




import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_  :      Tuple		=		logging.get_logger(__name__)

snake_case_  :      int		=		{
    """google/pix2struct-textcaps-base""": (
        """https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
    ),
}





class 					snake_case_   (     _UpperCamelCase	):



				'''simple docstring'''
				lowerCamelCase       =  """pix2struct_text_model"""
				lowerCamelCase       =  ["""past_key_values"""]
				lowerCamelCase       =  {
				    """hidden_size""": """hidden_size""",
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}





				def __init__(      self							:   Any      ,							__magic_name__							:   Optional[Any]=5_0244      ,							__magic_name__							:   int=768      ,							__magic_name__							:   Optional[int]=64      ,							__magic_name__							:   Any=2048      ,							__magic_name__							:   Optional[int]=12      ,							__magic_name__							:   str=12      ,							__magic_name__							:   Union[str, Any]=32      ,							__magic_name__							:   Union[str, Any]=128      ,							__magic_name__							:   Optional[Any]=0.1      ,							__magic_name__							:   Dict=1e-6      ,							__magic_name__							:   Optional[Any]=1.0      ,							__magic_name__							:   Dict="gelu_new"      ,							__magic_name__							:   List[Any]=0      ,							__magic_name__							:   Optional[Any]=False      ,							__magic_name__							:   List[str]=0      ,							__magic_name__							:   Any=1      ,							__magic_name__							:   List[Any]=False      ,							__magic_name__							:   Tuple=True      ,							**__magic_name__							:   Union[str, Any]      ,							)							->			Dict:
											lowerCamelCase_			:    Tuple   						=      vocab_size
											lowerCamelCase_			:    List[str]   						=      hidden_size
											lowerCamelCase_			:    Optional[Any]   						=      d_kv
											lowerCamelCase_			:    List[str]   						=      d_ff
											lowerCamelCase_			:    Optional[int]   						=      num_layers
											lowerCamelCase_			:    Tuple   						=      num_heads
											lowerCamelCase_			:    List[Any]   						=      relative_attention_num_buckets
											lowerCamelCase_			:    Optional[int]   						=      relative_attention_max_distance
											lowerCamelCase_			:    Optional[int]   						=      dropout_rate
											lowerCamelCase_			:    List[Any]   						=      layer_norm_epsilon
											lowerCamelCase_			:    List[str]   						=      initializer_factor
											lowerCamelCase_			:    Any   						=      use_cache

											lowerCamelCase_			:    Any   						=      eos_token_id
											lowerCamelCase_			:    List[Any]   						=      decoder_start_token_id

											# for backwards compatibility
											lowerCamelCase_			:    Tuple   						=      dense_act_fn

											super().__init__(
											    pad_token_id=__magic_name__      ,							eos_token_id=__magic_name__      ,							decoder_start_token_id=__magic_name__      ,							tie_word_embeddings=__magic_name__      ,							is_decoder=__magic_name__      ,							**__magic_name__      ,							)





				@classmethod
				def    __SCREAMING_SNAKE_CASE     (      cls							:   Any      ,							__magic_name__							:   List[str]      ,							**__magic_name__							:   Union[str, Any]							)							->			"PretrainedConfig":
											cls._set_token_in_kwargs(__magic_name__							)

											lowerCamelCase_			:    Optional[Any]   						=      cls.get_config_dict(__magic_name__      ,							**__magic_name__							)

											# get the text config dict if we are loading from Pix2StructConfig
											if config_dict.get("model_type"							) == "pix2struct":
																		lowerCamelCase_			:    Optional[int]   						=      config_dict["text_config"]

											if "model_type" in config_dict and hasattr(cls      ,							"model_type"							) and config_dict["model_type"] != cls.model_type:
																		logger.warning(
																		    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
																		    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."							)

											return cls.from_dict(__magic_name__      ,							**__magic_name__							)





class 					snake_case_   (     _UpperCamelCase	):



				'''simple docstring'''
				lowerCamelCase       =  """pix2struct_vision_model"""





				def __init__(      self							:   List[Any]      ,							__magic_name__							:   List[str]=768      ,							__magic_name__							:   Optional[Any]=768      ,							__magic_name__							:   Dict=2048      ,							__magic_name__							:   Optional[int]=64      ,							__magic_name__							:   Union[str, Any]=12      ,							__magic_name__							:   Dict=12      ,							__magic_name__							:   str="gelu_new"      ,							__magic_name__							:   int=1e-6      ,							__magic_name__							:   Tuple=0.0      ,							__magic_name__							:   List[Any]=0.0      ,							__magic_name__							:   Any=1e-10      ,							__magic_name__							:   Optional[Any]=1.0      ,							__magic_name__							:   Optional[int]=4096      ,							__magic_name__							:   Optional[int]=32      ,							__magic_name__							:   List[Any]=128      ,							**__magic_name__							:   Union[str, Any]      ,							)							->			Optional[Any]:
											super().__init__(**__magic_name__							)

