

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a_     =			{
    'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_     =			[
			    'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'GPTBigCodeForSequenceClassification',
			    'GPTBigCodeForTokenClassification',
			    'GPTBigCodeForCausalLM',
			    'GPTBigCodeModel',
			    'GPTBigCodePreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_bigcode import (
						    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTBigCodeForCausalLM,
						    GPTBigCodeForSequenceClassification,
						    GPTBigCodeForTokenClassification,
						    GPTBigCodeModel,
						    GPTBigCodePreTrainedModel,
						)


else:
			import sys

			a_     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






# 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__							)






