
"""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


a		:       int     						=       logging.get_logger(__name__)







@add_end_docstrings(
    _lowercase    ,						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(_lowercase       ):



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     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=__SCREAMING_SNAKE_CASE   )
         else:
             raise ValueError('Unsupported framework'   )
         return masked_index



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     np.ndarray:







         """simple docstring"""



         a__		=			self.get_masked_index(__SCREAMING_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 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         if isinstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_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(__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=None     ,				**__SCREAMING_SNAKE_CASE   )		->     Dict[str, GenericTensor]:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     str:







         """simple docstring"""



         a__		=			self.model(**__SCREAMING_SNAKE_CASE   )
         a__		=			model_inputs['input_ids']
         return model_outputs



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=5     ,				__SCREAMING_SNAKE_CASE=None   )		->     Optional[int]:







         """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(__SCREAMING_SNAKE_CASE     ,				axis=-1   )
             if target_ids is not None:
                 a__		=			tf.gather_nd(tf.squeeze(__SCREAMING_SNAKE_CASE     ,				0   )     ,				target_ids.reshape(-1     ,				1   )   )
                 a__		=			tf.expand_dims(__SCREAMING_SNAKE_CASE     ,				0   )

             a__		=			tf.math.top_k(__SCREAMING_SNAKE_CASE     ,				k=__SCREAMING_SNAKE_CASE   )
             a__	,     a__		=			topk.values.numpy(), topk.indices.numpy()
         else:
             a__		=			torch.nonzero(input_ids == self.tokenizer.mask_token_id     ,				as_tuple=__SCREAMING_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(__SCREAMING_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(__SCREAMING_SNAKE_CASE     ,				skip_special_tokens=__SCREAMING_SNAKE_CASE   )
                 a__		=			{'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]   ), 'sequence': sequence}
                 row.append(__SCREAMING_SNAKE_CASE   )
             result.append(__SCREAMING_SNAKE_CASE   )
         if single_mask:
             return result[0]
         return result



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE=None   )		->     str:







         """simple docstring"""



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



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE=None     ,				__SCREAMING_SNAKE_CASE=None   )		->     Tuple:







         """simple docstring"""



         a__		=			{}

         if targets is not None:
             a__		=			self.get_target_ids(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_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     ,				__SCREAMING_SNAKE_CASE     ,				*__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )		->     Union[str, Any]:







         """simple docstring"""



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




"""simple docstring"""




from __future__ import annotations



def     __magic_name__		(					UpperCamelCase     :		str							,		UpperCamelCase     :		list[str] | None = None     )     ->						list[list[str]]:
    a__		=			word_bank or []
    # create a table
    a__		=			len(UpperCamelCase     ) + 1

    a__		=			[]
    for _ in range(UpperCamelCase     ):
        table.append([]     )
    # seed value
    a__		=			[[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(UpperCamelCase     ):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(UpperCamelCase     )] == word:
                    a__		=			[
                        [word, *way] for way in table[i]
                    ]
                    # adds the word to every combination the current position holds
                    # now,push that combination to the table[i+len(word)]
                    table[i + len(UpperCamelCase     )] += new_combinations

    # combinations are in reverse order so reverse for better output
    for combination in table[len(UpperCamelCase     )]:
        combination.reverse()

    return table[len(UpperCamelCase     )]


if __name__ == "__main__":
    print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
    print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
    print(
        all_construct(
            'hexagonosaurus',
            ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
        )
    )


