


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__    :Optional[Any]       =				{
    '''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__    :str       =				logging.get_logger(__name__)

class 						__a (							UpperCAmelCase     ):
		_a      :    Union[str, Any]           =      'maskformer'
		_a      :    List[Any]           =      {'hidden_size': 'mask_feature_size'}
		_a      :    Dict           =      ['resnet', 'swin']
		_a      :    Union[str, Any]           =      ['detr']
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE = 256     ,       _SCREAMING_SNAKE_CASE = 256     ,       _SCREAMING_SNAKE_CASE = 0.1     ,       _SCREAMING_SNAKE_CASE = False     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = None     ,       _SCREAMING_SNAKE_CASE = 0.02     ,       _SCREAMING_SNAKE_CASE = 1.0     ,       _SCREAMING_SNAKE_CASE = 1.0     ,       _SCREAMING_SNAKE_CASE = 1.0     ,       _SCREAMING_SNAKE_CASE = 20.0     ,       _SCREAMING_SNAKE_CASE = None     ,       **_SCREAMING_SNAKE_CASE     ,       )					->			Tuple:




									"""simple docstring"""





									if backbone_config is None:
																# fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
																_UpperCAmelCase											=     SwinConfig(
																    image_size=384     ,       in_channels=3     ,       patch_size=4     ,       embed_dim=128     ,       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		):
																_UpperCAmelCase											=     backbone_config.pop('model_type'		)
																_UpperCAmelCase											=     CONFIG_MAPPING[backbone_model_type]
																_UpperCAmelCase											=     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
																_UpperCAmelCase											=     DetrConfig()
									else:
																# verify that the decoder is supported
																_UpperCAmelCase											=     (
																    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		):
																							_UpperCAmelCase											=     CONFIG_MAPPING[decoder_type]
																							_UpperCAmelCase											=     config_class.from_dict(_SCREAMING_SNAKE_CASE		)

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

									_UpperCAmelCase											=     self.decoder_config.encoder_attention_heads
									_UpperCAmelCase											=     self.decoder_config.num_hidden_layers
									super().__init__(**_SCREAMING_SNAKE_CASE		)
		@classmethod
		def       UpperCAmelCase__							(     cls     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)					->			Tuple:




									"""simple docstring"""





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






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




									"""simple docstring"""





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




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


lowerCAmelCase__    :Tuple       =				{
    '''configuration_speecht5''': [
        '''SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP''',
        '''SpeechT5Config''',
        '''SpeechT5HifiGanConfig''',
    ],
    '''feature_extraction_speecht5''': ['''SpeechT5FeatureExtractor'''],
    '''processing_speecht5''': ['''SpeechT5Processor'''],
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :Dict       =				['''SpeechT5Tokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__    :List[str]       =				[
		    '''SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''SpeechT5ForSpeechToText''',
		    '''SpeechT5ForSpeechToSpeech''',
		    '''SpeechT5ForTextToSpeech''',
		    '''SpeechT5Model''',
		    '''SpeechT5PreTrainedModel''',
		    '''SpeechT5HifiGan''',
		]

if TYPE_CHECKING:
		from .configuration_speechta import (
		    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
		    SpeechTaConfig,
		    SpeechTaHifiGanConfig,
		)
		from .feature_extraction_speechta import SpeechTaFeatureExtractor
		from .processing_speechta import SpeechTaProcessor

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_speechta import SpeechTaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_speechta import (
				    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SpeechTaForSpeechToSpeech,
				    SpeechTaForSpeechToText,
				    SpeechTaForTextToSpeech,
				    SpeechTaHifiGan,
				    SpeechTaModel,
				    SpeechTaPreTrainedModel,
				)

else:
		import sys

		lowerCAmelCase__    :str       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
