



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_		 =					transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[Any]				)  ->     List[Any]:
   if isinstance(_lowerCAmelCase	,   torch.Tensor				):
      return image
   elif isinstance(_lowerCAmelCase	,   PIL.Image.Image				):
      _A      =       [image]

   _A      =       [trans(img.convert('''RGB'''				)				) for img in image]
   _A      =       torch.stack(_lowerCAmelCase				)
   return image


class 	lowerCamelCase__	(       _a):



 """simple docstring"""

 def __init__(    self					:					str			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Optional[int]      )   ->	List[Any]:
    super().__init__()

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

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

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					Optional[Any]      )   ->	Union[str, Any]:
    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					:					Dict			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					List[str]      )   ->	Any:
    # get the original timestep using init_timestep
    _A      =       min(int(num_inference_steps * strength      )			,						snake_case_      )

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

    return timesteps, num_inference_steps - t_start

 def     snake_case_      (    self					:					List[Any]			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					Optional[Any]=None      )   ->	str:
    if not isinstance(snake_case_			,						(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(snake_case_      )}'''      )

    _A      =       image.to(device=snake_case_			,						dtype=snake_case_      )

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

    _A      =       init_latents.shape
    _A      =       randn_tensor(snake_case_			,						generator=snake_case_			,						device=snake_case_			,						dtype=snake_case_      )

    # get latents
    print('''add noise to latents at timestep'''			,						snake_case_      )
    _A      =       self.scheduler.add_noise(snake_case_			,						snake_case_			,						snake_case_      )
    _A      =       init_latents

    return latents

 @torch.no_grad()
 def __call__(    self					:					List[str]			,						__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			,						)   ->	int:
    self.check_inputs(snake_case_      )

    # 2. Preprocess image
    _A      =       preprocess(snake_case_      )

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

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

    # 5. Denoising loop
    for t in self.progress_bar(snake_case_      ):
       # 1. predict noise model_output
       _A      =       self.unet(snake_case_			,						snake_case_      ).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
       _A      =       self.scheduler.step(
           snake_case_			,						snake_case_			,						snake_case_			,						eta=snake_case_			,						use_clipped_model_output=snake_case_			,						generator=snake_case_			,						).prev_sample

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

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

    return ImagePipelineOutput(images=snake_case_      )








'''simple docstring'''





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


__a  			=						logging.get_logger(__name__)

__a  			=						{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						"layoutlmv3"




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







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						version.parse("1.12"      )




    @property
    def lowerCamelCase			(       self   :	Tuple		):
          # 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 lowerCamelCase			(       self   :	str		):
          return 1E-5




    @property
    def lowerCamelCase			(       self   :	List[Any]		):
          return 12




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

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

          # Generate dummy bounding boxes
          snake_case__    :       int       		= [[[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)
          snake_case__    :       Optional[int]       		= self._generate_dummy_images(snake_case_					,	snake_case_					,	snake_case_					,	snake_case_		)

          snake_case__    :       Optional[int]       		= dict(
              processor(
                  snake_case_					,	text=snake_case_					,	boxes=snake_case_					,	return_tensors=snake_case_					,	)		)

          return inputs
