






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


UpperCAmelCase_ :						str      =      transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]					)		->			Union[str, Any]:






	"""simple docstring"""



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

	UpperCamelCase     :Tuple          =	[trans(img.convert("""RGB"""					)					) for img in image]
	UpperCamelCase     :int          =	torch.stack(__magic_name__					)
	return image

class     _SCREAMING_SNAKE_CASE       (			_a      ):





	def __init__(		self				:					Any ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					Any	):
		super().__init__()

		# make sure scheduler can always be converted to DDIM
		UpperCamelCase     :List[Any]          =	DDIMScheduler.from_config(scheduler.config	)

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





	def 						_A (		self				:					Dict ,  __lowerCamelCase				:					List[str]	):
		if strength < 0 or strength > 1:
			raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""	)





	def 						_A (		self				:					Optional[Any] ,  __lowerCamelCase				:					Any ,  __lowerCamelCase				:					Optional[Any] ,  __lowerCamelCase				:					List[Any]	):
		# get the original timestep using init_timestep
		UpperCamelCase     :Optional[Any]          =	min(int(num_inference_steps * strength	) ,  __lowerCamelCase	)

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

		return timesteps, num_inference_steps - t_start





	def 						_A (		self				:					List[str] ,  __lowerCamelCase				:					Union[str, Any] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					Optional[int]=None	):
		if not isinstance(__lowerCamelCase ,  (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(__lowerCamelCase	)}"""	)

		UpperCamelCase     :Tuple          =	image.to(device=__lowerCamelCase ,  dtype=__lowerCamelCase	)

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

		UpperCamelCase     :Tuple          =	init_latents.shape
		UpperCamelCase     :List[str]          =	randn_tensor(__lowerCamelCase ,  generator=__lowerCamelCase ,  device=__lowerCamelCase ,  dtype=__lowerCamelCase	)

		# get latents
		print("""add noise to latents at timestep""" ,  __lowerCamelCase	)
		UpperCamelCase     :List[Any]          =	self.scheduler.add_noise(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)
		UpperCamelCase     :Tuple          =	init_latents

		return latents





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

		# 2. Preprocess image
		UpperCamelCase     :Any          =	preprocess(__lowerCamelCase	)

		# 3. set timesteps
		self.scheduler.set_timesteps(__lowerCamelCase ,  device=self.device	)
		UpperCamelCase     ,      UpperCamelCase     :Dict          =	self.get_timesteps(__lowerCamelCase ,  __lowerCamelCase ,  self.device	)
		UpperCamelCase     :List[Any]          =	timesteps[:1].repeat(__lowerCamelCase	)

		# 4. Prepare latent variables
		UpperCamelCase     :str          =	self.prepare_latents(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  self.unet.dtype ,  self.device ,  __lowerCamelCase	)
		UpperCamelCase     :List[str]          =	latents

		# 5. Denoising loop
		for t in self.progress_bar(__lowerCamelCase	):
			# 1. predict noise model_output
			UpperCamelCase     :List[str]          =	self.unet(__lowerCamelCase ,  __lowerCamelCase	).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
			UpperCamelCase     :Union[str, Any]          =	self.scheduler.step(
			    __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  eta=__lowerCamelCase ,  use_clipped_model_output=__lowerCamelCase ,  generator=__lowerCamelCase ,  ).prev_sample

		UpperCamelCase     :Dict          =	(image / 2 + 0.5).clamp(0 ,  1	)
		UpperCamelCase     :Optional[Any]          =	image.cpu().permute(0 ,  2 ,  3 ,  1	).numpy()
		if output_type == "pil":
			UpperCamelCase     :List[str]          =	self.numpy_to_pil(__lowerCamelCase	)

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

		return ImagePipelineOutput(images=__lowerCamelCase	)








from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
	from ...processing_utils import ProcessorMixin
	from ...utils import TensorType


UpperCAmelCase_ :						Dict      =      logging.get_logger(__name__)

UpperCAmelCase_ :						str      =      {
    '''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     Optional[int]     	=  """layoutlmv3"""





