





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


SCREAMING_SNAKE_CASE__       :			List[Any]			      =						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   __lowerCAmelCase			(					_UpperCamelCase  ):





   def 	_snake_case ( self					,			snake_case      )				->  np.ndarray:
      """simple docstring"""





      if self.framework == "tf":
         a__	:						Union[str, Any]      		=  tf.where(input_ids == self.tokenizer.mask_token_id      ).numpy()
      elif self.framework == "pt":
         a__	:						Optional[int]      		=  torch.nonzero(input_ids == self.tokenizer.mask_token_id					,			as_tuple=snake_case      )
      else:
         raise ValueError("Unsupported framework"      )
      return masked_index





   def 	_snake_case ( self					,			snake_case      )				->  np.ndarray:
      """simple docstring"""





      a__	:						Dict      		=  self.get_masked_index(snake_case      )
      a__	:						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 	_snake_case ( self					,			snake_case      )				->  int:
      """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 	_snake_case ( self					,			snake_case					,			snake_case=None					,			**snake_case      )				->  Dict[str, GenericTensor]:
      """simple docstring"""





      if return_tensors is None:
         a__	:						List[str]      		=  self.framework
      a__	:						Tuple      		=  self.tokenizer(snake_case					,			return_tensors=snake_case      )
      self.ensure_exactly_one_mask_token(snake_case      )
      return model_inputs





   def 	_snake_case ( self					,			snake_case      )				->  List[str]:
      """simple docstring"""





      a__	:						List[str]      		=  self.model(**snake_case      )
      a__	:						Dict      		=  model_inputs["input_ids"]
      return model_outputs





   def 	_snake_case ( self					,			snake_case					,			snake_case=5					,			snake_case=None      )				->  List[Any]:
      """simple docstring"""





      if target_ids is not None and target_ids.shape[0] < top_k:
         a__	:						Any      		=  target_ids.shape[0]
      a__	:						Tuple      		=  model_outputs["input_ids"][0]
      a__	:						Tuple      		=  model_outputs["logits"]

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

         a__	:						Dict      		=  outputs.numpy()

         a__	:						str      		=  outputs[0, masked_index, :]
         a__	:						List[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__	:						List[Any]      		=  tf.expand_dims(snake_case					,			0      )

         a__	:						Optional[int]      		=  tf.math.top_k(snake_case					,			k=snake_case      )
         a__		,     a__	:						Tuple      		=  topk.values.numpy(), topk.indices.numpy()
      else:
         a__	:						Tuple      		=  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__	:						Optional[int]      		=  logits.softmax(dim=-1      )
         if target_ids is not None:
            a__	:						Tuple      		=  probs[..., target_ids]

         a__		,     a__	:						Tuple      		=  probs.topk(snake_case      )

      a__	:						Optional[int]      		=  []
      a__	:						Union[str, Any]      		=  values.shape[0] == 1
      for i, (_values, _predictions) in enumerate(zip(values.tolist()					,			predictions.tolist()      )      ):
         a__	:						Union[str, Any]      		=  []
         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__	:						List[str]      		=  target_ids[p].tolist()

            a__	:						Dict      		=  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__	:						Union[str, Any]      		=  self.tokenizer.decode(snake_case					,			skip_special_tokens=snake_case      )
            a__	:						List[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 	_snake_case ( self					,			snake_case					,			snake_case=None      )				->  Dict:
      """simple docstring"""





      if isinstance(snake_case					,			snake_case      ):
         a__	:						Optional[int]      		=  [targets]
      try:
         a__	:						Dict      		=  self.tokenizer.get_vocab()
      except Exception:
         a__	:						Dict      		=  {}
      a__	:						List[Any]      		=  []
      for target in targets:
         a__	:						List[str]      		=  vocab.get(snake_case					,			snake_case      )
         if id_ is None:
            a__	:						List[Any]      		=  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__	:						List[Any]      		=  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__	:						Dict      		=  np.array(snake_case      )
      return target_ids





   def 	_snake_case ( self					,			snake_case=None					,			snake_case=None      )				->  List[str]:
      """simple docstring"""





      a__	:						Optional[int]      		=  {}

      if targets is not None:
         a__	:						Tuple      		=  self.get_target_ids(snake_case					,			snake_case      )
         a__	:						Optional[int]      		=  target_ids

      if top_k is not None:
         a__	:						Dict      		=  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      )				->  Optional[Any]:
      """simple docstring"""





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








# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__       :			int			      =						{
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			List[str]			      =						[
      """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """CpmAntForCausalLM""",
      """CpmAntModel""",
      """CpmAntPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
  from .tokenization_cpmant import CpmAntTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_cpmant import (
        CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
        CpmAntForCausalLM,
        CpmAntModel,
        CpmAntPreTrainedModel,
    )


else:
  import sys

  SCREAMING_SNAKE_CASE__       :			Union[str, Any]			      =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

