
'''simple docstring'''







_lowercase :  Union[str, Any]    							=       "0.18.2"

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


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

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_pt_objects import *  # noqa F403
else:
							from .models import (
							    AutoencoderKL,
							    ControlNetModel,
							    ModelMixin,
							    PriorTransformer,
							    TaFilmDecoder,
							    TransformeraDModel,
							    UNetaDModel,
							    UNetaDConditionModel,
							    UNetaDModel,
							    UNetaDConditionModel,
							    VQModel,
							)
							from .optimization import (
							    get_constant_schedule,
							    get_constant_schedule_with_warmup,
							    get_cosine_schedule_with_warmup,
							    get_cosine_with_hard_restarts_schedule_with_warmup,
							    get_linear_schedule_with_warmup,
							    get_polynomial_decay_schedule_with_warmup,
							    get_scheduler,
							)
							from .pipelines import (
							    AudioPipelineOutput,
							    ConsistencyModelPipeline,
							    DanceDiffusionPipeline,
							    DDIMPipeline,
							    DDPMPipeline,
							    DiffusionPipeline,
							    DiTPipeline,
							    ImagePipelineOutput,
							    KarrasVePipeline,
							    LDMPipeline,
							    LDMSuperResolutionPipeline,
							    PNDMPipeline,
							    RePaintPipeline,
							    ScoreSdeVePipeline,
							)
							from .schedulers import (
							    CMStochasticIterativeScheduler,
							    DDIMInverseScheduler,
							    DDIMParallelScheduler,
							    DDIMScheduler,
							    DDPMParallelScheduler,
							    DDPMScheduler,
							    DEISMultistepScheduler,
							    DPMSolverMultistepInverseScheduler,
							    DPMSolverMultistepScheduler,
							    DPMSolverSinglestepScheduler,
							    EulerAncestralDiscreteScheduler,
							    EulerDiscreteScheduler,
							    HeunDiscreteScheduler,
							    IPNDMScheduler,
							    KarrasVeScheduler,
							    KDPMaAncestralDiscreteScheduler,
							    KDPMaDiscreteScheduler,
							    PNDMScheduler,
							    RePaintScheduler,
							    SchedulerMixin,
							    ScoreSdeVeScheduler,
							    UnCLIPScheduler,
							    UniPCMultistepScheduler,
							    VQDiffusionScheduler,
							)
							from .training_utils import EMAModel

try:
							if not (is_torch_available() and is_scipy_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
							from .schedulers import LMSDiscreteScheduler

try:
							if not (is_torch_available() and is_torchsde_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
							from .schedulers import DPMSolverSDEScheduler

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 .pipelines import (
							    AltDiffusionImgaImgPipeline,
							    AltDiffusionPipeline,
							    AudioLDMPipeline,
							    CycleDiffusionPipeline,
							    IFImgaImgPipeline,
							    IFImgaImgSuperResolutionPipeline,
							    IFInpaintingPipeline,
							    IFInpaintingSuperResolutionPipeline,
							    IFPipeline,
							    IFSuperResolutionPipeline,
							    ImageTextPipelineOutput,
							    KandinskyImgaImgPipeline,
							    KandinskyInpaintPipeline,
							    KandinskyPipeline,
							    KandinskyPriorPipeline,
							    KandinskyVaaControlnetImgaImgPipeline,
							    KandinskyVaaControlnetPipeline,
							    KandinskyVaaImgaImgPipeline,
							    KandinskyVaaInpaintPipeline,
							    KandinskyVaaPipeline,
							    KandinskyVaaPriorEmbaEmbPipeline,
							    KandinskyVaaPriorPipeline,
							    LDMTextToImagePipeline,
							    PaintByExamplePipeline,
							    SemanticStableDiffusionPipeline,
							    ShapEImgaImgPipeline,
							    ShapEPipeline,
							    StableDiffusionAttendAndExcitePipeline,
							    StableDiffusionControlNetImgaImgPipeline,
							    StableDiffusionControlNetInpaintPipeline,
							    StableDiffusionControlNetPipeline,
							    StableDiffusionDepthaImgPipeline,
							    StableDiffusionDiffEditPipeline,
							    StableDiffusionImageVariationPipeline,
							    StableDiffusionImgaImgPipeline,
							    StableDiffusionInpaintPipeline,
							    StableDiffusionInpaintPipelineLegacy,
							    StableDiffusionInstructPixaPixPipeline,
							    StableDiffusionLatentUpscalePipeline,
							    StableDiffusionLDMaDPipeline,
							    StableDiffusionModelEditingPipeline,
							    StableDiffusionPanoramaPipeline,
							    StableDiffusionParadigmsPipeline,
							    StableDiffusionPipeline,
							    StableDiffusionPipelineSafe,
							    StableDiffusionPixaPixZeroPipeline,
							    StableDiffusionSAGPipeline,
							    StableDiffusionUpscalePipeline,
							    StableUnCLIPImgaImgPipeline,
							    StableUnCLIPPipeline,
							    TextToVideoSDPipeline,
							    TextToVideoZeroPipeline,
							    UnCLIPImageVariationPipeline,
							    UnCLIPPipeline,
							    UniDiffuserModel,
							    UniDiffuserPipeline,
							    UniDiffuserTextDecoder,
							    VersatileDiffusionDualGuidedPipeline,
							    VersatileDiffusionImageVariationPipeline,
							    VersatileDiffusionPipeline,
							    VersatileDiffusionTextToImagePipeline,
							    VideoToVideoSDPipeline,
							    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 .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

