





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase :List[Any]     =    {
    'configuration_mobilebert': [
        'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileBertConfig',
        'MobileBertOnnxConfig',
    ],
    'tokenization_mobilebert': ['MobileBertTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase :Dict     =    ['MobileBertTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase :Any     =    [
							    'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'MobileBertForMaskedLM',
							    'MobileBertForMultipleChoice',
							    'MobileBertForNextSentencePrediction',
							    'MobileBertForPreTraining',
							    'MobileBertForQuestionAnswering',
							    'MobileBertForSequenceClassification',
							    'MobileBertForTokenClassification',
							    'MobileBertLayer',
							    'MobileBertModel',
							    'MobileBertPreTrainedModel',
							    'load_tf_weights_in_mobilebert',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase :Optional[int]     =    [
							    'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFMobileBertForMaskedLM',
							    'TFMobileBertForMultipleChoice',
							    'TFMobileBertForNextSentencePrediction',
							    'TFMobileBertForPreTraining',
							    'TFMobileBertForQuestionAnswering',
							    'TFMobileBertForSequenceClassification',
							    'TFMobileBertForTokenClassification',
							    'TFMobileBertMainLayer',
							    'TFMobileBertModel',
							    'TFMobileBertPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_mobilebert import (
							    MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    MobileBertConfig,
							    MobileBertOnnxConfig,
							)
							from .tokenization_mobilebert import MobileBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mobilebert_fast import MobileBertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mobilebert import (
														    MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MobileBertForMaskedLM,
														    MobileBertForMultipleChoice,
														    MobileBertForNextSentencePrediction,
														    MobileBertForPreTraining,
														    MobileBertForQuestionAnswering,
														    MobileBertForSequenceClassification,
														    MobileBertForTokenClassification,
														    MobileBertLayer,
														    MobileBertModel,
														    MobileBertPreTrainedModel,
														    load_tf_weights_in_mobilebert,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mobilebert import (
														    TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFMobileBertForMaskedLM,
														    TFMobileBertForMultipleChoice,
														    TFMobileBertForNextSentencePrediction,
														    TFMobileBertForPreTraining,
														    TFMobileBertForQuestionAnswering,
														    TFMobileBertForSequenceClassification,
														    TFMobileBertForTokenClassification,
														    TFMobileBertMainLayer,
														    TFMobileBertModel,
														    TFMobileBertPreTrainedModel,
														)

else:
							import sys

							__lowerCAmelCase :Any     =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers.models.distilbert.modeling_tf_distilbert import (
							    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    TFDistilBertForMaskedLM,
							    TFDistilBertForMultipleChoice,
							    TFDistilBertForQuestionAnswering,
							    TFDistilBertForSequenceClassification,
							    TFDistilBertForTokenClassification,
							    TFDistilBertModel,
							)






class        _a:







		def __init__(    self ,						__snake_case ,						)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   parent
							_snake_case       :					Any				       =   1_3
							_snake_case       :					Union[str, Any]				       =   7
							_snake_case       :					Any				       =   True
							_snake_case       :					Optional[Any]				       =   True
							_snake_case       :					List[str]				       =   False
							_snake_case       :					str				       =   True
							_snake_case       :					int				       =   9_9
							_snake_case       :					Optional[int]				       =   3_2
							_snake_case       :					Dict				       =   2
							_snake_case       :					Optional[Any]				       =   4
							_snake_case       :					Any				       =   3_7
							_snake_case       :					Optional[int]				       =   "gelu"
							_snake_case       :					Optional[Any]				       =   0.1
							_snake_case       :					List[Any]				       =   0.1
							_snake_case       :					Any				       =   5_1_2
							_snake_case       :					Optional[int]				       =   1_6
							_snake_case       :					Dict				       =   2
							_snake_case       :					Optional[int]				       =   0.02
							_snake_case       :					Any				       =   3
							_snake_case       :					Tuple				       =   4
							_snake_case       :					str				       =   None







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


							'''simple docstring'''

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

							_snake_case       :					List[Any]				       =   None
							if self.use_input_mask:
												_snake_case       :					Tuple				       =   random_attention_mask([self.batch_size, self.seq_length]				)

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

							_snake_case       :					Optional[Any]				       =   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 ,						)

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







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					str				       =   TFDistilBertModel(config=__snake_case				)
							_snake_case       :					Dict				       =   {"input_ids": input_ids, "attention_mask": input_mask}

							_snake_case       :					Union[str, Any]				       =   model(__snake_case				)

							_snake_case       :					Union[str, Any]				       =   [input_ids, input_mask]

							_snake_case       :					int				       =   model(__snake_case				)

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







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					str				       =   TFDistilBertForMaskedLM(config=__snake_case				)
							_snake_case       :					Optional[Any]				       =   {"input_ids": input_ids, "attention_mask": input_mask}
							_snake_case       :					Tuple				       =   model(__snake_case				)
							self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.vocab_size)				)







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


							'''simple docstring'''

							_snake_case       :					int				       =   TFDistilBertForQuestionAnswering(config=__snake_case				)
							_snake_case       :					Any				       =   {
							    "input_ids": input_ids,
							    "attention_mask": input_mask,
							}
							_snake_case       :					Tuple				       =   model(__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        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Dict				       =   self.num_labels
							_snake_case       :					Dict				       =   TFDistilBertForSequenceClassification(__snake_case				)
							_snake_case       :					Dict				       =   {"input_ids": input_ids, "attention_mask": input_mask}
							_snake_case       :					Optional[Any]				       =   model(__snake_case				)
							self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_labels)				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					str				       =   self.num_choices
							_snake_case       :					List[str]				       =   TFDistilBertForMultipleChoice(__snake_case				)
							_snake_case       :					Optional[int]				       =   tf.tile(tf.expand_dims(__snake_case ,						1				) ,						(1, self.num_choices, 1)				)
							_snake_case       :					int				       =   tf.tile(tf.expand_dims(__snake_case ,						1				) ,						(1, self.num_choices, 1)				)
							_snake_case       :					str				       =   {
							    "input_ids": multiple_choice_inputs_ids,
							    "attention_mask": multiple_choice_input_mask,
							}
							_snake_case       :					List[Any]				       =   model(__snake_case				)
							self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.num_choices)				)







		def        lowercase       (    self ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   self.num_labels
							_snake_case       :					int				       =   TFDistilBertForTokenClassification(__snake_case				)
							_snake_case       :					Any				       =   {"input_ids": input_ids, "attention_mask": input_mask}
							_snake_case       :					Any				       =   model(__snake_case				)
							self.parent.assertEqual(result.logits.shape ,						(self.batch_size, self.seq_length, self.num_labels)				)







		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   self.prepare_config_and_inputs()
							((_snake_case)       , (_snake_case)       , (_snake_case)       , (_snake_case)       , (_snake_case)       , (_snake_case))       :					List[str]				       =   config_and_inputs
							_snake_case       :					Optional[int]				       =   {"input_ids": input_ids, "attention_mask": input_mask}
							return config, inputs_dict






@require_tf
class        _a(    __A	,   __A	,   unittest.TestCase    ):
		lowerCamelCase__     :str   				=		(
		    (
		        TFDistilBertModel,
		        TFDistilBertForMaskedLM,
		        TFDistilBertForQuestionAnswering,
		        TFDistilBertForSequenceClassification,
		        TFDistilBertForTokenClassification,
		        TFDistilBertForMultipleChoice,
		    )
		    if is_tf_available()
		    else None
		)
		lowerCamelCase__     :Tuple   				=		(
		    {
		        'feature-extraction': TFDistilBertModel,
		        'fill-mask': TFDistilBertForMaskedLM,
		        'question-answering': TFDistilBertForQuestionAnswering,
		        'text-classification': TFDistilBertForSequenceClassification,
		        'token-classification': TFDistilBertForTokenClassification,
		        'zero-shot': TFDistilBertForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		lowerCamelCase__     :Tuple   				=		False
		lowerCamelCase__     :Any   				=		False







		def        lowercase       (    self				)      ->      int:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   TFDistilBertModelTester(self				)
							_snake_case       :					str				       =   ConfigTester(self ,						config_class=__snake_case ,						dim=3_7				)







		def        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

							self.config_tester.run_common_tests()







		def        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_model(*__snake_case				)







		def        lowercase       (    self				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					Any				       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_masked_lm(*__snake_case				)







		def        lowercase       (    self				)      ->      List[str]:


							'''simple docstring'''

							_snake_case       :					str				       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_question_answering(*__snake_case				)







		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					List[Any]				       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_sequence_classification(*__snake_case				)







		def        lowercase       (    self				)      ->      Dict:


							'''simple docstring'''

							_snake_case       :					List[str]				       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_multiple_choice(*__snake_case				)







		def        lowercase       (    self				)      ->      List[Any]:


							'''simple docstring'''

							_snake_case       :					Dict				       =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_distilbert_for_token_classification(*__snake_case				)







		@slow
		def        lowercase       (    self				)      ->      Tuple:


							'''simple docstring'''

							for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]				):
												_snake_case       :					Any				       =   TFDistilBertModel.from_pretrained(__snake_case				)
												self.assertIsNotNone(__snake_case				)






@require_tf
class        _a(    unittest.TestCase    ):







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


							'''simple docstring'''

							_snake_case       :					Any				       =   TFDistilBertModel.from_pretrained("distilbert-base-uncased"				)
							_snake_case       :					int				       =   tf.constant([[0, 1, 2, 3, 4, 5]]				)
							_snake_case       :					Dict				       =   model(__snake_case				)[0]

							_snake_case       :					str				       =   [1, 6, 7_6_8]
							self.assertEqual(output.shape ,						__snake_case				)

							_snake_case       :					Optional[Any]				       =   tf.constant(
							    [
							        [
							            [0.19_26_18_85, -0.13_73_29_55, 0.4_11_97_99],
							            [0.22_15_01_56, -0.07_42_26_61, 0.39_03_72_04],
							            [0.22_75_60_18, -0.0_89_64_14, 0.3_70_14_67],
							        ]
							    ]				)
							tf.debugging.assert_near(output[:, :3, :3] ,						__snake_case ,						atol=1E-4				)