





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


UpperCAmelCase	:	Dict		     =       logging.get_logger(__name__)

UpperCAmelCase	:	Tuple		     =       {
    """caidas/swin2sr-classicalsr-x2-64""": (
        """https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"""
    ),
}







class 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Any						=			"""swin2sr"""

      _lowercase				:					Tuple						=			{
          """hidden_size""": """embed_dim""",
          """num_attention_heads""": """num_heads""",
          """num_hidden_layers""": """num_layers""",
      }







      def __init__(    self		,      lowerCAmelCase__=6_4		,      lowerCAmelCase__=1		,      lowerCAmelCase__=3		,      lowerCAmelCase__=1_8_0		,      lowerCAmelCase__=[6, 6, 6, 6, 6, 6]		,      lowerCAmelCase__=[6, 6, 6, 6, 6, 6]		,      lowerCAmelCase__=8		,      lowerCAmelCase__=2.0		,      lowerCAmelCase__=True		,      lowerCAmelCase__=0.0		,      lowerCAmelCase__=0.0		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__="gelu"		,      lowerCAmelCase__=False		,      lowerCAmelCase__=0.02		,      lowerCAmelCase__=1E-5		,      lowerCAmelCase__=2		,      lowerCAmelCase__=1.0		,      lowerCAmelCase__="1conv"		,      lowerCAmelCase__="pixelshuffle"		,      **lowerCAmelCase__		,      )   ->						int:



        '''simple docstring'''




        super().__init__(**lowerCAmelCase__							)

        a__	:					Optional[Any]										=image_size
        a__	:					Dict										=patch_size
        a__	:					Tuple										=num_channels
        a__	:					Union[str, Any]										=embed_dim
        a__	:					Optional[Any]										=depths
        a__	:					List[str]										=len(lowerCAmelCase__							)
        a__	:					Any										=num_heads
        a__	:					Any										=window_size
        a__	:					str										=mlp_ratio
        a__	:					List[str]										=qkv_bias
        a__	:					Dict										=hidden_dropout_prob
        a__	:					List[str]										=attention_probs_dropout_prob
        a__	:					Dict										=drop_path_rate
        a__	:					Optional[Any]										=hidden_act
        a__	:					Union[str, Any]										=use_absolute_embeddings
        a__	:					Optional[Any]										=layer_norm_eps
        a__	:					List[Any]										=initializer_range
        a__	:					int										=upscale
        a__	:					Optional[int]										=img_range
        a__	:					Any										=resi_connection
        a__	:					Optional[Any]										=upsampler













# 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 				__lowerCAmelCase       (    UpperCamelCase__):
      _lowercase				:					Optional[Any]						=			"""philschmid/bart-large-cnn-samsum"""
      _lowercase				:					List[Any]						=			(
          """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				:					Any						=			"""summarizer"""
      _lowercase				:					Any						=			AutoTokenizer
      _lowercase				:					str						=			AutoModelForSeqaSeqLM

      _lowercase				:					Optional[int]						=			["""text"""]
      _lowercase				:					Optional[int]						=			["""text"""]







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Tuple:



        '''simple docstring'''




        return self.pre_processor(lowerCAmelCase__		,      return_tensors="pt"		,      truncation=lowerCAmelCase__							)







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Union[str, Any]:



        '''simple docstring'''




        return self.model.generate(**lowerCAmelCase__							)[0]







      def    _lowercase  (    self		,      lowerCAmelCase__							)   ->						Any:



        '''simple docstring'''




        return self.pre_processor.decode(lowerCAmelCase__		,      skip_special_tokens=lowerCAmelCase__		,      clean_up_tokenization_spaces=lowerCAmelCase__							)






