
"""simple docstring"""


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


_lowercase							      = logging.get_logger(__name__)

_lowercase							      = {
    '''shi-labs/dinat-mini-in1k-224''': '''https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json''',
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}







class  lowerCAmelCase_ (					_lowercase						,    _lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			Union[str, Any]					=			'''dinat'''

						_lowerCamelCase:			str					=			{
						    '''num_attention_heads''': '''num_heads''',
						    '''num_hidden_layers''': '''num_layers''',
						}


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

							A        =      patch_size
							A        =      num_channels
							A        =      embed_dim
							A        =      depths
							A        =      len(A_					)
							A        =      num_heads
							A        =      kernel_size
							A        =      dilations
							A        =      mlp_ratio
							A        =      qkv_bias
							A        =      hidden_dropout_prob
							A        =      attention_probs_dropout_prob
							A        =      drop_path_rate
							A        =      hidden_act
							A        =      layer_norm_eps
							A        =      initializer_range
							# we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							A        =      int(embed_dim * 2 ** (len(A_					) - 1)					)
							A        =      layer_scale_init_value
							A        =      ['stem'] + [F'stage{idx}' for idx in range(1     ,len(A_					) + 1					)]
							A      ,					A        =      get_aligned_output_features_output_indices(
							    out_features=A_     ,out_indices=A_     ,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    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					'philschmid/bart-large-cnn-samsum'
    lowercase    =					(
        '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.'
    )
    lowercase    =					'summarizer'
    lowercase    =					AutoTokenizer
    lowercase    =					AutoModelForSeqaSeqLM

    lowercase    =					['text']
    lowercase    =					['text']

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  Dict		)   ->  Any:
      return self.pre_processor(lowerCamelCase    ,						return_tensors="""pt"""    ,						truncation=lowerCamelCase		)

    def __lowercase   (   self    :  Optional[Any]    ,						lowerCamelCase    :  Tuple		)   ->  List[str]:
      return self.model.generate(**lowerCamelCase		)[0]





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



