





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


UpperCAmelCase__						:			List[Any]									=logging.get_logger(__name__)

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

class       __A     (						_UpperCAmelCase    ):
					__A      =				"""swinv2"""

					__A      =				{
					    """num_attention_heads""": """num_heads""",
					    """num_hidden_layers""": """num_layers""",
					}


					def __init__(		self   , UpperCAmelCase_=224   , UpperCAmelCase_=4   , UpperCAmelCase_=3   , UpperCAmelCase_=96   , UpperCAmelCase_=[2, 2, 6, 2]   , UpperCAmelCase_=[3, 6, 12, 24]   , UpperCAmelCase_=7   , UpperCAmelCase_=4.0   , UpperCAmelCase_=True   , UpperCAmelCase_=0.0   , UpperCAmelCase_=0.0   , UpperCAmelCase_=0.1   , UpperCAmelCase_="gelu"   , UpperCAmelCase_=False   , UpperCAmelCase_=0.0_2   , UpperCAmelCase_=1E-5   , UpperCAmelCase_=32   , **UpperCAmelCase_   , ):
									super().__init__(**lowerCamelCase_							)

									lowerCamelCase	 =image_size
									lowerCamelCase	 =patch_size
									lowerCamelCase	 =num_channels
									lowerCamelCase	 =embed_dim
									lowerCamelCase	 =depths
									lowerCamelCase	 =len(lowerCamelCase_							)
									lowerCamelCase	 =num_heads
									lowerCamelCase	 =window_size
									lowerCamelCase	 =mlp_ratio
									lowerCamelCase	 =qkv_bias
									lowerCamelCase	 =hidden_dropout_prob
									lowerCamelCase	 =attention_probs_dropout_prob
									lowerCamelCase	 =drop_path_rate
									lowerCamelCase	 =hidden_act
									lowerCamelCase	 =use_absolute_embeddings
									lowerCamelCase	 =layer_norm_eps
									lowerCamelCase	 =initializer_range
									lowerCamelCase	 =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
									lowerCamelCase	 =int(embed_dim * 2 ** (len(lowerCamelCase_							) - 1)							)
									lowerCamelCase	 =(0, 0, 0, 0)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__						:			str									={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			List[Any]									=['''FNetTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Any									=['''FNetTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__						:			Dict									=[
							    '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''FNetForMaskedLM''',
							    '''FNetForMultipleChoice''',
							    '''FNetForNextSentencePrediction''',
							    '''FNetForPreTraining''',
							    '''FNetForQuestionAnswering''',
							    '''FNetForSequenceClassification''',
							    '''FNetForTokenClassification''',
							    '''FNetLayer''',
							    '''FNetModel''',
							    '''FNetPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet import FNetTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_fnet_fast import FNetTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_fnet import (
														    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FNetForMaskedLM,
														    FNetForMultipleChoice,
														    FNetForNextSentencePrediction,
														    FNetForPreTraining,
														    FNetForQuestionAnswering,
														    FNetForSequenceClassification,
														    FNetForTokenClassification,
														    FNetLayer,
														    FNetModel,
														    FNetPreTrainedModel,
														)


else:
							import sys

							UpperCAmelCase__						:			Optional[int]									=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





