



import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin




class 					snake_case__						(       lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_     ):






     """simple docstring"""


     @register_to_config
     def __init__(       self  :  str, _snake_case  :  int, _snake_case  :  int, _snake_case  :  int, _snake_case  :  float, _snake_case  :  int, _snake_case  :  int, _snake_case  :  int, _snake_case  :  int, _snake_case  :  str, _snake_case  :  bool = False, )   ->List[Any]:
            super().__init__()

            snake_case__							:	Union[str, Any]   	=	nn.Embedding(_snake_case, _snake_case					)

            snake_case__							:	List[Any]   	=	nn.Embedding(_snake_case, _snake_case					)
            snake_case__							:	Any   	=	False

            snake_case__							:	str   	=	nn.Dropout(p=_snake_case					)

            snake_case__							:	Optional[int]   	=	TaConfig(
                vocab_size=_snake_case, d_model=_snake_case, num_heads=_snake_case, d_kv=_snake_case, d_ff=_snake_case, dropout_rate=_snake_case, feed_forward_proj=_snake_case, is_decoder=_snake_case, is_encoder_decoder=_snake_case, )

            snake_case__							:	int   	=	nn.ModuleList()
            for lyr_num in range(_snake_case					):
                   snake_case__							:	Dict   	=	TaBlock(_snake_case					)
                   self.encoders.append(_snake_case					)

            snake_case__							:	str   	=	TaLayerNorm(_snake_case					)
            snake_case__							:	Optional[int]   	=	nn.Dropout(p=_snake_case					)






     def    lowercase_   (       self  :  Optional[int], _snake_case  :  Optional[Any], _snake_case  :  Tuple					)   ->str:
            snake_case__							:	Tuple   	=	self.token_embedder(_snake_case					)

            snake_case__							:	List[Any]   	=	encoder_input_tokens.shape[1]
            snake_case__							:	Tuple   	=	torch.arange(_snake_case, device=encoder_input_tokens.device					)
            x += self.position_encoding(_snake_case					)

            snake_case__							:	List[str]   	=	self.dropout_pre(_snake_case					)

            # inverted the attention mask
            snake_case__							:	List[str]   	=	encoder_input_tokens.size()
            snake_case__							:	Optional[Any]   	=	self.get_extended_attention_mask(_snake_case, _snake_case					)

            for lyr in self.encoders:
                   snake_case__							:	str   	=	lyr(_snake_case, _snake_case					)[0]
            snake_case__							:	Any   	=	self.layer_norm(_snake_case					)

            return self.dropout_post(_snake_case					), encoder_inputs_mask











# 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__						(       lowerCAmelCase_     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	"""philschmid/bart-large-cnn-samsum"""
     _SCREAMING_SNAKE_CASE						=	(
         """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."""
     )
     _SCREAMING_SNAKE_CASE						=	"""summarizer"""
     _SCREAMING_SNAKE_CASE						=	AutoTokenizer
     _SCREAMING_SNAKE_CASE						=	AutoModelForSeqaSeqLM

     _SCREAMING_SNAKE_CASE						=	["""text"""]
     _SCREAMING_SNAKE_CASE						=	["""text"""]






     def    lowercase_   (       self  :  Optional[Any], _snake_case  :  str					)   ->Any:
            return self.pre_processor(_snake_case, return_tensors='pt', truncation=_snake_case					)






     def    lowercase_   (       self  :  int, _snake_case  :  List[Any]					)   ->Any:
            return self.model.generate(**_snake_case					)[0]






     def    lowercase_   (       self  :  int, _snake_case  :  int					)   ->str:
            return self.pre_processor.decode(_snake_case, skip_special_tokens=_snake_case, clean_up_tokenization_spaces=_snake_case					)






