






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


__UpperCamelCase					:		Dict	  =						logging.get_logger(__name__)

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


class   __SCREAMING_SNAKE_CASE(	_a	):
							_UpperCAmelCase     =       "swinv2"

							_UpperCAmelCase     =       {
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}
							def __init__(     self:      Any       ,	UpperCamelCase:      Optional[int]=2_24       ,	UpperCamelCase:      str=4       ,	UpperCamelCase:      Dict=3       ,	UpperCamelCase:      List[Any]=96       ,	UpperCamelCase:      Tuple=[2, 2, 6, 2]       ,	UpperCamelCase:      Optional[int]=[3, 6, 12, 24]       ,	UpperCamelCase:      List[Any]=7       ,	UpperCamelCase:      List[str]=4.0       ,	UpperCamelCase:      Optional[int]=True       ,	UpperCamelCase:      str=0.0       ,	UpperCamelCase:      Optional[Any]=0.0       ,	UpperCamelCase:      Any=0.1       ,	UpperCamelCase:      Dict="gelu"       ,	UpperCamelCase:      Any=False       ,	UpperCamelCase:      Dict=0.02       ,	UpperCamelCase:      Dict=1e-5       ,	UpperCamelCase:      List[str]=32       ,	**UpperCamelCase:      List[str]       ,	)		->		str:
											super().__init__(**_A						)

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












from typing import TYPE_CHECKING

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


__UpperCamelCase					:		int	  =						{"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""XGLMTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Optional[int]	  =						["""XGLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		List[str]	  =						[
							    """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XGLMForCausalLM""",
							    """XGLMModel""",
							    """XGLMPreTrainedModel""",
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Any	  =						[
							    """FlaxXGLMForCausalLM""",
							    """FlaxXGLMModel""",
							    """FlaxXGLMPreTrainedModel""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		str	  =						[
							    """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFXGLMForCausalLM""",
							    """TFXGLMModel""",
							    """TFXGLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase					:		Union[str, Any]	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)




