


import unittest

from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           NystromformerForMaskedLM,
           NystromformerForMultipleChoice,
           NystromformerForQuestionAnswering,
           NystromformerForSequenceClassification,
           NystromformerForTokenClassification,
           NystromformerModel,
       )
       from transformers.models.nystromformer.modeling_nystromformer import NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST

class       __magic_name__	:


     def __init__(      self							:     int							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     Optional[Any]=13							,	lowerCamelCase__							:     int=7							,	lowerCamelCase__							:     int=True							,	lowerCamelCase__							:     Union[str, Any]=True							,	lowerCamelCase__							:     Union[str, Any]=True							,	lowerCamelCase__							:     Optional[int]=True							,	lowerCamelCase__							:     Tuple=99							,	lowerCamelCase__							:     Tuple=32							,	lowerCamelCase__							:     Optional[Any]=5							,	lowerCamelCase__							:     Tuple=4							,	lowerCamelCase__							:     Dict=37							,	lowerCamelCase__							:     Dict="gelu"							,	lowerCamelCase__							:     Any=0.1							,	lowerCamelCase__							:     Dict=0.1							,	lowerCamelCase__							:     Any=512							,	lowerCamelCase__							:     List[Any]=16							,	lowerCamelCase__							:     int=2							,	lowerCamelCase__							:     Dict=0.02							,	lowerCamelCase__							:     Optional[Any]=3							,	lowerCamelCase__							:     int=4							,	lowerCamelCase__							:     List[str]=None							,	)  ->      int:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   parent
      UpperCamelCase__					:    Optional[int]          =   batch_size
      UpperCamelCase__					:    int          =   seq_length
      UpperCamelCase__					:    Any          =   is_training
      UpperCamelCase__					:    List[Any]          =   use_input_mask
      UpperCamelCase__					:    int          =   use_token_type_ids
      UpperCamelCase__					:    int          =   use_labels
      UpperCamelCase__					:    List[Any]          =   vocab_size
      UpperCamelCase__					:    Tuple          =   hidden_size
      UpperCamelCase__					:    Optional[int]          =   num_hidden_layers
      UpperCamelCase__					:    List[Any]          =   num_attention_heads
      UpperCamelCase__					:    Optional[Any]          =   intermediate_size
      UpperCamelCase__					:    Union[str, Any]          =   hidden_act
      UpperCamelCase__					:    Dict          =   hidden_dropout_prob
      UpperCamelCase__					:    int          =   attention_probs_dropout_prob
      UpperCamelCase__					:    Tuple          =   max_position_embeddings
      UpperCamelCase__					:    Any          =   type_vocab_size
      UpperCamelCase__					:    List[Any]          =   type_sequence_label_size
      UpperCamelCase__					:    List[str]          =   initializer_range
      UpperCamelCase__					:    int          =   num_labels
      UpperCamelCase__					:    Tuple          =   num_choices
      UpperCamelCase__					:    Dict          =   scope


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





      '''simple docstring'''






      UpperCamelCase__					:    int          =   ids_tensor([self.batch_size, self.seq_length]							,	self.vocab_size			)

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

      UpperCamelCase__					:    List[Any]          =   None
      if self.use_token_type_ids:
       UpperCamelCase__					:    Optional[int]          =   ids_tensor([self.batch_size, self.seq_length]							,	self.type_vocab_size			)

      UpperCamelCase__					:    Union[str, Any]          =   None
      UpperCamelCase__					:    Optional[Any]          =   None
      UpperCamelCase__					:    Tuple          =   None
      if self.use_labels:
       UpperCamelCase__					:    Dict          =   ids_tensor([self.batch_size]							,	self.type_sequence_label_size			)
       UpperCamelCase__					:    Optional[Any]          =   ids_tensor([self.batch_size, self.seq_length]							,	self.num_labels			)
       UpperCamelCase__					:    List[Any]          =   ids_tensor([self.batch_size]							,	self.num_choices			)

      UpperCamelCase__					:    List[Any]          =   self.get_config()

