
'''simple docstring'''





from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool

class 						a   (				__magic_name__    ):
				_snake_case		    = "philschmid/bart-large-cnn-samsum"
				_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."
				)
				_snake_case		    = "summarizer"
				_snake_case		    = AutoTokenizer
				_snake_case		    = AutoModelForSeqaSeqLM

				_snake_case		    = ["text"]
				_snake_case		    = ["text"]




				def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
										return self.pre_processor(SCREAMING_SNAKE_CASE_,    return_tensors='''pt''',    truncation=SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										return self.model.generate(**SCREAMING_SNAKE_CASE_      )[0]




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



'''simple docstring'''





from .testing import (
    are_the_same_tensors,
    execute_subprocess_async,
    require_bnb,
    require_cpu,
    require_cuda,
    require_huggingface_suite,
    require_mps,
    require_multi_gpu,
    require_multi_xpu,
    require_safetensors,
    require_single_gpu,
    require_single_xpu,
    require_torch_min_version,
    require_tpu,
    require_xpu,
    skip,
    slow,
)
from .training import RegressionDataset, RegressionModel, RegressionModelaXPU


from .scripts import test_script, test_sync, test_ops  # isort: skip

