

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(
    __lowerCamelCase,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				(					__lowerCamelCase			):

   def     UpperCamelCase					(				self	:    Tuple			,						lowerCamelCase__	:    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=lowerCamelCase__       )
         else:
               raise ValueError('''Unsupported framework'''       )
         return masked_index

   def     UpperCamelCase					(				self	:    Tuple			,						lowerCamelCase__	:    GenericTensor       ) ->			np.ndarray:
         """simple docstring"""





         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	:    List[Any]			,						lowerCamelCase__	:    GenericTensor       ) ->			List[str]:
         """simple docstring"""





         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	:    str			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    int=None			,						**lowerCamelCase__	:    int       ) ->			Dict[str, GenericTensor]:
         """simple docstring"""





         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	:    Union[str, Any]			,						lowerCamelCase__	:    Optional[int]       ) ->			str:
         """simple docstring"""





         A_							       =		self.model(**lowerCamelCase__       )
         A_							       =		model_inputs['''input_ids''']
         return model_outputs

   def     UpperCamelCase					(				self	:    int			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    Optional[Any]=5			,						lowerCamelCase__	:    Union[str, Any]=None       ) ->			Union[str, 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(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__	:    Dict			,						lowerCamelCase__	:    List[Any]=None       ) ->			Optional[Any]:
         """simple docstring"""





         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	:    Union[str, Any]			,						lowerCamelCase__	:    List[str]=None			,						lowerCamelCase__	:    str=None       ) ->			List[str]:
         """simple docstring"""





         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	:    Optional[int]			,						lowerCamelCase__	:    List[Any]			,						*lowerCamelCase__	:    Tuple			,						**lowerCamelCase__	:    Any       ) ->			List[Any]:
         """simple docstring"""





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








from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
      import tensorflow as tf

      from transformers import (
          TFDebertaVaForMaskedLM,
          TFDebertaVaForQuestionAnswering,
          TFDebertaVaForSequenceClassification,
          TFDebertaVaForTokenClassification,
          TFDebertaVaModel,
      )

class    _lowercase				:

   def __init__(				self	:    str			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Union[str, Any]=1_3			,						lowerCamelCase__	:    Dict=7			,						lowerCamelCase__	:    List[Any]=True			,						lowerCamelCase__	:    Tuple=True			,						lowerCamelCase__	:    List[str]=True			,						lowerCamelCase__	:    List[Any]=True			,						lowerCamelCase__	:    Any=9_9			,						lowerCamelCase__	:    Union[str, Any]=3_2			,						lowerCamelCase__	:    Dict=2			,						lowerCamelCase__	:    Tuple=4			,						lowerCamelCase__	:    int=3_7			,						lowerCamelCase__	:    List[str]="gelu"			,						lowerCamelCase__	:    str=0.1			,						lowerCamelCase__	:    Any=0.1			,						lowerCamelCase__	:    int=5_1_2			,						lowerCamelCase__	:    Optional[Any]=1_6			,						lowerCamelCase__	:    Optional[Any]=2			,						lowerCamelCase__	:    List[str]=0.02			,						lowerCamelCase__	:    Dict=False			,						lowerCamelCase__	:    Union[str, Any]=True			,						lowerCamelCase__	:    List[Any]="None"			,						lowerCamelCase__	:    int=3			,						lowerCamelCase__	:    List[Any]=4			,						lowerCamelCase__	:    List[str]=None			,						) ->			List[str]:
         """simple docstring"""





         A_							       =		parent
         A_							       =		batch_size
         A_							       =		seq_length
         A_							       =		is_training
         A_							       =		use_input_mask
         A_							       =		use_token_type_ids
         A_							       =		use_labels
         A_							       =		vocab_size
         A_							       =		hidden_size
         A_							       =		num_hidden_layers
         A_							       =		num_attention_heads
         A_							       =		intermediate_size
         A_							       =		hidden_act
         A_							       =		hidden_dropout_prob
         A_							       =		attention_probs_dropout_prob
         A_							       =		max_position_embeddings
         A_							       =		type_vocab_size
         A_							       =		type_sequence_label_size
         A_							       =		initializer_range
         A_							       =		num_labels
         A_							       =		num_choices
         A_							       =		relative_attention
         A_							       =		position_biased_input
         A_							       =		pos_att_type
         A_							       =		scope

   def     UpperCamelCase					(				self	:    str       ) ->			str:
         """simple docstring"""





         A_							       =		ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size       )

         A_							       =		None
         if self.use_input_mask:
               A_							       =		random_attention_mask([self.batch_size, self.seq_length]       )

         A_							       =		None
         if self.use_token_type_ids:
               A_							       =		ids_tensor([self.batch_size, self.seq_length]			,						self.type_vocab_size       )

         A_							       =		None
         A_							       =		None
         A_							       =		None
         if self.use_labels:
               A_							       =		ids_tensor([self.batch_size]			,						self.type_sequence_label_size       )
               A_							       =		ids_tensor([self.batch_size, self.seq_length]			,						self.num_labels       )

         A_							       =		DebertaVaConfig(
             vocab_size=self.vocab_size			,						hidden_size=self.hidden_size			,						num_hidden_layers=self.num_hidden_layers			,						num_attention_heads=self.num_attention_heads			,						intermediate_size=self.intermediate_size			,						hidden_act=self.hidden_act			,						hidden_dropout_prob=self.hidden_dropout_prob			,						attention_probs_dropout_prob=self.attention_probs_dropout_prob			,						max_position_embeddings=self.max_position_embeddings			,						type_vocab_size=self.type_vocab_size			,						relative_attention=self.relative_attention			,						position_biased_input=self.position_biased_input			,						initializer_range=self.initializer_range			,						return_dict=lowerCamelCase__			,						)