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 .pipelines import StableDiffusionKDiffusionPipeline

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 .pipelines import (
							    OnnxStableDiffusionImgaImgPipeline,
							    OnnxStableDiffusionInpaintPipeline,
							    OnnxStableDiffusionInpaintPipelineLegacy,
							    OnnxStableDiffusionPipeline,
							    OnnxStableDiffusionUpscalePipeline,
							    StableDiffusionOnnxPipeline,
							)

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 .pipelines import AudioDiffusionPipeline, Mel

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 .pipelines import SpectrogramDiffusionPipeline

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_flax_objects import *  # noqa F403
else:
							from .models.controlnet_flax import FlaxControlNetModel
							from .models.modeling_flax_utils import FlaxModelMixin
							from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
							from .models.vae_flax import FlaxAutoencoderKL
							from .pipelines import FlaxDiffusionPipeline
							from .schedulers import (
							    FlaxDDIMScheduler,
							    FlaxDDPMScheduler,
							    FlaxDPMSolverMultistepScheduler,
							    FlaxKarrasVeScheduler,
							    FlaxLMSDiscreteScheduler,
							    FlaxPNDMScheduler,
							    FlaxSchedulerMixin,
							    FlaxScoreSdeVeScheduler,
							)


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 .pipelines import (
							    FlaxStableDiffusionControlNetPipeline,
							    FlaxStableDiffusionImgaImgPipeline,
							    FlaxStableDiffusionInpaintPipeline,
							    FlaxStableDiffusionPipeline,
							)

try:
							if not (is_note_seq_available()):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from .utils.dummy_note_seq_objects import *  # noqa F403
else:
							from .pipelines import MidiProcessor







'''simple docstring'''

import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowerCamelCase							:   List[Any]  						=	logging.getLogger(__name__)
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def __init__(self    :    Optional[Any]   ,						UpperCamelCase    :    Any=-1 ):

             '''simple docstring'''







             lowercase__      			= label_idx



      def       UpperCamelCase__		(self    :    Union[str, Any]   ,						UpperCamelCase    :    Optional[Any]   ,						UpperCamelCase    :    Union[Split, str] ):

             '''simple docstring'''







             if isinstance(UpperCamelCase   ,						UpperCamelCase ):
                    lowercase__      			= mode.value
             lowercase__      			= os.path.join(UpperCamelCase   ,						f"{mode}.txt" )
             lowercase__      			= 1
             lowercase__      			= []
             with open(UpperCamelCase   ,						encoding='''utf-8''' ) as f:
                    lowercase__      			= []
                    lowercase__      			= []
                    for line in f:
                           if line.startswith('''-DOCSTART-''' ) or line == "" or line == "\n":
                                  if words:
                                         examples.append(InputExample(guid=f"{mode}-{guid_index}"   ,						words=UpperCamelCase   ,						labels=UpperCamelCase ) )
                                         guid_index += 1
                                         lowercase__      			= []
                                         lowercase__      			= []
                           else:
                                  lowercase__      			= line.split(''' ''' )
                                  words.append(splits[0] )
                                  if len(UpperCamelCase ) > 1:
                                         labels.append(splits[self.label_idx].replace('''\n'''   ,						'''''' ) )
                                  else:
                                         # Examples could have no label for mode = "test"
                                         labels.append('''O''' )
                    if words:
                           examples.append(InputExample(guid=f"{mode}-{guid_index}"   ,						words=UpperCamelCase   ,						labels=UpperCamelCase ) )
             return examples



      def       UpperCamelCase__		(self    :    Optional[int]   ,						UpperCamelCase    :    TextIO   ,						UpperCamelCase    :    TextIO   ,						UpperCamelCase    :    List ):

