





'''simple docstring'''

import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""M-CLIP"""
 def __init__(							self :List[Any]		,			lowercase_ :Dict=10_24		,			lowercase_ :Optional[int]=7_68		,			**lowercase_ :Any				)->				Tuple:
     A__							=     transformerDimSize
     A__							=     imageDimSize
     super().__init__(**lowercase_				)






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							MCLIPConfig
 def __init__(							self :Tuple		,			lowercase_ :Dict		,			*lowercase_ :Tuple		,			**lowercase_ :Dict				)->				List[str]:
     super().__init__(lowercase_		,			*lowercase_		,			**lowercase_				)
     A__							=     XLMRobertaModel(lowercase_				)
     A__							=     torch.nn.Linear(
         in_features=config.transformerDimensions		,			out_features=config.numDims				)



 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :str		,			lowercase_ :Any				)->				int:
     A__							=     self.transformer(input_ids=lowercase_		,			attention_mask=lowercase_				)[0]
     A__							=     (embs * attention_mask.unsqueeze(2				)).sum(dim=1				) / attention_mask.sum(dim=1				)[:, None]
     return self.LinearTransformation(lowercase_				), embs










'''simple docstring'''

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


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      int						      =[
      "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				:      List[Any]						      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



