





import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax

  from transformers.models.big_bird.modeling_flax_big_bird import (
      FlaxBigBirdForCausalLM,
      FlaxBigBirdForMaskedLM,
      FlaxBigBirdForMultipleChoice,
      FlaxBigBirdForPreTraining,
      FlaxBigBirdForQuestionAnswering,
      FlaxBigBirdForSequenceClassification,
      FlaxBigBirdForTokenClassification,
      FlaxBigBirdModel,
  )






class        UpperCAmelCase     (	unittest.TestCase    ):
 def __init__(    self				:List[Any]						,	lowercase_				:int						,	lowercase_				:Union[str, Any]=2						,	lowercase_				:Optional[Any]=56						,	lowercase_				:Dict=True						,	lowercase_				:Optional[Any]=True						,	lowercase_				:Any=True						,	lowercase_				:List[Any]=True						,	lowercase_				:Tuple=99						,	lowercase_				:Any=32						,	lowercase_				:List[Any]=2						,	lowercase_				:Optional[Any]=2						,	lowercase_				:str=7						,	lowercase_				:Dict="gelu_new"						,	lowercase_				:List[str]=0.1						,	lowercase_				:Dict=0.1						,	lowercase_				:Optional[Any]=5_12						,	lowercase_				:Tuple=16						,	lowercase_				:Dict=2						,	lowercase_				:List[str]=0.0_2						,	lowercase_				:Optional[int]=4						,	lowercase_				:str="block_sparse"						,	lowercase_				:List[Any]=True						,	lowercase_				:int=False						,	lowercase_				:Tuple=2						,	lowercase_				:Optional[int]=3						,	)->							Optional[Any]:
     A__    =      parent
     A__    =      batch_size
     A__    =      seq_length
     A__    =      is_training
     A__    =      use_attention_mask
     A__    =      use_token_type_ids
     A__    =      use_labels
     A__    =      vocab_size
     A__    =      hidden_size
     A__    =      num_hidden_layers
     A__    =      num_attention_heads
     A__    =      intermediate_size
     A__    =      hidden_act
     A__    =      hidden_dropout_prob
     A__    =      attention_probs_dropout_prob
     A__    =      max_position_embeddings
     A__    =      type_vocab_size
     A__    =      type_sequence_label_size
     A__    =      initializer_range
     A__    =      num_choices

     A__    =      rescale_embeddings
     A__    =      attention_type
     A__    =      use_bias
     A__    =      block_size
     A__    =      num_random_blocks
 def 					UpperCAmelCase_							(    self				:str							)->							Tuple:
     A__    =      ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size							)

     A__    =      None
     if self.use_attention_mask:
         A__    =      random_attention_mask([self.batch_size, self.seq_length]							)

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

     A__    =      BigBirdConfig(
         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=lowercase_						,	initializer_range=self.initializer_range						,	attention_type=self.attention_type						,	block_size=self.block_size						,	num_random_blocks=self.num_random_blocks						,	use_bias=self.use_bias						,	rescale_embeddings=self.rescale_embeddings						,	)

     return config, input_ids, token_type_ids, attention_mask



 def 					UpperCAmelCase_							(    self				:List[str]							)->							int:
     A__    =      self.prepare_config_and_inputs()
     A__    =      config_and_inputs
     A__    =      {
         'input_ids': input_ids,
         'token_type_ids': token_type_ids,
         'attention_mask': attention_mask,
     }
     return config, inputs_dict






@require_flax
class        UpperCAmelCase     (	snake_case_      ,	unittest.TestCase    ):
 __lowercase       		=    (
     (
         FlaxBigBirdForCausalLM,
         FlaxBigBirdModel,
         FlaxBigBirdForPreTraining,
         FlaxBigBirdForMaskedLM,
         FlaxBigBirdForMultipleChoice,
         FlaxBigBirdForQuestionAnswering,
         FlaxBigBirdForSequenceClassification,
         FlaxBigBirdForTokenClassification,
     )
     if is_flax_available()
     else ()
 )

 __lowercase       		=    False
 __lowercase       		=    False
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							int:
     A__    =      FlaxBigBirdModelTester(self							)
 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 					UpperCAmelCase_							(    self				:int							)->							Any:
     super().test_from_pretrained_save_pretrained()
 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							List[str]:
     super().test_from_pretrained_with_no_automatic_init()
 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     super().test_no_automatic_init()
 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 					UpperCAmelCase_							(    self				:str							)->							Tuple:
     super().test_hidden_states_output()
 @slow
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Optional[int]:





     for model_class_name in self.all_model_classes:
         A__    =      model_class_name.from_pretrained("google/bigbird-roberta-base"							)
         self.assertIsNotNone(lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							int:





     if self.test_attn_probs:
         super().test_attention_outputs()
 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							List[Any]:
     A__    =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__							):
             A__    =      self._prepare_for_class(lowercase_						,	lowercase_							)
             A__    =      model_class(lowercase_							)

             @jax.jit
             def model_jitted(lowercase_				:str						,	lowercase_				:List[str]=None						,	**lowercase_				:Tuple							):
                 return model(input_ids=lowercase_						,	attention_mask=lowercase_						,	**lowercase_							)

             with self.subTest("JIT Enabled"							):
                 A__    =      model_jitted(**lowercase_							).to_tuple()

             with self.subTest("JIT Disabled"							):
                 with jax.disable_jit():
                     A__    =      model_jitted(**lowercase_							).to_tuple()

             self.assertEqual(len(lowercase_							)						,	len(lowercase_							)							)
             for jitted_output, output in zip(lowercase_						,	lowercase_							):
                 self.assertEqual(jitted_output.shape						,	output.shape							)



 def 					UpperCAmelCase_							(    self				:Any						,	lowercase_				:Optional[Any]						,	lowercase_				:Any						,	lowercase_				:Optional[Any]						,	lowercase_				:Optional[int]=1E-5						,	lowercase_				:Tuple="outputs"						,	lowercase_				:Dict=None							)->							Dict:





     if name.startswith("outputs.attentions"							):
         return
     else:
         super().check_pt_flax_outputs(lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	lowercase_						,	lowercase_							)










'''simple docstring'''

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        UpperCAmelCase     :
 def __init__(    self				:str						,	lowercase_				:str						,	)->							str:
     A__    =      parent
     A__    =      13
     A__    =      7
     A__    =      True
     A__    =      True
     A__    =      False
     A__    =      True
     A__    =      99
     A__    =      32
     A__    =      2
     A__    =      4
     A__    =      37
     A__    =      "gelu"
     A__    =      0.1
     A__    =      0.1
     A__    =      5_12
     A__    =      16
     A__    =      2
     A__    =      0.0_2
     A__    =      3
     A__    =      4
     A__    =      None
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							int:
     A__    =      ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size							)

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

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

     A__    =      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 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Optional[int]						,	lowercase_				:List[str]						,	lowercase_				:Any						,	lowercase_				:Union[str, Any]						,	lowercase_				:Optional[int]						,	lowercase_				:str							)->							List[str]:
     A__    =      TFDistilBertModel(config=lowercase_							)
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}

     A__    =      model(lowercase_							)

     A__    =      [input_ids, input_mask]

     A__    =      model(lowercase_							)

