





'''simple docstring'''





from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


_lowerCamelCase   				=			transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[str, Any]    ):
		if isinstance(UpperCamelCase__					,   torch.Tensor    ):
				return image
		elif isinstance(UpperCamelCase__					,   PIL.Image.Image    ):
				SCREAMING_SNAKE_CASE__			=      [image]

		SCREAMING_SNAKE_CASE__			=      [trans(img.convert("""RGB"""    )    ) for img in image]
		SCREAMING_SNAKE_CASE__			=      torch.stack(UpperCamelCase__    )
		return image




class   UpperCamelCase_       (     UpperCamelCase__    ):


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



									"""simple docstring"""



									super().__init__()

									# make sure scheduler can always be converted to DDIM
									SCREAMING_SNAKE_CASE__			=      DDIMScheduler.from_config(scheduler.config		)

									self.register_modules(unet=__A							,				scheduler=__A		)


							def      _snake_case  (							self    :str							,				__A    :Optional[Any]		)   ->   Any:



									"""simple docstring"""



									if strength < 0 or strength > 1:
											raise ValueError(f'''The value of strength should in [0.0, 1.0] but is {strength}'''		)


							def      _snake_case  (							self    :Union[str, Any]							,				__A    :List[Any]							,				__A    :Any							,				__A    :Optional[int]		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      min(int(num_inference_steps * strength		)							,				__A		)

									SCREAMING_SNAKE_CASE__			=      max(num_inference_steps - init_timestep							,				0		)
									SCREAMING_SNAKE_CASE__			=      self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start


							def      _snake_case  (							self    :List[str]							,				__A    :Optional[int]							,				__A    :Any							,				__A    :List[Any]							,				__A    :Any							,				__A    :Dict							,				__A    :Union[str, Any]=None		)   ->   Any:



									"""simple docstring"""



									if not isinstance(__A							,				(torch.Tensor, PIL.Image.Image, list)		):
											raise ValueError(
											    f'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__A		)}'''		)

									SCREAMING_SNAKE_CASE__			=      image.to(device=__A							,				dtype=__A		)

									if isinstance(__A							,				__A		) and len(__A		) != batch_size:
											raise ValueError(
											    f'''You have passed a list of generators of length {len(__A		)}, but requested an effective batch'''
											    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''		)

									SCREAMING_SNAKE_CASE__			=      init_latents.shape
									SCREAMING_SNAKE_CASE__			=      randn_tensor(__A							,				generator=__A							,				device=__A							,				dtype=__A		)

									# get latents
									print("""add noise to latents at timestep"""							,				__A		)
									SCREAMING_SNAKE_CASE__			=      self.scheduler.add_noise(__A							,				__A							,				__A		)
									SCREAMING_SNAKE_CASE__			=      init_latents

									return latents


							@torch.no_grad()
							def __call__(							self    :Any							,				__A    :Union[torch.FloatTensor, PIL.Image.Image] = None							,				__A    :float = 0.8							,				__A    :int = 1							,				__A    :Optional[Union[torch.Generator, List[torch.Generator]]] = None							,				__A    :float = 0.0							,				__A    :int = 50							,				__A    :Optional[bool] = None							,				__A    :Optional[str] = "pil"							,				__A    :bool = True							,				)   ->   Union[ImagePipelineOutput, Tuple]:



									"""simple docstring"""



									self.check_inputs(__A		)

									# 2. Preprocess image
									SCREAMING_SNAKE_CASE__			=      preprocess(__A		)

									# 3. set timesteps
									self.scheduler.set_timesteps(__A							,				device=self.device		)
									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      self.get_timesteps(__A							,				__A							,				self.device		)
									SCREAMING_SNAKE_CASE__			=      timesteps[:1].repeat(__A		)

									# 4. Prepare latent variables
									SCREAMING_SNAKE_CASE__			=      self.prepare_latents(__A							,				__A							,				__A							,				self.unet.dtype							,				self.device							,				__A		)
									SCREAMING_SNAKE_CASE__			=      latents

									# 5. Denoising loop
									for t in self.progress_bar(__A		):
											# 1. predict noise model_output
											SCREAMING_SNAKE_CASE__			=      self.unet(__A							,				__A		).sample

											# 2. predict previous mean of image x_t-1 and add variance depending on eta
											# eta corresponds to η in paper and should be between [0, 1]
											# do x_t -> x_t-1
											SCREAMING_SNAKE_CASE__			=      self.scheduler.step(
											    __A							,				__A							,				__A							,				eta=__A							,				use_clipped_model_output=__A							,				generator=__A							,				).prev_sample

									SCREAMING_SNAKE_CASE__			=      (image / 2 + 0.5).clamp(0							,				1		)
									SCREAMING_SNAKE_CASE__			=      image.cpu().permute(0							,				2							,				3							,				1		).numpy()
									if output_type == "pil":
											SCREAMING_SNAKE_CASE__			=      self.numpy_to_pil(__A		)

									if not return_dict:
											return (image, latent_timestep.item())

									return ImagePipelineOutput(images=__A		)






import copy
import os
from typing import Union

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


_lowerCamelCase   				=			logging.get_logger(__name__)

