






'''simple docstring'''

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

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


lowercase__		:    List[Any] =       logging.get_logger(__name__)

lowercase__		:    Optional[Any] =       {
    'microsoft/layoutlm-base-uncased': (
        'https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'
    ),
    'microsoft/layoutlm-large-uncased': (
        'https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'
    ),
}
class        __lowerCAmelCase  (					__magic_name__				):






       """simple docstring"""
       _snake_case			:  List[str]		       =					'layoutlm'





       def __init__(							self     :	str ,				lowerCAmelCase__     :	Optional[int]=30522 ,				lowerCAmelCase__     :	Optional[int]=768 ,				lowerCAmelCase__     :	int=12 ,				lowerCAmelCase__     :	Dict=12 ,				lowerCAmelCase__     :	str=3072 ,				lowerCAmelCase__     :	int="gelu" ,				lowerCAmelCase__     :	Tuple=0.1 ,				lowerCAmelCase__     :	List[Any]=0.1 ,				lowerCAmelCase__     :	Tuple=512 ,				lowerCAmelCase__     :	Tuple=2 ,				lowerCAmelCase__     :	Optional[Any]=0.02 ,				lowerCAmelCase__     :	Optional[int]=1e-1_2 ,				lowerCAmelCase__     :	List[str]=0 ,				lowerCAmelCase__     :	Optional[Any]="absolute" ,				lowerCAmelCase__     :	Tuple=True ,				lowerCAmelCase__     :	List[str]=1024 ,				**lowerCAmelCase__     :	str ,				)					->  Tuple:

            '''simple docstring'''




            super().__init__(pad_token_id=lowerCAmelCase__ ,				**lowerCAmelCase__  )
            _UpperCamelCase       					= vocab_size
            _UpperCamelCase       					= hidden_size
            _UpperCamelCase       					= num_hidden_layers
            _UpperCamelCase       					= num_attention_heads
            _UpperCamelCase       					= hidden_act
            _UpperCamelCase       					= intermediate_size
            _UpperCamelCase       					= hidden_dropout_prob
            _UpperCamelCase       					= attention_probs_dropout_prob
            _UpperCamelCase       					= max_position_embeddings
            _UpperCamelCase       					= type_vocab_size
            _UpperCamelCase       					= initializer_range
            _UpperCamelCase       					= layer_norm_eps
            _UpperCamelCase       					= position_embedding_type
            _UpperCamelCase       					= use_cache
            _UpperCamelCase       					= max_ad_position_embeddings

class        __lowerCAmelCase  (					__magic_name__				):






       """simple docstring"""





       def __init__(							self     :	List[Any] ,				lowerCAmelCase__     :	PretrainedConfig ,				lowerCAmelCase__     :	str = "default" ,				lowerCAmelCase__     :	List[PatchingSpec] = None ,				)					->  Optional[int]:

            '''simple docstring'''




            super().__init__(lowerCAmelCase__ ,				task=lowerCAmelCase__ ,				patching_specs=lowerCAmelCase__  )
            _UpperCamelCase       					= config.max_ad_position_embeddings - 1





       @property
       def snake_case__		(							self     :	Dict  )					->  Mapping[str, Mapping[int, str]]:

            '''simple docstring'''




            return OrderedDict(
                [
                    ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
                    ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
                    ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
                    ('''token_type_ids''', {0: '''batch''', 1: '''sequence'''}),
                ]  )







       def snake_case__		(							self     :	Optional[int] ,				lowerCAmelCase__     :	PreTrainedTokenizer ,				lowerCAmelCase__     :	int = -1 ,				lowerCAmelCase__     :	int = -1 ,				lowerCAmelCase__     :	bool = False ,				lowerCAmelCase__     :	Optional[TensorType] = None ,				)					->  Mapping[str, Any]:

            '''simple docstring'''




            _UpperCamelCase       					= super().generate_dummy_inputs(
                lowerCAmelCase__ ,				batch_size=lowerCAmelCase__ ,				seq_length=lowerCAmelCase__ ,				is_pair=lowerCAmelCase__ ,				framework=lowerCAmelCase__  )

            # Generate a dummy bbox
            _UpperCamelCase       					= [48, 84, 73, 128]

            if not framework == TensorType.PYTORCH:
                 raise NotImplementedError('''Exporting LayoutLM to ONNX is currently only supported for PyTorch.'''  )

            if not is_torch_available():
                 raise ValueError('''Cannot generate dummy inputs without PyTorch installed.'''  )
            import torch

            _UpperCamelCase					,       _UpperCamelCase       					= input_dict['''input_ids'''].shape
            _UpperCamelCase       					= torch.tensor([*[box] * seq_length]  ).tile(lowerCAmelCase__ ,				1 ,				1  )
            return input_dict










'''simple docstring'''

from ..utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
)


