

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(
    _UpperCAmelCase    ,							R'\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    '    ,							)
class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       List[Any],lowercase_ :       GenericTensor		)->    np.ndarray:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       List[str],lowercase_ :       GenericTensor		)->    np.ndarray:





        '''simple docstring'''







        A__          =   self.get_masked_index(lowercase_		)
        A__          =   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 						snake_case__     (      self :       Union[str, Any],lowercase_ :       GenericTensor		)->    Any:





        '''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 						snake_case__     (      self :       Tuple,lowercase_ :       Optional[int],lowercase_ :       str=None,**lowercase_ :       int		)->    Dict[str, GenericTensor]:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Tuple,lowercase_ :       List[Any]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   self.model(**lowercase_		)
        A__          =   model_inputs['input_ids']
        return model_outputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Optional[Any],lowercase_ :       List[Any]=5,lowercase_ :       Any=None		)->    Any:





        '''simple docstring'''







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

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

            A__          =   outputs.numpy()

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

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

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

            A__      ,						A__          =   probs.topk(lowercase_		)

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

                A__          =   p
                # Filter padding out:
                A__          =   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
                A__          =   self.tokenizer.decode(lowercase_,skip_special_tokens=lowercase_		)
                A__          =   {'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 						snake_case__     (      self :       Optional[Any],lowercase_ :       int,lowercase_ :       Tuple=None		)->    Optional[Any]:





        '''simple docstring'''







        if isinstance(lowercase_,lowercase_		):
            A__          =   [targets]
        try:
            A__          =   self.tokenizer.get_vocab()
        except Exception:
            A__          =   {}
        A__          =   []
        for target in targets:
            A__          =   vocab.get(lowercase_,lowercase_		)
            if id_ is None:
                A__          =   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
                A__          =   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_		)
        A__          =   list(set(lowercase_		)		)
        if len(lowercase_		) == 0:
            raise ValueError('At least one target must be provided when passed.'		)
        A__          =   np.array(lowercase_		)
        return target_ids


    def 						snake_case__     (      self :       Dict,lowercase_ :       int=None,lowercase_ :       Dict=None		)->    Optional[int]:





        '''simple docstring'''







        A__          =   {}

        if targets is not None:
            A__          =   self.get_target_ids(lowercase_,lowercase_		)
            A__          =   target_ids

        if top_k is not None:
            A__          =   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 :       int,lowercase_ :       str,*lowercase_ :       Tuple,**lowercase_ :       List[str]		)->    Optional[Any]:





        '''simple docstring'''







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




import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							(DPMSolverSinglestepScheduler,)
    lowerCamelCase			=							(('num_inference_steps', 25),)


    def 						snake_case__     (      self :       Tuple,**lowercase_ :       Dict		)->    Optional[int]:





        '''simple docstring'''







        A__          =   {
            'num_train_timesteps': 1_0_0_0,
            'beta_start': 0.0_001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
            'solver_order': 2,
            'prediction_type': 'epsilon',
            'thresholding': False,
            'sample_max_value': 1.0,
            'algorithm_type': 'dpmsolver++',
            'solver_type': 'midpoint',
            'lambda_min_clipped': -float('inf'		),
            'variance_type': None,
        }

        config.update(**lowercase_		)
        return config


    def 						snake_case__     (      self :       str,lowercase_ :       Optional[Any]=0,**lowercase_ :       Any		)->    List[Any]:





        '''simple docstring'''







        A__          =   dict(self.forward_default_kwargs		)
        A__          =   kwargs.pop('num_inference_steps',lowercase_		)
        A__          =   self.dummy_sample
        A__          =   0.1 * sample
        A__          =   [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
            A__          =   self.get_scheduler_config(**lowercase_		)
            A__          =   scheduler_class(**lowercase_		)
            scheduler.set_timesteps(lowercase_		)
            # copy over dummy past residuals
            A__          =   dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(lowercase_		)
                A__          =   scheduler_class.from_pretrained(lowercase_		)
                new_scheduler.set_timesteps(lowercase_		)
                # copy over dummy past residuals
                A__          =   dummy_past_residuals[: new_scheduler.config.solver_order]

            A__      ,						A__          =   sample, sample
            for t in range(lowercase_,time_step + scheduler.config.solver_order + 1		):
                A__          =   scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample
                A__          =   new_scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample

                assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"


    def 						snake_case__     (      self :       List[str]		)->    List[Any]:





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Union[str, Any]=0,**lowercase_ :       Union[str, Any]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   dict(self.forward_default_kwargs		)
        A__          =   kwargs.pop('num_inference_steps',lowercase_		)
        A__          =   self.dummy_sample
        A__          =   0.1 * sample
        A__          =   [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
            A__          =   self.get_scheduler_config()
            A__          =   scheduler_class(**lowercase_		)
            scheduler.set_timesteps(lowercase_		)

            # copy over dummy past residuals (must be after setting timesteps)
            A__          =   dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(lowercase_		)
                A__          =   scheduler_class.from_pretrained(lowercase_		)
                # copy over dummy past residuals
                new_scheduler.set_timesteps(lowercase_		)

                # copy over dummy past residual (must be after setting timesteps)
                A__          =   dummy_past_residuals[: new_scheduler.config.solver_order]

            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample
            A__          =   new_scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample

            assert torch.sum(torch.abs(output - new_output		)		) < 1E-5, "Scheduler outputs are not identical"


