





"""simple docstring"""






from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''ClapFeatureExtractor'''
					__lowerCAmelCase  =  ('''RobertaTokenizer''', '''RobertaTokenizerFast''')







					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									super().__init__(_UpperCAmelCase	,			_UpperCAmelCase     )







					def __call__(						self	,			_UpperCAmelCase=None	,			_UpperCAmelCase=None	,			_UpperCAmelCase=None	,			**_UpperCAmelCase     ):
									__a :						int			       =  kwargs.pop('''sampling_rate'''	,			_UpperCAmelCase     )

									if text is None and audios is None:
													raise ValueError('''You have to specify either text or audios. Both cannot be none.'''     )

									if text is not None:
													__a :						Union[str, Any]			       =  self.tokenizer(_UpperCAmelCase	,			return_tensors=_UpperCAmelCase	,			**_UpperCAmelCase     )

									if audios is not None:
													__a :						Union[str, Any]			       =  self.feature_extractor(
													    _UpperCAmelCase	,			sampling_rate=_UpperCAmelCase	,			return_tensors=_UpperCAmelCase	,			**_UpperCAmelCase     )

									if text is not None and audios is not None:
													__a :						int			       =  audio_features.input_features
													return encoding
									elif text is not None:
													return encoding
									else:
													return BatchEncoding(data=dict(**_UpperCAmelCase     )	,			tensor_type=_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			*_UpperCAmelCase	,			**_UpperCAmelCase     ):
									return self.tokenizer.batch_decode(*_UpperCAmelCase	,			**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			*_UpperCAmelCase	,			**_UpperCAmelCase     ):
									return self.tokenizer.decode(*_UpperCAmelCase	,			**_UpperCAmelCase     )







					@property
					def  _lowerCamelCase		(						self     ):
									__a :						int			       =  self.tokenizer.model_input_names
									__a :						Dict			       =  self.feature_extractor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names     )     )






"""simple docstring"""






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


A 			=  logging.get_logger(__name__)  # pylint: disable=invalid-name




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=768     ):
									super().__init__(_UpperCAmelCase     )
									__a :						str			       =  proj_size

									__a :						Optional[Any]			       =  CLIPVisionModel(_UpperCAmelCase     )
									__a :						List[Any]			       =  PaintByExampleMapper(_UpperCAmelCase     )
									__a :						int			       =  nn.LayerNorm(config.hidden_size     )
									__a :						List[Any]			       =  nn.Linear(config.hidden_size	,			self.proj_size     )

									# uncondition for scaling
									__a :						int			       =  nn.Parameter(torch.randn((1, 1, self.proj_size)     )     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=False     ):
									__a :						str			       =  self.model(pixel_values=_UpperCAmelCase     )
									__a :						Union[str, Any]			       =  clip_output.pooler_output
									__a :						Optional[int]			       =  self.mapper(latent_states[:, None]     )
									__a :						int			       =  self.final_layer_norm(_UpperCAmelCase     )
									__a :						Optional[Any]			       =  self.proj_out(_UpperCAmelCase     )
									if return_uncond_vector:
													return latent_states, self.uncond_vector

									return latent_states






class 				__lowercase			(			nn.Module							):







					'''simple docstring'''




					def __init__(						self	,			_UpperCAmelCase     ):
									super().__init__()
									__a :						List[str]			       =  (config.num_hidden_layers + 1) // 5
									__a :						Optional[Any]			       =  config.hidden_size
									__a :						str			       =  1
									__a :						Union[str, Any]			       =  nn.ModuleList(
									    [
									        BasicTransformerBlock(_UpperCAmelCase	,			_UpperCAmelCase	,			_UpperCAmelCase	,			activation_fn='''gelu'''	,			attention_bias=_UpperCAmelCase     )
									        for _ in range(_UpperCAmelCase     )
									    ]     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									for block in self.blocks:
													__a :						Union[str, Any]			       =  block(_UpperCAmelCase     )

									return hidden_states