

'''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

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase      					=     {
    """configuration_xmod""": [
        """XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XmodConfig""",
        """XmodOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_lowerCamelCase      					=     [
					    """XMOD_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """XmodForCausalLM""",
					    """XmodForMaskedLM""",
					    """XmodForMultipleChoice""",
					    """XmodForQuestionAnswering""",
					    """XmodForSequenceClassification""",
					    """XmodForTokenClassification""",
					    """XmodModel""",
					    """XmodPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xmod import (
										    XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XmodForCausalLM,
										    XmodForMaskedLM,
										    XmodForMultipleChoice,
										    XmodForQuestionAnswering,
										    XmodForSequenceClassification,
										    XmodForTokenClassification,
										    XmodModel,
										    XmodPreTrainedModel,
										)

else:
					import sys

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











"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__       :					Dict						=			logging.get_logger(__name__)

__magic_name__       :					List[str]						=			{
    """microsoft/swinv2-tiny-patch4-window8-256""": (
        """https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"""
    ),
}






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""
			__lowerCAmelCase     :				List[str]										=  """swinv2"""

			__lowerCAmelCase     :				Optional[Any]										=  {
			    """num_attention_heads""": """num_heads""",
			    """num_hidden_layers""": """num_layers""",
			}







			def __init__(      self						,   _A=2_2_4						,   _A=4						,   _A=3						,   _A=9_6						,   _A=[2, 2, 6, 2]						,   _A=[3, 6, 1_2, 2_4]						,   _A=7						,   _A=4.0						,   _A=True						,   _A=0.0						,   _A=0.0						,   _A=0.1						,   _A="gelu"						,   _A=False						,   _A=0.02						,   _A=1e-5						,   _A=3_2						,   **_A						,   ):






									'''simple docstring'''







									super().__init__(**_A    )

									UpperCamelCase				:		Optional[Any]				   =			image_size
									UpperCamelCase				:		Tuple				   =			patch_size
									UpperCamelCase				:		str				   =			num_channels
									UpperCamelCase				:		Dict				   =			embed_dim
									UpperCamelCase				:		List[str]				   =			depths
									UpperCamelCase				:		Optional[Any]				   =			len(_A    )
									UpperCamelCase				:		Optional[int]				   =			num_heads
									UpperCamelCase				:		Optional[int]				   =			window_size
									UpperCamelCase				:		List[Any]				   =			mlp_ratio
									UpperCamelCase				:		str				   =			qkv_bias
									UpperCamelCase				:		str				   =			hidden_dropout_prob
									UpperCamelCase				:		Tuple				   =			attention_probs_dropout_prob
									UpperCamelCase				:		List[Any]				   =			drop_path_rate
									UpperCamelCase				:		str				   =			hidden_act
									UpperCamelCase				:		Optional[Any]				   =			use_absolute_embeddings
									UpperCamelCase				:		Dict				   =			layer_norm_eps
									UpperCamelCase				:		Dict				   =			initializer_range
									UpperCamelCase				:		str				   =			encoder_stride
									# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									UpperCamelCase				:		Any				   =			int(embed_dim * 2 ** (len(_A    ) - 1)    )
									UpperCamelCase				:		str				   =			(0, 0, 0, 0)



