





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					,  lowercase					,  lowercase=13					,  lowercase=7					,  lowercase=True					,  lowercase=True					,  lowercase=True					,  lowercase=True					,  lowercase=99					,  lowercase=32					,  lowercase=5					,  lowercase=4					,  lowercase=37					,  lowercase="gelu"					,  lowercase=0.1					,  lowercase=0.1					,  lowercase=128					,  lowercase=32					,  lowercase=16					,  lowercase=2					,  lowercase=0.02					,  lowercase=3					,  lowercase=4					,  lowercase=None					,  )			->						Dict:







          '''simple docstring'''

          a__						:	Optional[Any] =				parent
          a__						:	str =				batch_size
          a__						:	Optional[int] =				seq_length
          a__						:	List[str] =				is_training
          a__						:	Any =				use_input_mask
          a__						:	Any =				use_token_type_ids
          a__						:	Any =				use_labels
          a__						:	Any =				vocab_size
          a__						:	Optional[Any] =				hidden_size
          a__						:	Any =				num_hidden_layers
          a__						:	int =				num_attention_heads
          a__						:	Any =				intermediate_size
          a__						:	Any =				hidden_act
          a__						:	Dict =				hidden_dropout_prob
          a__						:	List[Any] =				attention_probs_dropout_prob
          a__						:	Union[str, Any] =				max_position_embeddings
          a__						:	Optional[Any] =				type_vocab_size
          a__						:	str =				type_sequence_label_size
          a__						:	int =				initializer_range
          a__						:	List[Any] =				num_labels
          a__						:	Union[str, Any] =				num_choices
          a__						:	Tuple =				scope

   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	str =				ids_tensor([self.batch_size, self.seq_length]					,  self.vocab_size)

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

          a__						:	str =				None
          if self.use_token_type_ids:
                 a__						:	Optional[int] =				ids_tensor([self.batch_size, self.seq_length]					,  self.type_vocab_size)

          a__						:	List[str] =				None
          a__						:	List[str] =				None
          a__						:	str =				None
          if self.use_labels:
                 a__						:	Dict =				ids_tensor([self.batch_size]					,  self.type_sequence_label_size)
                 a__						:	Tuple =				ids_tensor([self.batch_size, self.seq_length]					,  self.num_labels)
                 a__						:	Any =				ids_tensor([self.batch_size]					,  self.num_choices)

          a__						:	Optional[Any] =				self.get_config()

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

   def 							__lowercase		(					self)			->						Tuple:







          '''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=lowercase					,  initializer_range=self.initializer_range					,  )

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          (
              (
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							
          )						:	List[Any] =				self.prepare_config_and_inputs()

          a__						:	Tuple =				True
          a__						:	Optional[int] =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
          a__						:	Optional[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 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						str:







          '''simple docstring'''

          a__						:	Dict =				NezhaModel(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	int =				model(lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase)
          a__						:	int =				model(lowercase					,  token_type_ids=lowercase)
          a__						:	Optional[Any] =				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 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  )			->						Tuple:







          '''simple docstring'''

          a__						:	List[str] =				True
          a__						:	str =				NezhaModel(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	int =				model(
              lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  encoder_hidden_states=lowercase					,  encoder_attention_mask=lowercase					,  )
          a__						:	Optional[int] =				model(
              lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  encoder_hidden_states=lowercase					,  )
          a__						:	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 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	str =				NezhaForMaskedLM(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	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 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Tuple:







          '''simple docstring'''

          a__						:	List[str] =				NezhaForNextSentencePrediction(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	int =				model(
              lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  labels=lowercase					,  )
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, 2))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Any:







          '''simple docstring'''

          a__						:	str =				NezhaForPreTraining(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Optional[Any] =				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 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						List[str]:







          '''simple docstring'''

          a__						:	str =				NezhaForQuestionAnswering(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	str =				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 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						List[Any]:







          '''simple docstring'''

          a__						:	List[str] =				self.num_labels
          a__						:	int =				NezhaForSequenceClassification(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	List[str] =				model(lowercase					,  attention_mask=lowercase					,  token_type_ids=lowercase					,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.num_labels))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						List[Any]:







          '''simple docstring'''

          a__						:	List[Any] =				self.num_labels
          a__						:	Optional[Any] =				NezhaForTokenClassification(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	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.num_labels))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase)			->						Optional[Any]:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.num_choices
          a__						:	Union[str, Any] =				NezhaForMultipleChoice(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Tuple =				input_ids.unsqueeze(1).expand(-1					,  self.num_choices					,  -1).contiguous()
          a__						:	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__						:	List[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 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	List[str] =				self.prepare_config_and_inputs()
          (
              (
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							
          )						:	Optional[Any] =				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  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



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

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase=False)			->						Dict:







          '''simple docstring'''

          a__						:	Union[str, Any] =				super()._prepare_for_class(lowercase					,  lowercase					,  return_labels=lowercase)

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

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Union[str, Any] =				NezhaModelTester(self)
          a__						:	Tuple =				ConfigTester(self					,  config_class=lowercase					,  hidden_size=37)

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          self.config_tester.run_common_tests()

