




"""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


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)




@add_end_docstrings(
    A_,       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_  (						A_							):




    def 							__magic_name__							(   self						:	Tuple     ,			snake_case_						:	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=snake_case_				)
        else:
            raise ValueError("Unsupported framework"				)
        return masked_index




    def 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	GenericTensor				)				->							np.ndarray:




        '''simple docstring'''

        A__	=    self.get_masked_index(snake_case_				)
        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 							__magic_name__							(   self						:	List[Any]     ,			snake_case_						:	GenericTensor				)				->							Dict:




        '''simple docstring'''

        if isinstance(snake_case_     ,			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(snake_case_				)




    def 							__magic_name__							(   self						:	Any     ,			snake_case_						:	Tuple     ,			snake_case_						:	Union[str, Any]=None     ,			**snake_case_						:	Dict				)				->							Dict[str, GenericTensor]:




        '''simple docstring'''

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




    def 							__magic_name__							(   self						:	Optional[Any]     ,			snake_case_						:	Dict				)				->							Optional[Any]:




        '''simple docstring'''

        A__	=    self.model(**snake_case_				)
        A__	=    model_inputs["input_ids"]
        return model_outputs




    def 							__magic_name__							(   self						:	int     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Dict=5     ,			snake_case_						:	Any=None				)				->							str:




        '''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(snake_case_     ,			axis=-1				)
            if target_ids is not None:
                A__	=    tf.gather_nd(tf.squeeze(snake_case_     ,			0				)     ,			target_ids.reshape(-1     ,			1				)				)
                A__	=    tf.expand_dims(snake_case_     ,			0				)

            A__	=    tf.math.top_k(snake_case_     ,			k=snake_case_				)
            A__,				A__	=    topk.values.numpy(), topk.indices.numpy()
        else:
            A__	=    torch.nonzero(input_ids == self.tokenizer.mask_token_id     ,			as_tuple=snake_case_				).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(snake_case_				)

        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(snake_case_     ,			skip_special_tokens=snake_case_				)
                A__	=    {"score": v, "token": p, "token_str": self.tokenizer.decode([p]				), "sequence": sequence}
                row.append(snake_case_				)
            result.append(snake_case_				)
        if single_mask:
            return result[0]
        return result




    def 							__magic_name__							(   self						:	Tuple     ,			snake_case_						:	List[Any]     ,			snake_case_						:	Optional[Any]=None				)				->							int:




        '''simple docstring'''

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




    def 							__magic_name__							(   self						:	List[str]     ,			snake_case_						:	Tuple=None     ,			snake_case_						:	List[str]=None				)				->							List[Any]:




        '''simple docstring'''

        A__	=    {}

        if targets is not None:
            A__	=    self.get_target_ids(snake_case_     ,			snake_case_				)
            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						:	Dict     ,			snake_case_						:	Tuple     ,			*snake_case_						:	Dict     ,			**snake_case_						:	Optional[int]				)				->							Optional[Any]:




        '''simple docstring'''

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











"""simple docstring"""



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				['''image_processor''', '''tokenizer''']
    lowercase__						 =				'''ViTImageProcessor'''
    lowercase__						 =				('''CLIPTokenizer''', '''CLIPTokenizerFast''')




    def __init__(   self						:	Optional[Any]     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	Dict=None     ,			**snake_case_						:	Optional[Any]				)				->							List[str]:




     '''simple docstring'''

     A__	=    None
     if "feature_extractor" in kwargs:
      warnings.warn(
          "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
          " instead."     ,			snake_case_     ,			)
      A__	=    kwargs.pop("feature_extractor"				)

     A__	=    image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
      raise ValueError("You need to specify an `image_processor`."				)
     if tokenizer is None:
      raise ValueError("You need to specify a `tokenizer`."				)

     super().__init__(snake_case_     ,			snake_case_				)




    def __call__(   self						:	int     ,			snake_case_						:	Union[str, Any]=None     ,			snake_case_						:	int=None     ,			snake_case_						:	Dict=None     ,			snake_case_						:	int=None     ,			**snake_case_						:	List[str]				)				->							Union[str, Any]:




     '''simple docstring'''

     if text is None and visual_prompt is None and images is None:
      raise ValueError("You have to specify either text, visual prompt or images."				)

     if text is not None and visual_prompt is not None:
      raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt."				)

     if text is not None:
      A__	=    self.tokenizer(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if visual_prompt is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if images is not None:
      A__	=    self.image_processor(snake_case_     ,			return_tensors=snake_case_     ,			**snake_case_				)

     if visual_prompt is not None and images is not None:
      A__	=    {
          "pixel_values": image_features.pixel_values,
          "conditional_pixel_values": prompt_features.pixel_values,
      }
      return encoding
     elif text is not None and images is not None:
      A__	=    image_features.pixel_values
      return encoding
     elif text is not None:
      return encoding
     elif visual_prompt is not None:
      A__	=    {
          "conditional_pixel_values": prompt_features.pixel_values,
      }
      return encoding
     else:
      return BatchEncoding(data=dict(**snake_case_				)     ,			tensor_type=snake_case_				)




    def 							__magic_name__							(   self						:	Tuple     ,			*snake_case_						:	List[str]     ,			**snake_case_						:	Optional[int]				)				->							str:




     '''simple docstring'''

     return self.tokenizer.batch_decode(*snake_case_     ,			**snake_case_				)




    def 							__magic_name__							(   self						:	Optional[Any]     ,			*snake_case_						:	Any     ,			**snake_case_						:	List[Any]				)				->							List[str]:




     '''simple docstring'''

     return self.tokenizer.decode(*snake_case_     ,			**snake_case_				)




    @property
    def 							__magic_name__							(   self						:	int				)				->							Optional[int]:




     '''simple docstring'''

     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."     ,			snake_case_     ,			)
     return self.image_processor_class




    @property
    def 							__magic_name__							(   self						:	int				)				->							int:




     '''simple docstring'''

     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."     ,			snake_case_     ,			)
     return self.image_processor





