


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					:		Dict            =						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 snake_case__			(    UpperCAmelCase__					):




       def     UpperCAmelCase__    ( self				:    Union[str, Any]    ,				_lowerCamelCase				:    Any			):
              if self.framework == "tf":
                     snake_case__ :       Tuple	   =							tf.where(input_ids == self.tokenizer.mask_token_id			).numpy()
              elif self.framework == "pt":
                     snake_case__ :       Optional[Any]	   =							torch.nonzero(input_ids == self.tokenizer.mask_token_id    ,				as_tuple=lowerCamelCase__			)
              else:
                     raise ValueError('Unsupported framework'			)
              return masked_index




       def     UpperCAmelCase__    ( self				:    str    ,				_lowerCamelCase				:    Any			):
              snake_case__ :       Optional[Any]	   =							self.get_masked_index(lowerCamelCase__			)
              snake_case__ :       Tuple	   =							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				:    Optional[Any]    ,				_lowerCamelCase				:    int			):
              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				:    Tuple    ,				_lowerCamelCase				:    Optional[Any]    ,				_lowerCamelCase				:    Any=None    ,				**_lowerCamelCase				:    int			):
              if return_tensors is None:
                     snake_case__ :       Tuple	   =							self.framework
              snake_case__ :       int	   =							self.tokenizer(lowerCamelCase__    ,				return_tensors=lowerCamelCase__			)
              self.ensure_exactly_one_mask_token(lowerCamelCase__			)
              return model_inputs




       def     UpperCAmelCase__    ( self				:    Union[str, Any]    ,				_lowerCamelCase				:    int			):
              snake_case__ :       int	   =							self.model(**lowerCamelCase__			)
              snake_case__ :       Tuple	   =							model_inputs["input_ids"]
              return model_outputs




       def     UpperCAmelCase__    ( self				:    int    ,				_lowerCamelCase				:    List[Any]    ,				_lowerCamelCase				:    str=5    ,				_lowerCamelCase				:    Tuple=None			):
              # Cap top_k if there are targets
              if target_ids is not None and target_ids.shape[0] < top_k:
                     snake_case__ :       Optional[Any]	   =							target_ids.shape[0]
              snake_case__ :       List[str]	   =							model_outputs["input_ids"][0]
              snake_case__ :       Optional[Any]	   =							model_outputs["logits"]

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

                     snake_case__ :       str	   =							outputs.numpy()

                     snake_case__ :       Optional[Any]	   =							outputs[0, masked_index, :]
                     snake_case__ :       Optional[Any]	   =							stable_softmax(lowerCamelCase__    ,				axis=-1			)
                     if target_ids is not None:
                            snake_case__ :       Tuple	   =							tf.gather_nd(tf.squeeze(lowerCamelCase__    ,				0			)    ,				target_ids.reshape(-1    ,				1			)			)
                            snake_case__ :       Any	   =							tf.expand_dims(lowerCamelCase__    ,				0			)

                     snake_case__ :       str	   =							tf.math.top_k(lowerCamelCase__    ,				k=lowerCamelCase__			)
                     snake_case__ :       Union[str, Any]	   =							topk.values.numpy(), topk.indices.numpy()
              else:
                     snake_case__ :       Optional[int]	   =							torch.nonzero(input_ids == self.tokenizer.mask_token_id    ,				as_tuple=lowerCamelCase__			).squeeze(-1			)
                     # Fill mask pipeline supports only one ${mask_token} per sample

                     snake_case__ :       Dict	   =							outputs[0, masked_index, :]
                     snake_case__ :       Union[str, Any]	   =							logits.softmax(dim=-1			)
                     if target_ids is not None:
                            snake_case__ :       Optional[int]	   =							probs[..., target_ids]

                     snake_case__ :       int	   =							probs.topk(lowerCamelCase__			)

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

                            snake_case__ :       Optional[int]	   =							p
                            # Filter padding out:
                            snake_case__ :       Tuple	   =							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
                            snake_case__ :       Union[str, Any]	   =							self.tokenizer.decode(lowerCamelCase__    ,				skip_special_tokens=lowerCamelCase__			)
                            snake_case__ :       int	   =							{"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				:    List[str]    ,				_lowerCamelCase				:    List[str]    ,				_lowerCamelCase				:    List[str]=None			):
              if isinstance(lowerCamelCase__    ,				lowerCamelCase__			):
                     snake_case__ :       Tuple	   =							[targets]
              try:
                     snake_case__ :       Tuple	   =							self.tokenizer.get_vocab()
              except Exception:
                     snake_case__ :       Dict	   =							{}
              snake_case__ :       Union[str, Any]	   =							[]
              for target in targets:
                     snake_case__ :       Union[str, Any]	   =							vocab.get(lowerCamelCase__    ,				lowerCamelCase__			)
                     if id_ is None:
                            snake_case__ :       Tuple	   =							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
                            snake_case__ :       int	   =							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_			)
              snake_case__ :       Any	   =							list(set(lowerCamelCase__			)			)
              if len(lowerCamelCase__			) == 0:
                     raise ValueError('At least one target must be provided when passed.'			)
              snake_case__ :       Optional[Any]	   =							np.array(lowerCamelCase__			)
              return target_ids