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

   def     UpperCamelCase					(				self	:    Tuple			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Union[str, Any]       ) ->			Any:
         """simple docstring"""





         A_							       =		TFDebertaVaModel(config=lowerCamelCase__       )
         A_							       =		{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

         A_							       =		[input_ids, input_mask]
         A_							       =		model(lowerCamelCase__       )

         A_							       =		model(lowerCamelCase__       )

         self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)       )

   def     UpperCamelCase					(				self	:    Tuple			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    List[str]       ) ->			str:
         """simple docstring"""





         A_							       =		TFDebertaVaForMaskedLM(config=lowerCamelCase__       )
         A_							       =		{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         A_							       =		model(lowerCamelCase__       )
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size)       )

   def     UpperCamelCase					(				self	:    Any			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Optional[int]			,						lowerCamelCase__	:    Union[str, Any]			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    List[Any]			,						lowerCamelCase__	:    Tuple       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.num_labels
         A_							       =		TFDebertaVaForSequenceClassification(config=lowerCamelCase__       )
         A_							       =		{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         A_							       =		model(lowerCamelCase__       )
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)       )

   def     UpperCamelCase					(				self	:    str			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    Dict			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    str       ) ->			Optional[Any]:
         """simple docstring"""





         A_							       =		self.num_labels
         A_							       =		TFDebertaVaForTokenClassification(config=lowerCamelCase__       )
         A_							       =		{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         A_							       =		model(lowerCamelCase__       )
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.num_labels)       )

   def     UpperCamelCase					(				self	:    Tuple			,						lowerCamelCase__	:    int			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Any			,						lowerCamelCase__	:    Optional[Any]			,						lowerCamelCase__	:    Tuple			,						lowerCamelCase__	:    str			,						lowerCamelCase__	:    Any       ) ->			Any:
         """simple docstring"""





         A_							       =		TFDebertaVaForQuestionAnswering(config=lowerCamelCase__       )
         A_							       =		{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         A_							       =		model(lowerCamelCase__       )
         self.parent.assertEqual(result.start_logits.shape			,						(self.batch_size, self.seq_length)       )
         self.parent.assertEqual(result.end_logits.shape			,						(self.batch_size, self.seq_length)       )





   def     UpperCamelCase					(				self	:    int       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		self.prepare_config_and_inputs()
         (
             (
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,(
             A_
         )       ,
         )							       =		config_and_inputs
         A_							       =		{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
         return config, inputs_dict



@require_tf
class    _lowercase				(					__lowerCamelCase,__lowerCamelCase,unittest.TestCase			):
   _lowercase     :       Union[str, Any]  					=    (
       (
           TFDebertaVaModel,
           TFDebertaVaForMaskedLM,
           TFDebertaVaForQuestionAnswering,
           TFDebertaVaForSequenceClassification,
           TFDebertaVaForTokenClassification,
       )
       if is_tf_available()
       else ()
   )
   _lowercase     :       Optional[Any]  					=    (
       {
           'feature-extraction': TFDebertaVaModel,
           'fill-mask': TFDebertaVaForMaskedLM,
           'question-answering': TFDebertaVaForQuestionAnswering,
           'text-classification': TFDebertaVaForSequenceClassification,
           'token-classification': TFDebertaVaForTokenClassification,
           'zero-shot': TFDebertaVaForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )

   _lowercase     :       List[str]  					=    False
   _lowercase     :       Any  					=    False

   def     UpperCamelCase					(				self	:    int       ) ->			List[str]:
         """simple docstring"""





         A_							       =		TFDebertaVaModelTester(self       )
         A_							       =		ConfigTester(self			,						config_class=lowerCamelCase__			,						hidden_size=3_7       )

   def     UpperCamelCase					(				self	:    Optional[Any]       ) ->			Optional[Any]:
         """simple docstring"""





         self.config_tester.run_common_tests()

   def     UpperCamelCase					(				self	:    Any       ) ->			List[Any]:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Any       ) ->			Optional[int]:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Any       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    List[Any]       ) ->			Dict:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__       )

   def     UpperCamelCase					(				self	:    Any       ) ->			List[str]:
         """simple docstring"""





         A_							       =		self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__       )





   @slow
   def     UpperCamelCase					(				self	:    Union[str, Any]       ) ->			int:
         """simple docstring"""





         A_							       =		TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''       )
         self.assertIsNotNone(lowerCamelCase__       )



@require_tf
class    _lowercase				(					unittest.TestCase			):

   @unittest.skip(reason='''Model not available yet'''       )
   def     UpperCamelCase					(				self	:    Tuple       ) ->			List[str]:
         """simple docstring"""





         pass





   @slow
   def     UpperCamelCase					(				self	:    Any       ) ->			Any:
         """simple docstring"""





         A_							       =		TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''       )
         A_							       =		tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]       )
         A_							       =		tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
         A_							       =		model(lowerCamelCase__			,						attention_mask=lowerCamelCase__       )[0]

         A_							       =		tf.constant(
             [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]       )
         tf.debugging.assert_near(output[:, 1:4, 1:4]			,						lowerCamelCase__			,						atol=1e-4       )





