


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCAmelCase__       :	Optional[int]     				=logging.get_logger(__name__)

lowerCAmelCase__       :	str     				={
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}







class      __lowercase  (_lowerCamelCase   ):





							"""simple docstring"""







							_UpperCAmelCase     =		"""oneformer"""
							_UpperCAmelCase     =		{"""hidden_size""": """hidden_dim"""}






							def __init__(	self       ,		lowerCAmelCase__ = None       ,		lowerCAmelCase__ = 2_5_5       ,		lowerCAmelCase__ = 1_5_0       ,		lowerCAmelCase__ = 0.1       ,		lowerCAmelCase__ = 2.0       ,		lowerCAmelCase__ = 5.0       ,		lowerCAmelCase__ = 5.0       ,		lowerCAmelCase__ = 0.5       ,		lowerCAmelCase__ = 0.07       ,		lowerCAmelCase__ = 1_2_5_4_4       ,		lowerCAmelCase__ = 3.0       ,		lowerCAmelCase__ = 0.75       ,		lowerCAmelCase__ = 0.02       ,		lowerCAmelCase__ = 1.0       ,		lowerCAmelCase__ = 1E-05       ,		lowerCAmelCase__ = False       ,		lowerCAmelCase__ = True       ,		lowerCAmelCase__ = True       ,		lowerCAmelCase__ = [4, 8, 1_6, 3_2]       ,		lowerCAmelCase__ = 7_7       ,		lowerCAmelCase__ = 2_5_6       ,		lowerCAmelCase__ = 7_7       ,		lowerCAmelCase__ = 6       ,		lowerCAmelCase__ = 4_9_4_0_8       ,		lowerCAmelCase__ = 2       ,		lowerCAmelCase__ = 1_6       ,		lowerCAmelCase__ = 2_5_6       ,		lowerCAmelCase__ = 2_5_6       ,		lowerCAmelCase__ = 2_5_6       ,		lowerCAmelCase__ = 1_0_2_4       ,		lowerCAmelCase__ = "GN"       ,		lowerCAmelCase__ = 6       ,		lowerCAmelCase__ = 1_0       ,		lowerCAmelCase__ = True       ,		lowerCAmelCase__ = 8       ,		lowerCAmelCase__ = 0.1       ,		lowerCAmelCase__ = 2_0_4_8       ,		lowerCAmelCase__ = False       ,		lowerCAmelCase__ = False       ,		lowerCAmelCase__ = 2       ,		lowerCAmelCase__ = 4       ,		**lowerCAmelCase__       ,		):





										"""simple docstring"""




										if backbone_config is None:
													logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'    )
													SCREAMING_SNAKE_CASE_						:			Any       						= CONFIG_MAPPING['swin'](
													    image_size=2_2_4       ,		in_channels=3       ,		patch_size=4       ,		embed_dim=9_6       ,		depths=[2, 2, 6, 2]       ,		num_heads=[3, 6, 1_2, 2_4]       ,		window_size=7       ,		drop_path_rate=0.3       ,		use_absolute_embeddings=A__       ,		out_features=['stage1', 'stage2', 'stage3', 'stage4']       ,		)
										elif isinstance(A__       ,		A__    ):
													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= backbone_config.get('model_type'    )
													SCREAMING_SNAKE_CASE_						:			int       						= CONFIG_MAPPING[backbone_model_type]
													SCREAMING_SNAKE_CASE_						:			Tuple       						= config_class.from_dict(A__    )

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= backbone_config

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= ignore_value
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= num_queries
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= no_object_weight
										SCREAMING_SNAKE_CASE_						:			Tuple       						= class_weight
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= mask_weight
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= dice_weight
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= contrastive_weight
										SCREAMING_SNAKE_CASE_						:			int       						= contrastive_temperature
										SCREAMING_SNAKE_CASE_						:			Dict       						= train_num_points
										SCREAMING_SNAKE_CASE_						:			Tuple       						= oversample_ratio
										SCREAMING_SNAKE_CASE_						:			Tuple       						= importance_sample_ratio
										SCREAMING_SNAKE_CASE_						:			List[str]       						= init_std
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= init_xavier_std
										SCREAMING_SNAKE_CASE_						:			Dict       						= layer_norm_eps
										SCREAMING_SNAKE_CASE_						:			Any       						= is_training
										SCREAMING_SNAKE_CASE_						:			List[str]       						= use_auxiliary_loss
										SCREAMING_SNAKE_CASE_						:			Tuple       						= output_auxiliary_logits
										SCREAMING_SNAKE_CASE_						:			Dict       						= strides
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= task_seq_len
										SCREAMING_SNAKE_CASE_						:			Any       						= text_encoder_width
										SCREAMING_SNAKE_CASE_						:			int       						= text_encoder_context_length
										SCREAMING_SNAKE_CASE_						:			Any       						= text_encoder_num_layers
										SCREAMING_SNAKE_CASE_						:			Tuple       						= text_encoder_vocab_size
										SCREAMING_SNAKE_CASE_						:			Dict       						= text_encoder_proj_layers
										SCREAMING_SNAKE_CASE_						:			str       						= text_encoder_n_ctx
										SCREAMING_SNAKE_CASE_						:			Any       						= conv_dim
										SCREAMING_SNAKE_CASE_						:			Any       						= mask_dim
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= hidden_dim
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= encoder_feedforward_dim
										SCREAMING_SNAKE_CASE_						:			int       						= norm
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= encoder_layers
										SCREAMING_SNAKE_CASE_						:			Any       						= decoder_layers
										SCREAMING_SNAKE_CASE_						:			Dict       						= use_task_norm
										SCREAMING_SNAKE_CASE_						:			Dict       						= num_attention_heads
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= dropout
										SCREAMING_SNAKE_CASE_						:			str       						= dim_feedforward
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= pre_norm
										SCREAMING_SNAKE_CASE_						:			List[str]       						= enforce_input_proj
										SCREAMING_SNAKE_CASE_						:			Any       						= query_dec_layers
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= common_stride
										SCREAMING_SNAKE_CASE_						:			str       						= decoder_layers

										super().__init__(**A__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= copy.deepcopy(self.__dict__    )
										SCREAMING_SNAKE_CASE_						:			str       						= self.backbone_config.to_dict()
										SCREAMING_SNAKE_CASE_						:			Tuple       						= self.__class__.model_type
										return output







'''simple docstring'''



from typing import Mapping

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


UpperCamelCase__					:						Any											=     logging.get_logger(__name__)

class       _a (_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE					=     'mt5'
       SCREAMING_SNAKE_CASE					=     ['past_key_values']






       def __init__(							self				,							A__=25_01_12				,							A__=5_12				,							A__=64				,							A__=10_24				,							A__=8				,							A__=None				,							A__=6				,							A__=32				,							A__=1_28				,							A__=0.1				,							A__=1E-6				,							A__=1.0				,							A__="gated-gelu"				,							A__=True				,							A__=True				,							A__="T5Tokenizer"				,							A__=False				,							A__=0				,							A__=1				,							A__=0				,							**A__				,							)			->	Union[str, Any]:
          super().__init__(
              is_encoder_decoder=A__				,							tokenizer_class=A__				,							tie_word_embeddings=A__				,							pad_token_id=A__				,							eos_token_id=A__				,							decoder_start_token_id=A__				,							**A__				,							)
          _SCREAMING_SNAKE_CASE          = vocab_size
          _SCREAMING_SNAKE_CASE          = d_model
          _SCREAMING_SNAKE_CASE          = d_kv
          _SCREAMING_SNAKE_CASE          = d_ff
          _SCREAMING_SNAKE_CASE          = num_layers
          _SCREAMING_SNAKE_CASE          = (
              num_decoder_layers if num_decoder_layers is not None else self.num_layers
          )  # default = symmetry
          _SCREAMING_SNAKE_CASE          = num_heads
          _SCREAMING_SNAKE_CASE          = relative_attention_num_buckets
          _SCREAMING_SNAKE_CASE          = relative_attention_max_distance
          _SCREAMING_SNAKE_CASE          = dropout_rate
          _SCREAMING_SNAKE_CASE          = layer_norm_epsilon
          _SCREAMING_SNAKE_CASE          = initializer_factor
          _SCREAMING_SNAKE_CASE          = feed_forward_proj
          _SCREAMING_SNAKE_CASE          = use_cache

          _SCREAMING_SNAKE_CASE          = self.feed_forward_proj.split("""-"""					)
          _SCREAMING_SNAKE_CASE          = act_info[-1]
          _SCREAMING_SNAKE_CASE          = act_info[0] == """gated"""

          if len(A__					) > 1 and act_info[0] != "gated" or len(A__					) > 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":
             _SCREAMING_SNAKE_CASE          = """gelu_new"""






       @property
       def 				UpperCamelCase (							self					)			->	int:
          return self.d_model






       @property
       def 				UpperCamelCase (							self					)			->	Optional[Any]:
          return self.num_heads






       @property
       def 				UpperCamelCase (							self					)			->	Dict:
          return self.num_layers






class       _a (_lowerCamelCase):





       """simple docstring"""




       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
       def 				UpperCamelCase (							self					)			->	Mapping[str, Mapping[int, str]]:
          _SCREAMING_SNAKE_CASE          = {
              """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
              """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
          }
          if self.use_past:
             _SCREAMING_SNAKE_CASE          = """past_encoder_sequence + sequence"""
             _SCREAMING_SNAKE_CASE          = {0: """batch"""}
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
          else:
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """decoder_sequence"""}
             _SCREAMING_SNAKE_CASE          = {0: """batch""", 1: """decoder_sequence"""}

          if self.use_past:
             self.fill_with_past_key_values_(A__				,							direction="""inputs"""					)

          return common_inputs






       @property
       # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
       def 				UpperCamelCase (							self					)			->	int:
          return 13






       @property
       def 				UpperCamelCase (							self					)			->	float:
          return 5E-4






