





from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_A					:				List[Any]				=			logging.get_logger(__name__)  # pylint: disable=invalid-name

_A					:				Optional[int]				=			"""
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
        >>> pipe_prior.to(\"cuda\")
        >>> prompt = \"red cat, 4k photo\"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
        >>> pipe.to(\"cuda\")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save(\"cat.png\")
        ```
"""


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=8	)				->						List[str]:
    SCREAMING_SNAKE_CASE__											=			height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    SCREAMING_SNAKE_CASE__											=			width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor



class 					__snake_case	(  __SCREAMING_SNAKE_CASE    ):


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_   ,				A_   ,				):





        '''simple docstring'''

        super().__init__()

        self.register_modules(
            unet=A_   ,				scheduler=A_   ,				movq=A_   ,				)
        SCREAMING_SNAKE_CASE__											=			2 ** (len(self.movq.config.block_out_channels     ) - 1)



    def  lowercase_					(       self   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_   ,				A_     ):





        '''simple docstring'''

        if latents is None:
            SCREAMING_SNAKE_CASE__											=			randn_tensor(A_   ,				generator=A_   ,				device=A_   ,				dtype=A_     )
        else:
            if latents.shape != shape:
                raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''     )
            SCREAMING_SNAKE_CASE__											=			latents.to(A_     )

        SCREAMING_SNAKE_CASE__											=			latents * scheduler.init_noise_sigma
        return latents



    def  lowercase_					(       self   ,				A_=0     ):





        '''simple docstring'''

        if is_accelerate_available():
            from accelerate import cpu_offload
        else:
            raise ImportError('''Please install accelerate via `pip install accelerate`'''     )

        SCREAMING_SNAKE_CASE__											=			torch.device(f'''cuda:{gpu_id}'''     )

        SCREAMING_SNAKE_CASE__											=			[
            self.unet,
            self.movq,
        ]
        for cpu_offloaded_model in models:
            if cpu_offloaded_model is not None:
                cpu_offload(A_   ,				A_     )



    def  lowercase_					(       self   ,				A_=0     ):





        '''simple docstring'''

        if is_accelerate_available() and is_accelerate_version('''>='''   ,				'''0.17.0.dev0'''     ):
            from accelerate import cpu_offload_with_hook
        else:
            raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''     )

        SCREAMING_SNAKE_CASE__											=			torch.device(f'''cuda:{gpu_id}'''     )

        if self.device.type != "cpu":
            self.to('''cpu'''   ,				silence_dtype_warnings=A_     )
            torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        SCREAMING_SNAKE_CASE__											=			None
        for cpu_offloaded_model in [self.unet, self.movq]:
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			cpu_offload_with_hook(A_   ,				A_   ,				prev_module_hook=A_     )

        # We'll offload the last model manually.
        SCREAMING_SNAKE_CASE__											=			hook



    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def  lowercase_					(       self     ):





        '''simple docstring'''

        if not hasattr(self.unet   ,				'''_hf_hook'''     ):
            return self.device
        for module in self.unet.modules():
            if (
                hasattr(A_   ,				'''_hf_hook'''     )
                and hasattr(module._hf_hook   ,				'''execution_device'''     )
                and module._hf_hook.execution_device is not None
            ):
                return torch.device(module._hf_hook.execution_device     )
        return self.device




    @torch.no_grad()
    @replace_example_docstring(A_     )
    def __call__(       self   ,				A_   ,				A_   ,				A_ = 5_12   ,				A_ = 5_12   ,				A_ = 1_00   ,				A_ = 4.0   ,				A_ = 1   ,				A_ = None   ,				A_ = None   ,				A_ = "pil"   ,				A_ = True   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self._execution_device

        SCREAMING_SNAKE_CASE__											=			guidance_scale > 1.0

        if isinstance(A_   ,				A_     ):
            SCREAMING_SNAKE_CASE__											=			torch.cat(A_   ,				dim=0     )
        SCREAMING_SNAKE_CASE__											=			image_embeds.shape[0] * num_images_per_prompt
        if isinstance(A_   ,				A_     ):
            SCREAMING_SNAKE_CASE__											=			torch.cat(A_   ,				dim=0     )

        if do_classifier_free_guidance:
            SCREAMING_SNAKE_CASE__											=			image_embeds.repeat_interleave(A_   ,				dim=0     )
            SCREAMING_SNAKE_CASE__											=			negative_image_embeds.repeat_interleave(A_   ,				dim=0     )

        SCREAMING_SNAKE_CASE__											=			torch.cat([negative_image_embeds, image_embeds]   ,				dim=0     ).to(dtype=self.unet.dtype   ,				device=A_     )

        self.scheduler.set_timesteps(A_   ,				device=A_     )
        SCREAMING_SNAKE_CASE__											=			self.scheduler.timesteps

        SCREAMING_SNAKE_CASE__											=			self.unet.config.in_channels

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			downscale_height_and_width(A_   ,				A_   ,				self.movq_scale_factor     )

