

import os
import tempfile
import unittest

from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 (
			    DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
			    DistilBertForMaskedLM,
			    DistilBertForMultipleChoice,
			    DistilBertForQuestionAnswering,
			    DistilBertForSequenceClassification,
			    DistilBertForTokenClassification,
			    DistilBertModel,
			)






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							parent
				SCREAMING_SNAKE_CASE :   Any					  =							batch_size
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							seq_length
				SCREAMING_SNAKE_CASE :   List[Any]					  =							is_training
				SCREAMING_SNAKE_CASE :   int					  =							use_input_mask
				SCREAMING_SNAKE_CASE :   Tuple					  =							use_token_type_ids
				SCREAMING_SNAKE_CASE :   str					  =							use_labels
				SCREAMING_SNAKE_CASE :   Any					  =							vocab_size
				SCREAMING_SNAKE_CASE :   List[Any]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   str					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Tuple					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							hidden_act
				SCREAMING_SNAKE_CASE :   Dict					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   str					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Tuple					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   List[str]					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   List[str]					  =							type_sequence_label_size
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   Tuple					  =							num_labels
				SCREAMING_SNAKE_CASE :   Tuple					  =							num_choices
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							scope
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							ids_tensor([self.batch_size, self.seq_length]   ,   self.vocab_size						)

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

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

				SCREAMING_SNAKE_CASE :   int					  =							self.get_config()

				return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
			def 				__UpperCamelCase			(      self				:			Dict						)				->							str:

				"""simple docstring"""

				return DistilBertConfig(
				    vocab_size=self.vocab_size   ,   dim=self.hidden_size   ,   n_layers=self.num_hidden_layers   ,   n_heads=self.num_attention_heads   ,   hidden_dim=self.intermediate_size   ,   hidden_act=self.hidden_act   ,   dropout=self.hidden_dropout_prob   ,   attention_dropout=self.attention_probs_dropout_prob   ,   max_position_embeddings=self.max_position_embeddings   ,   initializer_range=self.initializer_range   ,   )
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			int   ,   a				:			Optional[int]   ,   a				:			Optional[int]   ,   a				:			Dict   ,   a				:			str   ,   a				:			str						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							DistilBertModel(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							model(a   ,   a						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							model(a						)
				self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size)						)
			def 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			Optional[int]   ,   a				:			Dict   ,   a				:			Tuple   ,   a				:			int   ,   a				:			int   ,   a				:			Any						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							DistilBertForMaskedLM(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   str					  =							model(a   ,   attention_mask=a   ,   labels=a						)
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length, self.vocab_size)						)
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			int   ,   a				:			Optional[Any]   ,   a				:			Optional[Any]   ,   a				:			str   ,   a				:			str   ,   a				:			Tuple						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							DistilBertForQuestionAnswering(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   List[str]					  =							model(
				    a   ,   attention_mask=a   ,   start_positions=a   ,   end_positions=a						)
				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				:			Optional[int]   ,   a				:			str   ,   a				:			Any   ,   a				:			int   ,   a				:			Optional[Any]   ,   a				:			int   ,   a				:			str						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self.num_labels
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							DistilBertForSequenceClassification(a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							model(a   ,   attention_mask=a   ,   labels=a						)
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.num_labels)						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			List[Any]   ,   a				:			Optional[int]   ,   a				:			Union[str, Any]   ,   a				:			Dict   ,   a				:			Any   ,   a				:			Optional[Any]						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							self.num_labels
				SCREAMING_SNAKE_CASE :   List[str]					  =							DistilBertForTokenClassification(config=a						)
				model.to(a						)
				model.eval()

				SCREAMING_SNAKE_CASE :   List[str]					  =							model(a   ,   attention_mask=a   ,   labels=a						)
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length, self.num_labels)						)
			def 				__UpperCamelCase			(      self				:			int   ,   a				:			Any   ,   a				:			Optional[int]   ,   a				:			Union[str, Any]   ,   a				:			Tuple   ,   a				:			Optional[int]   ,   a				:			Tuple						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							self.num_choices
				SCREAMING_SNAKE_CASE :   Any					  =							DistilBertForMultipleChoice(config=a						)
				model.to(a						)
				model.eval()
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							input_ids.unsqueeze(1						).expand(-1   ,   self.num_choices   ,   -1						).contiguous()
				SCREAMING_SNAKE_CASE :   Dict					  =							input_mask.unsqueeze(1						).expand(-1   ,   self.num_choices   ,   -1						).contiguous()
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							model(
				    a   ,   attention_mask=a   ,   labels=a   ,   )
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.num_choices)						)






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.prepare_config_and_inputs()
				((SCREAMING_SNAKE_CASE)  ,(SCREAMING_SNAKE_CASE)  ,(SCREAMING_SNAKE_CASE)  ,(SCREAMING_SNAKE_CASE)  ,(SCREAMING_SNAKE_CASE)  ,(SCREAMING_SNAKE_CASE)) :   Tuple					  =							config_and_inputs
				SCREAMING_SNAKE_CASE :   int					  =							{"input_ids": input_ids, "attention_mask": input_mask}
				return config, inputs_dict






@require_torch
class _UpperCamelCase				(				__A				,						__A				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =(
			    (
			        DistilBertModel,
			        DistilBertForMaskedLM,
			        DistilBertForMultipleChoice,
			        DistilBertForQuestionAnswering,
			        DistilBertForSequenceClassification,
			        DistilBertForTokenClassification,
			    )
			    if is_torch_available()
			    else None
			)
			lowerCamelCase__							     =(
			    {
			        'feature-extraction': DistilBertModel,
			        'fill-mask': DistilBertForMaskedLM,
			        'question-answering': DistilBertForQuestionAnswering,
			        'text-classification': DistilBertForSequenceClassification,
			        'token-classification': DistilBertForTokenClassification,
			        'zero-shot': DistilBertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			lowerCamelCase__							     =True
			lowerCamelCase__							     =True
			lowerCamelCase__							     =True
			lowerCamelCase__							     =True
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							DistilBertModelTester(self						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							ConfigTester(self   ,   config_class=a   ,   dim=37						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Tuple:

