





# 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.
import re

from ..utils import cached_file


# docstyle-ignore
lowercase    : Union[str, Any]    	=  """
Human: <<task>>

Assistant: """


lowercase    : Any    	=  """huggingface-tools/default-prompts"""
lowercase    : Optional[int]    	=  {"""chat""": """chat_prompt_template.txt""", """run""": """run_prompt_template.txt"""}


def     SCREAMING_SNAKE_CASE			(      lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase="run"					):
       if prompt_or_repo_id is None:
              _UpperCamelCase              =						DEFAULT_PROMPTS_REPO

       # prompt is considered a repo ID when it does not contain any kind of space
       if re.search('''\\s'''    ,       lowerCAmelCase					) is not None:
              return prompt_or_repo_id

       _UpperCamelCase              =						cached_file(
           lowerCAmelCase    ,       PROMPT_FILES[mode]    ,       repo_type='''dataset'''    ,       user_agent={'''agent''': agent_name}					)
       with open(lowerCAmelCase    ,       '''r'''    ,       encoding='''utf-8'''					) as f:
              return f.read()













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  __A(						unittest.TestCase ):

     def 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						3
            _UpperCamelCase              =						250

            _UpperCamelCase              =						ids_tensor((batch_size, length),       A      )
            _UpperCamelCase              =						torch.ones((batch_size, length),       device=A,       dtype=torch.float      ) / length
            return input_ids, scores

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )

            _UpperCamelCase              =						StoppingCriteriaList(
                [
                    MaxLengthCriteria(max_length=10      ),
                    MaxTimeCriteria(max_time=0.1      ),
                ]      )

            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(9      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(10      )
            self.assertTrue(criteria(A,       A      )      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MaxLengthCriteria(max_length=10      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(9      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(10      )
            self.assertTrue(criteria(A,       A      )      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						MaxNewTokensCriteria(start_length=5,       max_new_tokens=5      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(9      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(10      )
            self.assertTrue(criteria(A,       A      )      )

            _UpperCamelCase              =						StoppingCriteriaList([criteria]      )
            self.assertEqual(criteria_list.max_length,       10      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase       ,		_UpperCamelCase              =						self._get_tensors(5      )

            _UpperCamelCase              =						MaxTimeCriteria(max_time=0.1      )
            self.assertFalse(criteria(A,       A      )      )

            _UpperCamelCase              =						MaxTimeCriteria(max_time=0.1,       initial_timestamp=time.time() - 0.2      )
            self.assertTrue(criteria(A,       A      )      )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      ),       10      )

            with self.assertWarns(A      ):
                   validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      ),       11      )

            _UpperCamelCase              =						validate_stopping_criteria(StoppingCriteriaList(),       11      )

            self.assertEqual(len(A      ),       1      )






