




'''simple docstring'''







import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowerCAmelCase_     						=				{
    '''facebook/maskformer-swin-base-ade''': (
        '''https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'''
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowerCAmelCase_     						=				logging.get_logger(__name__)

class 	_snake_case(			UpperCAmelCase      ):
 __snake_case:							Union[str, Any]						=					'maskformer'
 __snake_case:							List[Any]						=					{'hidden_size': 'mask_feature_size'}
 __snake_case:							Dict						=					['resnet', 'swin']
 __snake_case:							Union[str, Any]						=					['detr']






 def __init__(self       :  Any    ,		a       :  List[str] = 2_56    ,		a       :  int = 2_56    ,		a       :  List[str] = 0.1    ,		a       :  Optional[int] = False    ,		a       :  Optional[int] = None    ,		a       :  str = None    ,		a       :  Any = 0.02    ,		a       :  str = 1.0    ,		a       :  List[str] = 1.0    ,		a       :  Optional[Any] = 1.0    ,		a       :  Any = 20.0    ,		a       :  List[Any] = None    ,		**a       :  List[Any]    ,		)       ->   Tuple:






    """simple docstring"""


    if backbone_config is None:
       # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
       A__       =     SwinConfig(
           image_size=3_84    ,		in_channels=3    ,		patch_size=4    ,		embed_dim=1_28    ,		depths=[2, 2, 18, 2]    ,		num_heads=[4, 8, 16, 32]    ,		window_size=12    ,		drop_path_rate=0.3    ,		out_features=['stage1', 'stage2', 'stage3', 'stage4']    ,		)

    if isinstance(_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ):
       A__       =     backbone_config.pop('model_type'    )
       A__       =     CONFIG_MAPPING[backbone_model_type]
       A__       =     config_class.from_dict(_SCREAMING_SNAKE_CASE    )

    # 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 MaskFormer. """
           f"""Supported model types: {",".join(self.backbones_supported    )}"""    )

    if decoder_config is None:
       # fall back to https://huggingface.co/facebook/detr-resnet-50
       A__       =     DetrConfig()
    else:
       # verify that the decoder is supported
       A__       =     (
           decoder_config.pop('model_type'    ) if isinstance(_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ) else decoder_config.model_type
       )
       if decoder_type not in self.decoders_supported:
          raise ValueError(
              f"""Transformer Decoder {decoder_type} not supported, please use one of"""
              f""" {",".join(self.decoders_supported    )}"""    )
       if isinstance(_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE    ):
          A__       =     CONFIG_MAPPING[decoder_type]
          A__       =     config_class.from_dict(_SCREAMING_SNAKE_CASE    )

    A__       =     backbone_config
    A__       =     decoder_config
    # main feature dimension for the model
    A__       =     fpn_feature_size
    A__       =     mask_feature_size
    # initializer
    A__       =     init_std
    A__       =     init_xavier_std
    # Hungarian matcher && loss
    A__       =     cross_entropy_weight
    A__       =     dice_weight
    A__       =     mask_weight
    A__       =     use_auxiliary_loss
    A__       =     no_object_weight
    A__       =     output_auxiliary_logits

    A__       =     self.decoder_config.encoder_attention_heads
    A__       =     self.decoder_config.num_hidden_layers
    super().__init__(**_SCREAMING_SNAKE_CASE    )






 @classmethod
 def 						_UpperCamelCase (cls       :  Union[str, Any]    ,		a       :  List[Any]    ,		a       :  Any    ,		**a       :  str    )       ->   Tuple:






    """simple docstring"""


    return cls(
        backbone_config=_SCREAMING_SNAKE_CASE    ,		decoder_config=_SCREAMING_SNAKE_CASE    ,		**_SCREAMING_SNAKE_CASE    ,		)






 def 						_UpperCamelCase (self       :  Any    )       ->   Dict[str, any]:






    """simple docstring"""


    A__       =     copy.deepcopy(self.__dict__    )
    A__       =     self.backbone_config.to_dict()
    A__       =     self.decoder_config.to_dict()
    A__       =     self.__class__.model_type
    return output