       def     UpperCAmelCase__    ( self				:    List[str]    ,				_lowerCamelCase				:    Any=None    ,				_lowerCamelCase				:    int=None			):
              snake_case__ :       str	   =							{}

              if targets is not None:
                     snake_case__ :       Optional[Any]	   =							self.get_target_ids(lowerCamelCase__    ,				lowerCamelCase__			)
                     snake_case__ :       Optional[int]	   =							target_ids

              if top_k is not None:
                     snake_case__ :       List[str]	   =							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				:    List[str]    ,				_lowerCamelCase				:    int    ,				*_lowerCamelCase				:    Any    ,				**_lowerCamelCase				:    Dict			):
              snake_case__ :       Optional[int]	   =							super().__call__(lowerCamelCase__    ,				**lowerCamelCase__			)
              if isinstance(lowerCamelCase__    ,				lowerCamelCase__			) and len(lowerCamelCase__			) == 1:
                     return outputs[0]
              return outputs






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


a  :      Any            =    logging.get_logger(__name__)

a  :      List[str]            =    {
    """transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Optional[Any]          =    'transfo-xl'
    SCREAMING_SNAKE_CASE:				List[Any]          =    ['mems']
    SCREAMING_SNAKE_CASE:				List[Any]          =    {
        'n_token': 'vocab_size',
        'hidden_size': 'd_model',
        'num_attention_heads': 'n_head',
        'num_hidden_layers': 'n_layer',
    }




    def __init__(    self       ,						lowerCamelCase__=267_735       ,						lowerCamelCase__=[20_000, 40_000, 200_000]       ,						lowerCamelCase__=1_024       ,						lowerCamelCase__=1_024       ,						lowerCamelCase__=16       ,						lowerCamelCase__=64       ,						lowerCamelCase__=4_096       ,						lowerCamelCase__=4       ,						lowerCamelCase__=False       ,						lowerCamelCase__=18       ,						lowerCamelCase__=1_600       ,						lowerCamelCase__=1_000       ,						lowerCamelCase__=True       ,						lowerCamelCase__=True       ,						lowerCamelCase__=0       ,						lowerCamelCase__=-1       ,						lowerCamelCase__=True       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.0       ,						lowerCamelCase__=True       ,						lowerCamelCase__="normal"       ,						lowerCamelCase__=0.0_1       ,						lowerCamelCase__=0.0_1       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__=1E-5       ,						lowerCamelCase__=0       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							Dict       =	vocab_size
       lowerCAmelCase_:							Any       =	[]
       self.cutoffs.extend(lowerCamelCase__ )
       if proj_share_all_but_first:
          lowerCAmelCase_:							int       =	[False] + [True] * len(self.cutoffs )
       else:
          lowerCAmelCase_:							Any       =	[False] + [False] * len(self.cutoffs )
       lowerCAmelCase_:							Dict       =	d_model
       lowerCAmelCase_:							Union[str, Any]       =	d_embed
       lowerCAmelCase_:							Dict       =	d_head
       lowerCAmelCase_:							Dict       =	d_inner
       lowerCAmelCase_:							List[str]       =	div_val
       lowerCAmelCase_:							List[str]       =	pre_lnorm
       lowerCAmelCase_:							List[str]       =	n_layer
       lowerCAmelCase_:							List[str]       =	n_head
       lowerCAmelCase_:							str       =	mem_len
       lowerCAmelCase_:							Any       =	same_length
       lowerCAmelCase_:							Optional[Any]       =	attn_type
       lowerCAmelCase_:							int       =	clamp_len
       lowerCAmelCase_:							Optional[int]       =	sample_softmax
       lowerCAmelCase_:							Optional[Any]       =	adaptive
       lowerCAmelCase_:							Optional[int]       =	dropout
       lowerCAmelCase_:							Union[str, Any]       =	dropatt
       lowerCAmelCase_:							Tuple       =	untie_r
       lowerCAmelCase_:							Union[str, Any]       =	init
       lowerCAmelCase_:							Optional[Any]       =	init_range
       lowerCAmelCase_:							Optional[Any]       =	proj_init_std
       lowerCAmelCase_:							Tuple       =	init_std
       lowerCAmelCase_:							Tuple       =	layer_norm_epsilon
       super().__init__(eos_token_id=lowerCamelCase__       ,						**lowerCamelCase__ )




    @property
    def      _a    (    self ):
       # Message copied from Transformer-XL documentation
       logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
       return -1




    @max_position_embeddings.setter
    def      _a    (    self       ,						lowerCamelCase__ ):
       # Message copied from Transformer-XL documentation
       raise NotImplementedError(
           F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )