



"""simple docstring"""

from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
							import tensorflow as tf

							from ..tf_utils import stable_softmax


if is_torch_available():
							import torch


A_						:    Union[str, Any]				=      logging.get_logger(__name__)
@add_end_docstrings(
    _lowerCamelCase							,							r'''
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    '''							,							)
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	def 			_lowerCAmelCase     (      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       GenericTensor    )						->	np.ndarray:





					"""simple docstring"""


					if self.framework == "tf":
									SCREAMING_SNAKE_CASE						:							Dict							=     tf.where(input_ids == self.tokenizer.mask_token_id    ).numpy()
					elif self.framework == "pt":
									SCREAMING_SNAKE_CASE						:							List[Any]							=     torch.nonzero(input_ids == self.tokenizer.mask_token_id     ,       as_tuple=_SCREAMING_SNAKE_CASE    )
					else:
									raise ValueError('Unsupported framework'    )
					return masked_index





	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       GenericTensor    )						->	np.ndarray:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     self.get_masked_index(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     np.prod(masked_index.shape    )
					if numel < 1:
									raise PipelineException(
									    'fill-mask'     ,       self.model.base_model_prefix     ,       f"""No mask_token ({self.tokenizer.mask_token}) found on the input"""     ,       )





	def 			_lowerCAmelCase     (      self				:       List[str]     ,       _SCREAMING_SNAKE_CASE				:       GenericTensor    )						->	Optional[Any]:





					"""simple docstring"""


					if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
									for model_input in model_inputs:
													self._ensure_exactly_one_mask_token(model_input['input_ids'][0]    )
					else:
									for input_ids in model_inputs["input_ids"]:
													self._ensure_exactly_one_mask_token(_SCREAMING_SNAKE_CASE    )





	def 			_lowerCAmelCase     (      self				:       Dict     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]     ,       _SCREAMING_SNAKE_CASE				:       str=None     ,       **_SCREAMING_SNAKE_CASE				:       List[Any]    )						->	Dict[str, GenericTensor]:





					"""simple docstring"""


					if return_tensors is None:
									SCREAMING_SNAKE_CASE						:							str							=     self.framework
					SCREAMING_SNAKE_CASE						:							Tuple							=     self.tokenizer(_SCREAMING_SNAKE_CASE     ,       return_tensors=_SCREAMING_SNAKE_CASE    )
					self.ensure_exactly_one_mask_token(_SCREAMING_SNAKE_CASE    )
					return model_inputs





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       Tuple    )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							List[str]							=     self.model(**_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     model_inputs['input_ids']
					return model_outputs





	def 			_lowerCAmelCase     (      self				:       Any     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]     ,       _SCREAMING_SNAKE_CASE				:       List[str]=5     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=None    )						->	Optional[Any]:





					"""simple docstring"""


					if target_ids is not None and target_ids.shape[0] < top_k:
									SCREAMING_SNAKE_CASE						:							Dict							=     target_ids.shape[0]
					SCREAMING_SNAKE_CASE						:							Dict							=     model_outputs['input_ids'][0]
					SCREAMING_SNAKE_CASE						:							Any							=     model_outputs['logits']

					if self.framework == "tf":
									SCREAMING_SNAKE_CASE						:							List[str]							=     tf.where(input_ids == self.tokenizer.mask_token_id    ).numpy()[:, 0]

									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     outputs.numpy()

									SCREAMING_SNAKE_CASE						:							int							=     outputs[0, masked_index, :]
									SCREAMING_SNAKE_CASE						:							int							=     stable_softmax(_SCREAMING_SNAKE_CASE     ,       axis=-1    )
									if target_ids is not None:
													SCREAMING_SNAKE_CASE						:							List[Any]							=     tf.gather_nd(tf.squeeze(_SCREAMING_SNAKE_CASE     ,       0    )     ,       target_ids.reshape(-1     ,       1    )    )
													SCREAMING_SNAKE_CASE						:							List[str]							=     tf.expand_dims(_SCREAMING_SNAKE_CASE     ,       0    )

									SCREAMING_SNAKE_CASE						:							Dict							=     tf.math.top_k(_SCREAMING_SNAKE_CASE     ,       k=_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[Any]							=     topk.values.numpy(), topk.indices.numpy()
					else:
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     torch.nonzero(input_ids == self.tokenizer.mask_token_id     ,       as_tuple=_SCREAMING_SNAKE_CASE    ).squeeze(-1    )
									# Fill mask pipeline supports only one ${mask_token} per sample

