



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


a__   :					Optional[int] 						=      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    UpperCAmelCase__(				lowerCamelCase						):

   '''simple docstring'''




   def       UpperCAmelCase		(    self	:      Optional[Any]      ,					lowerCAmelCase	:      GenericTensor)						->					np.ndarray:




         """simple docstring"""

         if self.framework == "tf":
               lowercase__  =	tf.where(input_ids == self.tokenizer.mask_token_id).numpy()
         elif self.framework == "pt":
               lowercase__  =	torch.nonzero(input_ids == self.tokenizer.mask_token_id      ,					as_tuple=lowerCAmelCase)
         else:
               raise ValueError('Unsupported framework')
         return masked_index


   def       UpperCAmelCase		(    self	:      Optional[Any]      ,					lowerCAmelCase	:      GenericTensor)						->					np.ndarray:




         """simple docstring"""

         lowercase__  =	self.get_masked_index(lowerCAmelCase)
         lowercase__  =	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[str]      ,					lowerCAmelCase	:      GenericTensor)						->					Optional[int]:




         """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	:      Tuple      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      Union[str, Any]=None      ,					**lowerCAmelCase	:      Any)						->					Dict[str, GenericTensor]:




         """simple docstring"""

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


   def       UpperCAmelCase		(    self	:      Any      ,					lowerCAmelCase	:      str)						->					List[str]:




         """simple docstring"""

         lowercase__  =	self.model(**lowerCAmelCase)
         lowercase__  =	model_inputs['input_ids']
         return model_outputs


   def       UpperCAmelCase		(    self	:      Optional[int]      ,					lowerCAmelCase	:      Union[str, Any]      ,					lowerCAmelCase	:      Any=5      ,					lowerCAmelCase	:      Dict=None)						->					Tuple:




         """simple docstring"""

         if target_ids is not None and target_ids.shape[0] < top_k:
               lowercase__  =	target_ids.shape[0]
         lowercase__  =	model_outputs['input_ids'][0]
         lowercase__  =	model_outputs['logits']

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

               lowercase__  =	outputs.numpy()

               lowercase__  =	outputs[0, masked_index, :]
               lowercase__  =	stable_softmax(lowerCAmelCase      ,					axis=-1)
               if target_ids is not None:
                     lowercase__  =	tf.gather_nd(tf.squeeze(lowerCAmelCase      ,					0)      ,					target_ids.reshape(-1      ,					1))
                     lowercase__  =	tf.expand_dims(lowerCAmelCase      ,					0)

               lowercase__  =	tf.math.top_k(lowerCAmelCase      ,					k=lowerCAmelCase)
               lowercase__,						lowercase__  =	topk.values.numpy(), topk.indices.numpy()
         else:
               lowercase__  =	torch.nonzero(input_ids == self.tokenizer.mask_token_id      ,					as_tuple=lowerCAmelCase).squeeze(-1)
               # Fill mask pipeline supports only one ${mask_token} per sample

               lowercase__  =	outputs[0, masked_index, :]
               lowercase__  =	logits.softmax(dim=-1)
               if target_ids is not None:
                     lowercase__  =	probs[..., target_ids]

               lowercase__,						lowercase__  =	probs.topk(lowerCAmelCase)

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

                     lowercase__  =	p
                     # Filter padding out:
                     lowercase__  =	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
                     lowercase__  =	self.tokenizer.decode(lowerCAmelCase      ,					skip_special_tokens=lowerCAmelCase)
                     lowercase__  =	{'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	:      Optional[Any]      ,					lowerCAmelCase	:      Optional[Any]=None)						->					List[str]:




         """simple docstring"""

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


   def       UpperCAmelCase		(    self	:      str      ,					lowerCAmelCase	:      List[str]=None      ,					lowerCAmelCase	:      Dict=None)						->					Tuple:




         """simple docstring"""

         lowercase__  =	{}

         if targets is not None:
               lowercase__  =	self.get_target_ids(lowerCAmelCase      ,					lowerCAmelCase)
               lowercase__  =	target_ids

         if top_k is not None:
               lowercase__  =	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	:      Union[str, Any]      ,					*lowerCAmelCase	:      Tuple      ,					**lowerCAmelCase	:      Optional[Any])						->					Tuple:




         """simple docstring"""

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





import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a__   :					List[Any] 						=      get_tests_dir("fixtures/test_sentencepiece.model")



@require_sentencepiece
class    UpperCAmelCase__(				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	Tuple												=     XLMProphetNetTokenizer
   A     :	List[str]												=     False
   A     :	Union[str, Any]												=     True


   def       UpperCAmelCase		(    self	:      Any)						->					int:




         """simple docstring"""

         super().setUp()

         # We have a SentencePiece fixture for testing
         lowercase__  =	XLMProphetNetTokenizer(lowerCAmelCase      ,					keep_accents=lowerCAmelCase)
         tokenizer.save_pretrained(self.tmpdirname)


   def       UpperCAmelCase		(    self	:      Optional[int])						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	'[PAD]'
         lowercase__  =	0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase)      ,					lowerCAmelCase)
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase)      ,					lowerCAmelCase)


   def       UpperCAmelCase		(    self	:      Dict)						->					List[Any]:




         """simple docstring"""

         lowercase__  =	list(self.get_tokenizer().get_vocab().keys())

         self.assertEqual(vocab_keys[0]      ,					'[PAD]')
         self.assertEqual(vocab_keys[1]      ,					'[CLS]')
         self.assertEqual(vocab_keys[-1]      ,					'j')
         self.assertEqual(len(lowerCAmelCase)      ,					10_12)


   def       UpperCAmelCase		(    self	:      Union[str, Any])						->					str:




         """simple docstring"""

         self.assertEqual(self.get_tokenizer().vocab_size      ,					10_12)


   def       UpperCAmelCase		(    self	:      Tuple)						->					Optional[int]:




         """simple docstring"""

         lowercase__  =	XLMProphetNetTokenizer(lowerCAmelCase      ,					keep_accents=lowerCAmelCase)

         lowercase__  =	tokenizer.tokenize('This is a test')
         self.assertListEqual(lowerCAmelCase      ,					['▁This', '▁is', '▁a', '▁t', 'est'])

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(lowerCAmelCase)      ,					[value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]      ,					)

         lowercase__  =	tokenizer.tokenize('I was born in 92000, and this is falsé.')
         self.assertListEqual(
             lowerCAmelCase      ,					[
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '9',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 'é',
                 '.',
             ]      ,					)
         lowercase__  =	tokenizer.convert_tokens_to_ids(lowerCAmelCase)
         self.assertListEqual(
             lowerCAmelCase      ,					[
                 value + tokenizer.fairseq_offset
                 for value in [8, 21, 84, 55, 24, 19, 7, -9, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, -9, 4]
             ]      ,					)

         lowercase__  =	tokenizer.convert_ids_to_tokens(lowerCAmelCase)
         self.assertListEqual(
             lowerCAmelCase      ,					[
                 SPIECE_UNDERLINE + 'I',
                 SPIECE_UNDERLINE + 'was',
                 SPIECE_UNDERLINE + 'b',
                 'or',
                 'n',
                 SPIECE_UNDERLINE + 'in',
                 SPIECE_UNDERLINE + '',
                 '[UNK]',
                 '2',
                 '0',
                 '0',
                 '0',
                 ',',
                 SPIECE_UNDERLINE + 'and',
                 SPIECE_UNDERLINE + 'this',
                 SPIECE_UNDERLINE + 'is',
                 SPIECE_UNDERLINE + 'f',
                 'al',
                 's',
                 '[UNK]',
                 '.',
             ]      ,					)


   @cached_property
   def       UpperCAmelCase		(    self	:      str)						->					List[Any]:




         """simple docstring"""

         return XLMProphetNetTokenizer.from_pretrained('microsoft/xprophetnet-large-wiki100-cased')


   @slow
   def       UpperCAmelCase		(    self	:      List[Any])						->					List[str]:




         """simple docstring"""

         lowercase__  =	'Hello World!'
         lowercase__  =	[3_53_89, 66_72, 49, 2]
         self.assertListEqual(lowerCAmelCase      ,					self.big_tokenizer.encode(lowerCAmelCase))



   @slow
   def       UpperCAmelCase		(    self	:      Tuple)						->					str:




         """simple docstring"""

         lowercase__  =	{'input_ids': [[1_10_73, 8_27_83, 18, 26, 8_27_83, 5_49, 5_15_40, 2_48, 1_72_09, 13_01, 2_17, 20, 21_51_86, 13_25, 1_47, 1_72_09, 13_01, 2_17, 20, 5_63_70, 53, 12_20_20, 20, 1_64_77, 27, 8_73_55, 45_48, 20, 47_28, 7_83_92, 17, 15_99_69, 18, 26, 2_44_91, 6_29, 15, 5_38, 2_27_04, 54_39, 15, 27_88, 2_44_91, 98_85, 15, 4_35_34, 6_05, 15, 8_14, 1_84_03, 3_32_00, 29, 15, 4_35_34, 2_44_58, 1_24_10, 1_11, 2_49_66, 8_36_69, 96_37, 14_40_68, 26, 8_50, 2_23_46, 27, 1_47, 2_49_66, 8_36_69, 8_34_90, 26, 3_91_13, 7_35, 27, 6_89, 6_56, 28_00, 13_39, 46_00, 53, 12_20_20, 11_57_85, 34, 8_16, 13_39, 4_68_87, 18, 1_47, 5_39_05, 19_51, 4_22_38, 4_11_70, 1_77_32, 8_34, 4_36, 15, 2_75_23, 9_87_33, 2_17, 1_47, 55_42, 49_81, 9_30, 1_73_47, 16, 2], [2_00_91, 6_29, 94, 8_27_86, 58, 4_90, 20, 15_28, 84, 5_39_05, 3_44, 8_05_92, 11_01_28, 1_88_22, 52_67, 13_06, 62, 15_25_37, 3_08, 79_97, 4_01, 12_44_27, 5_49, 3_54_42, 2_25, 1_09, 1_50_55, 2_57_48, 1_47, 71_19, 4_37_12, 34, 7_67, 13_53_66, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_92, 6_37_84, 11_94_66, 17, 14_78_08, 8_82_14, 18, 6_56, 81, 32, 32_96, 1_02_80, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=lowerCAmelCase      ,					model_name='microsoft/xprophetnet-large-wiki100-cased'      ,					revision='1acad1643ddd54a44df6a1b797ada8373685d90e'      ,					)
