




from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy


class 							UpperCamelCase_       (					UpperCAmelCase__	):

    '''simple docstring'''






    UpperCAmelCase__				    = '''EncodecFeatureExtractor'''
    UpperCAmelCase__				    = ('''T5Tokenizer''', '''T5TokenizerFast''')


    def __init__(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      int)		->Dict:





           '''simple docstring'''

           super().__init__(UpperCAmelCase__   ,					UpperCAmelCase__)
           A__ 					=       self.feature_extractor
           A__ 					=       False


    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple   ,					UpperCAmelCase__ :      List[Any]=None   ,					UpperCAmelCase__ :      List[str]=None   ,					UpperCAmelCase__ :      Tuple=True)		->str:





           '''simple docstring'''

           return self.tokenizer.get_decoder_prompt_ids(task=UpperCAmelCase__   ,					language=UpperCAmelCase__   ,					no_timestamps=UpperCAmelCase__)


    def __call__(     self :      Any   ,					*UpperCAmelCase__ :      Tuple   ,					**UpperCAmelCase__ :      Union[str, Any])		->int:





           '''simple docstring'''

           if self._in_target_context_manager:
                  return self.current_processor(*UpperCAmelCase__   ,					**UpperCAmelCase__)

           A__ 					=       kwargs.pop('''audio'''   ,					UpperCAmelCase__)
           A__ 					=       kwargs.pop('''sampling_rate'''   ,					UpperCAmelCase__)
           A__ 					=       kwargs.pop('''text'''   ,					UpperCAmelCase__)
           if len(UpperCAmelCase__) > 0:
                  A__ 					=       args[0]
                  A__ 					=       args[1:]

           if audio is None and text is None:
                  raise ValueError('''You need to specify either an `audio` or `text` input to process.''')

           if text is not None:
                  A__ 					=       self.tokenizer(UpperCAmelCase__   ,					**UpperCAmelCase__)

           if audio is not None:
                  A__ 					=       self.feature_extractor(UpperCAmelCase__   ,					*UpperCAmelCase__   ,					sampling_rate=UpperCAmelCase__   ,					**UpperCAmelCase__)

           if audio is None:
                  return inputs

           elif text is None:
                  return audio_inputs

           else:
                  A__ 					=       audio_inputs['''input_values''']
                  if "padding_mask" in audio_inputs:
                         A__ 					=       audio_inputs['''padding_mask''']
                  return inputs


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any]   ,					*UpperCAmelCase__ :      List[str]   ,					**UpperCAmelCase__ :      Any)		->List[str]:





           '''simple docstring'''

           A__ 					=       kwargs.pop('''audio'''   ,					UpperCAmelCase__)
           A__ 					=       kwargs.pop('''padding_mask'''   ,					UpperCAmelCase__)

           if len(UpperCAmelCase__) > 0:
                  A__ 					=       args[0]
                  A__ 					=       args[1:]

           if audio_values is not None:
                  return self._decode_audio(UpperCAmelCase__   ,					padding_mask=UpperCAmelCase__)
           else:
                  return self.tokenizer.batch_decode(*UpperCAmelCase__   ,					**UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					*UpperCAmelCase__ :      Tuple   ,					**UpperCAmelCase__ :      int)		->str:





           '''simple docstring'''

           return self.tokenizer.decode(*UpperCAmelCase__   ,					**UpperCAmelCase__)





    def 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      Tuple   ,					UpperCAmelCase__ :      Optional = None)		->List[np.ndarray]:





           '''simple docstring'''

           A__ 					=       to_numpy(UpperCAmelCase__)
           A__					,				A__					,				A__ 					=       audio_values.shape

           if padding_mask is None:
                  return list(UpperCAmelCase__)

           A__ 					=       to_numpy(UpperCAmelCase__)

           # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
           # token (so that the generated audio values are **not** treated as padded tokens)
           A__ 					=       seq_len - padding_mask.shape[-1]
           A__ 					=       1 - self.feature_extractor.padding_value
           A__ 					=       np.pad(UpperCAmelCase__   ,					((0, 0), (0, difference))   ,					'''constant'''   ,					constant_values=UpperCAmelCase__)