        # create initial latent
        SCREAMING_SNAKE_CASE__											=			self.prepare_latents(
            (batch_size, num_channels_latents, height, width)   ,				image_embeds.dtype   ,				A_   ,				A_   ,				A_   ,				self.scheduler   ,				)

        for i, t in enumerate(self.progress_bar(A_     )     ):
            # expand the latents if we are doing classifier free guidance
            SCREAMING_SNAKE_CASE__											=			torch.cat([latents] * 2     ) if do_classifier_free_guidance else latents

            SCREAMING_SNAKE_CASE__											=			{'''image_embeds''': image_embeds}
            SCREAMING_SNAKE_CASE__											=			self.unet(
                sample=A_   ,				timestep=A_   ,				encoder_hidden_states=A_   ,				added_cond_kwargs=A_   ,				return_dict=A_   ,				)[0]

            if do_classifier_free_guidance:
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			noise_pred.split(latents.shape[1]   ,				dim=1     )
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			noise_pred.chunk(2     )
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			variance_pred.chunk(2     )
                SCREAMING_SNAKE_CASE__											=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                SCREAMING_SNAKE_CASE__											=			torch.cat([noise_pred, variance_pred_text]   ,				dim=1     )

            if not (
                hasattr(self.scheduler.config   ,				'''variance_type'''     )
                and self.scheduler.config.variance_type in ["learned", "learned_range"]
            ):
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			noise_pred.split(latents.shape[1]   ,				dim=1     )

            # compute the previous noisy sample x_t -> x_t-1
            SCREAMING_SNAKE_CASE__											=			self.scheduler.step(
                A_   ,				A_   ,				A_   ,				generator=A_   ,				)[0]
        # post-processing
        SCREAMING_SNAKE_CASE__											=			self.movq.decode(A_   ,				force_not_quantize=A_     )['''sample''']

        if output_type not in ["pt", "np", "pil"]:
            raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''     )

        if output_type in ["np", "pil"]:
            SCREAMING_SNAKE_CASE__											=			image * 0.5 + 0.5
            SCREAMING_SNAKE_CASE__											=			image.clamp(0   ,				1     )
            SCREAMING_SNAKE_CASE__											=			image.cpu().permute(0   ,				2   ,				3   ,				1     ).float().numpy()

        if output_type == "pil":
            SCREAMING_SNAKE_CASE__											=			self.numpy_to_pil(A_     )

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=A_     )








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


_SCREAMING_SNAKE_CASE   =  logging.get_logger(__name__)

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






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


       __lowerCAmelCase    :		Tuple					='''layoutlmv3'''





       def __init__(				self	:Optional[int],      snake_case	:int=5_0265,      snake_case	:int=768,      snake_case	:Any=12,      snake_case	:Union[str, Any]=12,      snake_case	:List[str]=3072,      snake_case	:List[str]="gelu",      snake_case	:List[str]=0.1,      snake_case	:Optional[int]=0.1,      snake_case	:Optional[int]=512,      snake_case	:Tuple=2,      snake_case	:Optional[Any]=0.0_2,      snake_case	:Optional[int]=1e-5,      snake_case	:Union[str, Any]=1,      snake_case	:Dict=0,      snake_case	:Tuple=2,      snake_case	:Tuple=1024,      snake_case	:Optional[Any]=128,      snake_case	:Optional[Any]=128,      snake_case	:List[str]=True,      snake_case	:str=32,      snake_case	:Optional[int]=128,      snake_case	:Dict=64,      snake_case	:List[str]=256,      snake_case	:Optional[Any]=True,      snake_case	:Optional[Any]=True,      snake_case	:Dict=True,      snake_case	:Optional[Any]=224,      snake_case	:int=3,      snake_case	:int=16,      snake_case	:str=None,      **snake_case	:Dict,      ):



          """simple docstring"""

          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,      )
          _lowercase              =max_ad_position_embeddings
          _lowercase              =coordinate_size
          _lowercase              =shape_size
          _lowercase              =has_relative_attention_bias
          _lowercase              =rel_pos_bins
          _lowercase              =max_rel_pos
          _lowercase              =has_spatial_attention_bias
          _lowercase              =rel_ad_pos_bins
          _lowercase              =max_rel_ad_pos
          _lowercase              =text_embed
          _lowercase              =visual_embed
          _lowercase              =input_size
          _lowercase              =num_channels
          _lowercase              =patch_size
          _lowercase              =classifier_dropout






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


       __lowerCAmelCase    :		Optional[Any]					=version.parse('''1.12''' )





       @property
       def 				UpperCamelCase__						(				self	:Any):



          """simple docstring"""

          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 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          return 1e-5





       @property
       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          return 12





       def 				UpperCamelCase__						(				self	:List[Any],      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,      ):



          """simple docstring"""

          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
          _lowercase              =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
          _lowercase              =processor.tokenizer.num_special_tokens_to_add(snake_case)
          _lowercase              =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
          _lowercase              =[[' '.join([processor.tokenizer.unk_token]) * seq_length]] * batch_size

          # Generate dummy bounding boxes
          _lowercase              =[[[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)
          _lowercase              =self._generate_dummy_images(snake_case,      snake_case,      snake_case,      snake_case)

          _lowercase              =dict(
              processor(
                  snake_case,      text=snake_case,      boxes=snake_case,      return_tensors=snake_case,      ))

          return inputs


