

'''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			:					Any					,      lowercase_			:					List[Any]					,      lowercase_			:					List[str]=13					,      lowercase_			:					str=7					,      lowercase_			:					List[str]=True					,      lowercase_			:					List[Any]=True					,      lowercase_			:					List[Any]=True					,      lowercase_			:					Union[str, Any]=True					,      lowercase_			:					int=99					,      lowercase_			:					Dict=32					,      lowercase_			:					List[Any]=5					,      lowercase_			:					int=4					,      lowercase_			:					Tuple=37					,      lowercase_			:					Union[str, Any]="gelu"					,      lowercase_			:					str=0.1					,      lowercase_			:					Union[str, Any]=0.1					,      lowercase_			:					Any=128					,      lowercase_			:					Any=32					,      lowercase_			:					Optional[Any]=16					,      lowercase_			:					Optional[int]=2					,      lowercase_			:					Union[str, Any]=0.02					,      lowercase_			:					Dict=3					,      lowercase_			:					Any=4					,      lowercase_			:					List[Any]=None					,      )						->     Union[str, Any]:
      UpperCAmelCase	:     Optional[int]						 =			parent
      UpperCAmelCase	:     Dict						 =			batch_size
      UpperCAmelCase	:     Any						 =			seq_length
      UpperCAmelCase	:     Tuple						 =			is_training
      UpperCAmelCase	:     Dict						 =			use_input_mask
      UpperCAmelCase	:     int						 =			use_token_type_ids
      UpperCAmelCase	:     Any						 =			use_labels
      UpperCAmelCase	:     List[str]						 =			vocab_size
      UpperCAmelCase	:     Optional[Any]						 =			hidden_size
      UpperCAmelCase	:     List[str]						 =			num_hidden_layers
      UpperCAmelCase	:     List[str]						 =			num_attention_heads
      UpperCAmelCase	:     List[str]						 =			intermediate_size
      UpperCAmelCase	:     Union[str, Any]						 =			hidden_act
      UpperCAmelCase	:     int						 =			hidden_dropout_prob
      UpperCAmelCase	:     Optional[int]						 =			attention_probs_dropout_prob
      UpperCAmelCase	:     List[Any]						 =			max_position_embeddings
      UpperCAmelCase	:     Tuple						 =			type_vocab_size
      UpperCAmelCase	:     Dict						 =			type_sequence_label_size
      UpperCAmelCase	:     List[str]						 =			initializer_range
      UpperCAmelCase	:     List[str]						 =			num_labels
      UpperCAmelCase	:     Optional[int]						 =			num_choices
      UpperCAmelCase	:     Tuple						 =			scope


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     int:
      UpperCAmelCase	:     str						 =			ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size	)

      UpperCAmelCase	:     List[str]						 =			None
      if self.use_input_mask:
       UpperCAmelCase	:     Dict						 =			random_attention_mask([self.batch_size, self.seq_length]	)

      UpperCAmelCase	:     Tuple						 =			None
      if self.use_token_type_ids:
       UpperCAmelCase	:     List[str]						 =			ids_tensor([self.batch_size, self.seq_length]					,      self.type_vocab_size	)

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

      UpperCAmelCase	:     Tuple						 =			self.get_config()

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


     def        UpperCAmelCase_	(		self			:					Tuple	)						->     Any:
      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=lowercase_					,      initializer_range=self.initializer_range					,      )


     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Optional[Any]:
      (
          (
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			
      )	:     str						 =			self.prepare_config_and_inputs()

      UpperCAmelCase	:     List[str]						 =			True
      UpperCAmelCase	:     List[str]						 =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
      UpperCAmelCase	:     Union[str, Any]						 =			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        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					Tuple					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					str					,      lowercase_			:					Tuple					,      lowercase_			:					Optional[int]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					int	)						->     Optional[int]:
      UpperCAmelCase	:     Optional[int]						 =			NezhaModel(config=lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     Tuple						 =			model(lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_	)
      UpperCAmelCase	:     Union[str, Any]						 =			model(lowercase_					,      token_type_ids=lowercase_	)
      UpperCAmelCase	:     Optional[int]						 =			model(lowercase_	)
      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        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					Dict					,      lowercase_			:					Tuple					,      lowercase_			:					List[Any]					,      lowercase_			:					Dict					,      lowercase_			:					List[str]					,      lowercase_			:					List[Any]					,      lowercase_			:					Dict					,      lowercase_			:					List[str]					,      lowercase_			:					int					,      )						->     Dict:
      UpperCAmelCase	:     List[str]						 =			True
      UpperCAmelCase	:     Tuple						 =			NezhaModel(lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     Union[str, Any]						 =			model(
          lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_					,      encoder_hidden_states=lowercase_					,      encoder_attention_mask=lowercase_					,      )
      UpperCAmelCase	:     List[str]						 =			model(
          lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_					,      encoder_hidden_states=lowercase_					,      )
      UpperCAmelCase	:     List[Any]						 =			model(lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_	)
      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        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					List[str]					,      lowercase_			:					Dict					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					str					,      lowercase_			:					Optional[int]					,      lowercase_			:					List[str]	)						->     Optional[Any]:
      UpperCAmelCase	:     int						 =			NezhaForMaskedLM(config=lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     List[Any]						 =			model(lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_					,      labels=lowercase_	)
      self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.vocab_size)	)


     def        UpperCAmelCase_	(		self			:					Tuple					,      lowercase_			:					Any					,      lowercase_			:					Optional[int]					,      lowercase_			:					Tuple					,      lowercase_			:					int					,      lowercase_			:					Dict					,      lowercase_			:					Tuple					,      lowercase_			:					Dict	)						->     List[str]:
      UpperCAmelCase	:     List[Any]						 =			NezhaForNextSentencePrediction(config=lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     Union[str, Any]						 =			model(
          lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_					,      labels=lowercase_					,      )
      self.parent.assertEqual(result.logits.shape					,      (self.batch_size, 2)	)


     def        UpperCAmelCase_	(		self			:					Optional[Any]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					Any					,      lowercase_			:					Any					,      lowercase_			:					List[Any]					,      lowercase_			:					List[str]	)						->     List[Any]:
      UpperCAmelCase	:     Optional[int]						 =			NezhaForPreTraining(config=lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     Tuple						 =			model(
          lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_					,      labels=lowercase_					,      next_sentence_label=lowercase_					,      )
      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        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					Dict					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					str					,      lowercase_			:					Any					,      lowercase_			:					Optional[Any]					,      lowercase_			:					Optional[int]					,      lowercase_			:					Optional[int]	)						->     Tuple:
      UpperCAmelCase	:     Dict						 =			NezhaForQuestionAnswering(config=lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     List[Any]						 =			model(
          lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_					,      start_positions=lowercase_					,      end_positions=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			:					int					,      lowercase_			:					Optional[Any]					,      lowercase_			:					List[Any]					,      lowercase_			:					List[str]					,      lowercase_			:					Optional[int]					,      lowercase_			:					Dict					,      lowercase_			:					Tuple					,      lowercase_			:					Optional[Any]	)						->     Union[str, Any]:
      UpperCAmelCase	:     Optional[int]						 =			self.num_labels
      UpperCAmelCase	:     Dict						 =			NezhaForSequenceClassification(lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     Optional[Any]						 =			model(lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_					,      labels=lowercase_	)
      self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)	)


     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					Dict					,      lowercase_			:					Tuple					,      lowercase_			:					int					,      lowercase_			:					Dict					,      lowercase_			:					Optional[Any]					,      lowercase_			:					Optional[Any]					,      lowercase_			:					int	)						->     Optional[int]:
      UpperCAmelCase	:     int						 =			self.num_labels
      UpperCAmelCase	:     List[str]						 =			NezhaForTokenClassification(config=lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     Optional[int]						 =			model(lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_					,      labels=lowercase_	)
      self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.seq_length, self.num_labels)	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					str					,      lowercase_			:					Tuple					,      lowercase_			:					List[Any]					,      lowercase_			:					List[str]					,      lowercase_			:					Tuple					,      lowercase_			:					Dict					,      lowercase_			:					str	)						->     Optional[int]:
      UpperCAmelCase	:     Tuple						 =			self.num_choices
      UpperCAmelCase	:     List[str]						 =			NezhaForMultipleChoice(config=lowercase_	)
      model.to(lowercase_	)
      model.eval()
      UpperCAmelCase	:     str						 =			input_ids.unsqueeze(1	).expand(-1					,      self.num_choices					,      -1	).contiguous()
      UpperCAmelCase	:     Dict						 =			token_type_ids.unsqueeze(1	).expand(-1					,      self.num_choices					,      -1	).contiguous()
      UpperCAmelCase	:     Dict						 =			input_mask.unsqueeze(1	).expand(-1					,      self.num_choices					,      -1	).contiguous()
      UpperCAmelCase	:     Union[str, Any]						 =			model(
          lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_					,      labels=lowercase_					,      )
      self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_choices)	)