           A__ 					=       audio_values.tolist()
           for i in range(UpperCAmelCase__):
                  A__ 					=       np.asarray(audio_values[i])[
                      padding_mask[i][None, :] != self.feature_extractor.padding_value
                  ]
                  A__ 					=       sliced_audio.reshape(UpperCAmelCase__   ,					-1)

           return audio_values





import copy
from typing import Dict, List, Optional

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


SCREAMING_SNAKE_CASE		:Optional[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
}

SCREAMING_SNAKE_CASE		:Union[str, Any]						=     logging.get_logger(__name__)






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Optional[int]										=      """mask2former"""
     UpperCamelCase_	:Dict										=      ["""swin"""]
     UpperCamelCase_	:List[Any]										=      {"""hidden_size""": """hidden_dim"""}






     def __init__(				self		,						_lowercase = None		,						_lowercase = 256		,						_lowercase = 256		,						_lowercase = 256		,						_lowercase = 1_024		,						_lowercase = "relu"		,						_lowercase = 6		,						_lowercase = 10		,						_lowercase = 8		,						_lowercase = 0.0		,						_lowercase = 2_048		,						_lowercase = False		,						_lowercase = False		,						_lowercase = 4		,						_lowercase = 255		,						_lowercase = 100		,						_lowercase = 0.1		,						_lowercase = 2.0		,						_lowercase = 5.0		,						_lowercase = 5.0		,						_lowercase = 12_544		,						_lowercase = 3.0		,						_lowercase = 0.75		,						_lowercase = 0.02		,						_lowercase = 1.0		,						_lowercase = True		,						_lowercase = [4, 8, 16, 32]		,						_lowercase = None		,						**_lowercase		,						)->     Union[str, Any]:
         if backbone_config is None:
             logger.info("`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."	)
             UpperCamelCase_			  =						CONFIG_MAPPING["swin"](
                 image_size=224		,						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=_lowercase		,						out_features=["stage1", "stage2", "stage3", "stage4"]		,						)

         if isinstance(_lowercase		,						_lowercase	):
             UpperCamelCase_			  =						backbone_config.pop("model_type"	)
             UpperCamelCase_			  =						CONFIG_MAPPING[backbone_model_type]
             UpperCamelCase_			  =						config_class.from_dict(_lowercase	)

         # 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	)}"	)

         UpperCamelCase_			  =						backbone_config
         UpperCamelCase_			  =						feature_size
         UpperCamelCase_			  =						mask_feature_size
         UpperCamelCase_			  =						hidden_dim
         UpperCamelCase_			  =						encoder_feedforward_dim
         UpperCamelCase_			  =						activation_function
         UpperCamelCase_			  =						encoder_layers
         UpperCamelCase_			  =						decoder_layers
         UpperCamelCase_			  =						num_attention_heads
         UpperCamelCase_			  =						dropout
         UpperCamelCase_			  =						dim_feedforward
         UpperCamelCase_			  =						pre_norm
         UpperCamelCase_			  =						enforce_input_projection
         UpperCamelCase_			  =						common_stride
         UpperCamelCase_			  =						ignore_value
         UpperCamelCase_			  =						num_queries
         UpperCamelCase_			  =						no_object_weight
         UpperCamelCase_			  =						class_weight
         UpperCamelCase_			  =						mask_weight
         UpperCamelCase_			  =						dice_weight
         UpperCamelCase_			  =						train_num_points
         UpperCamelCase_			  =						oversample_ratio
         UpperCamelCase_			  =						importance_sample_ratio
         UpperCamelCase_			  =						init_std
         UpperCamelCase_			  =						init_xavier_std
         UpperCamelCase_			  =						use_auxiliary_loss
         UpperCamelCase_			  =						feature_strides
         UpperCamelCase_			  =						output_auxiliary_logits
         UpperCamelCase_			  =						decoder_layers

         super().__init__(**_lowercase	)






     @classmethod
     def 	UpperCAmelCase_				(				cls		,						_lowercase		,						**_lowercase	)->     Optional[int]:
         return cls(
             backbone_config=_lowercase		,						**_lowercase		,						)






     def 	UpperCAmelCase_				(				self	)->     Dict[str, any]:
         UpperCamelCase_			  =						copy.deepcopy(self.__dict__	)
         UpperCamelCase_			  =						self.backbone_config.to_dict()
         UpperCamelCase_			  =						self.__class__.model_type
         return output
