






'''simple docstring'''







import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin

class _lowercase	(				unittest.TestCase				,      _lowercase		):




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	Union[str, Any]         =       load_tool("""text-classification"""     )
														self.tool.setup()
														lowerCamelCase__					:	Optional[int]         =       load_tool("""text-classification"""			,							remote=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	Optional[int]         =       self.tool("""That's quite cool"""			,							["""positive""", """negative"""]     )
														self.assertEqual(UpperCamelCase__			,							"""positive"""     )




							def 		lowerCamelCase_				(							self:				Tuple     ):
														lowerCamelCase__					:	Optional[int]         =       self.remote_tool("""That's quite cool"""			,							["""positive""", """negative"""]     )
														self.assertEqual(UpperCamelCase__			,							"""positive"""     )




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														lowerCamelCase__					:	str         =       self.tool(text="""That's quite cool"""			,							labels=["""positive""", """negative"""]     )
														self.assertEqual(UpperCamelCase__			,							"""positive"""     )




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	List[Any]         =       self.remote_tool(text="""That's quite cool"""			,							labels=["""positive""", """negative"""]     )
														self.assertEqual(UpperCamelCase__			,							"""positive"""     )









from ...configuration_utils import PretrainedConfig
from ...utils import logging


a		:       Optional[int]			     = logging.get_logger(__name__)

a		:       List[Any]			     = {
    "facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/config.json",
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class     a    ( lowercase__   ):



      """simple docstring"""


      a  : List[Any]							       =	'xglm'
      a  : str							       =	['past_key_values']

      a  : Any							       =	{
          'num_attention_heads': 'attention_heads',
          'hidden_size': 'd_model',
          'num_hidden_layers': 'num_layers',
      }
      def __init__(      self					:			Optional[int]		,      __lowercase					:			int=256008		,      __lowercase					:			Tuple=2048		,      __lowercase					:			List[Any]=1024		,      __lowercase					:			str=4096		,      __lowercase					:			Optional[Any]=24		,      __lowercase					:			Optional[int]=16		,      __lowercase					:			List[Any]="gelu"		,      __lowercase					:			str=0.1		,      __lowercase					:			Dict=0.1		,      __lowercase					:			Tuple=0.0		,      __lowercase					:			Optional[int]=0.0		,      __lowercase					:			Dict=0.02		,      __lowercase					:			Optional[int]=True		,      __lowercase					:			Any=True		,      __lowercase					:			Dict=2		,      __lowercase					:			Optional[Any]=1		,      __lowercase					:			List[Any]=0		,      __lowercase					:			Optional[Any]=2		,      **__lowercase					:			List[str]		,      )							->      Optional[int]:
        __UpperCAmelCase							:	List[str]           =  vocab_size
        __UpperCAmelCase							:	Optional[Any]           =  max_position_embeddings
        __UpperCAmelCase							:	Optional[Any]           =  d_model
        __UpperCAmelCase							:	str           =  ffn_dim
        __UpperCAmelCase							:	List[str]           =  num_layers
        __UpperCAmelCase							:	Dict           =  attention_heads
        __UpperCAmelCase							:	str           =  activation_function
        __UpperCAmelCase							:	Optional[Any]           =  dropout
        __UpperCAmelCase							:	Any           =  attention_dropout
        __UpperCAmelCase							:	int           =  activation_dropout
        __UpperCAmelCase							:	Tuple           =  layerdrop
        __UpperCAmelCase							:	Tuple           =  init_std
        __UpperCAmelCase							:	List[str]           =  scale_embedding  # scale factor will be sqrt(d_model) if True
        __UpperCAmelCase							:	Union[str, Any]           =  use_cache

        super().__init__(
            pad_token_id=__lowercase		,      bos_token_id=__lowercase		,      eos_token_id=__lowercase		,      decoder_start_token_id=__lowercase		,      **__lowercase		,      )


