




from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 		__A      (			UpperCamelCase__			):

						def __init__(		self   :Dict    , __snake_case   :TransformeraDModel    , __snake_case   :AutoencoderKL    , __snake_case   :KarrasDiffusionSchedulers    , __snake_case   :Optional[Dict[int, str]] = None    , ):
										'''simple docstring'''

										super().__init__()
										self.register_modules(transformer=__snake_case    , vae=__snake_case    , scheduler=__snake_case )

										# create a imagenet -> id dictionary for easier use
										__magic_name__	:	List[str]								={}
										if idalabel is not None:
														for key, value in idalabel.items():
																		for label in value.split(""",""" ):
																						__magic_name__	:	Optional[Any]								=int(__snake_case )
														__magic_name__	:	Optional[Any]								=dict(sorted(self.labels.items() ) )

						def     A__						(		self   :Optional[int]    , __snake_case   :Union[str, List[str]] ):
										'''simple docstring'''

										if not isinstance(__snake_case    , __snake_case ):
														__magic_name__	:	Dict								=list(__snake_case )

										for l in label:
														if l not in self.labels:
																		raise ValueError(
																		    f"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}." )

										return [self.labels[l] for l in label]


						@torch.no_grad()
						def __call__(		self   :Optional[Any]    , __snake_case   :List[int]    , __snake_case   :float = 4.0    , __snake_case   :Optional[Union[torch.Generator, List[torch.Generator]]] = None    , __snake_case   :int = 50    , __snake_case   :Optional[str] = "pil"    , __snake_case   :bool = True    , ):
										'''simple docstring'''

										__magic_name__	:	List[Any]								=len(__snake_case )
										__magic_name__	:	int								=self.transformer.config.sample_size
										__magic_name__	:	Tuple								=self.transformer.config.in_channels

										__magic_name__	:	int								=randn_tensor(
										    shape=(batch_size, latent_channels, latent_size, latent_size)    , generator=__snake_case    , device=self.device    , dtype=self.transformer.dtype    , )
										__magic_name__	:	Optional[Any]								=torch.cat([latents] * 2 ) if guidance_scale > 1 else latents

										__magic_name__	:	str								=torch.tensor(__snake_case    , device=self.device ).reshape(-1 )
										__magic_name__	:	Union[str, Any]								=torch.tensor([10_00] * batch_size    , device=self.device )
										__magic_name__	:	Optional[int]								=torch.cat([class_labels, class_null]    , 0 ) if guidance_scale > 1 else class_labels

										# set step values
										self.scheduler.set_timesteps(__snake_case )

										for t in self.progress_bar(self.scheduler.timesteps ):
														if guidance_scale > 1:
																		__magic_name__	:	Dict								=latent_model_input[: len(__snake_case ) // 2]
																		__magic_name__	:	Tuple								=torch.cat([half, half]    , dim=0 )
														__magic_name__	:	Optional[Any]								=self.scheduler.scale_model_input(__snake_case    , __snake_case )

														__magic_name__	:	List[Any]								=t
														if not torch.is_tensor(__snake_case ):
																		# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																		# This would be a good case for the `match` statement (Python 3.10+)
																		__magic_name__	:	str								=latent_model_input.device.type == """mps"""
																		if isinstance(__snake_case    , __snake_case ):
																						__magic_name__	:	int								=torch.floataa if is_mps else torch.floataa
																		else:
																						__magic_name__	:	List[str]								=torch.intaa if is_mps else torch.intaa
																		__magic_name__	:	List[str]								=torch.tensor([timesteps]    , dtype=__snake_case    , device=latent_model_input.device )
														elif len(timesteps.shape ) == 0:
																		__magic_name__	:	Any								=timesteps[None].to(latent_model_input.device )
														# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
														__magic_name__	:	List[str]								=timesteps.expand(latent_model_input.shape[0] )
														# predict noise model_output
														__magic_name__	:	Optional[int]								=self.transformer(
														    __snake_case    , timestep=__snake_case    , class_labels=__snake_case ).sample

