




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		:				List[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     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     GenericTensor							)					->     np.ndarray:
							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=__lowerCamelCase							)
							else:
										raise ValueError("Unsupported framework"							)
							return masked_index





				def 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     GenericTensor							)					->     np.ndarray:
							a    =			self.get_masked_index(__lowerCamelCase							)
							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 							__UpperCAmelCase							(      self				:     Any					,      __lowerCamelCase				:     GenericTensor							)					->     Union[str, Any]:
							if isinstance(__lowerCamelCase					,      __lowerCamelCase							):
										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(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Union[str, Any]=None					,      **__lowerCamelCase				:     List[Any]							)					->     Dict[str, GenericTensor]:
							if return_tensors is None:
										a    =			self.framework
							a    =			self.tokenizer(__lowerCamelCase					,      return_tensors=__lowerCamelCase							)
							self.ensure_exactly_one_mask_token(__lowerCamelCase							)
							return model_inputs





				def 							__UpperCAmelCase							(      self				:     Tuple					,      __lowerCamelCase				:     List[Any]							)					->     Dict:
							a    =			self.model(**__lowerCamelCase							)
							a    =			model_inputs["input_ids"]
							return model_outputs





				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     Union[str, Any]					,      __lowerCamelCase				:     Any=5					,      __lowerCamelCase				:     List[Any]=None							)					->     List[str]:
							# Cap top_k if there are targets
							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(__lowerCamelCase					,      axis=-1							)
										if target_ids is not None:
													a    =			tf.gather_nd(tf.squeeze(__lowerCamelCase					,      0							)					,      target_ids.reshape(-1					,      1							)							)
													a    =			tf.expand_dims(__lowerCamelCase					,      0							)

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

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





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





				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     Optional[int]=None					,      __lowerCamelCase				:     int=None							)					->     List[str]:
							a    =			{}

							if targets is not None:
										a    =			self.get_target_ids(__lowerCamelCase					,      __lowerCamelCase							)
										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				:     Union[str, Any]					,      __lowerCamelCase				:     Optional[int]					,      *__lowerCamelCase				:     int					,      **__lowerCamelCase				:     Optional[int]							)					->     Union[str, Any]:
							a    =			super().__call__(__lowerCamelCase					,      **__lowerCamelCase							)
							if isinstance(__lowerCamelCase					,      __lowerCamelCase							) and len(__lowerCamelCase							) == 1:
										return outputs[0]
							return outputs










from __future__ import annotations


class        _a							:

							def __init__(							self:	Union[str, Any]    ,						UpperCamelCase_:	str    ,						UpperCamelCase_:	str							)			->   List[Any]:





									"""simple docstring"""







									lowercase__     ,		lowercase__            =      text, pattern
									lowercase__     ,		lowercase__            =      len(UpperCamelCase_							), len(UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	str							)			->   int:





									"""simple docstring"""







									for i in range(self.patLen - 1    ,						-1    ,						-1							):
											if char == self.pattern[i]:
													return i
									return -1

							def       lowerCamelCase_ (							self:	Optional[int]    ,						UpperCamelCase_:	int							)			->   int:





									"""simple docstring"""







									for i in range(self.patLen - 1    ,						-1    ,						-1							):
											if self.pattern[i] != self.text[current_pos + i]:
													return current_pos + i
									return -1


							def       lowerCamelCase_ (							self:	List[Any]							)			->   list[int]:





									"""simple docstring"""







									lowercase__            =      []
									for i in range(self.textLen - self.patLen + 1							):
											lowercase__            =      self.mismatch_in_text(UpperCamelCase_							)
											if mismatch_index == -1:
													positions.append(UpperCamelCase_							)
											else:
													lowercase__            =      self.match_in_pattern(self.text[mismatch_index]							)
													lowercase__            =      (
													    mismatch_index - match_index
													)  # shifting index lgtm [py/multiple-definition]
									return positions
lowerCAmelCase       =							'ABAABA'
lowerCAmelCase       =							'AB'
lowerCAmelCase       =							BoyerMooreSearch(text, pattern)
lowerCAmelCase       =							bms.bad_character_heuristic()

if len(positions) == 0:
			print('No match found')
else:
			print('Pattern found in following positions: ')
			print(positions)


