



import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
      import torch

      from transformers.generation import (
          MaxLengthCriteria,
          MaxNewTokensCriteria,
          MaxTimeCriteria,
          StoppingCriteriaList,
          validate_stopping_criteria,
      )

@require_torch
class       _SCREAMING_SNAKE_CASE	(unittest.TestCase ):





     def 	__snake_case    (       self :							List[str]     ,  UpperCamelCase :							Any				)->Optional[int]:
      __SCREAMING_SNAKE_CASE       :    Any       =	3
      __SCREAMING_SNAKE_CASE       :    Optional[Any]       =	2_5_0

      __SCREAMING_SNAKE_CASE       :    Dict       =	ids_tensor((batch_size, length)     ,  UpperCamelCase				)
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	torch.ones((batch_size, length)     ,  device=UpperCamelCase     ,  dtype=torch.float				) / length
      return input_ids, scores





     def 	__snake_case    (       self :							List[Any]				)->List[Any]:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[Any]       =	self._get_tensors(5				)

      __SCREAMING_SNAKE_CASE       :    int       =	StoppingCriteriaList(
          [
              MaxLengthCriteria(max_length=1_0				),
              MaxTimeCriteria(max_time=0.1				),
          ]				)

      self.assertFalse(criteria(UpperCamelCase     ,  UpperCamelCase				)				)

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	self._get_tensors(9				)
      self.assertFalse(criteria(UpperCamelCase     ,  UpperCamelCase				)				)

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    str       =	self._get_tensors(1_0				)
      self.assertTrue(criteria(UpperCamelCase     ,  UpperCamelCase				)				)





     def 	__snake_case    (       self :							Optional[Any]				)->Union[str, Any]:
      __SCREAMING_SNAKE_CASE       :    Any       =	MaxLengthCriteria(max_length=1_0				)

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self._get_tensors(5				)
      self.assertFalse(criteria(UpperCamelCase     ,  UpperCamelCase				)				)

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    List[Any]       =	self._get_tensors(9				)
      self.assertFalse(criteria(UpperCamelCase     ,  UpperCamelCase				)				)

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Optional[int]       =	self._get_tensors(1_0				)
      self.assertTrue(criteria(UpperCamelCase     ,  UpperCamelCase				)				)





     def 	__snake_case    (       self :							Optional[Any]				)->Dict:
      __SCREAMING_SNAKE_CASE       :    Tuple       =	MaxNewTokensCriteria(start_length=5     ,  max_new_tokens=5				)

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Dict       =	self._get_tensors(5				)
      self.assertFalse(criteria(UpperCamelCase     ,  UpperCamelCase				)				)

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	self._get_tensors(9				)
      self.assertFalse(criteria(UpperCamelCase     ,  UpperCamelCase				)				)

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    Any       =	self._get_tensors(1_0				)
      self.assertTrue(criteria(UpperCamelCase     ,  UpperCamelCase				)				)

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	StoppingCriteriaList([criteria]				)
      self.assertEqual(criteria_list.max_length     ,  1_0				)





     def 	__snake_case    (       self :							Any				)->List[str]:
      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       :    int       =	self._get_tensors(5				)

      __SCREAMING_SNAKE_CASE       :    Any       =	MaxTimeCriteria(max_time=0.1				)
      self.assertFalse(criteria(UpperCamelCase     ,  UpperCamelCase				)				)

      __SCREAMING_SNAKE_CASE       :    str       =	MaxTimeCriteria(max_time=0.1     ,  initial_timestamp=time.time() - 0.2				)
      self.assertTrue(criteria(UpperCamelCase     ,  UpperCamelCase				)				)






     def 	__snake_case    (       self :							int				)->List[Any]:
      validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0				)]				)     ,  1_0				)

      with self.assertWarns(UpperCamelCase				):
       validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0				)]				)     ,  1_1				)

      __SCREAMING_SNAKE_CASE       :    List[Any]       =	validate_stopping_criteria(StoppingCriteriaList()     ,  1_1				)

      self.assertEqual(len(UpperCamelCase				)     ,  1				)











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

     lowerCAmelCase					=			["""text"""]
     lowerCAmelCase					=			["""text"""]





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							Tuple				)->Tuple:
      return self.pre_processor(UpperCamelCase     ,  return_tensors="pt"     ,  truncation=UpperCamelCase				)





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							List[Any]				)->Optional[Any]:
      return self.model.generate(**UpperCamelCase				)[0]






     def 	__snake_case    (       self :							Tuple     ,  UpperCamelCase :							Dict				)->Optional[int]:
      return self.pre_processor.decode(UpperCamelCase     ,  skip_special_tokens=UpperCamelCase     ,  clean_up_tokenization_spaces=UpperCamelCase				)






