





# Copyright 2023 The HuggingFace 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_torch_available, is_vision_available


__lowerCamelCase	:					int			  =					{
    """configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase	:					List[str]			  =					["""VivitImageProcessor"""]


try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase	:					Tuple			  =					[
        """VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """VivitModel""",
        """VivitPreTrainedModel""",
        """VivitForVideoClassification""",
    ]


if TYPE_CHECKING:
    from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_vivit import VivitImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_vivit import (
            VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            VivitForVideoClassification,
            VivitModel,
            VivitPreTrainedModel,
        )


else:
    import sys

    __lowerCamelCase	:					Any			  =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import tensorflow as tf

    from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



@require_tf
@require_sentencepiece
@require_tokenizers
class  A__       (	unittest.TestCase      ):





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       TFAutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       )
       UpperCamelCase     : Tuple	     =       AutoTokenizer.from_pretrained("google/mt5-small"       )

       UpperCamelCase     : Dict	     =       tokenizer("Hello there"	,							return_tensors="tf"       ).input_ids
       UpperCamelCase     : int	     =       tokenizer("Hi I am"	,							return_tensors="tf"       ).input_ids

       UpperCamelCase     : Union[str, Any]	     =       model(A_	,							labels=A_       ).loss
       UpperCamelCase     : List[str]	     =       -tf.math.reduce_mean(A_       ).numpy()

       UpperCamelCase     : Union[str, Any]	     =       -21.22_81_68
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 2e-4       )