											lowerCamelCase_			:    Optional[Any]   						=      hidden_size
											lowerCamelCase_			:    Tuple   						=      patch_embed_hidden_size
											lowerCamelCase_			:    Tuple   						=      d_ff
											lowerCamelCase_			:    List[Any]   						=      dropout_rate
											lowerCamelCase_			:    Optional[Any]   						=      num_hidden_layers
											lowerCamelCase_			:    Optional[Any]   						=      num_attention_heads
											lowerCamelCase_			:    List[str]   						=      initializer_range
											lowerCamelCase_			:    List[str]   						=      initializer_factor
											lowerCamelCase_			:    List[Any]   						=      attention_dropout
											lowerCamelCase_			:    Optional[int]   						=      layer_norm_eps
											lowerCamelCase_			:    int   						=      dense_act_fn
											lowerCamelCase_			:    Optional[Any]   						=      seq_len
											lowerCamelCase_			:    List[Any]   						=      relative_attention_num_buckets
											lowerCamelCase_			:    int   						=      relative_attention_max_distance
											lowerCamelCase_			:    int   						=      d_kv





				@classmethod
				def    __SCREAMING_SNAKE_CASE     (      cls							:   Any      ,							__magic_name__							:   List[str]      ,							**__magic_name__							:   Optional[Any]							)							->			"PretrainedConfig":
											cls._set_token_in_kwargs(__magic_name__							)

											lowerCamelCase_			:    int   						=      cls.get_config_dict(__magic_name__      ,							**__magic_name__							)

											# get the vision config dict if we are loading from Pix2StructConfig
											if config_dict.get("model_type"							) == "pix2struct":
																		lowerCamelCase_			:    int   						=      config_dict["vision_config"]

											if "model_type" in config_dict and hasattr(cls      ,							"model_type"							) and config_dict["model_type"] != cls.model_type:
																		logger.warning(
																		    F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
																		    F"{cls.model_type}. This is not supported for all configurations of models and can yield errors."							)

											return cls.from_dict(__magic_name__      ,							**__magic_name__							)





class 					snake_case_   (     _UpperCamelCase	):



				'''simple docstring'''
				lowerCamelCase       =  """pix2struct"""
				lowerCamelCase       =  True





				def __init__(      self							:   Any      ,							__magic_name__							:   List[Any]=None      ,							__magic_name__							:   Optional[Any]=None      ,							__magic_name__							:   str=1.0      ,							__magic_name__							:   Optional[int]=0.02      ,							__magic_name__							:   Any=False      ,							__magic_name__							:   Tuple=False      ,							__magic_name__							:   List[Any]=True      ,							**__magic_name__							:   Any      ,							)							->			Optional[int]:
											super().__init__(tie_word_embeddings=__magic_name__      ,							is_encoder_decoder=__magic_name__      ,							**__magic_name__							)

											if text_config is None:
																		lowerCamelCase_			:    Optional[Any]   						=      {}
																		logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values."							)

											if vision_config is None:
																		lowerCamelCase_			:    List[Any]   						=      {}
																		logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values."							)

											lowerCamelCase_			:    List[str]   						=      PixaStructTextConfig(**__magic_name__							)
											lowerCamelCase_			:    List[str]   						=      PixaStructVisionConfig(**__magic_name__							)

											lowerCamelCase_			:    Tuple   						=      self.text_config.decoder_start_token_id
											lowerCamelCase_			:    int   						=      self.text_config.pad_token_id
											lowerCamelCase_			:    List[Any]   						=      self.text_config.eos_token_id

											lowerCamelCase_			:    str   						=      initializer_factor
											lowerCamelCase_			:    Dict   						=      initializer_range

											lowerCamelCase_			:    Optional[int]   						=      self.initializer_range
											lowerCamelCase_			:    Union[str, Any]   						=      self.initializer_range

											lowerCamelCase_			:    Union[str, Any]   						=      is_vqa





				@classmethod
				def    __SCREAMING_SNAKE_CASE     (      cls							:   int      ,							__magic_name__							:   Optional[int]      ,							__magic_name__							:   str      ,							**__magic_name__							:   Optional[int]							)							->			Dict:
											return cls(text_config=text_config.to_dict()      ,							vision_config=vision_config.to_dict()      ,							**__magic_name__							)





				def    __SCREAMING_SNAKE_CASE     (      self							:   List[str]							)							->			int:
											lowerCamelCase_			:    Tuple   						=      copy.deepcopy(self.__dict__							)
											lowerCamelCase_			:    List[str]   						=      self.text_config.to_dict()
											lowerCamelCase_			:    Union[str, Any]   						=      self.vision_config.to_dict()
											lowerCamelCase_			:    List[Any]   						=      self.__class__.model_type
											return output








from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import (
      VersatileDiffusionDualGuidedPipeline,
      VersatileDiffusionImageVariationPipeline,
      VersatileDiffusionPipeline,
      VersatileDiffusionTextToImagePipeline,
  )
else:
  from .modeling_text_unet import UNetFlatConditionModel
  from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
  from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
  from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
  from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline

