


'''simple docstring'''




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 		UpperCAmelCase_      :





     def __init__( self       :      int    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Any=1_3    ,      UpperCAmelCase__       :      int=7    ,      UpperCAmelCase__       :      Optional[int]=True    ,      UpperCAmelCase__       :      Union[str, Any]=True    ,      UpperCAmelCase__       :      Tuple=True    ,      UpperCAmelCase__       :      int=True    ,      UpperCAmelCase__       :      Union[str, Any]=9_9    ,      UpperCAmelCase__       :      Tuple=3_2    ,      UpperCAmelCase__       :      int=5    ,      UpperCAmelCase__       :      Optional[int]=4    ,      UpperCAmelCase__       :      Dict=3_7    ,      UpperCAmelCase__       :      Union[str, Any]="gelu"    ,      UpperCAmelCase__       :      str=0.1    ,      UpperCAmelCase__       :      Tuple=0.1    ,      UpperCAmelCase__       :      int=5_1_2    ,      UpperCAmelCase__       :      Tuple=1_6    ,      UpperCAmelCase__       :      Optional[Any]=2    ,      UpperCAmelCase__       :      Union[str, Any]=0.02    ,      UpperCAmelCase__       :      List[str]=3    ,      UpperCAmelCase__       :      List[str]=4    ,      UpperCAmelCase__       :      int=None    ,      )				->  Optional[int]:
        lowerCAmelCase     =			parent
        lowerCAmelCase     =			batch_size
        lowerCAmelCase     =			seq_length
        lowerCAmelCase     =			is_training
        lowerCAmelCase     =			use_input_mask
        lowerCAmelCase     =			use_token_type_ids
        lowerCAmelCase     =			use_labels
        lowerCAmelCase     =			vocab_size
        lowerCAmelCase     =			hidden_size
        lowerCAmelCase     =			num_hidden_layers
        lowerCAmelCase     =			num_attention_heads
        lowerCAmelCase     =			intermediate_size
        lowerCAmelCase     =			hidden_act
        lowerCAmelCase     =			hidden_dropout_prob
        lowerCAmelCase     =			attention_probs_dropout_prob
        lowerCAmelCase     =			max_position_embeddings
        lowerCAmelCase     =			type_vocab_size
        lowerCAmelCase     =			type_sequence_label_size
        lowerCAmelCase     =			initializer_range
        lowerCAmelCase     =			num_labels
        lowerCAmelCase     =			num_choices
        lowerCAmelCase     =			scope





     def 							__UpperCAmelCase      ( self       :      str    )				->  int:
        lowerCAmelCase     =			ids_tensor([self.batch_size, self.seq_length]    ,      self.vocab_size    )

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

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

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

        lowerCAmelCase     =			self.get_config()

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





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Tuple:
        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=UpperCAmelCase__    ,      initializer_range=self.initializer_range    ,      )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Any    ,      UpperCAmelCase__       :      List[Any]    )				->  Optional[int]:
        lowerCAmelCase     =			NystromformerModel(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    )
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    )
        lowerCAmelCase     =			model(UpperCAmelCase__    )
        self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, self.seq_length, self.hidden_size)    )





     def 							__UpperCAmelCase      ( self       :      List[str]    ,      UpperCAmelCase__       :      Any    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      Optional[Any]    ,      UpperCAmelCase__       :      List[Any]    )				->  Any:
        lowerCAmelCase     =			NystromformerForMaskedLM(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    ,      labels=UpperCAmelCase__    )
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.seq_length, self.vocab_size)    )





     def 							__UpperCAmelCase      ( self       :      int    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      List[Any]    ,      UpperCAmelCase__       :      Optional[Any]    )				->  Optional[Any]:
        lowerCAmelCase     =			NystromformerForQuestionAnswering(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(
            UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    ,      start_positions=UpperCAmelCase__    ,      end_positions=UpperCAmelCase__    ,      )
        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       :      str    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      List[Any]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      Tuple    )				->  Union[str, Any]:
        lowerCAmelCase     =			self.num_labels
        lowerCAmelCase     =			NystromformerForSequenceClassification(UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    ,      labels=UpperCAmelCase__    )
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.num_labels)    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      List[Any]    ,      UpperCAmelCase__       :      Optional[int]    )				->  Any:
        lowerCAmelCase     =			self.num_labels
        lowerCAmelCase     =			NystromformerForTokenClassification(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			model(UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    ,      labels=UpperCAmelCase__    )
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.seq_length, self.num_labels)    )





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      Dict    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      str    ,      UpperCAmelCase__       :      Any    )				->  Dict:
        lowerCAmelCase     =			self.num_choices
        lowerCAmelCase     =			NystromformerForMultipleChoice(config=UpperCAmelCase__    )
        model.to(UpperCAmelCase__    )
        model.eval()
        lowerCAmelCase     =			input_ids.unsqueeze(1    ).expand(-1    ,      self.num_choices    ,      -1    ).contiguous()
        lowerCAmelCase     =			token_type_ids.unsqueeze(1    ).expand(-1    ,      self.num_choices    ,      -1    ).contiguous()
        lowerCAmelCase     =			input_mask.unsqueeze(1    ).expand(-1    ,      self.num_choices    ,      -1    ).contiguous()
        lowerCAmelCase     =			model(
            UpperCAmelCase__    ,      attention_mask=UpperCAmelCase__    ,      token_type_ids=UpperCAmelCase__    ,      labels=UpperCAmelCase__    ,      )
        self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.num_choices)    )





     def 							__UpperCAmelCase      ( self       :      str    )				->  Optional[int]:
        lowerCAmelCase     =			self.prepare_config_and_inputs()
        (
            (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  (
            lowerCAmelCase
        )     ,  
        )     =			config_and_inputs
        lowerCAmelCase     =			{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict




@require_torch
class 		UpperCAmelCase_      (  __lowercase       ,	__lowercase       ,	unittest.TestCase		):
     lowerCamelCase				:	int		=     (
         (
             NystromformerModel,
             NystromformerForMaskedLM,
             NystromformerForMultipleChoice,
             NystromformerForQuestionAnswering,
             NystromformerForSequenceClassification,
             NystromformerForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     lowerCamelCase				:	Any		=     (
         {
             '''feature-extraction''': NystromformerModel,
             '''fill-mask''': NystromformerForMaskedLM,
             '''question-answering''': NystromformerForQuestionAnswering,
             '''text-classification''': NystromformerForSequenceClassification,
             '''token-classification''': NystromformerForTokenClassification,
             '''zero-shot''': NystromformerForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     lowerCamelCase				:	Tuple		=     False
     lowerCamelCase				:	Optional[Any]		=     False





     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Any:
        lowerCAmelCase     =			NystromformerModelTester(self    )
        lowerCAmelCase     =			ConfigTester(self    ,      config_class=UpperCAmelCase__    ,      hidden_size=3_7    )





     def 							__UpperCAmelCase      ( self       :      Union[str, Any]    )				->  Dict:
        self.config_tester.run_common_tests()





     def 							__UpperCAmelCase      ( self       :      Tuple    )				->  Optional[int]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      int    )				->  List[Any]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
           lowerCAmelCase     =			type
           self.model_tester.create_and_check_model(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Optional[int]    )				->  Tuple:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  Optional[int]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  int:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  Optional[int]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase__    )





     def 							__UpperCAmelCase      ( self       :      int    )				->  Union[str, Any]:
        lowerCAmelCase     =			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__    )





     @slow
     def 							__UpperCAmelCase      ( self       :      Any    )				->  Dict:
        for model_name in NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCAmelCase     =			NystromformerModel.from_pretrained(UpperCAmelCase__    )
           self.assertIsNotNone(UpperCAmelCase__    )




@require_torch
class 		UpperCAmelCase_      (  unittest.TestCase		):





     @slow
     def 							__UpperCAmelCase      ( self       :      Optional[Any]    )				->  List[str]:
        lowerCAmelCase     =			NystromformerModel.from_pretrained('uw-madison/nystromformer-512'    )
        lowerCAmelCase     =			torch.tensor([[0, 1, 2, 3, 4, 5]]    )

        with torch.no_grad():
           lowerCAmelCase     =			model(UpperCAmelCase__    )[0]

        lowerCAmelCase     =			torch.Size((1, 6, 7_6_8)    )
        self.assertEqual(output.shape    ,      UpperCAmelCase__    )

        lowerCAmelCase     =			torch.tensor(
            [[[-0.4_532, -0.0_936, 0.5_137], [-0.2_676, 0.0_628, 0.6_186], [-0.3_629, -0.1_726, 0.4_716]]]    )

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





     @slow
     def 							__UpperCAmelCase      ( self       :      List[Any]    )				->  Optional[int]:
        lowerCAmelCase     =			'the [MASK] of Belgium is Brussels'

        lowerCAmelCase     =			AutoTokenizer.from_pretrained('uw-madison/nystromformer-512'    )
        lowerCAmelCase     =			NystromformerForMaskedLM.from_pretrained('uw-madison/nystromformer-512'    )

        lowerCAmelCase     =			tokenizer(UpperCAmelCase__    ,      return_tensors='pt'    )

        with torch.no_grad():
           lowerCAmelCase     =			model(encoding.input_ids    ).logits

        lowerCAmelCase     =			token_logits[:, 2, :].argmax(-1    )[0]

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









'''simple docstring'''




from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__snake_case    						={
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

__snake_case    						={
    """facebook/blenderbot_small-90M""": 512,
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Tuple		=     VOCAB_FILES_NAMES
     lowerCamelCase				:	Optional[int]		=     PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase				:	Union[str, Any]		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase				:	Optional[Any]		=     BlenderbotSmallTokenizer





     def __init__( self       :      Any    ,      UpperCAmelCase__       :      Union[str, Any]=None    ,      UpperCAmelCase__       :      Dict=None    ,      UpperCAmelCase__       :      int="<|endoftext|>"    ,      UpperCAmelCase__       :      Dict="<|endoftext|>"    ,      UpperCAmelCase__       :      str="<|endoftext|>"    ,      UpperCAmelCase__       :      str=False    ,      UpperCAmelCase__       :      Tuple=True    ,      **UpperCAmelCase__       :      Optional[Any]    ,      )				->  Any:
        super().__init__(
            ByteLevelBPETokenizer(
                vocab=UpperCAmelCase__    ,      merges=UpperCAmelCase__    ,      add_prefix_space=UpperCAmelCase__    ,      trim_offsets=UpperCAmelCase__    ,      )    ,      bos_token=UpperCAmelCase__    ,      eos_token=UpperCAmelCase__    ,      unk_token=UpperCAmelCase__    ,      **UpperCAmelCase__    ,      )
        lowerCAmelCase     =			add_prefix_space





     def 							__UpperCAmelCase      ( self       :      List[Any]    ,      UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      Dict=None    )				->  Any:
        lowerCAmelCase     =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
           return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def 							__UpperCAmelCase      ( self       :      Any    ,      UpperCAmelCase__       :      List[int]    ,      UpperCAmelCase__       :      Optional[List[int]] = None    )				->  List[int]:
        lowerCAmelCase     =			[self.sep_token_id]
        lowerCAmelCase     =			[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]





