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							:


   def __init__(self     :						Any       ,  __UpperCAmelCase     :						Optional[Any]       ,  __UpperCAmelCase     :						Optional[Any]=1_3       ,  __UpperCAmelCase     :						int=7       ,  __UpperCAmelCase     :						Tuple=True       ,  __UpperCAmelCase     :						Tuple=True       ,  __UpperCAmelCase     :						List[Any]=True       ,  __UpperCAmelCase     :						List[Any]=True       ,  __UpperCAmelCase     :						List[Any]=9_9       ,  __UpperCAmelCase     :						int=3_2       ,  __UpperCAmelCase     :						Union[str, Any]=5       ,  __UpperCAmelCase     :						Dict=4       ,  __UpperCAmelCase     :						str=3_7       ,  __UpperCAmelCase     :						Dict="gelu"       ,  __UpperCAmelCase     :						Union[str, Any]=0.1       ,  __UpperCAmelCase     :						Dict=0.1       ,  __UpperCAmelCase     :						str=1_2_8       ,  __UpperCAmelCase     :						str=3_2       ,  __UpperCAmelCase     :						Any=1_6       ,  __UpperCAmelCase     :						Union[str, Any]=2       ,  __UpperCAmelCase     :						Union[str, Any]=0.02       ,  __UpperCAmelCase     :						Union[str, Any]=3       ,  __UpperCAmelCase     :						int=4       ,  __UpperCAmelCase     :						int=None       ,  )						->				List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  parent
         UpperCAmelCase__								=  batch_size
         UpperCAmelCase__								=  seq_length
         UpperCAmelCase__								=  is_training
         UpperCAmelCase__								=  use_input_mask
         UpperCAmelCase__								=  use_token_type_ids
         UpperCAmelCase__								=  use_labels
         UpperCAmelCase__								=  vocab_size
         UpperCAmelCase__								=  hidden_size
         UpperCAmelCase__								=  num_hidden_layers
         UpperCAmelCase__								=  num_attention_heads
         UpperCAmelCase__								=  intermediate_size
         UpperCAmelCase__								=  hidden_act
         UpperCAmelCase__								=  hidden_dropout_prob
         UpperCAmelCase__								=  attention_probs_dropout_prob
         UpperCAmelCase__								=  max_position_embeddings
         UpperCAmelCase__								=  type_vocab_size
         UpperCAmelCase__								=  type_sequence_label_size
         UpperCAmelCase__								=  initializer_range
         UpperCAmelCase__								=  num_labels
         UpperCAmelCase__								=  num_choices
         UpperCAmelCase__								=  scope


   def 			lowercase_      (self     :						str		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size		)

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

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

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

         UpperCAmelCase__								=  self.get_config()

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


   def 			lowercase_      (self     :						List[Any]		)						->				List[Any]:
         """simple docstring"""



         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=lowerCAmelCase__       ,  initializer_range=self.initializer_range       ,  )


   def 			lowercase_      (self     :						List[str]		)						->				int:
         """simple docstring"""



         (
             UpperCAmelCase__       
         )								=  self.prepare_config_and_inputs()

