

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


lowerCAmelCase_       	=	logging.get_logger(__name__)

lowerCAmelCase_       	=	{
    '''google/vivit-b-16x2-kinetics400''': (
        '''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}





class 	snake_case_   (		__A					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   int									=				"vivit"







			def __init__(     self :				int							,   _UpperCamelCase :				Union[str, Any]=2_2_4							,   _UpperCamelCase :				List[str]=3_2							,   _UpperCamelCase :				Optional[Any]=[2, 1_6, 1_6]							,   _UpperCamelCase :				Dict=3							,   _UpperCamelCase :				int=7_6_8							,   _UpperCamelCase :				Any=1_2							,   _UpperCamelCase :				Dict=1_2							,   _UpperCamelCase :				Union[str, Any]=3_0_7_2							,   _UpperCamelCase :				List[Any]="gelu_fast"							,   _UpperCamelCase :				str=0.0							,   _UpperCamelCase :				Dict=0.0							,   _UpperCamelCase :				str=0.02							,   _UpperCamelCase :				Optional[int]=1e-06							,   _UpperCamelCase :				Any=True							,   **_UpperCamelCase :				List[Any]							,   )      ->int:
									snake_case_   =	hidden_size
									snake_case_   =	num_hidden_layers
									snake_case_   =	num_attention_heads
									snake_case_   =	intermediate_size
									snake_case_   =	hidden_act
									snake_case_   =	hidden_dropout_prob
									snake_case_   =	attention_probs_dropout_prob
									snake_case_   =	initializer_range
									snake_case_   =	layer_norm_eps

									snake_case_   =	image_size
									snake_case_   =	num_frames
									snake_case_   =	tubelet_size
									snake_case_   =	num_channels
									snake_case_   =	qkv_bias

									super().__init__(**_UpperCamelCase							)






"""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_     :						str						=					logging.get_logger(__name__)

@add_end_docstrings(
    UpperCAmelCase__			,   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  (   UpperCAmelCase__		):



      '''simple docstring'''

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        if self.framework == "tf":
          A_  :       str										=  tf.where(input_ids == self.tokenizer.mask_token_id  ).numpy()
        elif self.framework == "pt":
          A_  :       List[str]										=  torch.nonzero(input_ids == self.tokenizer.mask_token_id					,      as_tuple=snake_case_  )
        else:
          raise ValueError('Unsupported framework'  )
        return masked_index

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       List[str]										=  self.get_masked_index(snake_case_  )
        A_  :       str										=  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					,      snake_case_  ):

        """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 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=None					,      **snake_case_  ):

        """simple docstring"""

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

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       Dict										=  self.model(**snake_case_  )
        A_  :       Optional[int]										=  model_inputs['input_ids']
        return model_outputs

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=5					,      snake_case_=None  ):

        """simple docstring"""

        if target_ids is not None and target_ids.shape[0] < top_k:
          A_  :       str										=  target_ids.shape[0]
        A_  :       Optional[Any]										=  model_outputs['input_ids'][0]
        A_  :       List[Any]										=  model_outputs['logits']

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

          A_  :       Union[str, Any]										=  outputs.numpy()

          A_  :       Optional[int]										=  outputs[0, masked_index, :]
          A_  :       Optional[Any]										=  stable_softmax(snake_case_					,      axis=-1  )
          if target_ids is not None:
            A_  :       Union[str, Any]										=  tf.gather_nd(tf.squeeze(snake_case_					,      0  )					,      target_ids.reshape(-1					,      1  )  )
            A_  :       Optional[int]										=  tf.expand_dims(snake_case_					,      0  )

          A_  :       Any										=  tf.math.top_k(snake_case_					,      k=snake_case_  )
          A_						,       A_  :       str										=  topk.values.numpy(), topk.indices.numpy()
        else:
          A_  :       int										=  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_  :       Tuple										=  outputs[0, masked_index, :]
          A_  :       List[str]										=  logits.softmax(dim=-1  )
          if target_ids is not None:
            A_  :       str										=  probs[..., target_ids]

          A_						,       A_  :       List[str]										=  probs.topk(snake_case_  )

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

            A_  :       Union[str, Any]										=  p
            # Filter padding out:
            A_  :       Any										=  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_  :       Any										=  self.tokenizer.decode(snake_case_					,      skip_special_tokens=snake_case_  )
            A_  :       Any										=  {'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 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=None  ):

        """simple docstring"""

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

      def 					lowerCamelCase_			(     self					,      snake_case_=None					,      snake_case_=None  ):

        """simple docstring"""

        A_  :       List[str]										=  {}

        if targets is not None:
          A_  :       Any										=  self.get_target_ids(snake_case_					,      snake_case_  )
          A_  :       Optional[Any]										=  target_ids

        if top_k is not None:
          A_  :       int										=  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					,      snake_case_					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

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