														# perform guidance
														if guidance_scale > 1:
																		__magic_name__	:	Tuple								=noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																		__magic_name__	:	Tuple								=torch.split(__snake_case    , len(__snake_case ) // 2    , dim=0 )

																		__magic_name__	:	Optional[Any]								=uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																		__magic_name__	:	Union[str, Any]								=torch.cat([half_eps, half_eps]    , dim=0 )

																		__magic_name__	:	List[str]								=torch.cat([eps, rest]    , dim=1 )

														# learned sigma
														if self.transformer.config.out_channels // 2 == latent_channels:
																		__magic_name__	:	str								=torch.split(__snake_case    , __snake_case    , dim=1 )
														else:
																		__magic_name__	:	Optional[int]								=noise_pred

														# compute previous image: x_t -> x_t-1
														__magic_name__	:	Optional[Any]								=self.scheduler.step(__snake_case    , __snake_case    , __snake_case ).prev_sample

										if guidance_scale > 1:
														__magic_name__	:	Any								=latent_model_input.chunk(2    , dim=0 )
										else:
														__magic_name__	:	Optional[Any]								=latent_model_input

										__magic_name__	:	int								=1 / self.vae.config.scaling_factor * latents
										__magic_name__	:	Any								=self.vae.decode(__snake_case ).sample

										__magic_name__	:	Optional[Any]								=(samples / 2 + 0.5).clamp(0    , 1 )

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										__magic_name__	:	Union[str, Any]								=samples.cpu().permute(0    , 2    , 3    , 1 ).float().numpy()

										if output_type == "pil":
														__magic_name__	:	Optional[int]								=self.numpy_to_pil(__snake_case )

										if not return_dict:
														return (samples,)

										return ImagePipelineOutput(images=__snake_case )











import copy
import os
from typing import Union

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


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

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




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""pix2struct_text_model"""
						UpperCamelCase     						=					["""past_key_values"""]
						UpperCamelCase     						=					{
						    """hidden_size""": """hidden_size""",
						    """num_attention_heads""": """num_heads""",
						    """num_hidden_layers""": """num_layers""",
						}

						def __init__(		self   :List[Any]    , __snake_case   :Dict=5_02_44    , __snake_case   :Dict=7_68    , __snake_case   :Union[str, Any]=64    , __snake_case   :Union[str, Any]=20_48    , __snake_case   :List[Any]=12    , __snake_case   :Any=12    , __snake_case   :str=32    , __snake_case   :Dict=1_28    , __snake_case   :Optional[int]=0.1    , __snake_case   :Any=1E-6    , __snake_case   :Optional[int]=1.0    , __snake_case   :List[Any]="gelu_new"    , __snake_case   :Any=0    , __snake_case   :Tuple=False    , __snake_case   :Any=0    , __snake_case   :int=1    , __snake_case   :Dict=False    , __snake_case   :Optional[Any]=True    , **__snake_case   :Dict    , ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=vocab_size
										__magic_name__	:	List[str]								=hidden_size
										__magic_name__	:	Optional[int]								=d_kv
										__magic_name__	:	Dict								=d_ff
										__magic_name__	:	Tuple								=num_layers
										__magic_name__	:	str								=num_heads
										__magic_name__	:	Optional[Any]								=relative_attention_num_buckets
										__magic_name__	:	List[Any]								=relative_attention_max_distance
										__magic_name__	:	str								=dropout_rate
										__magic_name__	:	Optional[Any]								=layer_norm_epsilon
										__magic_name__	:	Union[str, Any]								=initializer_factor
										__magic_name__	:	Tuple								=use_cache

										__magic_name__	:	Any								=eos_token_id
										__magic_name__	:	int								=decoder_start_token_id

										# for backwards compatibility
										__magic_name__	:	Union[str, Any]								=dense_act_fn

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


						@classmethod
						def     A__						(		cls   :Union[str, Any]    , __snake_case   :Union[str, os.PathLike]    , **__snake_case   :Any ):
										'''simple docstring'''

										cls._set_token_in_kwargs(__snake_case )

