





import copy
from typing import Dict, List, Optional

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


_lowercase		:  List[Any]			       ={
    """facebook/mask2former-swin-small-coco-instance""": (
        """https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"""
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

_lowercase		:  Optional[Any]			       =logging.get_logger(__name__)






class 						UpperCamelCase_  (  __a							):
					_a      :						int						    =					"""mask2former"""
					_a      :						Optional[Any]						    =					["""swin"""]
					_a      :						Any						    =					{"""hidden_size""": """hidden_dim"""}



					def __init__(		self     :     int       ,      lowerCamelCase     :     Optional[int] = None       ,      lowerCamelCase     :     Union[str, Any] = 2_56       ,      lowerCamelCase     :     List[Any] = 2_56       ,      lowerCamelCase     :     Dict = 2_56       ,      lowerCamelCase     :     List[str] = 10_24       ,      lowerCamelCase     :     int = "relu"       ,      lowerCamelCase     :     Union[str, Any] = 6       ,      lowerCamelCase     :     Tuple = 10       ,      lowerCamelCase     :     Dict = 8       ,      lowerCamelCase     :     Any = 0.0       ,      lowerCamelCase     :     List[Any] = 20_48       ,      lowerCamelCase     :     Union[str, Any] = False       ,      lowerCamelCase     :     List[str] = False       ,      lowerCamelCase     :     List[str] = 4       ,      lowerCamelCase     :     Dict = 2_55       ,      lowerCamelCase     :     Dict = 1_00       ,      lowerCamelCase     :     Optional[Any] = 0.1       ,      lowerCamelCase     :     Optional[int] = 2.0       ,      lowerCamelCase     :     List[str] = 5.0       ,      lowerCamelCase     :     Dict = 5.0       ,      lowerCamelCase     :     Tuple = 1_25_44       ,      lowerCamelCase     :     Any = 3.0       ,      lowerCamelCase     :     Optional[Any] = 0.75       ,      lowerCamelCase     :     str = 0.02       ,      lowerCamelCase     :     Union[str, Any] = 1.0       ,      lowerCamelCase     :     List[str] = True       ,      lowerCamelCase     :     List[Any] = [4, 8, 16, 32]       ,      lowerCamelCase     :     int = None       ,      **lowerCamelCase     :     Dict       ,      ):
							if backbone_config is None:
									logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'						)
									lowerCamelCase_	:     Dict 						=      CONFIG_MAPPING['swin'](
									    image_size=2_24       ,      in_channels=3       ,      patch_size=4       ,      embed_dim=96       ,      depths=[2, 2, 18, 2]       ,      num_heads=[3, 6, 12, 24]       ,      window_size=7       ,      drop_path_rate=0.3       ,      use_absolute_embeddings=_lowerCamelCase       ,      out_features=['stage1', 'stage2', 'stage3', 'stage4']       ,      )

							if isinstance(_lowerCamelCase       ,      _lowerCamelCase						):
									lowerCamelCase_	:     Dict 						=      backbone_config.pop('model_type'						)
									lowerCamelCase_	:     int 						=      CONFIG_MAPPING[backbone_model_type]
									lowerCamelCase_	:     List[str] 						=      config_class.from_dict(_lowerCamelCase						)

							# verify that the backbone is supported
							if backbone_config.model_type not in self.backbones_supported:
									logger.warning_once(
									    F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. "
									    F"Supported model types: {','.join(self.backbones_supported						)}"						)

							lowerCamelCase_	:     Any 						=      backbone_config
							lowerCamelCase_	:     Any 						=      feature_size
							lowerCamelCase_	:     Dict 						=      mask_feature_size
							lowerCamelCase_	:     Union[str, Any] 						=      hidden_dim
							lowerCamelCase_	:     Tuple 						=      encoder_feedforward_dim
							lowerCamelCase_	:     Optional[int] 						=      activation_function
							lowerCamelCase_	:     str 						=      encoder_layers
							lowerCamelCase_	:     int 						=      decoder_layers
							lowerCamelCase_	:     str 						=      num_attention_heads
							lowerCamelCase_	:     str 						=      dropout
							lowerCamelCase_	:     List[Any] 						=      dim_feedforward
							lowerCamelCase_	:     Tuple 						=      pre_norm
							lowerCamelCase_	:     str 						=      enforce_input_projection
							lowerCamelCase_	:     Tuple 						=      common_stride
							lowerCamelCase_	:     int 						=      ignore_value
							lowerCamelCase_	:     int 						=      num_queries
							lowerCamelCase_	:     int 						=      no_object_weight
							lowerCamelCase_	:     Dict 						=      class_weight
							lowerCamelCase_	:     Tuple 						=      mask_weight
							lowerCamelCase_	:     str 						=      dice_weight
							lowerCamelCase_	:     str 						=      train_num_points
							lowerCamelCase_	:     Tuple 						=      oversample_ratio
							lowerCamelCase_	:     int 						=      importance_sample_ratio
							lowerCamelCase_	:     Union[str, Any] 						=      init_std
							lowerCamelCase_	:     Dict 						=      init_xavier_std
							lowerCamelCase_	:     Optional[Any] 						=      use_auxiliary_loss
							lowerCamelCase_	:     List[str] 						=      feature_strides
							lowerCamelCase_	:     Tuple 						=      output_auxiliary_logits
							lowerCamelCase_	:     Any 						=      decoder_layers

							super().__init__(**_lowerCamelCase						)



					@classmethod
					def   __a     (		cls     :     Union[str, Any]       ,      lowerCamelCase     :     List[str]       ,      **lowerCamelCase     :     int						):
							return cls(
							    backbone_config=_lowerCamelCase       ,      **_lowerCamelCase       ,      )





					def   __a     (		self     :     Optional[Any]						):
							lowerCamelCase_	:     Tuple 						=      copy.deepcopy(self.__dict__						)
							lowerCamelCase_	:     Tuple 						=      self.backbone_config.to_dict()
							lowerCamelCase_	:     List[Any] 						=      self.__class__.model_type
							return output


"""simple docstring"""






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


_lowercase							=				logging.get_logger(__name__)


_lowercase							=				{
    '''Salesforce/codegen-350M-nl''': '''https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json''',
    '''Salesforce/codegen-350M-multi''': '''https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json''',
    '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json''',
    '''Salesforce/codegen-2B-nl''': '''https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-2B-multi''': '''https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-2B-mono''': '''https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-6B-nl''': '''https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-6B-multi''': '''https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-6B-mono''': '''https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json''',
    '''Salesforce/codegen-16B-nl''': '''https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json''',
    '''Salesforce/codegen-16B-multi''': '''https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json''',
    '''Salesforce/codegen-16B-mono''': '''https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json''',
}

class 					__a     ( __a       ):



							'''simple docstring'''







							_lowerCamelCase					:			List[Any]          =   """codegen"""
							_lowerCamelCase					:			List[str]          =   {
							    """max_position_embeddings""": """n_positions""",
							    """hidden_size""": """n_embd""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}
							def __init__(     self  ,    _lowerCamelCase=50_400  ,    _lowerCamelCase=2_048  ,    _lowerCamelCase=2_048  ,    _lowerCamelCase=4_096  ,    _lowerCamelCase=28  ,    _lowerCamelCase=16  ,    _lowerCamelCase=64  ,    _lowerCamelCase=None  ,    _lowerCamelCase="gelu_new"  ,    _lowerCamelCase=0.0  ,    _lowerCamelCase=0.0  ,    _lowerCamelCase=0.0  ,    _lowerCamelCase=1e-5  ,    _lowerCamelCase=0.02  ,    _lowerCamelCase=True  ,    _lowerCamelCase=50_256  ,    _lowerCamelCase=50_256  ,    _lowerCamelCase=False  ,    **_lowerCamelCase  ,    )			->							Union[str, Any]:




										'''simple docstring'''
										__lowercase       =       vocab_size
										__lowercase       =       n_ctx
										__lowercase       =       n_positions
										__lowercase       =       n_embd
										__lowercase       =       n_layer
										__lowercase       =       n_head
										__lowercase       =       n_inner
										__lowercase       =       rotary_dim
										__lowercase       =       activation_function
										__lowercase       =       resid_pdrop
										__lowercase       =       embd_pdrop
										__lowercase       =       attn_pdrop
										__lowercase       =       layer_norm_epsilon
										__lowercase       =       initializer_range
										__lowercase       =       use_cache

										__lowercase       =       bos_token_id
										__lowercase       =       eos_token_id

										super().__init__(
										    bos_token_id=_lowerCamelCase  ,    eos_token_id=_lowerCamelCase  ,    tie_word_embeddings=_lowerCamelCase  ,    **_lowerCamelCase       )





class 					__a     ( __a       ):



							'''simple docstring'''







							def __init__(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = "default"  ,    _lowerCamelCase = None  ,    _lowerCamelCase = False  ,    )			->							str:




										'''simple docstring'''
										super().__init__(_lowerCamelCase  ,    task=_lowerCamelCase  ,    patching_specs=_lowerCamelCase  ,    use_past=_lowerCamelCase       )
										if not getattr(self._config  ,    "pad_token_id"  ,    _lowerCamelCase       ):
													# TODO: how to do that better?
													__lowercase       =       0
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							Mapping[str, Mapping[int, str]]:




										'''simple docstring'''
										__lowercase       =       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}       )
										if self.use_past:
													self.fill_with_past_key_values_(_lowerCamelCase  ,    direction="inputs"       )
													__lowercase       =       {0: "batch", 1: "past_sequence + sequence"}
										else:
													__lowercase       =       {0: "batch", 1: "sequence"}

										return common_inputs
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										return self._config.n_layer
							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										return self._config.n_head
							def   SCREAMING_SNAKE_CASE						(     self  ,    _lowerCamelCase  ,    _lowerCamelCase = -1  ,    _lowerCamelCase = -1  ,    _lowerCamelCase = False  ,    _lowerCamelCase = None  ,    )			->							Mapping[str, Any]:




										'''simple docstring'''
										__lowercase       =       super(_lowerCamelCase  ,    self       ).generate_dummy_inputs(
										    _lowerCamelCase  ,    batch_size=_lowerCamelCase  ,    seq_length=_lowerCamelCase  ,    is_pair=_lowerCamelCase  ,    framework=_lowerCamelCase       )

										# We need to order the input in the way they appears in the forward()
										__lowercase       =       OrderedDict({"input_ids": common_inputs["input_ids"]}       )

										# Need to add the past_keys
										if self.use_past:
													if not is_torch_available():
																raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."       )
													else:
																import torch

																__lowercase   ,					__lowercase       =       common_inputs["input_ids"].shape
																# Not using the same length for past_key_values
																__lowercase       =       seqlen + 2
																__lowercase       =       (
																    batch,
																    self.num_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // self.num_attention_heads,
																)
																__lowercase       =       [
																    (torch.zeros(_lowerCamelCase       ), torch.zeros(_lowerCamelCase       )) for _ in range(self.num_layers       )
																]

										__lowercase       =       common_inputs["attention_mask"]
										if self.use_past:
													__lowercase       =       ordered_inputs["attention_mask"].dtype
													__lowercase       =       torch.cat(
													    [ordered_inputs["attention_mask"], torch.ones(_lowerCamelCase  ,    _lowerCamelCase  ,    dtype=_lowerCamelCase       )]  ,    dim=1       )

										return ordered_inputs





							@property
							def   SCREAMING_SNAKE_CASE						(     self       )			->							int:




										'''simple docstring'''
										return 13





