

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


lowercase_       		=  logging.get_logger(__name__)


@add_end_docstrings(
    _UpperCAmelCase    ,							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  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       List[Any],lowercase_ :       GenericTensor		)->    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=lowercase_		)
        else:
            raise ValueError('Unsupported framework'		)
        return masked_index


    def 						snake_case__     (      self :       List[str],lowercase_ :       GenericTensor		)->    np.ndarray:





        '''simple docstring'''







        A__          =   self.get_masked_index(lowercase_		)
        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 						snake_case__     (      self :       Union[str, Any],lowercase_ :       GenericTensor		)->    Any:





        '''simple docstring'''







        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 						snake_case__     (      self :       Tuple,lowercase_ :       Optional[int],lowercase_ :       str=None,**lowercase_ :       int		)->    Dict[str, GenericTensor]:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Tuple,lowercase_ :       List[Any]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   self.model(**lowercase_		)
        A__          =   model_inputs['input_ids']
        return model_outputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Optional[Any],lowercase_ :       List[Any]=5,lowercase_ :       Any=None		)->    Any:





        '''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(lowercase_,axis=-1		)
            if target_ids is not None:
                A__          =   tf.gather_nd(tf.squeeze(lowercase_,0		),target_ids.reshape(-1,1		)		)
                A__          =   tf.expand_dims(lowercase_,0		)

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

        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(lowercase_,skip_special_tokens=lowercase_		)
                A__          =   {'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 						snake_case__     (      self :       Optional[Any],lowercase_ :       int,lowercase_ :       Tuple=None		)->    Optional[Any]:





        '''simple docstring'''







        if isinstance(lowercase_,lowercase_		):
            A__          =   [targets]
        try:
            A__          =   self.tokenizer.get_vocab()
        except Exception:
            A__          =   {}
        A__          =   []
        for target in targets:
            A__          =   vocab.get(lowercase_,lowercase_		)
            if id_ is None:
                A__          =   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
                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(lowercase_		)		)
        if len(lowercase_		) == 0:
            raise ValueError('At least one target must be provided when passed.'		)
        A__          =   np.array(lowercase_		)
        return target_ids


    def 						snake_case__     (      self :       Dict,lowercase_ :       int=None,lowercase_ :       Dict=None		)->    Optional[int]:





        '''simple docstring'''







        A__          =   {}

        if targets is not None:
            A__          =   self.get_target_ids(lowercase_,lowercase_		)
            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 :       int,lowercase_ :       str,*lowercase_ :       Tuple,**lowercase_ :       List[str]		)->    Optional[Any]:





        '''simple docstring'''







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







def 			__magic_name__				( A		:					str   ):






			'''simple docstring'''







			a    =			""
			for ch in key:
						if ch == " " or ch not in key_no_dups and ch.isalpha():
									key_no_dups += ch
			return key_no_dups




def 			__magic_name__				( A		:					str   ):






			'''simple docstring'''







			a    =			[chr(i + 65   ) for i in range(26   )]
			# Remove duplicate characters from key
			a    =			remove_duplicates(key.upper()   )
			a    =			len(A   )
			# First fill cipher with key characters
			a    =			{alphabet[i]: char for i, char in enumerate(A   )}
			# Then map remaining characters in alphabet to
			# the alphabet from the beginning
			for i in range(len(A   ),  26   ):
						a    =			alphabet[i - offset]
						# Ensure we are not mapping letters to letters previously mapped
						while char in key:
									offset -= 1
									a    =			alphabet[i - offset]
						a    =			char
			return cipher_alphabet




def 			__magic_name__				( A		:					str,  A		:					dict[str, str]   ):






			'''simple docstring'''







			return "".join(cipher_map.get(A,  A   ) for ch in message.upper()   )




def 			__magic_name__				( A		:					str,  A		:					dict[str, str]   ):






			'''simple docstring'''







			a    =			{v: k for k, v in cipher_map.items()}
			return "".join(rev_cipher_map.get(A,  A   ) for ch in message.upper()   )




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			input("Enter message to encode or decode: "   ).strip()
			a    =			input("Enter keyword: "   ).strip()
			a    =			input("Encipher or decipher? E/D:"   ).strip()[0].lower()
			try:
						a    =			{"e": encipher, "d": decipher}[option]
			except KeyError:
						raise KeyError("invalid input option"   )
			a    =			create_cipher_map(A   )
			print(func(A,  A   )   )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		main()