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




@require_torch
class 	A_			(    _snake_case	,       _snake_case	,       _snake_case	,       unittest.TestCase							):






     '''simple docstring'''



     UpperCAmelCase_						:     Dict  		=  (
         (
             NezhaModel,
             NezhaForMaskedLM,
             NezhaForMultipleChoice,
             NezhaForNextSentencePrediction,
             NezhaForPreTraining,
             NezhaForQuestionAnswering,
             NezhaForSequenceClassification,
             NezhaForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     UpperCAmelCase_						:     Optional[int]  		=  (
         {
             """feature-extraction""": NezhaModel,
             """fill-mask""": NezhaForMaskedLM,
             """question-answering""": NezhaForQuestionAnswering,
             """text-classification""": NezhaForSequenceClassification,
             """token-classification""": NezhaForTokenClassification,
             """zero-shot""": NezhaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     UpperCAmelCase_						:     Union[str, Any]  		=  True


     def        UpperCAmelCase_	(		self			:					Optional[Any]					,      lowercase_			:					int					,      lowercase_			:					Any					,      lowercase_			:					List[Any]=False	)						->     Tuple:
      UpperCAmelCase	:     Dict						 =			super()._prepare_for_class(lowercase_					,      lowercase_					,      return_labels=lowercase_	)

      if return_labels:
       if model_class in get_values(lowercase_	):
        UpperCAmelCase	:     Tuple						 =			torch.zeros(
            (self.model_tester.batch_size, self.model_tester.seq_length)					,      dtype=torch.long					,      device=lowercase_	)
        UpperCAmelCase	:     Union[str, Any]						 =			torch.zeros(
            self.model_tester.batch_size					,      dtype=torch.long					,      device=lowercase_	)
      return inputs_dict


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     Any:
      UpperCAmelCase	:     int						 =			NezhaModelTester(self	)
      UpperCAmelCase	:     Tuple						 =			ConfigTester(self					,      config_class=lowercase_					,      hidden_size=37	)


     def        UpperCAmelCase_	(		self			:					Dict	)						->     Tuple:
      self.config_tester.run_common_tests()


     def        UpperCAmelCase_	(		self			:					Tuple	)						->     List[str]:
      UpperCAmelCase	:     Tuple						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowercase_	)


     def        UpperCAmelCase_	(		self			:					Tuple	)						->     Dict:
      UpperCAmelCase	:     Dict						 =			self.model_tester.prepare_config_and_inputs_for_decoder()
      self.model_tester.create_and_check_model_as_decoder(*lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     str:
      # This regression test was failing with PyTorch < 1.3
      (
          (
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			(
          UpperCAmelCase
      ) ,			
      )	:     Optional[int]						 =			self.model_tester.prepare_config_and_inputs_for_decoder()

      UpperCAmelCase	:     str						 =			None

      self.model_tester.create_and_check_model_as_decoder(
          lowercase_					,      lowercase_					,      lowercase_					,      lowercase_					,      lowercase_					,      lowercase_					,      lowercase_					,      lowercase_					,      lowercase_					,      )


     def        UpperCAmelCase_	(		self			:					List[str]	)						->     int:
      UpperCAmelCase	:     int						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]	)						->     Optional[int]:
      UpperCAmelCase	:     Optional[Any]						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*lowercase_	)


     def        UpperCAmelCase_	(		self			:					List[str]	)						->     Optional[Any]:
      UpperCAmelCase	:     Optional[Any]						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_next_sequence_prediction(*lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     List[str]:
      UpperCAmelCase	:     Union[str, Any]						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_pretraining(*lowercase_	)


     def        UpperCAmelCase_	(		self			:					Tuple	)						->     List[str]:
      UpperCAmelCase	:     List[Any]						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*lowercase_	)


     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Any:
      UpperCAmelCase	:     str						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*lowercase_	)


     def        UpperCAmelCase_	(		self			:					Any	)						->     Optional[int]:
      UpperCAmelCase	:     int						 =			self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*lowercase_	)


     @slow
     def        UpperCAmelCase_	(		self			:					Tuple	)						->     List[str]:
      for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCAmelCase	:     str						 =			NezhaModel.from_pretrained(lowercase_	)
       self.assertIsNotNone(lowercase_	)




     @slow
     @require_torch_gpu
     def        UpperCAmelCase_	(		self			:					str	)						->     Dict:
      UpperCAmelCase ,			UpperCAmelCase	:     Dict						 =			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	:     Any						 =			True
       UpperCAmelCase	:     Optional[Any]						 =			model_class(config=lowercase_	)

       UpperCAmelCase	:     Dict						 =			self._prepare_for_class(lowercase_					,      lowercase_	)
       UpperCAmelCase	:     int						 =			torch.jit.trace(
           lowercase_					,      (inputs_dict['input_ids'].to('cpu'	), inputs_dict['attention_mask'].to('cpu'	))	)

       with tempfile.TemporaryDirectory() as tmp:
        torch.jit.save(lowercase_					,      os.path.join(lowercase_					,      'bert.pt'	)	)
        UpperCAmelCase	:     Union[str, Any]						 =			torch.jit.load(os.path.join(lowercase_					,      'bert.pt'	)					,      map_location=lowercase_	)
        loaded(inputs_dict['input_ids'].to(lowercase_	)					,      inputs_dict['attention_mask'].to(lowercase_	)	)




@require_torch
class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     @slow
     def        UpperCAmelCase_	(		self			:					str	)						->     Union[str, Any]:
      UpperCAmelCase	:     Optional[int]						 =			NezhaModel.from_pretrained('sijunhe/nezha-cn-base'	)
      UpperCAmelCase	:     List[str]						 =			torch.tensor([[0, 1, 2, 3, 4, 5]]	)
      UpperCAmelCase	:     Dict						 =			torch.tensor([[0, 1, 1, 1, 1, 1]]	)
      with torch.no_grad():
       UpperCAmelCase	:     List[str]						 =			model(lowercase_					,      attention_mask=lowercase_	)[0]
      UpperCAmelCase	:     List[str]						 =			torch.Size((1, 6, 768)	)
      self.assertEqual(output.shape					,      lowercase_	)
      UpperCAmelCase	:     List[str]						 =			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]					,      lowercase_					,      atol=1E-4	)	)




     @slow
     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Tuple:
      UpperCAmelCase	:     Any						 =			NezhaForMaskedLM.from_pretrained('sijunhe/nezha-cn-base'	)
      UpperCAmelCase	:     Optional[int]						 =			torch.tensor([[0, 1, 2, 3, 4, 5]]	)
      UpperCAmelCase	:     Tuple						 =			torch.tensor([[1, 1, 1, 1, 1, 1]]	)
      with torch.no_grad():
       UpperCAmelCase	:     Union[str, Any]						 =			model(lowercase_					,      attention_mask=lowercase_	)[0]
      UpperCAmelCase	:     Tuple						 =			torch.Size((1, 6, 21_128)	)
      self.assertEqual(output.shape					,      lowercase_	)
      UpperCAmelCase	:     Optional[int]						 =			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]					,      lowercase_					,      atol=1E-4	)	)







'''simple docstring'''

import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


lowercase__         =      {
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

lowercase__         =      logging.get_logger(__name__)