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


     def      UpperCAmelCase__	(      self							:     int			)  ->      Optional[int]:





      '''simple docstring'''






      return NystromformerConfig(
          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							,	is_decoder=lowerCamelCase__							,	initializer_range=self.initializer_range							,	)


     def      UpperCAmelCase__	(      self							:     Optional[Any]							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     Any							,	lowerCamelCase__							:     Any							,	lowerCamelCase__							:     List[str]			)  ->      Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   NystromformerModel(config=lowerCamelCase__			)
      model.to(lowerCamelCase__			)
      model.eval()
      UpperCamelCase__					:    Any          =   model(lowerCamelCase__							,	attention_mask=lowerCamelCase__							,	token_type_ids=lowerCamelCase__			)
      UpperCamelCase__					:    int          =   model(lowerCamelCase__							,	token_type_ids=lowerCamelCase__			)
      UpperCamelCase__					:    List[Any]          =   model(lowerCamelCase__			)
      self.parent.assertEqual(result.last_hidden_state.shape							,	(self.batch_size, self.seq_length, self.hidden_size)			)


     def      UpperCAmelCase__	(      self							:     List[Any]							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     List[str]							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     Tuple							,	lowerCamelCase__							:     Tuple			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   NystromformerForMaskedLM(config=lowerCamelCase__			)
      model.to(lowerCamelCase__			)
      model.eval()
      UpperCamelCase__					:    str          =   model(lowerCamelCase__							,	attention_mask=lowerCamelCase__							,	token_type_ids=lowerCamelCase__							,	labels=lowerCamelCase__			)
      self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.seq_length, self.vocab_size)			)


     def      UpperCAmelCase__	(      self							:     Tuple							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     Union[str, Any]							,	lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     Dict							,	lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     List[str]							,	lowerCamelCase__							:     Union[str, Any]			)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   NystromformerForQuestionAnswering(config=lowerCamelCase__			)
      model.to(lowerCamelCase__			)
      model.eval()
      UpperCamelCase__					:    str          =   model(
          lowerCamelCase__							,	attention_mask=lowerCamelCase__							,	token_type_ids=lowerCamelCase__							,	start_positions=lowerCamelCase__							,	end_positions=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							:     Dict							,	lowerCamelCase__							:     List[str]							,	lowerCamelCase__							:     Tuple							,	lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     Union[str, Any]							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     Any							,	lowerCamelCase__							:     Tuple			)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   self.num_labels
      UpperCamelCase__					:    List[str]          =   NystromformerForSequenceClassification(lowerCamelCase__			)
      model.to(lowerCamelCase__			)
      model.eval()
      UpperCamelCase__					:    str          =   model(lowerCamelCase__							,	attention_mask=lowerCamelCase__							,	token_type_ids=lowerCamelCase__							,	labels=lowerCamelCase__			)
      self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.num_labels)			)


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     Union[str, Any]							,	lowerCamelCase__							:     Optional[Any]							,	lowerCamelCase__							:     Any							,	lowerCamelCase__							:     int							,	lowerCamelCase__							:     Tuple							,	lowerCamelCase__							:     Tuple							,	lowerCamelCase__							:     Optional[Any]			)  ->      int:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   self.num_labels
      UpperCamelCase__					:    Optional[Any]          =   NystromformerForTokenClassification(config=lowerCamelCase__			)
      model.to(lowerCamelCase__			)
      model.eval()
      UpperCamelCase__					:    Optional[Any]          =   model(lowerCamelCase__							,	attention_mask=lowerCamelCase__							,	token_type_ids=lowerCamelCase__							,	labels=lowerCamelCase__			)
      self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.seq_length, self.num_labels)			)


