


"""simple docstring"""







import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_FOR_PRETRAINING_MAPPING,
           NezhaForMaskedLM,
           NezhaForMultipleChoice,
           NezhaForNextSentencePrediction,
           NezhaForPreTraining,
           NezhaForQuestionAnswering,
           NezhaForSequenceClassification,
           NezhaForTokenClassification,
           NezhaModel,
       )
       from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST






class       __A				:






 """simple docstring"""




 def __init__(		self ,    __A ,    __A=13 ,    __A=7 ,    __A=True ,    __A=True ,    __A=True ,    __A=True ,    __A=99 ,    __A=32 ,    __A=5 ,    __A=4 ,    __A=37 ,    __A="gelu" ,    __A=0.1 ,    __A=0.1 ,    __A=128 ,    __A=32 ,    __A=16 ,    __A=2 ,    __A=0.02 ,    __A=3 ,    __A=4 ,    __A=None ,    )					->     Union[str, Any]:
     a 		=parent
     a 		=batch_size
     a 		=seq_length
     a 		=is_training
     a 		=use_input_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_labels
     a 		=num_choices
     a 		=scope


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[Any]:
     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
     if self.use_token_type_ids:
         a 		=ids_tensor([self.batch_size, self.seq_length] ,    self.type_vocab_size						)

     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 		=self.get_config()

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


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Tuple:
     return NezhaConfig(
         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=__A ,    initializer_range=self.initializer_range ,    )


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Tuple:
     (
         (
         a
     )		,      (
         a
     )		,      (
         a
     )		,      (
         a
     )		,      (
         a
     )		,      (
         a
     )		,      (
         a
     )		,      
     ) 		=self.prepare_config_and_inputs()

     a 		=True
     a 		=floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
     a 		=ids_tensor([self.batch_size, self.seq_length] ,    vocab_size=2						)

     return (
         config,
         input_ids,
         token_type_ids,
         input_mask,
         sequence_labels,
         token_labels,
         choice_labels,
         encoder_hidden_states,
         encoder_attention_mask,
     )


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A						)					->     Tuple:
     a 		=NezhaModel(config=__A						)
     model.to(__A						)
     model.eval()
     a 		=model(__A ,    attention_mask=__A ,    token_type_ids=__A						)
     a 		=model(__A ,    token_type_ids=__A						)
     a 		=model(__A						)
     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 ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    )					->     List[Any]:
     a 		=True
     a 		=NezhaModel(__A						)
     model.to(__A						)
     model.eval()
     a 		=model(
         __A ,    attention_mask=__A ,    token_type_ids=__A ,    encoder_hidden_states=__A ,    encoder_attention_mask=__A ,    )
     a 		=model(
         __A ,    attention_mask=__A ,    token_type_ids=__A ,    encoder_hidden_states=__A ,    )
     a 		=model(__A ,    attention_mask=__A ,    token_type_ids=__A						)
     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 ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A						)					->     List[Any]:
     a 		=NezhaForMaskedLM(config=__A						)
     model.to(__A						)
     model.eval()
     a 		=model(__A ,    attention_mask=__A ,    token_type_ids=__A ,    labels=__A						)
     self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.seq_length, self.vocab_size)						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A						)					->     int:
     a 		=NezhaForNextSentencePrediction(config=__A						)
     model.to(__A						)
     model.eval()
     a 		=model(
         __A ,    attention_mask=__A ,    token_type_ids=__A ,    labels=__A ,    )
     self.parent.assertEqual(result.logits.shape ,    (self.batch_size, 2)						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A						)					->     Any:
     a 		=NezhaForPreTraining(config=__A						)
     model.to(__A						)
     model.eval()
     a 		=model(
         __A ,    attention_mask=__A ,    token_type_ids=__A ,    labels=__A ,    next_sentence_label=__A ,    )
     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 ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A						)					->     List[str]:
     a 		=NezhaForQuestionAnswering(config=__A						)
     model.to(__A						)
     model.eval()
     a 		=model(
         __A ,    attention_mask=__A ,    token_type_ids=__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       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A						)					->     Tuple:
     a 		=self.num_labels
     a 		=NezhaForSequenceClassification(__A						)
     model.to(__A						)
     model.eval()
     a 		=model(__A ,    attention_mask=__A ,    token_type_ids=__A ,    labels=__A						)
     self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.num_labels)						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A						)					->     Union[str, Any]:
     a 		=self.num_labels
     a 		=NezhaForTokenClassification(config=__A						)
     model.to(__A						)
     model.eval()
     a 		=model(__A ,    attention_mask=__A ,    token_type_ids=__A ,    labels=__A						)
     self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.seq_length, self.num_labels)						)


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A						)					->     Union[str, Any]:
     a 		=self.num_choices
     a 		=NezhaForMultipleChoice(config=__A						)
     model.to(__A						)
     model.eval()
     a 		=input_ids.unsqueeze(1						).expand(-1 ,    self.num_choices ,    -1						).contiguous()
     a 		=token_type_ids.unsqueeze(1						).expand(-1 ,    self.num_choices ,    -1						).contiguous()
     a 		=input_mask.unsqueeze(1						).expand(-1 ,    self.num_choices ,    -1						).contiguous()
     a 		=model(
         __A ,    attention_mask=__A ,    token_type_ids=__A ,    labels=__A ,    )
     self.parent.assertEqual(result.logits.shape ,    (self.batch_size, self.num_choices)						)





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






@require_torch
class       __A				(       _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    _SCREAMING_SNAKE_CASE,    unittest.TestCase			):






 """simple docstring"""