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Any  		=  """maskformer"""
     UpperCAmelCase_						:     Union[str, Any]  		=  {"""hidden_size""": """mask_feature_size"""}
     UpperCAmelCase_						:     Union[str, Any]  		=  ["""resnet""", """swin"""]
     UpperCAmelCase_						:     Optional[int]  		=  ["""detr"""]


     def __init__(		self			:					Union[str, Any]					,      lowercase_			:					int = 256					,      lowercase_			:					int = 256					,      lowercase_			:					float = 0.1					,      lowercase_			:					bool = False					,      lowercase_			:					Optional[Dict] = None					,      lowercase_			:					Optional[Dict] = None					,      lowercase_			:					float = 0.02					,      lowercase_			:					float = 1.0					,      lowercase_			:					float = 1.0					,      lowercase_			:					float = 1.0					,      lowercase_			:					float = 20.0					,      lowercase_			:					Optional[bool] = None					,      **lowercase_			:					int					,      )						->     Optional[Any]:
      if backbone_config is None:
       # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
       UpperCAmelCase	:     List[Any]						 =			SwinConfig(
           image_size=384					,      in_channels=3					,      patch_size=4					,      embed_dim=128					,      depths=[2, 2, 18, 2]					,      num_heads=[4, 8, 16, 32]					,      window_size=12					,      drop_path_rate=0.3					,      out_features=['stage1', 'stage2', 'stage3', 'stage4']					,      )

      if isinstance(lowercase_					,      lowercase_	):
       UpperCAmelCase	:     List[Any]						 =			backbone_config.pop('model_type'	)
       UpperCAmelCase	:     Optional[Any]						 =			CONFIG_MAPPING[backbone_model_type]
       UpperCAmelCase	:     List[Any]						 =			config_class.from_dict(lowercase_	)

      # verify that the backbone is supported
      if backbone_config.model_type not in self.backbones_supported:
       logger.warning_once(
           f"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. """
           f"""Supported model types: {",".join(self.backbones_supported	)}"""	)

      if decoder_config is None:
       # fall back to https://huggingface.co/facebook/detr-resnet-50
       UpperCAmelCase	:     Dict						 =			DetrConfig()
      else:
       # verify that the decoder is supported
       UpperCAmelCase	:     Tuple						 =			(
           decoder_config.pop('model_type'	) if isinstance(lowercase_					,      lowercase_	) else decoder_config.model_type
       )
       if decoder_type not in self.decoders_supported:
        raise ValueError(
            f"""Transformer Decoder {decoder_type} not supported, please use one of"""
            f""" {",".join(self.decoders_supported	)}"""	)
       if isinstance(lowercase_					,      lowercase_	):
        UpperCAmelCase	:     Union[str, Any]						 =			CONFIG_MAPPING[decoder_type]
        UpperCAmelCase	:     str						 =			config_class.from_dict(lowercase_	)

      UpperCAmelCase	:     int						 =			backbone_config
      UpperCAmelCase	:     Tuple						 =			decoder_config
      # main feature dimension for the model
      UpperCAmelCase	:     Union[str, Any]						 =			fpn_feature_size
      UpperCAmelCase	:     Optional[Any]						 =			mask_feature_size
      # initializer
      UpperCAmelCase	:     Union[str, Any]						 =			init_std
      UpperCAmelCase	:     List[str]						 =			init_xavier_std
      # Hungarian matcher && loss
      UpperCAmelCase	:     int						 =			cross_entropy_weight
      UpperCAmelCase	:     Tuple						 =			dice_weight
      UpperCAmelCase	:     int						 =			mask_weight
      UpperCAmelCase	:     Any						 =			use_auxiliary_loss
      UpperCAmelCase	:     Tuple						 =			no_object_weight
      UpperCAmelCase	:     str						 =			output_auxiliary_logits

      UpperCAmelCase	:     Dict						 =			self.decoder_config.encoder_attention_heads
      UpperCAmelCase	:     Union[str, Any]						 =			self.decoder_config.num_hidden_layers
      super().__init__(**lowercase_	)


     @classmethod
     def        UpperCAmelCase_	(		cls			:					Any					,      lowercase_			:					PretrainedConfig					,      lowercase_			:					PretrainedConfig					,      **lowercase_			:					Optional[Any]	)						->     str:
      return cls(
          backbone_config=lowercase_					,      decoder_config=lowercase_					,      **lowercase_					,      )




     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Dict[str, any]:
      UpperCAmelCase	:     Optional[int]						 =			copy.deepcopy(self.__dict__	)
      UpperCAmelCase	:     List[str]						 =			self.backbone_config.to_dict()
      UpperCAmelCase	:     Any						 =			self.decoder_config.to_dict()
      UpperCAmelCase	:     Union[str, Any]						 =			self.__class__.model_type
      return output