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_pt_objects import *  # noqa F403
else:
   from .consistency_models import ConsistencyModelPipeline
   from .dance_diffusion import DanceDiffusionPipeline
   from .ddim import DDIMPipeline
   from .ddpm import DDPMPipeline
   from .dit import DiTPipeline
   from .latent_diffusion import LDMSuperResolutionPipeline
   from .latent_diffusion_uncond import LDMPipeline
   from .pipeline_utils import AudioPipelineOutput, DiffusionPipeline, ImagePipelineOutput
   from .pndm import PNDMPipeline
   from .repaint import RePaintPipeline
   from .score_sde_ve import ScoreSdeVePipeline
   from .stochastic_karras_ve import KarrasVePipeline

try:
   if not (is_torch_available() and is_librosa_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
   from .audio_diffusion import AudioDiffusionPipeline, Mel

try:
   if not (is_torch_available() and is_transformers_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .alt_diffusion import AltDiffusionImgaImgPipeline, AltDiffusionPipeline
   from .audioldm import AudioLDMPipeline
   from .controlnet import (
       StableDiffusionControlNetImgaImgPipeline,
       StableDiffusionControlNetInpaintPipeline,
       StableDiffusionControlNetPipeline,
   )
   from .deepfloyd_if import (
       IFImgaImgPipeline,
       IFImgaImgSuperResolutionPipeline,
       IFInpaintingPipeline,
       IFInpaintingSuperResolutionPipeline,
       IFPipeline,
       IFSuperResolutionPipeline,
   )
   from .kandinsky import (
       KandinskyImgaImgPipeline,
       KandinskyInpaintPipeline,
       KandinskyPipeline,
       KandinskyPriorPipeline,
   )
   from .kandinskya_a import (
       KandinskyVaaControlnetImgaImgPipeline,
       KandinskyVaaControlnetPipeline,
       KandinskyVaaImgaImgPipeline,
       KandinskyVaaInpaintPipeline,
       KandinskyVaaPipeline,
       KandinskyVaaPriorEmbaEmbPipeline,
       KandinskyVaaPriorPipeline,
   )
   from .latent_diffusion import LDMTextToImagePipeline
   from .paint_by_example import PaintByExamplePipeline
   from .semantic_stable_diffusion import SemanticStableDiffusionPipeline
   from .shap_e import ShapEImgaImgPipeline, ShapEPipeline
   from .stable_diffusion import (
       CycleDiffusionPipeline,
       StableDiffusionAttendAndExcitePipeline,
       StableDiffusionDepthaImgPipeline,
       StableDiffusionDiffEditPipeline,
       StableDiffusionImageVariationPipeline,
       StableDiffusionImgaImgPipeline,
       StableDiffusionInpaintPipeline,
       StableDiffusionInpaintPipelineLegacy,
       StableDiffusionInstructPixaPixPipeline,
       StableDiffusionLatentUpscalePipeline,
       StableDiffusionLDMaDPipeline,
       StableDiffusionModelEditingPipeline,
       StableDiffusionPanoramaPipeline,
       StableDiffusionParadigmsPipeline,
       StableDiffusionPipeline,
       StableDiffusionPixaPixZeroPipeline,
       StableDiffusionSAGPipeline,
       StableDiffusionUpscalePipeline,
       StableUnCLIPImgaImgPipeline,
       StableUnCLIPPipeline,
   )
   from .stable_diffusion_safe import StableDiffusionPipelineSafe
   from .text_to_video_synthesis import TextToVideoSDPipeline, TextToVideoZeroPipeline, VideoToVideoSDPipeline
   from .unclip import UnCLIPImageVariationPipeline, UnCLIPPipeline
   from .unidiffuser import ImageTextPipelineOutput, UniDiffuserModel, UniDiffuserPipeline, UniDiffuserTextDecoder
   from .versatile_diffusion import (
       VersatileDiffusionDualGuidedPipeline,
       VersatileDiffusionImageVariationPipeline,
       VersatileDiffusionPipeline,
       VersatileDiffusionTextToImagePipeline,
   )
   from .vq_diffusion import VQDiffusionPipeline


try:
   if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
   from .stable_diffusion_xl import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
   if not is_onnx_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_onnx_objects import *  # noqa F403
else:
   from .onnx_utils import OnnxRuntimeModel

try:
   if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
   from .stable_diffusion import (
       OnnxStableDiffusionImgaImgPipeline,
       OnnxStableDiffusionInpaintPipeline,
       OnnxStableDiffusionInpaintPipelineLegacy,
       OnnxStableDiffusionPipeline,
       OnnxStableDiffusionUpscalePipeline,
       StableDiffusionOnnxPipeline,
   )

try:
   if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
   from .stable_diffusion import StableDiffusionKDiffusionPipeline

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_flax_objects import *  # noqa F403
else:
   from .pipeline_flax_utils import FlaxDiffusionPipeline


try:
   if not (is_flax_available() and is_transformers_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
   from .controlnet import FlaxStableDiffusionControlNetPipeline
   from .stable_diffusion import (
       FlaxStableDiffusionImgaImgPipeline,
       FlaxStableDiffusionInpaintPipeline,
       FlaxStableDiffusionPipeline,
   )
try:
   if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ..utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
   from .spectrogram_diffusion import MidiProcessor, SpectrogramDiffusionPipeline