         UpperCAmelCase__								=  True
         UpperCAmelCase__								=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
         UpperCAmelCase__								=  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 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						Tuple       ,  __UpperCAmelCase     :						Optional[Any]       ,  __UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						Union[str, Any]       ,  __UpperCAmelCase     :						Optional[int]       ,  __UpperCAmelCase     :						Optional[int]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  NezhaModel(config=lowerCAmelCase__		)
         model.to(lowerCAmelCase__		)
         model.eval()
         UpperCAmelCase__								=  model(lowerCAmelCase__       ,  attention_mask=lowerCAmelCase__       ,  token_type_ids=lowerCAmelCase__		)
         UpperCAmelCase__								=  model(lowerCAmelCase__       ,  token_type_ids=lowerCAmelCase__		)
         UpperCAmelCase__								=  model(lowerCAmelCase__		)
         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 			lowercase_      (self     :						Dict       ,  __UpperCAmelCase     :						Dict       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Dict       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						Optional[Any]       ,  __UpperCAmelCase     :						Optional[int]       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						int       ,  )						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  True
         UpperCAmelCase__								=  NezhaModel(lowerCAmelCase__		)
         model.to(lowerCAmelCase__		)
         model.eval()
         UpperCAmelCase__								=  model(
             lowerCAmelCase__       ,  attention_mask=lowerCAmelCase__       ,  token_type_ids=lowerCAmelCase__       ,  encoder_hidden_states=lowerCAmelCase__       ,  encoder_attention_mask=lowerCAmelCase__       ,  )
         UpperCAmelCase__								=  model(
             lowerCAmelCase__       ,  attention_mask=lowerCAmelCase__       ,  token_type_ids=lowerCAmelCase__       ,  encoder_hidden_states=lowerCAmelCase__       ,  )
         UpperCAmelCase__								=  model(lowerCAmelCase__       ,  attention_mask=lowerCAmelCase__       ,  token_type_ids=lowerCAmelCase__		)
         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 			lowercase_      (self     :						List[Any]       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						Optional[Any]       ,  __UpperCAmelCase     :						List[Any]       ,  __UpperCAmelCase     :						Optional[int]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						Optional[Any]		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  NezhaForMaskedLM(config=lowerCAmelCase__		)
         model.to(lowerCAmelCase__		)
         model.eval()
         UpperCAmelCase__								=  model(lowerCAmelCase__       ,  attention_mask=lowerCAmelCase__       ,  token_type_ids=lowerCAmelCase__       ,  labels=lowerCAmelCase__		)
         self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.vocab_size)		)


   def 			lowercase_      (self     :						str       ,  __UpperCAmelCase     :						Union[str, Any]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						Optional[Any]       ,  __UpperCAmelCase     :						Tuple       ,  __UpperCAmelCase     :						Union[str, Any]       ,  __UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						List[Any]		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  NezhaForNextSentencePrediction(config=lowerCAmelCase__		)
         model.to(lowerCAmelCase__		)
         model.eval()
         UpperCAmelCase__								=  model(
             lowerCAmelCase__       ,  attention_mask=lowerCAmelCase__       ,  token_type_ids=lowerCAmelCase__       ,  labels=lowerCAmelCase__       ,  )
         self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, 2)		)


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						Optional[Any]       ,  __UpperCAmelCase     :						Union[str, Any]       ,  __UpperCAmelCase     :						Dict       ,  __UpperCAmelCase     :						Tuple       ,  __UpperCAmelCase     :						Optional[int]       ,  __UpperCAmelCase     :						Union[str, Any]       ,  __UpperCAmelCase     :						Dict		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  NezhaForPreTraining(config=lowerCAmelCase__		)
         model.to(lowerCAmelCase__		)
         model.eval()
         UpperCAmelCase__								=  model(
             lowerCAmelCase__       ,  attention_mask=lowerCAmelCase__       ,  token_type_ids=lowerCAmelCase__       ,  labels=lowerCAmelCase__       ,  next_sentence_label=lowerCAmelCase__       ,  )
         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 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						Dict       ,  __UpperCAmelCase     :						List[Any]       ,  __UpperCAmelCase     :						Any       ,  __UpperCAmelCase     :						Tuple       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						int		)						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  NezhaForQuestionAnswering(config=lowerCAmelCase__		)
         model.to(lowerCAmelCase__		)
         model.eval()
         UpperCAmelCase__								=  model(
             lowerCAmelCase__       ,  attention_mask=lowerCAmelCase__       ,  token_type_ids=lowerCAmelCase__       ,  start_positions=lowerCAmelCase__       ,  end_positions=lowerCAmelCase__       ,  )
         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     :						List[Any]       ,  __UpperCAmelCase     :						Optional[int]       ,  __UpperCAmelCase     :						Tuple       ,  __UpperCAmelCase     :						Optional[int]       ,  __UpperCAmelCase     :						Union[str, Any]       ,  __UpperCAmelCase     :						List[Any]       ,  __UpperCAmelCase     :						List[Any]       ,  __UpperCAmelCase     :						Optional[int]		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.num_labels
         UpperCAmelCase__								=  NezhaForSequenceClassification(lowerCAmelCase__		)
         model.to(lowerCAmelCase__		)
         model.eval()
         UpperCAmelCase__								=  model(lowerCAmelCase__       ,  attention_mask=lowerCAmelCase__       ,  token_type_ids=lowerCAmelCase__       ,  labels=lowerCAmelCase__		)
         self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels)		)


   def 			lowercase_      (self     :						Union[str, Any]       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						Union[str, Any]       ,  __UpperCAmelCase     :						str       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						Union[str, Any]       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				str:
         """simple docstring"""



         UpperCAmelCase__								=  self.num_labels
         UpperCAmelCase__								=  NezhaForTokenClassification(config=lowerCAmelCase__		)
         model.to(lowerCAmelCase__		)
         model.eval()
         UpperCAmelCase__								=  model(lowerCAmelCase__       ,  attention_mask=lowerCAmelCase__       ,  token_type_ids=lowerCAmelCase__       ,  labels=lowerCAmelCase__		)
         self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.seq_length, self.num_labels)		)


   def 			lowercase_      (self     :						Any       ,  __UpperCAmelCase     :						Dict       ,  __UpperCAmelCase     :						int       ,  __UpperCAmelCase     :						List[str]       ,  __UpperCAmelCase     :						Optional[Any]       ,  __UpperCAmelCase     :						Optional[int]       ,  __UpperCAmelCase     :						Union[str, Any]       ,  __UpperCAmelCase     :						Union[str, Any]		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  self.num_choices
         UpperCAmelCase__								=  NezhaForMultipleChoice(config=lowerCAmelCase__		)
         model.to(lowerCAmelCase__		)
         model.eval()
         UpperCAmelCase__								=  input_ids.unsqueeze(1		).expand(-1       ,  self.num_choices       ,  -1		).contiguous()
         UpperCAmelCase__								=  token_type_ids.unsqueeze(1		).expand(-1       ,  self.num_choices       ,  -1		).contiguous()
         UpperCAmelCase__								=  input_mask.unsqueeze(1		).expand(-1       ,  self.num_choices       ,  -1		).contiguous()
         UpperCAmelCase__								=  model(
             lowerCAmelCase__       ,  attention_mask=lowerCAmelCase__       ,  token_type_ids=lowerCAmelCase__       ,  labels=lowerCAmelCase__       ,  )
         self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_choices)		)





   def 			lowercase_      (self     :						List[Any]		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  self.prepare_config_and_inputs()
         (
             UpperCAmelCase__       
         )								=  config_and_inputs
         UpperCAmelCase__								=  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
         return config, inputs_dict





@require_torch
class      A							(				a__		,				a__		,				a__		,				unittest.TestCase     ):
   __UpperCAmelCase : str      =			(
       (
           NezhaModel,
           NezhaForMaskedLM,
           NezhaForMultipleChoice,
           NezhaForNextSentencePrediction,
           NezhaForPreTraining,
           NezhaForQuestionAnswering,
           NezhaForSequenceClassification,
           NezhaForTokenClassification,
       )
       if is_torch_available()
       else ()
   )
   __UpperCAmelCase : Union[str, Any]      =			(
       {
           """feature-extraction""": NezhaModel,
           """fill-mask""": NezhaForMaskedLM,
           """question-answering""": NezhaForQuestionAnswering,
           """text-classification""": NezhaForSequenceClassification,
           """token-classification""": NezhaForTokenClassification,
           """zero-shot""": NezhaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   __UpperCAmelCase : Optional[int]      =			True


   def 			lowercase_      (self     :						Optional[int]       ,  __UpperCAmelCase     :						Optional[int]       ,  __UpperCAmelCase     :						Optional[int]       ,  __UpperCAmelCase     :						Optional[Any]=False		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  super()._prepare_for_class(lowerCAmelCase__       ,  lowerCAmelCase__       ,  return_labels=lowerCAmelCase__		)