             '''simple docstring'''







             lowercase__      			= 0
             for line in test_input_reader:
                    if line.startswith('''-DOCSTART-''' ) or line == "" or line == "\n":
                           writer.write(UpperCamelCase )
                           if not preds_list[example_id]:
                                  example_id += 1
                    elif preds_list[example_id]:
                           lowercase__      			= line.split()[0] + ''' ''' + preds_list[example_id].pop(0 ) + '''\n'''
                           writer.write(UpperCamelCase )
                    else:
                           logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.'''   ,						line.split()[0] )






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

             '''simple docstring'''







             if path:
                    with open(UpperCamelCase   ,						'''r''' ) as f:
                           lowercase__      			= f.read().splitlines()
                    if "O" not in labels:
                           lowercase__      			= ['''O'''] + labels
                    return labels
             else:
                    return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def __init__(self    :    List[Any] ):

             '''simple docstring'''







             super().__init__(label_idx=-2 )






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

             '''simple docstring'''







             if path:
                    with open(UpperCamelCase   ,						'''r''' ) as f:
                           lowercase__      			= f.read().splitlines()
                    if "O" not in labels:
                           lowercase__      			= ['''O'''] + labels
                    return labels
             else:
                    return [
                        "O",
                        "B-ADVP",
                        "B-INTJ",
                        "B-LST",
                        "B-PRT",
                        "B-NP",
                        "B-SBAR",
                        "B-VP",
                        "B-ADJP",
                        "B-CONJP",
                        "B-PP",
                        "I-ADVP",
                        "I-INTJ",
                        "I-LST",
                        "I-PRT",
                        "I-NP",
                        "I-SBAR",
                        "I-VP",
                        "I-ADJP",
                        "I-CONJP",
                        "I-PP",
                    ]






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    Tuple   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    Union[Split, str] ):

             '''simple docstring'''







             if isinstance(UpperCamelCase   ,						UpperCamelCase ):
                    lowercase__      			= mode.value
             lowercase__      			= os.path.join(UpperCamelCase   ,						f"{mode}.txt" )
             lowercase__      			= 1
             lowercase__      			= []

             with open(UpperCamelCase   ,						encoding='''utf-8''' ) as f:
                    for sentence in parse_incr(UpperCamelCase ):
                           lowercase__      			= []
                           lowercase__      			= []
                           for token in sentence:
                                  words.append(token['''form'''] )
                                  labels.append(token['''upos'''] )
                           assert len(UpperCamelCase ) == len(UpperCamelCase )
                           if words:
                                  examples.append(InputExample(guid=f"{mode}-{guid_index}"   ,						words=UpperCamelCase   ,						labels=UpperCamelCase ) )
                                  guid_index += 1
             return examples



      def       UpperCamelCase__		(self    :    Tuple   ,						UpperCamelCase    :    TextIO   ,						UpperCamelCase    :    TextIO   ,						UpperCamelCase    :    List ):

             '''simple docstring'''







             lowercase__      			= 0
             for sentence in parse_incr(UpperCamelCase ):
                    lowercase__      			= preds_list[example_id]
                    lowercase__      			= ''''''
                    for token in sentence:
                           out += f"{token['form']} ({token['upos']}|{s_p.pop(0 )}) "
                    out += "\n"
                    writer.write(UpperCamelCase )
                    example_id += 1






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

             '''simple docstring'''







             if path:
                    with open(UpperCamelCase   ,						'''r''' ) as f:
                           return f.read().splitlines()
             else:
                    return [
                        "ADJ",
                        "ADP",
                        "ADV",
                        "AUX",
                        "CCONJ",
                        "DET",
                        "INTJ",
                        "NOUN",
                        "NUM",
                        "PART",
                        "PRON",
                        "PROPN",
                        "PUNCT",
                        "SCONJ",
                        "SYM",
                        "VERB",
                        "X",
                    ]




