



"""simple docstring"""




import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


UpperCAmelCase								=   """
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 	__snake_case (				unittest.TestCase     ,SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def _a (							self						):
											a__			 =						load_tool("""summarization"""						)
											self.tool.setup()
											a__			 =						load_tool("""summarization"""					,					remote=a_						)


							def _a (							self						):
											a__			 =						self.tool(a_						)
											self.assertEqual(
											    a_					,					"""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						):
											a__			 =						self.remote_tool(a_						)
											self.assertEqual(
											    a_					,					"""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						):
											a__			 =						self.tool(text=a_						)
											self.assertEqual(
											    a_					,					"""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						):
											a__			 =						self.remote_tool(text=a_						)
											self.assertEqual(
											    a_					,					"""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 ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase								=   logging.get_logger(__name__)

UpperCAmelCase								=   {
    """facebook/regnet-y-040""": """https://huggingface.co/facebook/regnet-y-040/blob/main/config.json""",
}

class 	__snake_case (				SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__			: Tuple             =   """regnet"""
							UpperCamelCase__			: int             =   ["""x""", """y"""]


							def __init__(							self					,					a_=3					,					a_=32					,					a_=[128, 192, 512, 1_088]					,					a_=[2, 6, 12, 2]					,					a_=64					,					a_="y"					,					a_="relu"					,					**a_					,					):
											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						)}'''						)
											a__			 =						num_channels
											a__			 =						embedding_size
											a__			 =						hidden_sizes
											a__			 =						depths
											a__			 =						groups_width
											a__			 =						layer_type
											a__			 =						hidden_act
											# always downsample in the first stage
											a__			 =						True