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

										# get the text config dict if we are loading from Pix2StructConfig
										if config_dict.get("""model_type""" ) == "pix2struct":
														__magic_name__	:	Tuple								=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(__snake_case    , **__snake_case )




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""pix2struct_vision_model"""

						def __init__(		self   :Union[str, Any]    , __snake_case   :Tuple=7_68    , __snake_case   :Any=7_68    , __snake_case   :Tuple=20_48    , __snake_case   :Dict=64    , __snake_case   :List[str]=12    , __snake_case   :str=12    , __snake_case   :str="gelu_new"    , __snake_case   :Optional[int]=1E-6    , __snake_case   :Optional[int]=0.0    , __snake_case   :Any=0.0    , __snake_case   :Any=1E-10    , __snake_case   :List[str]=1.0    , __snake_case   :Tuple=40_96    , __snake_case   :Optional[int]=32    , __snake_case   :Union[str, Any]=1_28    , **__snake_case   :Optional[int]    , ):
										'''simple docstring'''

										super().__init__(**__snake_case )

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


						@classmethod
						def     A__						(		cls   :Optional[Any]    , __snake_case   :Union[str, os.PathLike]    , **__snake_case   :Any ):
										'''simple docstring'''

										cls._set_token_in_kwargs(__snake_case )

										__magic_name__				, __magic_name__	:	Optional[int]								=cls.get_config_dict(__snake_case    , **__snake_case )

										# get the vision config dict if we are loading from Pix2StructConfig
										if config_dict.get("""model_type""" ) == "pix2struct":
														__magic_name__	:	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(__snake_case    , **__snake_case )




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					"""pix2struct"""
						UpperCamelCase     						=					True

						def __init__(		self   :Union[str, Any]    , __snake_case   :Optional[int]=None    , __snake_case   :List[Any]=None    , __snake_case   :Optional[Any]=1.0    , __snake_case   :Tuple=0.02    , __snake_case   :str=False    , __snake_case   :List[str]=False    , __snake_case   :str=True    , **__snake_case   :List[str]    , ):
										'''simple docstring'''

										super().__init__(tie_word_embeddings=__snake_case    , is_encoder_decoder=__snake_case    , **__snake_case )

										if text_config is None:
														__magic_name__	:	int								={}
														logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values.""" )

										if vision_config is None:
														__magic_name__	:	str								={}
														logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values.""" )

										__magic_name__	:	Union[str, Any]								=PixaStructTextConfig(**__snake_case )
										__magic_name__	:	str								=PixaStructVisionConfig(**__snake_case )

										__magic_name__	:	int								=self.text_config.decoder_start_token_id
										__magic_name__	:	Optional[Any]								=self.text_config.pad_token_id
										__magic_name__	:	str								=self.text_config.eos_token_id

										__magic_name__	:	int								=initializer_factor
										__magic_name__	:	List[Any]								=initializer_range

										__magic_name__	:	Dict								=self.initializer_range
										__magic_name__	:	Union[str, Any]								=self.initializer_range

										__magic_name__	:	Tuple								=is_vqa

						@classmethod
						def     A__						(		cls   :Optional[int]    , __snake_case   :PixaStructTextConfig    , __snake_case   :PixaStructVisionConfig    , **__snake_case   :List[str] ):
										'''simple docstring'''

										return cls(text_config=text_config.to_dict()    , vision_config=vision_config.to_dict()    , **__snake_case )


						def     A__						(		self   :Dict ):
										'''simple docstring'''

										__magic_name__	:	Union[str, Any]								=copy.deepcopy(self.__dict__ )
										__magic_name__	:	Any								=self.text_config.to_dict()
										__magic_name__	:	List[str]								=self.vision_config.to_dict()
										__magic_name__	:	Optional[int]								=self.__class__.model_type
										return output




