
'''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


_lowercase    =		logging.get_logger(__name__)




@add_end_docstrings(
    _SCREAMING_SNAKE_CASE	,							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   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												if self.framework == "tf":
																		_lowerCAmelCase  						=			tf.where(input_ids == self.tokenizer.mask_token_id				).numpy()
												elif self.framework == "pt":
																		_lowerCAmelCase  						=			torch.nonzero(input_ids == self.tokenizer.mask_token_id   ,       as_tuple=_lowercase				)
												else:
																		raise ValueError("""Unsupported framework"""				)
												return masked_index




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.get_masked_index(_lowercase				)
												_lowerCAmelCase  						=			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  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												if isinstance(_lowercase   ,       _lowercase				):
																		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(_lowercase				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase=None   ,       **_lowercase				):

												"""simple docstring"""



												if return_tensors is None:
																		_lowerCAmelCase  						=			self.framework
												_lowerCAmelCase  						=			self.tokenizer(_lowercase   ,       return_tensors=_lowercase				)
												self.ensure_exactly_one_mask_token(_lowercase				)
												return model_inputs




						def  _lowercase     ( self   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model(**_lowercase				)
												_lowerCAmelCase  						=			model_inputs["""input_ids"""]
												return model_outputs




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase=5   ,       _lowercase=None				):

												"""simple docstring"""



												if target_ids is not None and target_ids.shape[0] < top_k:
																		_lowerCAmelCase  						=			target_ids.shape[0]
												_lowerCAmelCase  						=			model_outputs["""input_ids"""][0]
												_lowerCAmelCase  						=			model_outputs["""logits"""]

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

																		_lowerCAmelCase  						=			outputs.numpy()

																		_lowerCAmelCase  						=			outputs[0, masked_index, :]
																		_lowerCAmelCase  						=			stable_softmax(_lowercase   ,       axis=-1				)
																		if target_ids is not None:
																								_lowerCAmelCase  						=			tf.gather_nd(tf.squeeze(_lowercase   ,       0				)   ,       target_ids.reshape(-1   ,       1				)				)
																								_lowerCAmelCase  						=			tf.expand_dims(_lowercase   ,       0				)

																		_lowerCAmelCase  						=			tf.math.top_k(_lowercase   ,       k=_lowercase				)
																		_lowerCAmelCase						,   _lowerCAmelCase  						=			topk.values.numpy(), topk.indices.numpy()
												else:
																		_lowerCAmelCase  						=			torch.nonzero(input_ids == self.tokenizer.mask_token_id   ,       as_tuple=_lowercase				).squeeze(-1				)
																		# Fill mask pipeline supports only one ${mask_token} per sample

																		_lowerCAmelCase  						=			outputs[0, masked_index, :]
																		_lowerCAmelCase  						=			logits.softmax(dim=-1				)
																		if target_ids is not None:
																								_lowerCAmelCase  						=			probs[..., target_ids]

																		_lowerCAmelCase						,   _lowerCAmelCase  						=			probs.topk(_lowercase				)

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

																								_lowerCAmelCase  						=			p
																								# Filter padding out:
																								_lowerCAmelCase  						=			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
																								_lowerCAmelCase  						=			self.tokenizer.decode(_lowercase   ,       skip_special_tokens=_lowercase				)
																								_lowerCAmelCase  						=			{"""score""": v, """token""": p, """token_str""": self.tokenizer.decode([p]				), """sequence""": sequence}
																								row.append(_lowercase				)
																		result.append(_lowercase				)
												if single_mask:
																		return result[0]
												return result




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase=None				):

												"""simple docstring"""



												if isinstance(_lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			[targets]
												try:
																		_lowerCAmelCase  						=			self.tokenizer.get_vocab()
												except Exception:
																		_lowerCAmelCase  						=			{}
												_lowerCAmelCase  						=			[]
												for target in targets:
																		_lowerCAmelCase  						=			vocab.get(_lowercase   ,       _lowercase				)
																		if id_ is None:
																								_lowerCAmelCase  						=			self.tokenizer(
																								    _lowercase   ,       add_special_tokens=_lowercase   ,       return_attention_mask=_lowercase   ,       return_token_type_ids=_lowercase   ,       max_length=1   ,       truncation=_lowercase   ,       )["""input_ids"""]
																								if len(_lowercase				) == 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
																								_lowerCAmelCase  						=			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_				)
												_lowerCAmelCase  						=			list(set(_lowercase				)				)
												if len(_lowercase				) == 0:
																		raise ValueError("""At least one target must be provided when passed."""				)
												_lowerCAmelCase  						=			np.array(_lowercase				)
												return target_ids




						def  _lowercase     ( self   ,       _lowercase=None   ,       _lowercase=None				):

												"""simple docstring"""



												_lowerCAmelCase  						=			{}

												if targets is not None:
																		_lowerCAmelCase  						=			self.get_target_ids(_lowercase   ,       _lowercase				)
																		_lowerCAmelCase  						=			target_ids

												if top_k is not None:
																		_lowerCAmelCase  						=			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   ,       _lowercase   ,       *_lowercase   ,       **_lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			super().__call__(_lowercase   ,       **_lowercase				)
												if isinstance(_lowercase   ,       _lowercase				) and len(_lowercase				) == 1:
																		return outputs[0]
												return outputs


'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE			):






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												super().__init__()

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

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




						@torch.no_grad()
						def __call__( self   ,       _lowercase = 1   ,       _lowercase = None   ,       _lowercase = 0.0   ,       _lowercase = 50   ,       _lowercase = None   ,       _lowercase = "pil"   ,       _lowercase = True   ,       ):

												"""simple docstring"""



												if isinstance(self.unet.config.sample_size   ,       _lowercase				):
																		_lowerCAmelCase  						=			(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		_lowerCAmelCase  						=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

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

												_lowerCAmelCase  						=			randn_tensor(_lowercase   ,       generator=_lowercase   ,       device=self.device   ,       dtype=self.unet.dtype				)

												# set step values
												self.scheduler.set_timesteps(_lowercase				)

												for t in self.progress_bar(self.scheduler.timesteps				):
																		# 1. predict noise model_output
																		_lowerCAmelCase  						=			self.unet(_lowercase   ,       _lowercase				).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
																		_lowerCAmelCase  						=			self.scheduler.step(
																		    _lowercase   ,       _lowercase   ,       _lowercase   ,       eta=_lowercase   ,       use_clipped_model_output=_lowercase   ,       generator=_lowercase				).prev_sample

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

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=_lowercase				)
