



"""simple docstring"""







import unittest

from transformers import MobileBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, 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 (
         MODEL_FOR_PRETRAINING_MAPPING,
         MobileBertForMaskedLM,
         MobileBertForMultipleChoice,
         MobileBertForNextSentencePrediction,
         MobileBertForPreTraining,
         MobileBertForQuestionAnswering,
         MobileBertForSequenceClassification,
         MobileBertForTokenClassification,
         MobileBertModel,
     )







class       UpperCamelCase__   :




      """simple docstring"""




      def __init__(      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=1_3    ,			SCREAMING_SNAKE_CASE_				:      List[Any]=7    ,			SCREAMING_SNAKE_CASE_				:      str=True    ,			SCREAMING_SNAKE_CASE_				:      Any=True    ,			SCREAMING_SNAKE_CASE_				:      Tuple=True    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=True    ,			SCREAMING_SNAKE_CASE_				:      Any=9_9    ,			SCREAMING_SNAKE_CASE_				:      Any=6_4    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=3_2    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=5    ,			SCREAMING_SNAKE_CASE_				:      Tuple=4    ,			SCREAMING_SNAKE_CASE_				:      List[str]=3_7    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]="gelu"    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=0.1    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=0.1    ,			SCREAMING_SNAKE_CASE_				:      Tuple=5_1_2    ,			SCREAMING_SNAKE_CASE_				:      Tuple=1_6    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=2    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]=0.02    ,			SCREAMING_SNAKE_CASE_				:      List[str]=3    ,			SCREAMING_SNAKE_CASE_				:      int=4    ,			SCREAMING_SNAKE_CASE_				:      List[str]=None    ,			):
        lowerCAmelCase_   :				Dict								= parent
        lowerCAmelCase_   :				str								= batch_size
        lowerCAmelCase_   :				Optional[int]								= seq_length
        lowerCAmelCase_   :				Any								= is_training
        lowerCAmelCase_   :				Dict								= use_input_mask
        lowerCAmelCase_   :				Dict								= use_token_type_ids
        lowerCAmelCase_   :				Optional[Any]								= use_labels
        lowerCAmelCase_   :				Dict								= vocab_size
        lowerCAmelCase_   :				Optional[Any]								= hidden_size
        lowerCAmelCase_   :				Optional[int]								= embedding_size
        lowerCAmelCase_   :				List[Any]								= num_hidden_layers
        lowerCAmelCase_   :				Any								= num_attention_heads
        lowerCAmelCase_   :				Union[str, Any]								= intermediate_size
        lowerCAmelCase_   :				str								= hidden_act
        lowerCAmelCase_   :				List[Any]								= hidden_dropout_prob
        lowerCAmelCase_   :				List[str]								= attention_probs_dropout_prob
        lowerCAmelCase_   :				Optional[Any]								= max_position_embeddings
        lowerCAmelCase_   :				List[Any]								= type_vocab_size
        lowerCAmelCase_   :				Tuple								= type_sequence_label_size
        lowerCAmelCase_   :				Dict								= initializer_range
        lowerCAmelCase_   :				List[str]								= num_labels
        lowerCAmelCase_   :				Union[str, Any]								= num_choices
        lowerCAmelCase_   :				Union[str, Any]								= scope



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				str								= ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size       )

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

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

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

        lowerCAmelCase_   :				str								= self.get_config()

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



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        return MobileBertConfig(
            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    ,			embedding_size=self.embedding_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=SCREAMING_SNAKE_CASE_    ,			initializer_range=self.initializer_range    ,			)



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Any       ):
        lowerCAmelCase_   :				Optional[int]								= MobileBertModel(config=SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.eval()
        lowerCAmelCase_   :				Union[str, Any]								= model(SCREAMING_SNAKE_CASE_    ,			attention_mask=SCREAMING_SNAKE_CASE_    ,			token_type_ids=SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Optional[Any]								= model(SCREAMING_SNAKE_CASE_    ,			token_type_ids=SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				List[str]								= model(SCREAMING_SNAKE_CASE_       )

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



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      Dict       ):
        lowerCAmelCase_   :				Union[str, Any]								= MobileBertForMaskedLM(config=SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.eval()
        lowerCAmelCase_   :				str								= model(SCREAMING_SNAKE_CASE_    ,			attention_mask=SCREAMING_SNAKE_CASE_    ,			token_type_ids=SCREAMING_SNAKE_CASE_    ,			labels=SCREAMING_SNAKE_CASE_       )
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.seq_length, self.vocab_size)       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      int       ):
        lowerCAmelCase_   :				Optional[int]								= MobileBertForNextSentencePrediction(config=SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.eval()
        lowerCAmelCase_   :				Optional[int]								= model(
            SCREAMING_SNAKE_CASE_    ,			attention_mask=SCREAMING_SNAKE_CASE_    ,			token_type_ids=SCREAMING_SNAKE_CASE_    ,			labels=SCREAMING_SNAKE_CASE_    ,			)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, 2)       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Tuple       ):
        lowerCAmelCase_   :				Optional[Any]								= MobileBertForPreTraining(config=SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.eval()
        lowerCAmelCase_   :				Any								= model(
            SCREAMING_SNAKE_CASE_    ,			attention_mask=SCREAMING_SNAKE_CASE_    ,			token_type_ids=SCREAMING_SNAKE_CASE_    ,			labels=SCREAMING_SNAKE_CASE_    ,			next_sentence_label=SCREAMING_SNAKE_CASE_    ,			)
        self.parent.assertEqual(result.prediction_logits.shape    ,			(self.batch_size, self.seq_length, self.vocab_size)       )
        self.parent.assertEqual(result.seq_relationship_logits.shape    ,			(self.batch_size, 2)       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Dict       ):
        lowerCAmelCase_   :				Union[str, Any]								= MobileBertForQuestionAnswering(config=SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.eval()
        lowerCAmelCase_   :				Optional[Any]								= model(
            SCREAMING_SNAKE_CASE_    ,			attention_mask=SCREAMING_SNAKE_CASE_    ,			token_type_ids=SCREAMING_SNAKE_CASE_    ,			start_positions=SCREAMING_SNAKE_CASE_    ,			end_positions=SCREAMING_SNAKE_CASE_    ,			)
        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      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      Tuple       ):
        lowerCAmelCase_   :				Dict								= self.num_labels
        lowerCAmelCase_   :				Any								= MobileBertForSequenceClassification(SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.eval()
        lowerCAmelCase_   :				int								= model(SCREAMING_SNAKE_CASE_    ,			attention_mask=SCREAMING_SNAKE_CASE_    ,			token_type_ids=SCREAMING_SNAKE_CASE_    ,			labels=SCREAMING_SNAKE_CASE_       )
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_labels)       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]       ):
        lowerCAmelCase_   :				Tuple								= self.num_labels
        lowerCAmelCase_   :				List[Any]								= MobileBertForTokenClassification(config=SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.eval()
        lowerCAmelCase_   :				int								= model(SCREAMING_SNAKE_CASE_    ,			attention_mask=SCREAMING_SNAKE_CASE_    ,			token_type_ids=SCREAMING_SNAKE_CASE_    ,			labels=SCREAMING_SNAKE_CASE_       )
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.seq_length, self.num_labels)       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      Tuple    ,			SCREAMING_SNAKE_CASE_				:      List[Any]    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Dict    ,			SCREAMING_SNAKE_CASE_				:      Union[str, Any]       ):
        lowerCAmelCase_   :				Any								= self.num_choices
        lowerCAmelCase_   :				Dict								= MobileBertForMultipleChoice(config=SCREAMING_SNAKE_CASE_       )
        model.to(SCREAMING_SNAKE_CASE_       )
        model.eval()
        lowerCAmelCase_   :				str								= input_ids.unsqueeze(1       ).expand(-1    ,			self.num_choices    ,			-1       ).contiguous()
        lowerCAmelCase_   :				str								= token_type_ids.unsqueeze(1       ).expand(-1    ,			self.num_choices    ,			-1       ).contiguous()
        lowerCAmelCase_   :				int								= input_mask.unsqueeze(1       ).expand(-1    ,			self.num_choices    ,			-1       ).contiguous()
        lowerCAmelCase_   :				List[Any]								= model(
            SCREAMING_SNAKE_CASE_    ,			attention_mask=SCREAMING_SNAKE_CASE_    ,			token_type_ids=SCREAMING_SNAKE_CASE_    ,			labels=SCREAMING_SNAKE_CASE_    ,			)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.num_choices)       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				Union[str, Any]								= self.prepare_config_and_inputs()
        (
            (
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,(
            lowerCAmelCase_
        )						,
        )   :				Tuple								= config_and_inputs
        lowerCAmelCase_   :				Tuple								= {'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  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     (
          (
              MobileBertModel,
              MobileBertForMaskedLM,
              MobileBertForMultipleChoice,
              MobileBertForNextSentencePrediction,
              MobileBertForPreTraining,
              MobileBertForQuestionAnswering,
              MobileBertForSequenceClassification,
              MobileBertForTokenClassification,
          )
          if is_torch_available()
          else ()
      )
      _SCREAMING_SNAKE_CASE  				=     (
          {
              """feature-extraction""": MobileBertModel,
              """fill-mask""": MobileBertForMaskedLM,
              """question-answering""": MobileBertForQuestionAnswering,
              """text-classification""": MobileBertForSequenceClassification,
              """token-classification""": MobileBertForTokenClassification,
              """zero-shot""": MobileBertForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _SCREAMING_SNAKE_CASE  				=     True



