

'''simple docstring'''


from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      int=   "t5"
							_lowerCamelCase		:      Any=   ["past_key_values"]
							_lowerCamelCase		:      Optional[int]=   {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


							def __init__(			self    ,			_snake_case=3_2128    ,			_snake_case=512    ,			_snake_case=64    ,			_snake_case=2048    ,			_snake_case=6    ,			_snake_case=None    ,			_snake_case=8    ,			_snake_case=32    ,			_snake_case=128    ,			_snake_case=0.1    ,			_snake_case=1e-6    ,			_snake_case=1.0    ,			_snake_case="relu"    ,			_snake_case=True    ,			_snake_case=True    ,			_snake_case=0    ,			_snake_case=1    ,			**_snake_case    ,			)							->				Tuple:
								UpperCAmelCase_		:      Optional[int]       	=					vocab_size
								UpperCAmelCase_		:      Union[str, Any]       	=					d_model
								UpperCAmelCase_		:      Union[str, Any]       	=					d_kv
								UpperCAmelCase_		:      List[str]       	=					d_ff
								UpperCAmelCase_		:      List[str]       	=					num_layers
								UpperCAmelCase_		:      List[str]       	=					(
								    num_decoder_layers if num_decoder_layers is not None else self.num_layers
								)  # default = symmetry
								UpperCAmelCase_		:      List[Any]       	=					num_heads
								UpperCAmelCase_		:      Tuple       	=					relative_attention_num_buckets
								UpperCAmelCase_		:      Union[str, Any]       	=					relative_attention_max_distance
								UpperCAmelCase_		:      Tuple       	=					dropout_rate
								UpperCAmelCase_		:      Tuple       	=					layer_norm_epsilon
								UpperCAmelCase_		:      Optional[Any]       	=					initializer_factor
								UpperCAmelCase_		:      Union[str, Any]       	=					feed_forward_proj
								UpperCAmelCase_		:      str       	=					use_cache

								UpperCAmelCase_		:      Tuple       	=					self.feed_forward_proj.split('-')
								UpperCAmelCase_		:      Union[str, Any]       	=					act_info[-1]
								UpperCAmelCase_		:      List[Any]       	=					act_info[0] == 'gated'

								if len(_snake_case) > 1 and act_info[0] != "gated" or len(_snake_case) > 2:
									raise ValueError(
									    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
									    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
									    '\'gated-gelu\' or \'relu\'')

								# for backwards compatibility
								if feed_forward_proj == "gated-gelu":
									UpperCAmelCase_		:      List[Any]       	=					'gelu_new'

								super().__init__(
								    pad_token_id=_snake_case    ,			eos_token_id=_snake_case    ,			is_encoder_decoder=_snake_case    ,			**_snake_case    ,			)



class 					lowercase						(  a_ ):


							@property
							def 						_snake_case							(			self)							->				Mapping[str, Mapping[int, str]]:
								UpperCAmelCase_		:      List[str]       	=					{
								    'input_ids': {0: 'batch', 1: 'encoder_sequence'},
								    'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
								}
								if self.use_past:
									UpperCAmelCase_		:      int       	=					'past_encoder_sequence + sequence'
									UpperCAmelCase_		:      List[str]       	=					{0: 'batch'}
									UpperCAmelCase_		:      Dict       	=					{0: 'batch', 1: 'past_decoder_sequence + sequence'}
								else:
									UpperCAmelCase_		:      Union[str, Any]       	=					{0: 'batch', 1: 'decoder_sequence'}
									UpperCAmelCase_		:      Tuple       	=					{0: 'batch', 1: 'decoder_sequence'}

								if self.use_past:
									self.fill_with_past_key_values_(_snake_case    ,			direction='inputs')

								return common_inputs





							@property
							def 						_snake_case							(			self)							->				int:
								return 13









'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase__										= logging.get_logger(__name__)

lowerCAmelCase__										= {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}



class 					lowercase						(  a_,					a_ ):
							_lowerCamelCase		:      Optional[int]=   "swin"

							_lowerCamelCase		:      Dict=   {
							    "num_attention_heads": "num_heads",
							    "num_hidden_layers": "num_layers",
							}


							def __init__(			self    ,			_snake_case=224    ,			_snake_case=4    ,			_snake_case=3    ,			_snake_case=96    ,			_snake_case=[2, 2, 6, 2]    ,			_snake_case=[3, 6, 12, 24]    ,			_snake_case=7    ,			_snake_case=4.0    ,			_snake_case=True    ,			_snake_case=0.0    ,			_snake_case=0.0    ,			_snake_case=0.1    ,			_snake_case="gelu"    ,			_snake_case=False    ,			_snake_case=0.02    ,			_snake_case=1e-5    ,			_snake_case=32    ,			_snake_case=None    ,			_snake_case=None    ,			**_snake_case    ,			)							->				Tuple:
								super().__init__(**_snake_case)

								UpperCAmelCase_		:      Any       	=					image_size
								UpperCAmelCase_		:      Dict       	=					patch_size
								UpperCAmelCase_		:      int       	=					num_channels
								UpperCAmelCase_		:      Tuple       	=					embed_dim
								UpperCAmelCase_		:      Dict       	=					depths
								UpperCAmelCase_		:      Dict       	=					len(_snake_case)
								UpperCAmelCase_		:      List[Any]       	=					num_heads
								UpperCAmelCase_		:      Optional[int]       	=					window_size
								UpperCAmelCase_		:      Any       	=					mlp_ratio
								UpperCAmelCase_		:      Any       	=					qkv_bias
								UpperCAmelCase_		:      str       	=					hidden_dropout_prob
								UpperCAmelCase_		:      Optional[int]       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      Dict       	=					drop_path_rate
								UpperCAmelCase_		:      int       	=					hidden_act
								UpperCAmelCase_		:      Tuple       	=					use_absolute_embeddings
								UpperCAmelCase_		:      Optional[int]       	=					layer_norm_eps
								UpperCAmelCase_		:      Optional[int]       	=					initializer_range
								UpperCAmelCase_		:      Optional[Any]       	=					encoder_stride
								# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								UpperCAmelCase_		:      Union[str, Any]       	=					int(embed_dim * 2 ** (len(_snake_case) - 1))
								UpperCAmelCase_		:      Tuple       	=					['stem'] + [F"""stage{idx}""" for idx in range(1    ,			len(_snake_case) + 1)]
								UpperCAmelCase_	,    UpperCAmelCase_		:      Dict       	=					get_aligned_output_features_output_indices(
								    out_features=_snake_case    ,			out_indices=_snake_case    ,			stage_names=self.stage_names)



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      Tuple=   version.parse("1.11" )


							@property
							def 						_snake_case							(			self)							->				Mapping[str, Mapping[int, str]]:
								return OrderedDict(
								    [
								        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
								    ])





							@property
							def 						_snake_case							(			self)							->				float:
								return 1e-4






