





# 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


snake_case_							=			{
    '''configuration_vivit''': ['''VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VivitConfig'''],
}
try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_							=			['''VivitImageProcessor''']


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				snake_case_							=			[
				    '''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

				snake_case_							=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class        snake_case_		(			_A						,					_A):







		@register_to_config
		def __init__(		self			,     __lowercase = 7_6_8			,     )				->   List[Any]:
				super().__init__()

				lowerCamelCase   : str								=nn.Parameter(torch.zeros(1			,     __lowercase   )   )
				lowerCamelCase   : Union[str, Any]								=nn.Parameter(torch.ones(1			,     __lowercase   )   )







		def 			__lowercase   (		self			,     __lowercase = None			,     __lowercase = None			,     )				->   List[str]:
				lowerCamelCase   : Tuple								=nn.Parameter(self.mean.to(__lowercase   ).to(__lowercase   )   )
				lowerCamelCase   : Dict								=nn.Parameter(self.std.to(__lowercase   ).to(__lowercase   )   )
				return self







		def 			__lowercase   (		self			,     __lowercase   )				->   Optional[Any]:
				lowerCamelCase   : Any								=(embeds - self.mean) * 1.0 / self.std
				return embeds







		def 			__lowercase   (		self			,     __lowercase   )				->   Optional[int]:
				lowerCamelCase   : Any								=(embeds * self.std) + self.mean
				return embeds