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       Optional[int]=None,**lowercase_ :       int		)->    int:





        '''simple docstring'''







        if scheduler is None:
            A__          =   self.scheduler_classes[0]
            A__          =   self.get_scheduler_config(**lowercase_		)
            A__          =   scheduler_class(**lowercase_		)

        A__          =   self.scheduler_classes[0]
        A__          =   self.get_scheduler_config(**lowercase_		)
        A__          =   scheduler_class(**lowercase_		)

        A__          =   1_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter
        scheduler.set_timesteps(lowercase_		)

        for i, t in enumerate(scheduler.timesteps		):
            A__          =   model(lowercase_,lowercase_		)
            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_		).prev_sample

        return sample


    def 						snake_case__     (      self :       Any		)->    str:





        '''simple docstring'''







        A__          =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
        A__          =   5_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter
        scheduler.set_timesteps(lowercase_		)

        # make sure that the first t is uneven
        for i, t in enumerate(scheduler.timesteps[3:]		):
            A__          =   model(lowercase_,lowercase_		)
            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_		).prev_sample

        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_574		) < 1E-3


    def 						snake_case__     (      self :       Optional[Any]		)->    List[Any]:





        '''simple docstring'''







        for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=lowercase_		)


    def 						snake_case__     (      self :       int		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
        A__          =   self.full_loop(scheduler=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_791		) < 1E-3

        A__          =   DEISMultistepScheduler.from_config(scheduler.config		)
        A__          =   DPMSolverMultistepScheduler.from_config(scheduler.config		)
        A__          =   UniPCMultistepScheduler.from_config(scheduler.config		)
        A__          =   DPMSolverSinglestepScheduler.from_config(scheduler.config		)

        A__          =   self.full_loop(scheduler=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_791		) < 1E-3


    def 						snake_case__     (      self :       Tuple		)->    Any:





        '''simple docstring'''







        self.check_over_configs(thresholding=lowercase_		)
        for order in [1, 2, 3]:
            for solver_type in ["midpoint", "heun"]:
                for threshold in [0.5, 1.0, 2.0]:
                    for prediction_type in ["epsilon", "sample"]:
                        self.check_over_configs(
                            thresholding=lowercase_,prediction_type=lowercase_,sample_max_value=lowercase_,algorithm_type='dpmsolver++',solver_order=lowercase_,solver_type=lowercase_,)


    def 						snake_case__     (      self :       List[Any]		)->    int:





        '''simple docstring'''







        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=lowercase_		)


    def 						snake_case__     (      self :       Dict		)->    List[Any]:





        '''simple docstring'''







        for algorithm_type in ["dpmsolver", "dpmsolver++"]:
            for solver_type in ["midpoint", "heun"]:
                for order in [1, 2, 3]:
                    for prediction_type in ["epsilon", "sample"]:
                        self.check_over_configs(
                            solver_order=lowercase_,solver_type=lowercase_,prediction_type=lowercase_,algorithm_type=lowercase_,)
                        A__          =   self.full_loop(
                            solver_order=lowercase_,solver_type=lowercase_,prediction_type=lowercase_,algorithm_type=lowercase_,)
                        assert not torch.isnan(lowercase_		).any(), "Samples have nan numbers"


    def 						snake_case__     (      self :       Optional[int]		)->    Tuple:





        '''simple docstring'''







        self.check_over_configs(lower_order_final=lowercase_		)
        self.check_over_configs(lower_order_final=lowercase_		)


    def 						snake_case__     (      self :       Tuple		)->    Optional[int]:





        '''simple docstring'''







        self.check_over_configs(lambda_min_clipped=-float('inf'		)		)
        self.check_over_configs(lambda_min_clipped=-5.1		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Tuple:





        '''simple docstring'''







        self.check_over_configs(variance_type=lowercase_		)
        self.check_over_configs(variance_type='learned_range'		)


    def 						snake_case__     (      self :       str		)->    Any:





        '''simple docstring'''







        for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_forward(num_inference_steps=lowercase_,time_step=0		)


    def 						snake_case__     (      self :       Tuple		)->    Tuple:





        '''simple docstring'''







        A__          =   self.full_loop()
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_791		) < 1E-3


    def 						snake_case__     (      self :       Any		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   self.full_loop(use_karras_sigmas=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_248		) < 1E-3


    def 						snake_case__     (      self :       Union[str, Any]		)->    Tuple:





        '''simple docstring'''







        A__          =   self.full_loop(prediction_type='v_prediction'		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.1_453		) < 1E-3


    def 						snake_case__     (      self :       Tuple		)->    int:





        '''simple docstring'''







        A__          =   self.full_loop(prediction_type='v_prediction',use_karras_sigmas=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.0_649		) < 1E-3


    def 						snake_case__     (      self :       List[Any]		)->    int:





        '''simple docstring'''







        A__          =   self.scheduler_classes[0]
        A__          =   self.get_scheduler_config(thresholding=lowercase_,dynamic_thresholding_ratio=0		)
        A__          =   scheduler_class(**lowercase_		)

        A__          =   1_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter.half()
        scheduler.set_timesteps(lowercase_		)

        for i, t in enumerate(scheduler.timesteps		):
            A__          =   model(lowercase_,lowercase_		)
            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_		).prev_sample

        assert sample.dtype == torch.floataa