     def      UpperCAmelCase__	(      self							:     int							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     Any							,	lowerCamelCase__							:     Dict							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     List[Any]							,	lowerCamelCase__							:     List[str]			)  ->      List[str]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   self.num_choices
      UpperCamelCase__					:    int          =   NystromformerForMultipleChoice(config=lowerCamelCase__			)
      model.to(lowerCamelCase__			)
      model.eval()
      UpperCamelCase__					:    List[Any]          =   input_ids.unsqueeze(1			).expand(-1							,	self.num_choices							,	-1			).contiguous()
      UpperCamelCase__					:    int          =   token_type_ids.unsqueeze(1			).expand(-1							,	self.num_choices							,	-1			).contiguous()
      UpperCamelCase__					:    Union[str, Any]          =   input_mask.unsqueeze(1			).expand(-1							,	self.num_choices							,	-1			).contiguous()
      UpperCamelCase__					:    Any          =   model(
          lowerCamelCase__							,	attention_mask=lowerCamelCase__							,	token_type_ids=lowerCamelCase__							,	labels=lowerCamelCase__							,	)
      self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.num_choices)			)


     def      UpperCAmelCase__	(      self							:     int			)  ->      List[str]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   self.prepare_config_and_inputs()
      (
          (
          UpperCamelCase__
      )       ,				(
          UpperCamelCase__
      )       ,				(
          UpperCamelCase__
      )       ,				(
          UpperCamelCase__
      )       ,				(
          UpperCamelCase__
      )       ,				(
          UpperCamelCase__
      )       ,				(
          UpperCamelCase__
      )       ,				
      )					:    Optional[int]          =   config_and_inputs
      UpperCamelCase__					:    List[Any]          =   {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
      return config, inputs_dict



@require_torch
class       __magic_name__	(     __lowerCAmelCase					,				__lowerCAmelCase					,				unittest.TestCase):
     A:		Union[str, Any]			=      (
         (
             NystromformerModel,
             NystromformerForMaskedLM,
             NystromformerForMultipleChoice,
             NystromformerForQuestionAnswering,
             NystromformerForSequenceClassification,
             NystromformerForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     A:		Union[str, Any]			=      (
         {
             "feature-extraction": NystromformerModel,
             "fill-mask": NystromformerForMaskedLM,
             "question-answering": NystromformerForQuestionAnswering,
             "text-classification": NystromformerForSequenceClassification,
             "token-classification": NystromformerForTokenClassification,
             "zero-shot": NystromformerForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     A:		str			=      False
     A:		Tuple			=      False


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





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   NystromformerModelTester(self			)
      UpperCamelCase__					:    Tuple          =   ConfigTester(self							,	config_class=lowerCamelCase__							,	hidden_size=37			)


     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      Any:





      '''simple docstring'''






      self.config_tester.run_common_tests()


     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Any			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   self.model_tester.prepare_config_and_inputs()
      for type in ["absolute", "relative_key", "relative_key_query"]:
       UpperCamelCase__					:    Optional[Any]          =   type
       self.model_tester.create_and_check_model(*lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Any			)  ->      int:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      int:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__			)


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





      '''simple docstring'''






      for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCamelCase__					:    Optional[Any]          =   NystromformerModel.from_pretrained(lowerCamelCase__			)
       self.assertIsNotNone(lowerCamelCase__			)



@require_torch
class       __magic_name__	(     unittest.TestCase):


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





      '''simple docstring'''






      UpperCamelCase__					:    int          =   NystromformerModel.from_pretrained('''uw-madison/nystromformer-512'''			)
      UpperCamelCase__					:    Tuple          =   torch.tensor([[0, 1, 2, 3, 4, 5]]			)

      with torch.no_grad():
       UpperCamelCase__					:    Optional[int]          =   model(lowerCamelCase__			)[0]

      UpperCamelCase__					:    List[str]          =   torch.Size((1, 6, 768)			)
      self.assertEqual(output.shape							,	lowerCamelCase__			)

      UpperCamelCase__					:    List[str]          =   torch.tensor(
          [[[-0.4532, -0.0936, 0.5137], [-0.2676, 0.0628, 0.6186], [-0.3629, -0.1726, 0.4716]]]			)

      self.assertTrue(torch.allclose(output[:, :3, :3]							,	lowerCamelCase__							,	atol=1E-4			)			)


     @slow
     def      UpperCAmelCase__	(      self							:     int			)  ->      Union[str, Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   '''the [MASK] of Belgium is Brussels'''