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

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowerCAmelCase__    :Dict       =				logging.get_logger(__name__)

lowerCAmelCase__    :Optional[int]       =				{
    '''deepmind/language-perceiver''': '''https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json''',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}

class 						__a (							UpperCAmelCase     ):
		_a      :    Tuple           =      'perceiver'
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE=256     ,       _SCREAMING_SNAKE_CASE=1280     ,       _SCREAMING_SNAKE_CASE=768     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=26     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=8     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE=None     ,       _SCREAMING_SNAKE_CASE="kv"     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE=1     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.02     ,       _SCREAMING_SNAKE_CASE=1e-1_2     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=262     ,       _SCREAMING_SNAKE_CASE=2048     ,       _SCREAMING_SNAKE_CASE=56     ,       _SCREAMING_SNAKE_CASE=[368, 496]     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=1920     ,       _SCREAMING_SNAKE_CASE=16     ,       _SCREAMING_SNAKE_CASE=[1, 16, 224, 224]     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Any:




									"""simple docstring"""





									super().__init__(**_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     num_latents
									_UpperCAmelCase											=     d_latents
									_UpperCAmelCase											=     d_model
									_UpperCAmelCase											=     num_blocks
									_UpperCAmelCase											=     num_self_attends_per_block
									_UpperCAmelCase											=     num_self_attention_heads
									_UpperCAmelCase											=     num_cross_attention_heads
									_UpperCAmelCase											=     qk_channels
									_UpperCAmelCase											=     v_channels
									_UpperCAmelCase											=     cross_attention_shape_for_attention
									_UpperCAmelCase											=     self_attention_widening_factor
									_UpperCAmelCase											=     cross_attention_widening_factor
									_UpperCAmelCase											=     hidden_act
									_UpperCAmelCase											=     attention_probs_dropout_prob
									_UpperCAmelCase											=     initializer_range
									_UpperCAmelCase											=     layer_norm_eps
									_UpperCAmelCase											=     use_query_residual
									# masked language modeling attributes
									_UpperCAmelCase											=     vocab_size
									_UpperCAmelCase											=     max_position_embeddings
									# image classification attributes
									_UpperCAmelCase											=     image_size
									# flow attributes
									_UpperCAmelCase											=     train_size
									# multimodal autoencoding attributes
									_UpperCAmelCase											=     num_frames
									_UpperCAmelCase											=     audio_samples_per_frame
									_UpperCAmelCase											=     samples_per_patch
									_UpperCAmelCase											=     output_shape



class 						__a (							UpperCAmelCase     ):
		@property
		def       UpperCAmelCase__							(     self		)					->			Mapping[str, Mapping[int, str]]:




									"""simple docstring"""





									if self.task == "multiple-choice":
																_UpperCAmelCase											=     {0: 'batch', 1: 'choice', 2: 'sequence'}
									else:
																_UpperCAmelCase											=     {0: 'batch', 1: 'sequence'}
									return OrderedDict(
									    [
									        ('inputs', dynamic_axis),
									        ('attention_mask', dynamic_axis),
									    ]		)
		@property
		def       UpperCAmelCase__							(     self		)					->			float:




									"""simple docstring"""





									return 1e-4






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = -1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = 3     ,       _SCREAMING_SNAKE_CASE = 40     ,       _SCREAMING_SNAKE_CASE = 40     ,       )					->			Mapping[str, Any]:




									"""simple docstring"""





									if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																_UpperCAmelCase											=     compute_effective_axis_dimension(
																    _SCREAMING_SNAKE_CASE     ,       fixed_dimension=OnnxConfig.default_fixed_batch     ,       num_token_to_add=0		)
																# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
																_UpperCAmelCase											=     preprocessor.num_special_tokens_to_add(_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     compute_effective_axis_dimension(
																    _SCREAMING_SNAKE_CASE     ,       fixed_dimension=OnnxConfig.default_fixed_sequence     ,       num_token_to_add=_SCREAMING_SNAKE_CASE		)
																# Generate dummy inputs according to compute batch and sequence
																_UpperCAmelCase											=     [' '.join(['a']		) * seq_length] * batch_size
																_UpperCAmelCase											=     dict(preprocessor(_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE		)		)
																_UpperCAmelCase											=     inputs.pop('input_ids'		)
																return inputs
									elif isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		) and preprocessor.model_input_names[0] == "pixel_values":
																# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																_UpperCAmelCase											=     compute_effective_axis_dimension(_SCREAMING_SNAKE_CASE     ,       fixed_dimension=OnnxConfig.default_fixed_batch		)
																_UpperCAmelCase											=     self._generate_dummy_images(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     dict(preprocessor(images=_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE		)		)
																_UpperCAmelCase											=     inputs.pop('pixel_values'		)
																return inputs
									else:
																raise ValueError(
																    'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'		)