									SCREAMING_SNAKE_CASE						:							Dict							=     outputs[0, masked_index, :]
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     logits.softmax(dim=-1    )
									if target_ids is not None:
													SCREAMING_SNAKE_CASE						:							List[Any]							=     probs[..., target_ids]

									SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							int							=     probs.topk(_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Dict							=     []
					SCREAMING_SNAKE_CASE						:							Dict							=     values.shape[0] == 1
					for i, (_values, _predictions) in enumerate(zip(values.tolist()     ,       predictions.tolist()    )    ):
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     []
									for v, p in zip(_values     ,       _predictions    ):
													# Copy is important since we're going to modify this array in place
													SCREAMING_SNAKE_CASE						:							Optional[int]							=     input_ids.numpy().copy()
													if target_ids is not None:
																	SCREAMING_SNAKE_CASE						:							int							=     target_ids[p].tolist()

													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     p
													# Filter padding out:
													SCREAMING_SNAKE_CASE						:							Optional[int]							=     tokens[np.where(tokens != self.tokenizer.pad_token_id    )]
													# Originally we skip special tokens to give readable output.
													# For multi masks though, the other [MASK] would be removed otherwise
													# making the output look odd, so we add them back
													SCREAMING_SNAKE_CASE						:							Dict							=     self.tokenizer.decode(_SCREAMING_SNAKE_CASE     ,       skip_special_tokens=_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							List[str]							=     {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]    ), 'sequence': sequence}
													row.append(_SCREAMING_SNAKE_CASE    )
									result.append(_SCREAMING_SNAKE_CASE    )
					if single_mask:
									return result[0]
					return result





	def 			_lowerCAmelCase     (      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       int=None    )						->	Optional[int]:





					"""simple docstring"""


					if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ):
									SCREAMING_SNAKE_CASE						:							List[Any]							=     [targets]
					try:
									SCREAMING_SNAKE_CASE						:							int							=     self.tokenizer.get_vocab()
					except Exception:
									SCREAMING_SNAKE_CASE						:							Tuple							=     {}
					SCREAMING_SNAKE_CASE						:							str							=     []
					for target in targets:
									SCREAMING_SNAKE_CASE						:							Tuple							=     vocab.get(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									if id_ is None:
													SCREAMING_SNAKE_CASE						:							Dict							=     self.tokenizer(
													    _SCREAMING_SNAKE_CASE     ,       add_special_tokens=_SCREAMING_SNAKE_CASE     ,       return_attention_mask=_SCREAMING_SNAKE_CASE     ,       return_token_type_ids=_SCREAMING_SNAKE_CASE     ,       max_length=1     ,       truncation=_SCREAMING_SNAKE_CASE     ,       )['input_ids']
													if len(_SCREAMING_SNAKE_CASE    ) == 0:
																	logger.warning(
																	    f"""The specified target token `{target}` does not exist in the model vocabulary. """
																	    'We cannot replace it with anything meaningful, ignoring it'    )
																	continue
													SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     input_ids[0]
													# XXX: If users encounter this pass
													# it becomes pretty slow, so let's make sure
													# The warning enables them to fix the input to
													# get faster performance.
													logger.warning(
													    f"""The specified target token `{target}` does not exist in the model vocabulary. """
													    f"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_    )}`."""    )
									target_ids.append(id_    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     list(set(_SCREAMING_SNAKE_CASE    )    )
					if len(_SCREAMING_SNAKE_CASE    ) == 0:
									raise ValueError('At least one target must be provided when passed.'    )
					SCREAMING_SNAKE_CASE						:							Tuple							=     np.array(_SCREAMING_SNAKE_CASE    )
					return target_ids





	def 			_lowerCAmelCase     (      self				:       int     ,       _SCREAMING_SNAKE_CASE				:       Dict=None     ,       _SCREAMING_SNAKE_CASE				:       Dict=None    )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Tuple							=     {}

					if targets is not None:
									SCREAMING_SNAKE_CASE						:							int							=     self.get_target_ids(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							List[str]							=     target_ids

					if top_k is not None:
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     top_k

					if self.tokenizer.mask_token_id is None:
									raise PipelineException(
									    'fill-mask'     ,       self.model.base_model_prefix     ,       'The tokenizer does not define a `mask_token`.'    )
					return {}, {}, postprocess_params







	def __call__(      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       *_SCREAMING_SNAKE_CASE				:       str     ,       **_SCREAMING_SNAKE_CASE				:       Tuple    )						->	Tuple:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							str							=     super().__call__(_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )
					if isinstance(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    ) and len(_SCREAMING_SNAKE_CASE    ) == 1:
									return outputs[0]
					return outputs










"""simple docstring"""

A_						:    Optional[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





