Hugging Face do in April 2021?"				)
          self.assertEqual(_a	,    "launched the BigScience Research Workshop"				)

   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							self.remote_tool(text=_a	,    question="What did Hugging Face do in April 2021?"				)
          self.assertEqual(_a	,    "launched the BigScience Research Workshop"				)


