



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_			=					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__   ):




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''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=__UpperCAmelCase				)
    else:
       raise ValueError('''Unsupported framework'''				)
    return masked_index




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_masked_index(__UpperCAmelCase				)
    __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        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    if isinstance(__UpperCAmelCase	,				__UpperCAmelCase				):
       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(__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase=None	,				**__UpperCAmelCase				):



    '''simple docstring'''
    if return_tensors is None:
       __lowerCamelCase													=    self.framework
    __lowerCamelCase													=    self.tokenizer(__UpperCAmelCase	,				return_tensors=__UpperCAmelCase				)
    self.ensure_exactly_one_mask_token(__UpperCAmelCase				)
    return model_inputs




 def        lowerCamelCase      (						self	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    self.model(**__UpperCAmelCase				)
    __lowerCamelCase													=    model_inputs['''input_ids''']
    return model_outputs




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase=5	,				__UpperCAmelCase=None				):



    '''simple docstring'''
    # Cap top_k if there are targets
    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(__UpperCAmelCase	,				axis=-1				)
       if target_ids is not None:
          __lowerCamelCase													=    tf.gather_nd(tf.squeeze(__UpperCAmelCase	,				0				)	,				target_ids.reshape(-1	,				1				)				)
          __lowerCamelCase													=    tf.expand_dims(__UpperCAmelCase	,				0				)

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

    __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(__UpperCAmelCase	,				skip_special_tokens=__UpperCAmelCase				)
          __lowerCamelCase													=    {'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p]				), '''sequence''': sequence}
          row.append(__UpperCAmelCase				)
       result.append(__UpperCAmelCase				)
    if single_mask:
       return result[0]
    return result




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase=None				):



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




 def        lowerCamelCase      (						self	,				__UpperCAmelCase=None	,				__UpperCAmelCase=None				):



    '''simple docstring'''
    __lowerCamelCase													=    {}

    if targets is not None:
       __lowerCamelCase													=    self.get_target_ids(__UpperCAmelCase	,				__UpperCAmelCase				)
       __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	,				__UpperCAmelCase	,				*__UpperCAmelCase	,				**__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    super().__call__(__UpperCAmelCase	,				**__UpperCAmelCase				)
    if isinstance(__UpperCAmelCase	,				__UpperCAmelCase				) and len(__UpperCAmelCase				) == 1:
       return outputs[0]
    return outputs











from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_			=					logging.get_logger(__name__)

a_			=					{
    """uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-pre""": """https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-vcr""": """https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-pre""": """https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-nlvr2""": """https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-pre""": """https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"""
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  """visual_bert"""




 def __init__(						self	,				__UpperCAmelCase=30522	,				__UpperCAmelCase=768	,				__UpperCAmelCase=512	,				__UpperCAmelCase=12	,				__UpperCAmelCase=12	,				__UpperCAmelCase=3072	,				__UpperCAmelCase="gelu"	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=512	,				__UpperCAmelCase=2	,				__UpperCAmelCase=0.02	,				__UpperCAmelCase=1E-1_2	,				__UpperCAmelCase=False	,				__UpperCAmelCase=True	,				__UpperCAmelCase=1	,				__UpperCAmelCase=0	,				__UpperCAmelCase=2	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    super().__init__(pad_token_id=__UpperCAmelCase	,				bos_token_id=__UpperCAmelCase	,				eos_token_id=__UpperCAmelCase	,				**__UpperCAmelCase				)

    __lowerCamelCase													=    vocab_size
    __lowerCamelCase													=    max_position_embeddings
    __lowerCamelCase													=    hidden_size
    __lowerCamelCase													=    visual_embedding_dim
    __lowerCamelCase													=    num_hidden_layers
    __lowerCamelCase													=    num_attention_heads
    __lowerCamelCase													=    intermediate_size
    __lowerCamelCase													=    hidden_act
    __lowerCamelCase													=    hidden_dropout_prob
    __lowerCamelCase													=    attention_probs_dropout_prob
    __lowerCamelCase													=    initializer_range
    __lowerCamelCase													=    type_vocab_size
    __lowerCamelCase													=    layer_norm_eps
    __lowerCamelCase													=    bypass_transformer
    __lowerCamelCase													=    special_visual_initialize






