code: 
  
  
  import unittest
  
  from transformers import ElectraConfig, is_torch_available
  from transformers.models.auto import get_values
  from transformers.testing_utils import require_torch, slow, torch_device
  
  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,
          ElectraForCausalLM,
          ElectraForMaskedLM,
          ElectraForMultipleChoice,
          ElectraForPreTraining,
          ElectraForQuestionAnswering,
          ElectraForSequenceClassification,
          ElectraForTokenClassification,
          ElectraModel,
      )
      from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST
  
  
  
  
  
  
  
  class   lowerCAmelCase__ :
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase=1_3				,		__lowerCamelCase=7				,		__lowerCamelCase=True				,		__lowerCamelCase=True				,		__lowerCamelCase=True				,		__lowerCamelCase=True				,		__lowerCamelCase=9_9				,		__lowerCamelCase=3_2				,		__lowerCamelCase=5				,		__lowerCamelCase=4				,		__lowerCamelCase=3_7				,		__lowerCamelCase="gelu"				,		__lowerCamelCase=0.1				,		__lowerCamelCase=0.1				,		__lowerCamelCase=5_1_2				,		__lowerCamelCase=1_6				,		__lowerCamelCase=2				,		__lowerCamelCase=0.0_2				,		__lowerCamelCase=3				,		__lowerCamelCase=4				,		__lowerCamelCase=None				,		)    ->       Optional[int]:
              _A   : Any       					=				parent
              _A   : Union[str, Any]       					=				batch_size
              _A   : Dict       					=				seq_length
              _A   : Union[str, Any]       					=				is_training
              _A   : Any       					=				use_input_mask
              _A   : List[str]       					=				use_token_type_ids
              _A   : Optional[int]       					=				use_labels
              _A   : int       					=				vocab_size
              _A   : Tuple       					=				hidden_size
              _A   : List[Any]       					=				num_hidden_layers
              _A   : Optional[Any]       					=				num_attention_heads
              _A   : List[Any]       					=				intermediate_size
              _A   : Tuple       					=				hidden_act
              _A   : Optional[int]       					=				hidden_dropout_prob
              _A   : Dict       					=				attention_probs_dropout_prob
              _A   : List[Any]       					=				max_position_embeddings
              _A   : Tuple       					=				type_vocab_size
              _A   : Dict       					=				type_sequence_label_size
              _A   : Union[str, Any]       					=				initializer_range
              _A   : Optional[Any]       					=				num_labels
              _A   : Any       					=				num_choices
              _A   : str       					=				scope
  
  
       def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
              _A   : List[str]       					=				ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size)
  
              _A   : Optional[int]       					=				None
              if self.use_input_mask:
                     _A   : Any       					=				random_attention_mask([self.batch_size, self.seq_length])
  
              _A   : Tuple       					=				None
              if self.use_token_type_ids:
                     _A   : Tuple       					=				ids_tensor([self.batch_size, self.seq_length]				,		self.type_vocab_size)
  
              _A   : Any       					=				None
              _A   : Union[str, Any]       					=				None
              _A   : Optional[int]       					=				None
              if self.use_labels:
                     _A   : Optional[int]       					=				ids_tensor([self.batch_size]				,		self.type_sequence_label_size)
                     _A   : List[str]       					=				ids_tensor([self.batch_size, self.seq_length]				,		self.num_labels)
                     _A   : List[Any]       					=				ids_tensor([self.batch_size]				,		self.num_choices)
                     _A   : Union[str, Any]       					=				ids_tensor([self.batch_size, self.seq_length]				,		1)
  
              _A   : Optional[int]       					=				self.get_config()
  
              return (
                  config,
                  input_ids,
                  token_type_ids,
                  input_mask,
                  sequence_labels,
                  token_labels,
                  choice_labels,
                  fake_token_labels,
              )
  
  
       def 				_lowerCamelCase				(		self)    ->       str:
              return ElectraConfig(
                  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 				_lowerCamelCase				(		self)    ->       List[Any]:
              (
                  (
                  _A
              )					,    (
                  _A
              )					,    (
                  _A
              )					,    (
                  _A
              )					,    (
                  _A
              )					,    (
                  _A
              )					,    (
                  _A
              )					,    (
                  _A
              )					,    
              )   : Optional[int]       					=				self.prepare_config_and_inputs()
  
              _A   : int       					=				True
              _A   : List[Any]       					=				floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
              _A   : Optional[int]       					=				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 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		)    ->       Optional[Any]:
              _A   : List[Any]       					=				ElectraModel(config=__lowerCamelCase)
              model.to(__lowerCamelCase)
              model.eval()
              _A   : Optional[int]       					=				model(__lowerCamelCase				,		attention_mask=__lowerCamelCase				,		token_type_ids=__lowerCamelCase)
              _A   : str       					=				model(__lowerCamelCase				,		token_type_ids=__lowerCamelCase)
              _A   : List[str]       					=				model(__lowerCamelCase)
              self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size))
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		)    ->       str:
              _A   : Optional[Any]       					=				True
              _A   : str       					=				ElectraModel(__lowerCamelCase)
              model.to(__lowerCamelCase)
              model.eval()
              _A   : Any       					=				model(
                  __lowerCamelCase				,		attention_mask=__lowerCamelCase				,		token_type_ids=__lowerCamelCase				,		encoder_hidden_states=__lowerCamelCase				,		encoder_attention_mask=__lowerCamelCase				,		)
              _A   : Dict       					=				model(
                  __lowerCamelCase				,		attention_mask=__lowerCamelCase				,		token_type_ids=__lowerCamelCase				,		encoder_hidden_states=__lowerCamelCase				,		)
              _A   : Tuple       					=				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))
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		)    ->       Union[str, Any]:
              _A   : Optional[Any]       					=				ElectraForMaskedLM(config=__lowerCamelCase)
              model.to(__lowerCamelCase)
              model.eval()
              _A   : Union[str, Any]       					=				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 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		)    ->       Optional[Any]:
              _A   : List[str]       					=				ElectraForCausalLM(config=__lowerCamelCase)
              model.to(__lowerCamelCase)
              model.eval()
              _A   : Optional[Any]       					=				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 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		)    ->       Optional[Any]:
              _A   : Any       					=				self.num_labels
              _A   : Tuple       					=				ElectraForTokenClassification(config=__lowerCamelCase)
              model.to(__lowerCamelCase)
              model.eval()
              _A   : Any       					=				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 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		)    ->       int:
              _A   : Optional[int]       					=				self.num_labels
              _A   : List[str]       					=				ElectraForPreTraining(config=__lowerCamelCase)
              model.to(__lowerCamelCase)
              model.eval()
              _A   : Any       					=				model(__lowerCamelCase				,		attention_mask=__lowerCamelCase				,		token_type_ids=__lowerCamelCase				,		labels=__lowerCamelCase)
              self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.seq_length))
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		)    ->       str:
              _A   : Union[str, Any]       					=				self.num_labels
              _A   : List[str]       					=				ElectraForSequenceClassification(__lowerCamelCase)
              model.to(__lowerCamelCase)
              model.eval()
              _A   : List[Any]       					=				model(__lowerCamelCase				,		attention_mask=__lowerCamelCase				,		token_type_ids=__lowerCamelCase				,		labels=__lowerCamelCase)
              self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_labels))
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		)    ->       Dict:
              _A   : Optional[Any]       					=				ElectraForQuestionAnswering(config=__lowerCamelCase)
              model.to(__lowerCamelCase)
              model.eval()
              _A   : Optional[int]       					=				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 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase				,		)    ->       Dict:
              _A   : List[str]       					=				self.num_choices
              _A   : Optional[Any]       					=				ElectraForMultipleChoice(config=__lowerCamelCase)
              model.to(__lowerCamelCase)
              model.eval()
              _A   : Union[str, Any]       					=				input_ids.unsqueeze(1).expand(-1				,		self.num_choices				,		-1).contiguous()
              _A   : List[str]       					=				token_type_ids.unsqueeze(1).expand(-1				,		self.num_choices				,		-1).contiguous()
              _A   : Dict       					=				input_mask.unsqueeze(1).expand(-1				,		self.num_choices				,		-1).contiguous()
              _A   : Union[str, Any]       					=				model(
                  __lowerCamelCase				,		attention_mask=__lowerCamelCase				,		token_type_ids=__lowerCamelCase				,		labels=__lowerCamelCase				,		)
              self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_choices))
  
  
       def 				_lowerCamelCase				(		self)    ->       Optional[Any]:
              _A   : Optional[Any]       					=				self.prepare_config_and_inputs()
              (
                  (
                  _A
              )					,    (
                  _A
              )					,    (
                  _A
              )					,    (
                  _A
              )					,    (
                  _A
              )					,    (
                  _A
              )					,    (
                  _A
              )					,    (
                  _A
              )					,    
              )   : Tuple       					=				config_and_inputs
              _A   : Optional[int]       					=				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
              return config, inputs_dict
  
  
  
  
  
  
  
  @require_torch
  class   lowerCAmelCase__ (       a  ,					a  ,					unittest.TestCase):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				(
           (
               ElectraModel,
               ElectraForPreTraining,
               ElectraForMaskedLM,
               ElectraForCausalLM,
               ElectraForMultipleChoice,
               ElectraForTokenClassification,
               ElectraForSequenceClassification,
               ElectraForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       __SCREAMING_SNAKE_CASE						    =				(
           {
               "feature-extraction": ElectraModel,
               "fill-mask": ElectraForMaskedLM,
               "question-answering": ElectraForQuestionAnswering,
               "text-classification": ElectraForSequenceClassification,
               "text-generation": ElectraForCausalLM,
               "token-classification": ElectraForTokenClassification,
               "zero-shot": ElectraForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       __SCREAMING_SNAKE_CASE						    =				True
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase				,		__lowerCamelCase=False)    ->       str:
              _A   : str       					=				super()._prepare_for_class(__lowerCamelCase				,		__lowerCamelCase				,		return_labels=__lowerCamelCase)
  
              if return_labels:
                     if model_class in get_values(__lowerCamelCase):
                            _A   : Optional[int]       					=				torch.zeros(
                                (self.model_tester.batch_size, self.model_tester.seq_length)				,		dtype=torch.long				,		device=__lowerCamelCase)
              return inputs_dict
  
  
       def 				_lowerCamelCase				(		self)    ->       Optional[int]:
              _A   : Optional[Any]       					=				ElectraModelTester(self)
              _A   : Dict       					=				ConfigTester(self				,		config_class=__lowerCamelCase				,		hidden_size=3_7)
  
  
       def 				_lowerCamelCase				(		self)    ->       Tuple:
              self.config_tester.run_common_tests()
  
  
       def 				_lowerCamelCase				(		self)    ->       int:
              _A   : Optional[int]       					=				self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_model(*__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       int:
              _A   : List[str]       					=				self.model_tester.prepare_config_and_inputs_for_decoder()
              self.model_tester.create_and_check_electra_model_as_decoder(*__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       Dict:
              _A   : Tuple       					=				self.model_tester.prepare_config_and_inputs()
              for type in ["absolute", "relative_key", "relative_key_query"]:
                     _A   : List[str]       					=				type
                     self.model_tester.create_and_check_electra_model(*__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       List[str]:
              _A   : List[Any]       					=				self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_for_masked_lm(*__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       List[Any]:
              _A   : int       					=				self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_for_token_classification(*__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       Dict:
              _A   : Dict       					=				self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_for_pretraining(*__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       List[Any]:
              _A   : Dict       					=				self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_for_sequence_classification(*__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       List[str]:
              _A   : Dict       					=				self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_for_question_answering(*__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       str:
              _A   : Optional[int]       					=				self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_electra_for_multiple_choice(*__lowerCamelCase)
  
  
       @slow
       def 				_lowerCamelCase				(		self)    ->       List[str]:
              for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     _A   : int       					=				ElectraModel.from_pretrained(__lowerCamelCase)
                     self.assertIsNotNone(__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self)    ->       Tuple:
              _A   : int       					=				self.model_tester.prepare_config_and_inputs_for_decoder()
              self.model_tester.create_and_check_electra_for_causal_lm(*__lowerCamelCase)
  
  
  
  
  
  
  
  @require_torch
  class   lowerCAmelCase__ (       unittest.TestCase):
  
  
  
  
       '''simple docstring'''
  
  
       @slow
       def 				_lowerCamelCase				(		self)    ->       List[Any]:
              _A   : Tuple       					=				ElectraModel.from_pretrained("google/electra-small-discriminator")
              _A   : int       					=				torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]])
              _A   : int       					=				torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
              _A   : str       					=				model(__lowerCamelCase				,		attention_mask=__lowerCamelCase)[0]
              _A   : Any       					=				torch.Size((1, 1_1, 2_5_6))
              self.assertEqual(output.shape				,		__lowerCamelCase)
              _A   : List[Any]       					=				torch.tensor(
                  [[[0.4_4_7_1, 0.6_8_2_1, -0.3_2_6_5], [0.4_6_2_7, 0.5_2_5_5, -0.3_6_6_8], [0.4_5_3_2, 0.3_3_1_3, -0.4_3_4_4]]])
  
              self.assertTrue(torch.allclose(output[:, 1:4, 1:4]				,		__lowerCamelCase				,		atol=1e-4))
  
style_context: 
  
  
  import argparse
  import json
  
  import torch
  
  from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Union[str, Any]       ,							UpperCamelCase__  :						Dict		):
         _A   : Dict       					=				UNetaDModel(**UpperCamelCase__		)
         _A   : Any       					=				checkpoint["all_modules.0.W"].data
         _A   : Any       					=				checkpoint["all_modules.0.W"].data
         _A   : Union[str, Any]       					=				checkpoint["all_modules.1.weight"].data
         _A   : Optional[Any]       					=				checkpoint["all_modules.1.bias"].data
  
         _A   : Dict       					=				checkpoint["all_modules.2.weight"].data
         _A   : List[str]       					=				checkpoint["all_modules.2.bias"].data
  
         _A   : Dict       					=				checkpoint["all_modules.3.weight"].data
         _A   : Union[str, Any]       					=				checkpoint["all_modules.3.bias"].data
  
         _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-4]].data
         _A   : Any       					=				checkpoint[list(checkpoint.keys()		)[-3]].data
         _A   : List[str]       					=				checkpoint[list(checkpoint.keys()		)[-2]].data
         _A   : Optional[int]       					=				checkpoint[list(checkpoint.keys()		)[-1]].data
  
         _A   : str       					=				4
  
         def set_attention_weights(UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Optional[Any]       ,							UpperCamelCase__  :						Tuple		):
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.W"].data.T
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_1.W"].data.T
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.NIN_2.W"].data.T
  
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_0.b"].data
                _A   : Any       					=				old_checkpoint[f"all_modules.{index}.NIN_1.b"].data
                _A   : List[str]       					=				old_checkpoint[f"all_modules.{index}.NIN_2.b"].data
  
                _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.NIN_3.W"].data.T
                _A   : Dict       					=				old_checkpoint[f"all_modules.{index}.NIN_3.b"].data
  
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
         def set_resnet_weights(UpperCamelCase__  :						str       ,							UpperCamelCase__  :						Any       ,							UpperCamelCase__  :						Dict		):
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.weight"].data
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_0.bias"].data
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.weight"].data
                _A   : int       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_0.bias"].data
  
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.weight"].data
                _A   : Optional[int]       					=				old_checkpoint[f"all_modules.{index}.Conv_1.bias"].data
                _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.weight"].data
                _A   : str       					=				old_checkpoint[f"all_modules.{index}.GroupNorm_1.bias"].data
  
                _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Dense_0.weight"].data
                _A   : Union[str, Any]       					=				old_checkpoint[f"all_modules.{index}.Dense_0.bias"].data
  
                if new_layer.in_channels != new_layer.out_channels or new_layer.up or new_layer.down:
                       _A   : Tuple       					=				old_checkpoint[f"all_modules.{index}.Conv_2.weight"].data
                       _A   : Optional[Any]       					=				old_checkpoint[f"all_modules.{index}.Conv_2.bias"].data
  
         for i, block in enumerate(new_model_architecture.downsample_blocks		):
                _A   : Any       					=				hasattr(UpperCamelCase__       ,							"attentions"		)
                for j in range(len(block.resnets		)		):
                       set_resnet_weights(block.resnets[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                       if has_attentions:
                              set_attention_weights(block.attentions[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                              module_index += 1
  
                if hasattr(UpperCamelCase__       ,							"downsamplers"		) and block.downsamplers is not None:
                       set_resnet_weights(block.resnet_down       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                       _A   : List[str]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.weight"].data
                       _A   : Optional[int]       					=				checkpoint[f"all_modules.{module_index}.Conv_0.bias"].data
                       module_index += 1
  
         set_resnet_weights(new_model_architecture.mid_block.resnets[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
         set_attention_weights(new_model_architecture.mid_block.attentions[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
         set_resnet_weights(new_model_architecture.mid_block.resnets[1]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
         module_index += 1
  
         for i, block in enumerate(new_model_architecture.up_blocks		):
                _A   : Dict       					=				hasattr(UpperCamelCase__       ,							"attentions"		)
                for j in range(len(block.resnets		)		):
                       set_resnet_weights(block.resnets[j]       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
                if has_attentions:
                       set_attention_weights(
                           block.attentions[0]       ,							UpperCamelCase__       ,							UpperCamelCase__		)  # why can there only be a single attention layer for up?
                       module_index += 1
  
                if hasattr(UpperCamelCase__       ,							"resnet_up"		) and block.resnet_up is not None:
                       _A   : Dict       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                       _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                       module_index += 1
                       _A   : int       					=				checkpoint[f"all_modules.{module_index}.weight"].data
                       _A   : List[Any]       					=				checkpoint[f"all_modules.{module_index}.bias"].data
                       module_index += 1
                       set_resnet_weights(block.resnet_up       ,							UpperCamelCase__       ,							UpperCamelCase__		)
                       module_index += 1
  
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.weight"].data
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
         module_index += 1
         _A   : Tuple       					=				checkpoint[f"all_modules.{module_index}.weight"].data
         _A   : str       					=				checkpoint[f"all_modules.{module_index}.bias"].data
  
         return new_model_architecture.state_dict()
  
  
  if __name__ == "__main__":
      lowerCAmelCase__   	=				argparse.ArgumentParser()
  
      parser.add_argument(
          '--checkpoint_path',
          default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_pytorch_model.bin',
          type=str,
          required=False,
          help='Path to the checkpoint to convert.',
      )
  
      parser.add_argument(
          '--config_file',
          default='/Users/arthurzucker/Work/diffusers/ArthurZ/config.json',
          type=str,
          required=False,
          help='The config json file corresponding to the architecture.',
      )
  
      parser.add_argument(
          '--dump_path',
          default='/Users/arthurzucker/Work/diffusers/ArthurZ/diffusion_model_new.pt',
          type=str,
          required=False,
          help='Path to the output model.',
      )
  
      lowerCAmelCase__   	=				parser.parse_args()
  
      lowerCAmelCase__   	=				torch.load(args.checkpoint_path, map_location='cpu')
  
      with open(args.config_file) as f:
          lowerCAmelCase__   	=				json.loads(f.read())
  
      lowerCAmelCase__   	=				convert_ncsnpp_checkpoint(
          checkpoint,
          config,
      )
  
      if "sde" in config:
          del config["sde"]
  
      lowerCAmelCase__   	=				UNetaDModel(**config)
      model.load_state_dict(converted_checkpoint)
  
      try:
          lowerCAmelCase__   	=				ScoreSdeVeScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
  
          lowerCAmelCase__   	=				ScoreSdeVePipeline(unet=model, scheduler=scheduler)
          pipe.save_pretrained(args.dump_path)
      except:  # noqa: E722
          model.save_pretrained(args.dump_path)
  