				"""simple docstring"""

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_model(*a						)
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_masked_lm(*a						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_question_answering(*a						)
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_sequence_classification(*a						)
			def 				__UpperCamelCase			(      self				:			str						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_token_classification(*a						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_distilbert_for_multiple_choice(*a						)
			@slow
			def 				__UpperCamelCase			(      self				:			int						)				->							Any:

				"""simple docstring"""

				for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							DistilBertModel.from_pretrained(a						)
					self.assertIsNotNone(a						)






			@slow
			@require_torch_gpu
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Dict					  =							self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes:
					# BertForMultipleChoice behaves incorrectly in JIT environments.
					if model_class == DistilBertForMultipleChoice:
						return

					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							True
					SCREAMING_SNAKE_CASE :   Any					  =							model_class(config=a						)

					SCREAMING_SNAKE_CASE :   List[Any]					  =							self._prepare_for_class(a   ,   a						)
					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.jit.trace(
					    a   ,   (inputs_dict["input_ids"].to("cpu"						), inputs_dict["attention_mask"].to("cpu"						))						)

					with tempfile.TemporaryDirectory() as tmp:
						torch.jit.save(a   ,   os.path.join(a   ,   "traced_model.pt"						)						)
						SCREAMING_SNAKE_CASE :   Tuple					  =							torch.jit.load(os.path.join(a   ,   "traced_model.pt"						)   ,   map_location=a						)
						loaded(inputs_dict["input_ids"].to(a						)   ,   inputs_dict["attention_mask"].to(a						)						)






@require_torch
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			@slow
			def 				__UpperCamelCase			(      self				:			int						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							DistilBertModel.from_pretrained("distilbert-base-uncased"						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
				with torch.no_grad():
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							model(a   ,   attention_mask=a						)[0]
				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.Size((1, 11, 768)						)
				self.assertEqual(output.shape   ,   a						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.tensor(
				    [[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]						)

				self.assertTrue(torch.allclose(output[:, 1:4, 1:4]   ,   a   ,   atol=1e-4						)						)


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_rembert import RemBertTokenizer
else:
      a_     :    int     		=   None

a_     :    List[Any]     		=   logging.get_logger(__name__)
a_     :    List[Any]     		=   {"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

a_     :    Optional[int]     		=   {
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

a_     :    Union[str, Any]     		=   {
    "google/rembert": 256,
}

a_     :    List[Any]     		=   "▁"




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =VOCAB_FILES_NAMES
    __UpperCamelCase    =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase    =RemBertTokenizer

    def __init__(      self			:   int				,     snake_case__			:   Any=None				,     snake_case__			:   Optional[Any]=None				,     snake_case__			:   Optional[int]=True				,     snake_case__			:   Any=True				,     snake_case__			:   Any=False				,     snake_case__			:   Dict="[CLS]"				,     snake_case__			:   List[str]="[SEP]"				,     snake_case__			:   List[str]="<unk>"				,     snake_case__			:   Union[str, Any]="[SEP]"				,     snake_case__			:   List[Any]="<pad>"				,     snake_case__			:   Tuple="[CLS]"				,     snake_case__			:   str="[MASK]"				,     **snake_case__			:   Union[str, Any]				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      AddedToken(snake_case__				,     lstrip=snake_case__				,     rstrip=snake_case__     ) if isinstance(snake_case__				,     snake_case__     ) else mask_token

     super().__init__(
         snake_case__				,     tokenizer_file=snake_case__				,     do_lower_case=snake_case__				,     remove_space=snake_case__				,     keep_accents=snake_case__				,     bos_token=snake_case__				,     eos_token=snake_case__				,     unk_token=snake_case__				,     sep_token=snake_case__				,     pad_token=snake_case__				,     cls_token=snake_case__				,     mask_token=snake_case__				,     **snake_case__				,     )

     SCREAMING_SNAKE_CASE		    =      do_lower_case
     SCREAMING_SNAKE_CASE		    =      remove_space
     SCREAMING_SNAKE_CASE		    =      keep_accents
     SCREAMING_SNAKE_CASE		    =      vocab_file
     SCREAMING_SNAKE_CASE		    =      False if not self.vocab_file else True

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None     ):

     """simple docstring"""






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

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None				,     snake_case__			:   bool = False     ):

     """simple docstring"""






     if already_has_special_tokens:
      if token_ids_a is not None:
       raise ValueError(
           'You should not supply a second sequence if the provided sequence of '
           'ids is already formatted with special tokens for the model.'     )
      return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

     if token_ids_a is not None:
      return [1] + ([0] * len(snake_case__     )) + [1] + ([0] * len(snake_case__     )) + [1]
     return [1] + ([0] * len(snake_case__     )) + [1]

    def UpperCamelCase      (      self			:   int				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      [self.sep_token_id]
     SCREAMING_SNAKE_CASE		    =      [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     ) * [0] + len(token_ids_a + sep     ) * [1]




    def UpperCamelCase      (      self			:   Dict				,     snake_case__			:   str				,     snake_case__			:   Optional[str] = None     ):

     """simple docstring"""






     if not os.path.isdir(snake_case__     ):
      logger.error('Vocabulary path ({}) should be a directory'.format(snake_case__     )     )
      return
     SCREAMING_SNAKE_CASE		    =      os.path.join(
         snake_case__				,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

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

     return (out_vocab_file,)

