





'''simple docstring'''






from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput




class  _A (					nn.Module		):

		def __init__(   self					:   Optional[Any]  ,      __magic_name__					:   int = 16  ,      __magic_name__					:   int = 88  ,      __magic_name__					:   Optional[int] = None  ,      __magic_name__					:   int = 1  ,      __magic_name__					:   float = 0.0  ,      __magic_name__					:   int = 32  ,      __magic_name__					:   Optional[int] = None  ,      __magic_name__					:   bool = False  ,      __magic_name__					:   Optional[int] = None  ,      __magic_name__					:   Optional[int] = None  ,      __magic_name__					:   str = "geglu"  ,      __magic_name__					:   Optional[int] = None  ,      )					->    Any:


					"""simple docstring"""

					super().__init__()
					__snake_case					:      List[Any]							      =     nn.ModuleList(
					    [
					        TransformeraDModel(
					            num_attention_heads=__magic_name__  ,      attention_head_dim=__magic_name__  ,      in_channels=__magic_name__  ,      num_layers=__magic_name__  ,      dropout=__magic_name__  ,      norm_num_groups=__magic_name__  ,      cross_attention_dim=__magic_name__  ,      attention_bias=__magic_name__  ,      sample_size=__magic_name__  ,      num_vector_embeds=__magic_name__  ,      activation_fn=__magic_name__  ,      num_embeds_ada_norm=__magic_name__  ,      )
					        for _ in range(2			)
					    ]			)

					# Variables that can be set by a pipeline:

					# The ratio of transformer1 to transformer2's output states to be combined during inference
					__snake_case					:      List[Any]							      =     0.5

					# The shape of `encoder_hidden_states` is expected to be
					# `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
					__snake_case					:      Any							      =     [77, 2_57]

					# Which transformer to use to encode which condition.
					# E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
					__snake_case					:      int							      =     [1, 0]

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   str  ,      __magic_name__					:   List[str]=None  ,      __magic_name__					:   int=None  ,      __magic_name__					:   Optional[Any]=None  ,      __magic_name__					:   bool = True  ,      )					->    Dict:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     hidden_states

					__snake_case					:      int							      =     []
					__snake_case					:      int							      =     0
					# attention_mask is not used yet
					for i in range(2			):
								# for each of the two transformers, pass the corresponding condition tokens
								__snake_case					:      Tuple							      =     encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
								__snake_case					:      Tuple							      =     self.transformer_index_for_condition[i]
								__snake_case					:      Optional[Any]							      =     self.transformers[transformer_index](
								    __magic_name__  ,      encoder_hidden_states=__magic_name__  ,      timestep=__magic_name__  ,      cross_attention_kwargs=__magic_name__  ,      return_dict=__magic_name__  ,      )[0]
								encoded_states.append(encoded_state - input_states			)
								tokens_start += self.condition_lengths[i]

					__snake_case					:      List[Any]							      =     encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
					__snake_case					:      List[str]							      =     output_states + input_states

					if not return_dict:
								return (output_states,)

					return TransformeraDModelOutput(sample=__magic_name__			)









'''simple docstring'''






import warnings

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




class  _A (					__lowercase		):
		lowercase__:   Any				     =   ['''image_processor''', '''tokenizer''']
		lowercase__:   Any				     =   '''CLIPImageProcessor'''
		lowercase__:   Optional[Any]				     =   ('''CLIPTokenizer''', '''CLIPTokenizerFast''')

		def __init__(   self					:   int  ,      __magic_name__					:   Dict=None  ,      __magic_name__					:   Dict=None  ,      **__magic_name__					:   Union[str, Any]			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     None
					if "feature_extractor" in kwargs:
								warnings.warn(
								    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
								    """ instead."""  ,      __magic_name__  ,      )
								__snake_case					:      List[Any]							      =     kwargs.pop("""feature_extractor"""			)

					__snake_case					:      List[str]							      =     image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
								raise ValueError("""You need to specify an `image_processor`."""			)
					if tokenizer is None:
								raise ValueError("""You need to specify a `tokenizer`."""			)

					super().__init__(__magic_name__  ,      __magic_name__			)

		def __call__(   self					:   int  ,      __magic_name__					:   List[str]=None  ,      __magic_name__					:   Tuple=None  ,      __magic_name__					:   Any=None  ,      **__magic_name__					:   Union[str, Any]			)					->    Optional[int]:


					"""simple docstring"""

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

					if text is not None:
								__snake_case					:      int							      =     self.tokenizer(__magic_name__  ,      return_tensors=__magic_name__  ,      **__magic_name__			)

					if images is not None:
								__snake_case					:      str							      =     self.image_processor(__magic_name__  ,      return_tensors=__magic_name__  ,      **__magic_name__			)

					if text is not None and images is not None:
								__snake_case					:      Union[str, Any]							      =     image_features.pixel_values
								return encoding
					elif text is not None:
								return encoding
					else:
								return BatchEncoding(data=dict(**__magic_name__			)  ,      tensor_type=__magic_name__			)

		def 	lowercase__     (   self					:   Optional[int]  ,      *__magic_name__					:   List[Any]  ,      **__magic_name__					:   Any			)					->    Optional[Any]:


					"""simple docstring"""

					return self.tokenizer.batch_decode(*__magic_name__  ,      **__magic_name__			)

		def 	lowercase__     (   self					:   List[str]  ,      *__magic_name__					:   Tuple  ,      **__magic_name__					:   List[Any]			)					->    int:


					"""simple docstring"""

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

		@property
		def 	lowercase__     (   self					:   List[Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     self.tokenizer.model_input_names
					__snake_case					:      str							      =     self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)

		@property
		def 	lowercase__     (   self					:   int			)					->    List[str]:


					"""simple docstring"""

					warnings.warn(
					    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""  ,      __magic_name__  ,      )
					return self.image_processor_class

		@property
		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Dict:


					"""simple docstring"""

					warnings.warn(
					    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""  ,      __magic_name__  ,      )
					return self.image_processor


