



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
    import torch

    from transformers.generation import DisjunctiveConstraint





@require_torch
class 		UpperCAmelCase						(						unittest.TestCase				):





   def 	__lowerCAmelCase							(	self			):
         # For consistency across different places the DisjunctiveConstraint is called,
         # dc.token_ids is a list of integers. It is also initialized only by integers.

         _lowerCAmelCase 			=  [[1, 2, 4], [1, 2, 3, 4]]
         _lowerCAmelCase 			=  DisjunctiveConstraint(_lowerCAmelCase			)
         self.assertTrue(isinstance(dc.token_ids       ,     _lowerCAmelCase			)			)

         with self.assertRaises(_lowerCAmelCase			):
               DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]			)			)

         with self.assertRaises(_lowerCAmelCase			):
               DisjunctiveConstraint([torch.LongTensor([1, 2, 4]			), torch.LongTensor([1, 2, 3, 4, 5]			)]			)





   def 	__lowerCAmelCase							(	self			):
         # We can't have constraints that are complete subsets of another. This leads to a preverse
         # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
         # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
         # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
         # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
         _lowerCAmelCase 			=  [[1, 2], [1, 2, 3, 4]]

         with self.assertRaises(_lowerCAmelCase			):
               DisjunctiveConstraint(_lowerCAmelCase			)  # fails here





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  [[1, 2, 3], [1, 2, 4]]

         _lowerCAmelCase 			=  DisjunctiveConstraint(_lowerCAmelCase			)

         _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  dc.update(1			)
         _lowerCAmelCase 			=  stepped is True and completed is False and reset is False
         self.assertTrue(_lowerCAmelCase			)
         self.assertTrue(not dc.completed			)
         self.assertTrue(dc.current_seq == [1]			)

         _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  dc.update(2			)
         _lowerCAmelCase 			=  stepped is True and completed is False and reset is False
         self.assertTrue(_lowerCAmelCase			)
         self.assertTrue(not dc.completed			)
         self.assertTrue(dc.current_seq == [1, 2]			)

         _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  dc.update(3			)
         _lowerCAmelCase 			=  stepped is True and completed is True and reset is False
         self.assertTrue(_lowerCAmelCase			)
         self.assertTrue(dc.completed			)  # Completed!
         self.assertTrue(dc.current_seq == [1, 2, 3]			)





   def 	__lowerCAmelCase							(	self			):
         _lowerCAmelCase 			=  [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

         _lowerCAmelCase 			=  DisjunctiveConstraint(_lowerCAmelCase			)

         _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  dc.update(1			)
         self.assertTrue(not dc.completed			)
         self.assertTrue(dc.current_seq == [1]			)

         _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  dc.update(2			)
         self.assertTrue(not dc.completed			)
         self.assertTrue(dc.current_seq == [1, 2]			)

         _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  dc.update(4			)
         self.assertTrue(not dc.completed			)
         self.assertTrue(dc.current_seq == [1, 2, 4]			)

         _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  dc.update(5			)
         self.assertTrue(dc.completed			)  # Completed!
         self.assertTrue(dc.current_seq == [1, 2, 4, 5]			)

         dc.reset()

         _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  dc.update(1			)
         self.assertTrue(not dc.completed			)
         self.assertTrue(dc.remaining() == 3			)
         self.assertTrue(dc.current_seq == [1]			)

         _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  dc.update(2			)
         self.assertTrue(not dc.completed			)
         self.assertTrue(dc.remaining() == 2			)
         self.assertTrue(dc.current_seq == [1, 2]			)

         _lowerCAmelCase			,   _lowerCAmelCase			,   _lowerCAmelCase 			=  dc.update(5			)
         self.assertTrue(dc.completed			)  # Completed!
         self.assertTrue(dc.remaining() == 0			)
         self.assertTrue(dc.current_seq == [1, 2, 5]			)




# Copyright 2022 The HuggingFace Team and The OpenBMB 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 typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


UpperCAmelCase_									=							{
    "configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CpmAntConfig"],
    "tokenization_cpmant": ["CpmAntTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    UpperCAmelCase_									=							[
        "CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "CpmAntForCausalLM",
        "CpmAntModel",
        "CpmAntPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
    from .tokenization_cpmant import CpmAntTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_cpmant import (
            CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
            CpmAntForCausalLM,
            CpmAntModel,
            CpmAntPreTrainedModel,
        )


else:
    import sys

    UpperCAmelCase_									=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)