	def __init__(		self				:					List[Any] ,  __lowerCamelCase				:					Optional[Any]=50_265 ,  __lowerCamelCase				:					Dict=768 ,  __lowerCamelCase				:					Any=12 ,  __lowerCamelCase				:					int=12 ,  __lowerCamelCase				:					str=3_072 ,  __lowerCamelCase				:					List[Any]="gelu" ,  __lowerCamelCase				:					List[str]=0.1 ,  __lowerCamelCase				:					Optional[int]=0.1 ,  __lowerCamelCase				:					Optional[Any]=512 ,  __lowerCamelCase				:					Optional[int]=2 ,  __lowerCamelCase				:					Union[str, Any]=0.02 ,  __lowerCamelCase				:					Union[str, Any]=1E-5 ,  __lowerCamelCase				:					Any=1 ,  __lowerCamelCase				:					Optional[int]=0 ,  __lowerCamelCase				:					List[Any]=2 ,  __lowerCamelCase				:					Dict=1_024 ,  __lowerCamelCase				:					List[Any]=128 ,  __lowerCamelCase				:					str=128 ,  __lowerCamelCase				:					Optional[Any]=True ,  __lowerCamelCase				:					str=32 ,  __lowerCamelCase				:					List[Any]=128 ,  __lowerCamelCase				:					str=64 ,  __lowerCamelCase				:					List[str]=256 ,  __lowerCamelCase				:					Dict=True ,  __lowerCamelCase				:					List[Any]=True ,  __lowerCamelCase				:					Tuple=True ,  __lowerCamelCase				:					Tuple=224 ,  __lowerCamelCase				:					Tuple=3 ,  __lowerCamelCase				:					Dict=16 ,  __lowerCamelCase				:					Union[str, Any]=None ,  **__lowerCamelCase				:					Optional[Any] ,  ):
		super().__init__(
		    vocab_size=__lowerCamelCase ,  hidden_size=__lowerCamelCase ,  num_hidden_layers=__lowerCamelCase ,  num_attention_heads=__lowerCamelCase ,  intermediate_size=__lowerCamelCase ,  hidden_act=__lowerCamelCase ,  hidden_dropout_prob=__lowerCamelCase ,  attention_probs_dropout_prob=__lowerCamelCase ,  max_position_embeddings=__lowerCamelCase ,  type_vocab_size=__lowerCamelCase ,  initializer_range=__lowerCamelCase ,  layer_norm_eps=__lowerCamelCase ,  pad_token_id=__lowerCamelCase ,  bos_token_id=__lowerCamelCase ,  eos_token_id=__lowerCamelCase ,  **__lowerCamelCase ,  )
		UpperCamelCase     :int          =	max_ad_position_embeddings
		UpperCamelCase     :Tuple          =	coordinate_size
		UpperCamelCase     :List[Any]          =	shape_size
		UpperCamelCase     :Union[str, Any]          =	has_relative_attention_bias
		UpperCamelCase     :Any          =	rel_pos_bins
		UpperCamelCase     :Optional[Any]          =	max_rel_pos
		UpperCamelCase     :str          =	has_spatial_attention_bias
		UpperCamelCase     :Tuple          =	rel_ad_pos_bins
		UpperCamelCase     :Optional[int]          =	max_rel_ad_pos
		UpperCamelCase     :Tuple          =	text_embed
		UpperCamelCase     :str          =	visual_embed
		UpperCamelCase     :Optional[Any]          =	input_size
		UpperCamelCase     :str          =	num_channels
		UpperCamelCase     :List[Any]          =	patch_size
		UpperCamelCase     :Optional[Any]          =	classifier_dropout

class     _SCREAMING_SNAKE_CASE       (			_a      ):
	snake_case__							:     int     	=  version.parse("""1.12"""      )





	@property
	def 						_A (		self				:					Optional[int]	):
		# The order of inputs is different for question answering and sequence classification
		if self.task in ["question-answering", "sequence-classification"]:
			return OrderedDict(
			    [
			        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
			        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
			        ("""bbox""", {0: """batch""", 1: """sequence"""}),
			        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
			    ]	)
		else:
			return OrderedDict(
			    [
			        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
			        ("""bbox""", {0: """batch""", 1: """sequence"""}),
			        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
			        ("""pixel_values""", {0: """batch""", 1: """num_channels"""}),
			    ]	)





	@property
	def 						_A (		self				:					str	):
		return 1E-5





	@property
	def 						_A (		self				:					Dict	):
		return 12





	def 						_A (		self				:					Dict ,  __lowerCamelCase				:					"ProcessorMixin" ,  __lowerCamelCase				:					int = -1 ,  __lowerCamelCase				:					int = -1 ,  __lowerCamelCase				:					bool = False ,  __lowerCamelCase				:					Optional["TensorType"] = None ,  __lowerCamelCase				:					int = 3 ,  __lowerCamelCase				:					int = 40 ,  __lowerCamelCase				:					int = 40 ,  ):
		setattr(processor.image_processor ,  """apply_ocr""" ,  __lowerCamelCase	)

		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
		UpperCamelCase     :Optional[Any]          =	compute_effective_axis_dimension(
		    __lowerCamelCase ,  fixed_dimension=OnnxConfig.default_fixed_batch ,  num_token_to_add=0	)
		# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
		UpperCamelCase     :Optional[int]          =	processor.tokenizer.num_special_tokens_to_add(__lowerCamelCase	)
		UpperCamelCase     :int          =	compute_effective_axis_dimension(
		    __lowerCamelCase ,  fixed_dimension=OnnxConfig.default_fixed_sequence ,  num_token_to_add=__lowerCamelCase	)
		# Generate dummy inputs according to compute batch and sequence
		UpperCamelCase     :Any          =	[[""" """.join([processor.tokenizer.unk_token]	) * seq_length]] * batch_size

		# Generate dummy bounding boxes
		UpperCamelCase     :Optional[Any]          =	[[[48, 84, 73, 128]]] * batch_size

		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
		# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
		UpperCamelCase     :List[str]          =	self._generate_dummy_images(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)

		UpperCamelCase     :Any          =	dict(
		    processor(
		        __lowerCamelCase ,  text=__lowerCamelCase ,  boxes=__lowerCamelCase ,  return_tensors=__lowerCamelCase ,  )	)

		return inputs
