

'''simple docstring'''



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


__snake_case					:      Any 		=     logging.get_logger(__name__)

__snake_case					:      Optional[int] 		=     {
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}



class lowerCamelCase					(    snake_case__			,		snake_case__		):



			'''simple docstring'''






			__snake_case             =			"""nat"""

			__snake_case             =			{
			    """num_attention_heads""": """num_heads""",
			    """num_hidden_layers""": """num_layers""",
			}


			def __init__( self			:		Optional[Any]						,     lowerCAmelCase_			:		Tuple=4						,     lowerCAmelCase_			:		Optional[Any]=3						,     lowerCAmelCase_			:		str=64						,     lowerCAmelCase_			:		List[Any]=[3, 4, 6, 5]						,     lowerCAmelCase_			:		List[str]=[2, 4, 8, 16]						,     lowerCAmelCase_			:		Any=7						,     lowerCAmelCase_			:		List[Any]=3.0						,     lowerCAmelCase_			:		str=True						,     lowerCAmelCase_			:		str=0.0						,     lowerCAmelCase_			:		int=0.0						,     lowerCAmelCase_			:		Optional[int]=0.1						,     lowerCAmelCase_			:		str="gelu"						,     lowerCAmelCase_			:		Optional[Any]=0.02						,     lowerCAmelCase_			:		List[Any]=1e-5						,     lowerCAmelCase_			:		int=0.0						,     lowerCAmelCase_			:		str=None						,     lowerCAmelCase_			:		Any=None						,     **lowerCAmelCase_			:		int						,     )     ->						List[Any]:





									'''simple docstring'''
									super().__init__(**__lowercase					)

									A__	:  Dict           =patch_size
									A__	:  Dict           =num_channels
									A__	:  Any           =embed_dim
									A__	:  List[Any]           =depths
									A__	:  str           =len(__lowercase					)
									A__	:  Union[str, Any]           =num_heads
									A__	:  Optional[int]           =kernel_size
									A__	:  Optional[Any]           =mlp_ratio
									A__	:  List[str]           =qkv_bias
									A__	:  List[str]           =hidden_dropout_prob
									A__	:  Optional[int]           =attention_probs_dropout_prob
									A__	:  Dict           =drop_path_rate
									A__	:  str           =hidden_act
									A__	:  Optional[Any]           =layer_norm_eps
									A__	:  Optional[Any]           =initializer_range
									# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									A__	:  Optional[Any]           =int(embed_dim * 2 ** (len(__lowercase					) - 1)					)
									A__	:  Union[str, Any]           =layer_scale_init_value
									A__	:  Dict           =['''stem'''] + [f"stage{idx}" for idx in range(1						,     len(__lowercase					) + 1					)]
									A__	:  List[Any]           =get_aligned_output_features_output_indices(
									    out_features=__lowercase						,     out_indices=__lowercase						,     stage_names=self.stage_names					)








"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							int									=		"""philschmid/bart-large-cnn-samsum"""
				__snake_case :							Optional[int]									=		(
				    """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
				    """and returns a summary of the text."""
				)
				__snake_case :							Dict									=		"""summarizer"""
				__snake_case :							str									=		AutoTokenizer
				__snake_case :							List[str]									=		AutoModelForSeqaSeqLM

				__snake_case :							List[Any]									=		["""text"""]
				__snake_case :							Dict									=		["""text"""]




				def        __lowercase	(  self	:int    ,     __lowercase	:List[Any]      ):
											return self.pre_processor(__lowercase    ,     return_tensors='''pt'''    ,     truncation=__lowercase      )




				def        __lowercase	(  self	:Optional[int]    ,     __lowercase	:Optional[int]      ):
											return self.model.generate(**__lowercase      )[0]




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:str      ):
											return self.pre_processor.decode(__lowercase    ,     skip_special_tokens=__lowercase    ,     clean_up_tokenization_spaces=__lowercase      )