      UpperCamelCase__					:    List[str]          =   AutoTokenizer.from_pretrained('''uw-madison/nystromformer-512'''			)
      UpperCamelCase__					:    int          =   NystromformerForMaskedLM.from_pretrained('''uw-madison/nystromformer-512'''			)

      UpperCamelCase__					:    Optional[int]          =   tokenizer(lowerCamelCase__							,	return_tensors='''pt'''			)

      with torch.no_grad():
       UpperCamelCase__					:    List[str]          =   model(encoding.input_ids			).logits

      UpperCamelCase__					:    Optional[int]          =   token_logits[:, 2, :].argmax(-1			)[0]

      self.assertEqual(tokenizer.decode(lowerCamelCase__			)							,	'''capital'''			)






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_camembert import CamembertTokenizer
else:
       __UpperCamelCase       :       Tuple						       =				None


__UpperCamelCase       :       Optional[Any]						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Any						       =				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__UpperCamelCase       :       Tuple						       =				{
    "vocab_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_file": {
        "camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
    },
}

__UpperCamelCase       :       Optional[Any]						       =				{
    "camembert-base": 512,
}

__UpperCamelCase       :       Optional[Any]						       =				"▁"

class       __magic_name__	(     __lowerCAmelCase):
     A:		int			=      VOCAB_FILES_NAMES
     A:		Union[str, Any]			=      PRETRAINED_VOCAB_FILES_MAP
     A:		Optional[int]			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		List[str]			=      ["input_ids", "attention_mask"]
     A:		Dict			=      CamembertTokenizer


     def __init__(      self							:     int							,	lowerCamelCase__							:     Any=None							,	lowerCamelCase__							:     Any=None							,	lowerCamelCase__							:     Dict="<s>"							,	lowerCamelCase__							:     List[str]="</s>"							,	lowerCamelCase__							:     int="</s>"							,	lowerCamelCase__							:     List[str]="<s>"							,	lowerCamelCase__							:     Optional[int]="<unk>"							,	lowerCamelCase__							:     Union[str, Any]="<pad>"							,	lowerCamelCase__							:     List[Any]="<mask>"							,	lowerCamelCase__							:     List[Any]=["<s>NOTUSED", "</s>NOTUSED"]							,	**lowerCamelCase__							:     Optional[int]							,	)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   AddedToken(lowerCamelCase__							,	lstrip=lowerCamelCase__							,	rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__							,	lowerCamelCase__			) else mask_token

      super().__init__(
          lowerCamelCase__							,	tokenizer_file=lowerCamelCase__							,	bos_token=lowerCamelCase__							,	eos_token=lowerCamelCase__							,	sep_token=lowerCamelCase__							,	cls_token=lowerCamelCase__							,	unk_token=lowerCamelCase__							,	pad_token=lowerCamelCase__							,	mask_token=lowerCamelCase__							,	additional_special_tokens=lowerCamelCase__							,	**lowerCamelCase__							,	)

      UpperCamelCase__					:    Tuple          =   vocab_file
      UpperCamelCase__					:    Optional[Any]          =   False if not self.vocab_file else True


     def      UpperCAmelCase__	(      self							:     Any							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase__					:    Any          =   [self.cls_token_id]
      UpperCamelCase__					:    Dict          =   [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[Any]          =   [self.sep_token_id]
      UpperCamelCase__					:    Tuple          =   [self.cls_token_id]

      if token_ids_a is None:
       return len(cls + token_ids_a + sep			) * [0]
      return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Optional[str] = None			)  ->      Tuple[str]:





      '''simple docstring'''






      if not self.can_save_slow_tokenizer:
       raise ValueError(
           '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
           '''tokenizer.'''			)

      if not os.path.isdir(lowerCamelCase__			):
       logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
       return
      UpperCamelCase__					:    List[str]          =   os.path.join(
          lowerCamelCase__							,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

      if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCamelCase__			):
       copyfile(self.vocab_file							,	lowerCamelCase__			)

      return (out_vocab_file,)