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




class   UpperCamelCase_       (     UpperCamelCase__    ):
							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    :Union[str, Any]							,				__A    :Any=5_0244							,				__A    :Optional[Any]=768							,				__A    :Tuple=64							,				__A    :List[str]=2048							,				__A    :int=12							,				__A    :str=12							,				__A    :Any=32							,				__A    :Tuple=128							,				__A    :int=0.1							,				__A    :str=1E-6							,				__A    :Optional[Any]=1.0							,				__A    :Union[str, Any]="gelu_new"							,				__A    :Any=0							,				__A    :List[str]=False							,				__A    :Optional[Any]=0							,				__A    :int=1							,				__A    :Optional[int]=False							,				__A    :Optional[Any]=True							,				**__A    :List[Any]							,				)   ->   Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      vocab_size
									SCREAMING_SNAKE_CASE__			=      hidden_size
									SCREAMING_SNAKE_CASE__			=      d_kv
									SCREAMING_SNAKE_CASE__			=      d_ff
									SCREAMING_SNAKE_CASE__			=      num_layers
									SCREAMING_SNAKE_CASE__			=      num_heads
									SCREAMING_SNAKE_CASE__			=      relative_attention_num_buckets
									SCREAMING_SNAKE_CASE__			=      relative_attention_max_distance
									SCREAMING_SNAKE_CASE__			=      dropout_rate
									SCREAMING_SNAKE_CASE__			=      layer_norm_epsilon
									SCREAMING_SNAKE_CASE__			=      initializer_factor
									SCREAMING_SNAKE_CASE__			=      use_cache

									SCREAMING_SNAKE_CASE__			=      eos_token_id
									SCREAMING_SNAKE_CASE__			=      decoder_start_token_id

									# for backwards compatibility
									SCREAMING_SNAKE_CASE__			=      dense_act_fn

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


							@classmethod
							def      _snake_case  (							cls    :Optional[int]							,				__A    :Union[str, os.PathLike]							,				**__A    :Optional[int]		)   ->   "PretrainedConfig":



									"""simple docstring"""



									cls._set_token_in_kwargs(__A		)

									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      cls.get_config_dict(__A							,				**__A		)

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




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"pix2struct_vision_model"


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



									"""simple docstring"""



									super().__init__(**__A		)

									SCREAMING_SNAKE_CASE__			=      hidden_size
									SCREAMING_SNAKE_CASE__			=      patch_embed_hidden_size
									SCREAMING_SNAKE_CASE__			=      d_ff
									SCREAMING_SNAKE_CASE__			=      dropout_rate
									SCREAMING_SNAKE_CASE__			=      num_hidden_layers
									SCREAMING_SNAKE_CASE__			=      num_attention_heads
									SCREAMING_SNAKE_CASE__			=      initializer_range
									SCREAMING_SNAKE_CASE__			=      initializer_factor
									SCREAMING_SNAKE_CASE__			=      attention_dropout
									SCREAMING_SNAKE_CASE__			=      layer_norm_eps
									SCREAMING_SNAKE_CASE__			=      dense_act_fn
									SCREAMING_SNAKE_CASE__			=      seq_len
									SCREAMING_SNAKE_CASE__			=      relative_attention_num_buckets
									SCREAMING_SNAKE_CASE__			=      relative_attention_max_distance
									SCREAMING_SNAKE_CASE__			=      d_kv


							@classmethod
							def      _snake_case  (							cls    :str							,				__A    :Union[str, os.PathLike]							,				**__A    :str		)   ->   "PretrainedConfig":



									"""simple docstring"""



									cls._set_token_in_kwargs(__A		)

									SCREAMING_SNAKE_CASE__	,						SCREAMING_SNAKE_CASE__			=      cls.get_config_dict(__A							,				**__A		)

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




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =					"pix2struct"
							lowerCamelCase_           =					True


							def __init__(							self    :str							,				__A    :Optional[Any]=None							,				__A    :List[str]=None							,				__A    :Optional[Any]=1.0							,				__A    :Optional[Any]=0.0_2							,				__A    :Any=False							,				__A    :Tuple=False							,				__A    :Any=True							,				**__A    :Dict							,				)   ->   Union[str, Any]:



									"""simple docstring"""



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

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

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

									SCREAMING_SNAKE_CASE__			=      PixaStructTextConfig(**__A		)
									SCREAMING_SNAKE_CASE__			=      PixaStructVisionConfig(**__A		)

									SCREAMING_SNAKE_CASE__			=      self.text_config.decoder_start_token_id
									SCREAMING_SNAKE_CASE__			=      self.text_config.pad_token_id
									SCREAMING_SNAKE_CASE__			=      self.text_config.eos_token_id

									SCREAMING_SNAKE_CASE__			=      initializer_factor
									SCREAMING_SNAKE_CASE__			=      initializer_range

									SCREAMING_SNAKE_CASE__			=      self.initializer_range
									SCREAMING_SNAKE_CASE__			=      self.initializer_range

									SCREAMING_SNAKE_CASE__			=      is_vqa


							@classmethod
							def      _snake_case  (							cls    :Union[str, Any]							,				__A    :PixaStructTextConfig							,				__A    :PixaStructVisionConfig							,				**__A    :Optional[int]		)   ->   Optional[Any]:



									"""simple docstring"""



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


							def      _snake_case  (							self    :str		)   ->   List[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      copy.deepcopy(self.__dict__		)
									SCREAMING_SNAKE_CASE__			=      self.text_config.to_dict()
									SCREAMING_SNAKE_CASE__			=      self.vision_config.to_dict()
									SCREAMING_SNAKE_CASE__			=      self.__class__.model_type
									return output