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      int    ,			SCREAMING_SNAKE_CASE_				:      Dict=False       ):
        lowerCAmelCase_   :				int								= super()._prepare_for_class(SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			return_labels=SCREAMING_SNAKE_CASE_       )

        if return_labels:
          if model_class in get_values(SCREAMING_SNAKE_CASE_       ):
            lowerCAmelCase_   :				List[str]								= torch.zeros(
                (self.model_tester.batch_size, self.model_tester.seq_length)    ,			dtype=torch.long    ,			device=SCREAMING_SNAKE_CASE_       )
            lowerCAmelCase_   :				int								= torch.zeros(
                self.model_tester.batch_size    ,			dtype=torch.long    ,			device=SCREAMING_SNAKE_CASE_       )
        return inputs_dict



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				List[Any]								= MobileBertModelTester(self       )
        lowerCAmelCase_   :				Tuple								= ConfigTester(self    ,			config_class=SCREAMING_SNAKE_CASE_    ,			hidden_size=3_7       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Union[str, Any]       ):
        self.config_tester.run_common_tests()



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]       ):
        lowerCAmelCase_   :				List[Any]								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_model(*SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      int       ):
        lowerCAmelCase_   :				Any								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_masked_lm(*SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				Dict								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_multiple_choice(*SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        lowerCAmelCase_   :				Optional[int]								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        lowerCAmelCase_   :				List[str]								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_pretraining(*SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      str       ):
        lowerCAmelCase_   :				List[Any]								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_question_answering(*SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      List[Any]       ):
        lowerCAmelCase_   :				Optional[Any]								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_sequence_classification(*SCREAMING_SNAKE_CASE_       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      List[str]       ):
        lowerCAmelCase_   :				str								= self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_mobilebert_for_token_classification(*SCREAMING_SNAKE_CASE_       )




def 				UpperCamelCase_    (    lowerCAmelCase__      :				Dict						)						->   Optional[int]:

  """simple docstring"""




  return torch.tensor(
      lowerCAmelCase__    ,   dtype=torch.long    ,   device=lowerCAmelCase__    ,   )


lowercase__		:				Union[str, Any]          =	1e-3







@require_torch
@require_sentencepiece
@require_tokenizers
class       UpperCamelCase__   (	unittest.TestCase  ):




      """simple docstring"""




      @slow
      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[int]       ):
        lowerCAmelCase_   :				Any								= MobileBertModel.from_pretrained('google/mobilebert-uncased'       ).to(SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				Union[str, Any]								= _long_tensor([[1_0_1, 7_1_1_0, 1_0_0_5, 1_0_5_6, 2_0_2_3, 1_1_3_3_3, 1_7_4_1_3, 1_0_2_9, 1_0_2]]       )
        with torch.no_grad():
          lowerCAmelCase_   :				Optional[Any]								= model(SCREAMING_SNAKE_CASE_       )[0]
        lowerCAmelCase_   :				int								= torch.Size((1, 9, 5_1_2)       )
        self.assertEqual(output.shape    ,			SCREAMING_SNAKE_CASE_       )
        lowerCAmelCase_   :				str								= torch.tensor(
            [
                [
                    [-2.4_73_65_26E07, 8.2_69_16_56E04, 1.6_52_18_38E05],
                    [-5.7_54_17_04E-01, 3.9_05_60_22E00, 4.4_01_15_07E00],
                    [2.6_04_73_59E00, 1.5_67_76_52E00, -1.7_32_41_88E-01],
                ]
            ]    ,			device=SCREAMING_SNAKE_CASE_    ,			)

        # MobileBERT results range from 10e0 to 10e8. Even a 0.0000001% difference with a value of 10e8 results in a
        # ~1 difference, it's therefore not a good idea to measure using addition.
        # Here, we instead divide the expected result with the result in order to obtain ~1. We then check that the
        # result is held between bounds: 1 - TOLERANCE < expected_result / result < 1 + TOLERANCE
        lowerCAmelCase_   :				Dict								= torch.all((expected_slice / output[..., :3, :3]) >= 1 - TOLERANCE       )
        lowerCAmelCase_   :				int								= torch.all((expected_slice / output[..., :3, :3]) <= 1 + TOLERANCE       )

        self.assertTrue(lower_bound and upper_bound       )