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


   def 			lowercase_      (self     :						List[str]		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  NezhaModelTester(self		)
         UpperCAmelCase__								=  ConfigTester(self       ,  config_class=lowerCAmelCase__       ,  hidden_size=3_7		)


   def 			lowercase_      (self     :						int		)						->				Optional[Any]:
         """simple docstring"""



         self.config_tester.run_common_tests()


   def 			lowercase_      (self     :						Dict		)						->				List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*lowerCAmelCase__		)


   def 			lowercase_      (self     :						int		)						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs_for_decoder()
         self.model_tester.create_and_check_model_as_decoder(*lowerCAmelCase__		)


   def 			lowercase_      (self     :						Optional[Any]		)						->				str:
         """simple docstring"""



         (
             UpperCAmelCase__       
         )								=  self.model_tester.prepare_config_and_inputs_for_decoder()

         UpperCAmelCase__								=  None

         self.model_tester.create_and_check_model_as_decoder(
             lowerCAmelCase__       ,  lowerCAmelCase__       ,  lowerCAmelCase__       ,  lowerCAmelCase__       ,  lowerCAmelCase__       ,  lowerCAmelCase__       ,  lowerCAmelCase__       ,  lowerCAmelCase__       ,  lowerCAmelCase__       ,  )


   def 			lowercase_      (self     :						List[str]		)						->				Dict:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__		)


   def 			lowercase_      (self     :						List[str]		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_multiple_choice(*lowerCAmelCase__		)


   def 			lowercase_      (self     :						List[Any]		)						->				Any:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_next_sequence_prediction(*lowerCAmelCase__		)


   def 			lowercase_      (self     :						List[str]		)						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase__		)


   def 			lowercase_      (self     :						Tuple		)						->				Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase__		)


   def 			lowercase_      (self     :						Optional[Any]		)						->				Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase__		)


   def 			lowercase_      (self     :						Dict		)						->				Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__		)


   @slow
   def 			lowercase_      (self     :						Optional[Any]		)						->				List[str]:
         """simple docstring"""



         for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase__								=  NezhaModel.from_pretrained(lowerCAmelCase__		)
               self.assertIsNotNone(lowerCAmelCase__		)





   @slow
   @require_torch_gpu
   def 			lowercase_      (self     :						str		)						->				Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  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

               UpperCAmelCase__								=  True
               UpperCAmelCase__								=  model_class(config=lowerCAmelCase__		)

               UpperCAmelCase__								=  self._prepare_for_class(lowerCAmelCase__       ,  lowerCAmelCase__		)
               UpperCAmelCase__								=  torch.jit.trace(
                   lowerCAmelCase__       ,  (inputs_dict["input_ids"].to("cpu"		), inputs_dict["attention_mask"].to("cpu"		))		)

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





@require_torch
class      A							(				unittest.TestCase     ):


   @slow
   def 			lowercase_      (self     :						Any		)						->				int:
         """simple docstring"""



         UpperCAmelCase__								=  NezhaModel.from_pretrained("sijunhe/nezha-cn-base"		)
         UpperCAmelCase__								=  torch.tensor([[0, 1, 2, 3, 4, 5]]		)
         UpperCAmelCase__								=  torch.tensor([[0, 1, 1, 1, 1, 1]]		)
         with torch.no_grad():
               UpperCAmelCase__								=  model(lowerCAmelCase__       ,  attention_mask=lowerCAmelCase__		)[0]
         UpperCAmelCase__								=  torch.Size((1, 6, 7_6_8)		)
         self.assertEqual(output.shape       ,  lowerCAmelCase__		)
         UpperCAmelCase__								=  torch.tensor([[[0.0685, 0.2441, 0.1102], [0.0600, 0.1906, 0.1349], [0.0221, 0.0819, 0.0586]]]		)

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





   @slow
   def 			lowercase_      (self     :						List[str]		)						->				List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  NezhaForMaskedLM.from_pretrained("sijunhe/nezha-cn-base"		)
         UpperCAmelCase__								=  torch.tensor([[0, 1, 2, 3, 4, 5]]		)
         UpperCAmelCase__								=  torch.tensor([[1, 1, 1, 1, 1, 1]]		)
         with torch.no_grad():
               UpperCAmelCase__								=  model(lowerCAmelCase__       ,  attention_mask=lowerCAmelCase__		)[0]
         UpperCAmelCase__								=  torch.Size((1, 6, 2_1_1_2_8)		)
         self.assertEqual(output.shape       ,  lowerCAmelCase__		)
         UpperCAmelCase__								=  torch.tensor(
             [[-2.7939, -1.7902, -2.2189], [-2.8585, -1.8908, -2.3723], [-2.6499, -1.7750, -2.2558]]		)

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










"""simple docstring"""




import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin


class        __UpperCamelCase				(						a__		,       a__						):






							@register_to_config
							def __init__(   self      ,					*,
							    lowerCAmelCase__ = 4      ,					lowerCAmelCase__ = 768      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					)	-> Optional[Any]:
								super().__init__()

								a      :		Tuple	=				nn.Parameter(torch.zeros(lowerCAmelCase__      )      )

								# parameters for additional clip time embeddings
								a      :		str	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		Any	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )

								# parameters for encoder hidden states
								a      :		int	=				clip_extra_context_tokens
								a      :		int	=				nn.Linear(
								    lowerCAmelCase__      ,					self.clip_extra_context_tokens * cross_attention_dim      )
								a      :		Any	=				nn.Linear(lowerCAmelCase__      ,					lowerCAmelCase__      )
								a      :		str	=				nn.LayerNorm(lowerCAmelCase__      )






							def     __a   (   self      ,					*, lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      )	-> Union[str, Any]:
								if do_classifier_free_guidance:
									# Add the classifier free guidance embeddings to the image embeddings
									a      :		str	=				image_embeddings.shape[0]
									a      :		Optional[int]	=				self.learned_classifier_free_guidance_embeddings.unsqueeze(0      )
									a      :		Any	=				classifier_free_guidance_embeddings.expand(
									    lowerCAmelCase__      ,					-1      )
									a      :		Any	=				torch.cat([classifier_free_guidance_embeddings, image_embeddings]      ,					dim=0      )

								# The image embeddings batch size and the text embeddings batch size are equal
								assert image_embeddings.shape[0] == prompt_embeds.shape[0]

								a      :		List[str]	=				prompt_embeds.shape[0]

								# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
								# adding CLIP embeddings to the existing timestep embedding, ...
								a      :		Dict	=				self.embedding_proj(lowerCAmelCase__      )
								a      :		List[str]	=				self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase__      )
								a      :		Dict	=				time_projected_image_embeddings + time_projected_prompt_embeds

								# ... and by projecting CLIP embeddings into four
								# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
								a      :		Union[str, Any]	=				self.clip_extra_context_tokens_proj(lowerCAmelCase__      )
								a      :		List[str]	=				clip_extra_context_tokens.reshape(lowerCAmelCase__      ,					-1      ,					self.clip_extra_context_tokens      )
								a      :		Optional[Any]	=				clip_extra_context_tokens.permute(0      ,					2      ,					1      )

								a      :		Optional[int]	=				self.encoder_hidden_states_proj(lowerCAmelCase__      )
								a      :		str	=				self.text_encoder_hidden_states_norm(lowerCAmelCase__      )
								a      :		List[str]	=				torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]      ,					dim=1      )

								return text_encoder_hidden_states, additive_clip_time_embeddings


