

'''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


lowerCAmelCase__											=	logging.get_logger(__name__)






@add_end_docstrings(
    lowerCamelCase__				,       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 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




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






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     GenericTensor			):
   __lowercase							  = self.get_masked_index(lowercase__			)
   __lowercase							  = 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 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     GenericTensor			):
   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 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]					,lowercase__   :     str					,lowercase__   :     Union[str, Any]=None					,**lowercase__   :     Any			):
   if return_tensors is None:
    __lowercase							  = self.framework
   __lowercase							  = self.tokenizer(lowercase__					,return_tensors=lowercase__			)
   self.ensure_exactly_one_mask_token(lowercase__			)
   return model_inputs






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple					,lowercase__   :     int			):
   __lowercase							  = self.model(**lowercase__			)
   __lowercase							  = model_inputs['''input_ids''']
   return model_outputs






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,lowercase__   :     Union[str, Any]					,lowercase__   :     Optional[Any]=5					,lowercase__   :     str=None			):
   # Cap top_k if there are targets
   if target_ids is not None and target_ids.shape[0] < top_k:
    __lowercase							  = target_ids.shape[0]
   __lowercase							  = model_outputs['''input_ids'''][0]
   __lowercase							  = model_outputs['''logits''']

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

    __lowercase							  = outputs.numpy()

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

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

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

    __lowercase  ,	__lowercase							  = probs.topk(lowercase__			)

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

     __lowercase							  = p
     # Filter padding out:
     __lowercase							  = 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
     __lowercase							  = self.tokenizer.decode(lowercase__					,skip_special_tokens=lowercase__			)
     __lowercase							  = {'''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 					SCREAMING_SNAKE_CASE      (      self   :     Dict					,lowercase__   :     Tuple					,lowercase__   :     int=None			):
   if isinstance(lowercase__					,lowercase__			):
    __lowercase							  = [targets]
   try:
    __lowercase							  = self.tokenizer.get_vocab()
   except Exception:
    __lowercase							  = {}
   __lowercase							  = []
   for target in targets:
    __lowercase							  = vocab.get(lowercase__					,lowercase__			)
    if id_ is None:
     __lowercase							  = 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
     __lowercase							  = 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_			)
   __lowercase							  = list(set(lowercase__			)			)
   if len(lowercase__			) == 0:
    raise ValueError('''At least one target must be provided when passed.'''			)
   __lowercase							  = np.array(lowercase__			)
   return target_ids






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     int=None					,lowercase__   :     List[str]=None			):
   __lowercase							  = {}

   if targets is not None:
    __lowercase							  = self.get_target_ids(lowercase__					,lowercase__			)
    __lowercase							  = target_ids

   if top_k is not None:
    __lowercase							  = 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   :     str					,lowercase__   :     Optional[int]					,*lowercase__   :     Union[str, Any]					,**lowercase__   :     List[Any]			):
   __lowercase							  = super().__call__(lowercase__					,**lowercase__			)
   if isinstance(lowercase__					,lowercase__			) and len(lowercase__			) == 1:
    return outputs[0]
   return outputs




'''simple docstring'''






import warnings

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






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			['image_processor', 'tokenizer']
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			'CLIPImageProcessor'
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			('CLIPTokenizer', 'CLIPTokenizerFast')






  def __init__(      self   :     List[Any]					,lowercase__   :     Dict=None					,lowercase__   :     Union[str, Any]=None					,**lowercase__   :     Tuple			):
   __lowercase							  = None
   if "feature_extractor" in kwargs:
    warnings.warn(
        '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
        ''' instead.'''					,lowercase__					,)
    __lowercase							  = kwargs.pop('''feature_extractor'''			)

   __lowercase							  = 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__(lowercase__					,lowercase__			)






  def __call__(      self   :     List[Any]					,lowercase__   :     str=None					,lowercase__   :     List[Any]=None					,lowercase__   :     Optional[Any]=None					,**lowercase__   :     int			):

   if text is None and images is None:
    raise ValueError('''You have to specify either text or images. Both cannot be none.'''			)

   if text is not None:
    __lowercase							  = self.tokenizer(lowercase__					,return_tensors=lowercase__					,**lowercase__			)

   if images is not None:
    __lowercase							  = self.image_processor(lowercase__					,return_tensors=lowercase__					,**lowercase__			)

   if text is not None and images is not None:
    __lowercase							  = image_features.pixel_values
    return encoding
   elif text is not None:
    return encoding
   else:
    return BatchEncoding(data=dict(**lowercase__			)					,tensor_type=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,*lowercase__   :     List[str]					,**lowercase__   :     int			):
   return self.tokenizer.batch_decode(*lowercase__					,**lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]					,*lowercase__   :     Optional[int]					,**lowercase__   :     Union[str, Any]			):
   return self.tokenizer.decode(*lowercase__					,**lowercase__			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.tokenizer.model_input_names
   __lowercase							  = self.image_processor.model_input_names
   return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   warnings.warn(
       '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''					,lowercase__					,)
   return self.image_processor_class






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   warnings.warn(
       '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''					,lowercase__					,)
   return self.image_processor