 __lowerCAmelCase 					=  (
     (
         NezhaModel,
         NezhaForMaskedLM,
         NezhaForMultipleChoice,
         NezhaForNextSentencePrediction,
         NezhaForPreTraining,
         NezhaForQuestionAnswering,
         NezhaForSequenceClassification,
         NezhaForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 __lowerCAmelCase 					=  (
     {
         "feature-extraction": NezhaModel,
         "fill-mask": NezhaForMaskedLM,
         "question-answering": NezhaForQuestionAnswering,
         "text-classification": NezhaForSequenceClassification,
         "token-classification": NezhaForTokenClassification,
         "zero-shot": NezhaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 __lowerCAmelCase 					=  True


 def       SCREAMING_SNAKE_CASE							(		self ,    __A ,    __A ,    __A=False						)					->     Union[str, Any]:
     a 		=super()._prepare_for_class(__A ,    __A ,    return_labels=__A						)

     if return_labels:
         if model_class in get_values(__A						):
             a 		=torch.zeros(
                 (self.model_tester.batch_size, self.model_tester.seq_length) ,    dtype=torch.long ,    device=__A						)
             a 		=torch.zeros(
                 self.model_tester.batch_size ,    dtype=torch.long ,    device=__A						)
     return inputs_dict


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Tuple:
     a 		=NezhaModelTester(self						)
     a 		=ConfigTester(self ,    config_class=__A ,    hidden_size=37						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[Any]:
     self.config_tester.run_common_tests()


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     a 		=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     int:
     a 		=self.model_tester.prepare_config_and_inputs_for_decoder()
     self.model_tester.create_and_check_model_as_decoder(*__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[Any]:
     # This regression test was failing with PyTorch < 1.3
     (
         (
         a
     )		,      (
         a
     )		,      (
         a
     )		,      (
         a
     )		,      (
         a
     )		,      (
         a
     )		,      (
         a
     )		,      (
         a
     )		,      (
         a
     )		,      
     ) 		=self.model_tester.prepare_config_and_inputs_for_decoder()

     a 		=None

     self.model_tester.create_and_check_model_as_decoder(
         __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    __A ,    )


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


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[Any]:
     a 		=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_multiple_choice(*__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[Any]:
     a 		=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_next_sequence_prediction(*__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[str]:
     a 		=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_pretraining(*__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     List[str]:
     a 		=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_question_answering(*__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Tuple:
     a 		=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_sequence_classification(*__A						)


 def       SCREAMING_SNAKE_CASE							(		self						)					->     Optional[int]:
     a 		=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_token_classification(*__A						)


 @slow
 def       SCREAMING_SNAKE_CASE							(		self						)					->     Any:
     for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         a 		=NezhaModel.from_pretrained(__A						)
         self.assertIsNotNone(__A						)





 @slow
 @require_torch_gpu
 def       SCREAMING_SNAKE_CASE							(		self						)					->     str:
     a		,      a 		=self.model_tester.prepare_config_and_inputs_for_common()
     for model_class in self.all_model_classes:
         # NezhaForMultipleChoice behaves incorrectly in JIT environments.
         if model_class == NezhaForMultipleChoice:
             return

         a 		=True
         a 		=model_class(config=__A						)

         a 		=self._prepare_for_class(__A ,    __A						)
         a 		=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 ,    '''bert.pt'''						)						)
             a 		=torch.jit.load(os.path.join(__A ,    '''bert.pt'''						) ,    map_location=__A						)
             loaded(inputs_dict['''input_ids'''].to(__A						) ,    inputs_dict['''attention_mask'''].to(__A						)						)






@require_torch
class       __A				(       unittest.TestCase			):






 """simple docstring"""




 @slow
 def       SCREAMING_SNAKE_CASE							(		self						)					->     Dict:
     a 		=NezhaModel.from_pretrained('''sijunhe/nezha-cn-base'''						)
     a 		=torch.tensor([[0, 1, 2, 3, 4, 5]]						)
     a 		=torch.tensor([[0, 1, 1, 1, 1, 1]]						)
     with torch.no_grad():
         a 		=model(__A ,    attention_mask=__A						)[0]
     a 		=torch.Size((1, 6, 768)						)
     self.assertEqual(output.shape ,    __A						)
     a 		=torch.tensor([[[0.0_685, 0.2_441, 0.1_102], [0.0_600, 0.1_906, 0.1_349], [0.0_221, 0.0_819, 0.0_586]]]						)

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





 @slow
 def       SCREAMING_SNAKE_CASE							(		self						)					->     Tuple:
     a 		=NezhaForMaskedLM.from_pretrained('''sijunhe/nezha-cn-base'''						)
     a 		=torch.tensor([[0, 1, 2, 3, 4, 5]]						)
     a 		=torch.tensor([[1, 1, 1, 1, 1, 1]]						)
     with torch.no_grad():
         a 		=model(__A ,    attention_mask=__A						)[0]
     a 		=torch.Size((1, 6, 2_1128)						)
     self.assertEqual(output.shape ,    __A						)
     a 		=torch.tensor(
         [[-2.7_939, -1.7_902, -2.2_189], [-2.8_585, -1.8_908, -2.3_723], [-2.6_499, -1.7_750, -2.2_558]]						)

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



# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_A		       =						{
    'configuration_vivit': ['VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VivitConfig'],
}
try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						['VivitImageProcessor']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'VivitModel',
       'VivitPreTrainedModel',
       'VivitForVideoClassification',
   ]


if TYPE_CHECKING:
   from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_vivit import VivitImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vivit import (
          VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          VivitForVideoClassification,
          VivitModel,
          VivitPreTrainedModel,
      )


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