     self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)							)
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:str						,	lowercase_				:Optional[Any]						,	lowercase_				:Optional[int]						,	lowercase_				:Optional[int]						,	lowercase_				:Optional[int]						,	lowercase_				:Union[str, Any]							)->							Optional[int]:
     A__    =      TFDistilBertForMaskedLM(config=lowercase_							)
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.vocab_size)							)
 def 					UpperCAmelCase_							(    self				:Any						,	lowercase_				:str						,	lowercase_				:str						,	lowercase_				:Optional[int]						,	lowercase_				:str						,	lowercase_				:List[Any]						,	lowercase_				:Union[str, Any]							)->							Optional[int]:
     A__    =      TFDistilBertForQuestionAnswering(config=lowercase_							)
     A__    =      {
         "input_ids": input_ids,
         "attention_mask": input_mask,
     }
     A__    =      model(lowercase_							)
     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				:Union[str, Any]						,	lowercase_				:Optional[int]						,	lowercase_				:Any						,	lowercase_				:Dict						,	lowercase_				:Tuple						,	lowercase_				:Optional[Any]						,	lowercase_				:Optional[int]							)->							Any:
     A__    =      self.num_labels
     A__    =      TFDistilBertForSequenceClassification(lowercase_							)
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_labels)							)
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Optional[Any]						,	lowercase_				:List[Any]						,	lowercase_				:Dict						,	lowercase_				:Tuple						,	lowercase_				:int						,	lowercase_				:Union[str, Any]							)->							str:
     A__    =      self.num_choices
     A__    =      TFDistilBertForMultipleChoice(lowercase_							)
     A__    =      tf.tile(tf.expand_dims(lowercase_						,	1							)						,	(1, self.num_choices, 1)							)
     A__    =      tf.tile(tf.expand_dims(lowercase_						,	1							)						,	(1, self.num_choices, 1)							)
     A__    =      {
         "input_ids": multiple_choice_inputs_ids,
         "attention_mask": multiple_choice_input_mask,
     }
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.num_choices)							)
 def 					UpperCAmelCase_							(    self				:str						,	lowercase_				:Any						,	lowercase_				:List[str]						,	lowercase_				:Any						,	lowercase_				:int						,	lowercase_				:List[Any]						,	lowercase_				:Tuple							)->							Tuple:
     A__    =      self.num_labels
     A__    =      TFDistilBertForTokenClassification(lowercase_							)
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.logits.shape						,	(self.batch_size, self.seq_length, self.num_labels)							)



 def 					UpperCAmelCase_							(    self				:Any							)->							Union[str, Any]:
     A__    =      self.prepare_config_and_inputs()
     ((A__),     (A__),     (A__),     (A__),     (A__),     (A__))    =      config_and_inputs
     A__    =      {"input_ids": input_ids, "attention_mask": input_mask}
     return config, inputs_dict






@require_tf
class        UpperCAmelCase     (	UpperCamelCase__      ,	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    (
     (
         TFDistilBertModel,
         TFDistilBertForMaskedLM,
         TFDistilBertForQuestionAnswering,
         TFDistilBertForSequenceClassification,
         TFDistilBertForTokenClassification,
         TFDistilBertForMultipleChoice,
     )
     if is_tf_available()
     else None
 )
 __lowercase       		=    (
     {
         """feature-extraction""": TFDistilBertModel,
         """fill-mask""": TFDistilBertForMaskedLM,
         """question-answering""": TFDistilBertForQuestionAnswering,
         """text-classification""": TFDistilBertForSequenceClassification,
         """token-classification""": TFDistilBertForTokenClassification,
         """zero-shot""": TFDistilBertForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 __lowercase       		=    False
 __lowercase       		=    False
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							List[Any]:
     A__    =      TFDistilBertModelTester(self							)
     A__    =      ConfigTester(self						,	config_class=lowercase_						,	dim=37							)
 def 					UpperCAmelCase_							(    self				:Tuple							)->							Tuple:
     self.config_tester.run_common_tests()
 def 					UpperCAmelCase_							(    self				:int							)->							Tuple:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_model(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[int]							)->							Optional[Any]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_masked_lm(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:str							)->							str:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_question_answering(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Dict:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_sequence_classification(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Optional[int]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_multiple_choice(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:str							)->							int:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_distilbert_for_token_classification(*lowercase_							)



 @slow
 def 					UpperCAmelCase_							(    self				:List[str]							)->							Dict:
     for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]							):
         A__    =      TFDistilBertModel.from_pretrained(lowercase_							)
         self.assertIsNotNone(lowercase_							)






@require_tf
class        UpperCAmelCase     (	unittest.TestCase    ):
 @slow
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Any:
     A__    =      TFDistilBertModel.from_pretrained("distilbert-base-uncased"							)
     A__    =      tf.constant([[0, 1, 2, 3, 4, 5]]							)
     A__    =      model(lowercase_							)[0]

     A__    =      [1, 6, 7_68]
     self.assertEqual(output.shape						,	lowercase_							)

     A__    =      tf.constant(
         [
             [
                 [0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
                 [0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
                 [0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
             ]
         ]							)
     tf.debugging.assert_near(output[:, :3, :3]						,	lowercase_						,	atol=1E-4							)