"""simple docstring"""







import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation


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

lowercase__		:				List[str]          =	{"""tokenizer_file""": """tokenizer.json"""}

lowercase__		:				List[str]          =	{
    """tokenizer_file""": {
        """bigscience/tokenizer""": """https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json""",
        """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json""",
        """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json""",
        """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json""",
        """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json""",
        """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json""",
        """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json""",
    },
}







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE  				=     PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE  				=     ["""input_ids""", """attention_mask"""]
      _SCREAMING_SNAKE_CASE  				=     None



      def __init__(      self				:      Union[str, Any]    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]=None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]=None    ,			SCREAMING_SNAKE_CASE_				:      Tuple=None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Any]="<unk>"    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="<s>"    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="</s>"    ,			SCREAMING_SNAKE_CASE_				:      Optional[int]="<pad>"    ,			SCREAMING_SNAKE_CASE_				:      List[str]=False    ,			SCREAMING_SNAKE_CASE_				:      Any=False    ,			**SCREAMING_SNAKE_CASE_				:      Any    ,			):
        super().__init__(
            SCREAMING_SNAKE_CASE_    ,			SCREAMING_SNAKE_CASE_    ,			tokenizer_file=SCREAMING_SNAKE_CASE_    ,			unk_token=SCREAMING_SNAKE_CASE_    ,			bos_token=SCREAMING_SNAKE_CASE_    ,			eos_token=SCREAMING_SNAKE_CASE_    ,			pad_token=SCREAMING_SNAKE_CASE_    ,			add_prefix_space=SCREAMING_SNAKE_CASE_    ,			clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_    ,			)
        lowerCAmelCase_   :				Optional[Any]								= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
        if pre_tok_state.get('add_prefix_space'    ,			SCREAMING_SNAKE_CASE_       ) != add_prefix_space:
          lowerCAmelCase_   :				str								= getattr(SCREAMING_SNAKE_CASE_    ,			pre_tok_state.pop('type'       )       )
          lowerCAmelCase_   :				str								= add_prefix_space
          lowerCAmelCase_   :				str								= pre_tok_class(**SCREAMING_SNAKE_CASE_       )

        lowerCAmelCase_   :				str								= add_prefix_space



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			*SCREAMING_SNAKE_CASE_				:      List[Any]    ,			**SCREAMING_SNAKE_CASE_				:      Tuple       ):
        lowerCAmelCase_   :				List[Any]								= kwargs.get('is_split_into_words'    ,			SCREAMING_SNAKE_CASE_       )
        if not (self.add_prefix_space or not is_split_into_words):
          raise Exception(
              F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
              ' pretokenized inputs.'       )

        return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Any    ,			*SCREAMING_SNAKE_CASE_				:      Optional[int]    ,			**SCREAMING_SNAKE_CASE_				:      int       ):
        lowerCAmelCase_   :				List[Any]								= kwargs.get('is_split_into_words'    ,			SCREAMING_SNAKE_CASE_       )

        if not (self.add_prefix_space or not is_split_into_words):
          raise Exception(
              F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"
              ' pretokenized inputs.'       )

        return super()._encode_plus(*SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )



      def      SCREAMING_SNAKE_CASE__ (      self				:      Optional[Any]    ,			SCREAMING_SNAKE_CASE_				:      str    ,			SCREAMING_SNAKE_CASE_				:      Optional[str] = None       ):
        lowerCAmelCase_   :				Optional[int]								= self._tokenizer.model.save(SCREAMING_SNAKE_CASE_    ,			name=SCREAMING_SNAKE_CASE_       )
        return tuple(SCREAMING_SNAKE_CASE_       )





      def      SCREAMING_SNAKE_CASE__ (      self				:      int    ,			SCREAMING_SNAKE_CASE_				:      "Conversation"       ):
        lowerCAmelCase_   :				int								= []
        for is_user, text in conversation.iter_texts():
          input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_    ,			add_special_tokens=SCREAMING_SNAKE_CASE_       ) + [self.eos_token_id]       )

        if len(SCREAMING_SNAKE_CASE_       ) > self.model_max_length:
          lowerCAmelCase_   :				Any								= input_ids[-self.model_max_length :]
        return input_ids