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	Union[str, Any] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	Any =				self.model_tester.prepare_config_and_inputs_for_decoder()
          self.model_tester.create_and_check_model_as_decoder(*lowercase)

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          (
              (
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							(
              a__
          ) ,							
          )						:	str =				self.model_tester.prepare_config_and_inputs_for_decoder()

          a__						:	Optional[int] =				None

          self.model_tester.create_and_check_model_as_decoder(
              lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  lowercase					,  )

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*lowercase)

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	List[Any] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*lowercase)

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	int =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_next_sequence_prediction(*lowercase)

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          a__						:	Dict =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*lowercase)

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Dict =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*lowercase)

   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          a__						:	str =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*lowercase)

   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          a__						:	List[str] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_token_classification(*lowercase)

   @slow
   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	Any =				NezhaModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)

   @slow
   @require_torch_gpu
   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__ ,							a__						:	Tuple =				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__						:	int =				True
                 a__						:	Optional[Any] =				model_class(config=lowercase)

                 a__						:	Dict =				self._prepare_for_class(lowercase					,  lowercase)
                 a__						:	Union[str, Any] =				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'))
                        a__						:	Tuple =				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 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	Optional[int] =				NezhaModel.from_pretrained('sijunhe/nezha-cn-base')
          a__						:	Optional[int] =				torch.tensor([[0, 1, 2, 3, 4, 5]])
          a__						:	List[Any] =				torch.tensor([[0, 1, 1, 1, 1, 1]])
          with torch.no_grad():
                 a__						:	List[Any] =				model(lowercase					,  attention_mask=lowercase)[0]
          a__						:	str =				torch.Size((1, 6, 768))
          self.assertEqual(output.shape					,  lowercase)
          a__						:	List[Any] =				torch.tensor([[[0.06_85, 0.24_41, 0.11_02], [0.06_00, 0.19_06, 0.13_49], [0.02_21, 0.08_19, 0.05_86]]])

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

   @slow
   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          a__						:	int =				NezhaForMaskedLM.from_pretrained('sijunhe/nezha-cn-base')
          a__						:	str =				torch.tensor([[0, 1, 2, 3, 4, 5]])
          a__						:	Dict =				torch.tensor([[1, 1, 1, 1, 1, 1]])
          with torch.no_grad():
                 a__						:	str =				model(lowercase					,  attention_mask=lowercase)[0]
          a__						:	str =				torch.Size((1, 6, 2_1128))
          self.assertEqual(output.shape					,  lowercase)
          a__						:	Any =				torch.tensor(
              [[-2.79_39, -1.79_02, -2.21_89], [-2.85_85, -1.89_08, -2.37_23], [-2.64_99, -1.77_50, -2.25_58]])

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









import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase      :       Any									=  logging.get_logger(__name__)



def A_	(		A__				)  ->		List[str]:
       a__						:	Union[str, Any] =				torch.load(A__		,      map_location='cpu'				)
       if "model" in sd.keys():
              a__						:	Tuple =				torch.load(A__		,      map_location='cpu'				)['model']

       # pop unnecessary weights
       a__						:	Optional[Any] =				[
           'decoder.version',
           'decoder.output_projection.weight',
       ]
       for key in keys_to_delete:
              if key in sd:
                     sd.pop(A__				)

       a__						:	Union[str, Any] =				{
           'decoder.project_in_dim.weight': 'decoder.project_in.weight',
           'decoder.project_out_dim.weight': 'decoder.project_out.weight',
           'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
           'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
       }
       for old_key, new_key in keys_to_rename.items():
              if old_key in sd:
                     a__						:	Any =				sd.pop(A__				)

       a__						:	Optional[int] =				list(sd.keys()				)
       for key in keys:
              if ".qkv_proj." in key:
                     a__						:	str =				sd[key]
                     # We split QKV in separate Q,K,V

                     a__						:	Union[str, Any] =				key.replace('.qkv_proj.'		,      '.q_proj.'				)
                     a__						:	int =				key.replace('.qkv_proj.'		,      '.k_proj.'				)
                     a__						:	Optional[int] =				key.replace('.qkv_proj.'		,      '.v_proj.'				)

                     a__						:	List[Any] =				value.shape[0]
                     assert depth % 3 == 0
                     # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                     # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                     a__ ,							a__ ,							a__						:	Any =				torch.split(A__		,      depth // 3		,      dim=0				)

                     a__						:	int =				q
                     a__						:	List[Any] =				k
                     a__						:	int =				v
                     del sd[key]

       return sd



@torch.no_grad()
def A_	(		A__		,      A__		,      A__=None				)  ->		Union[str, Any]:
       a__						:	Union[str, Any] =				load_checkpoint(A__				)

       if config is not None:
              a__						:	List[Any] =				OPTConfig.from_pretrained(A__				)
       else:
              a__						:	int =				OPTConfig()

       a__						:	Optional[int] =				OPTModel(A__				).half().eval()
       model.load_state_dict(A__				)

       # Check results
       Path(A__				).mkdir(exist_ok=A__				)
       model.save_pretrained(A__				)


if __name__ == "__main__":
    lowercase      :       str									=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--fairseq_path""",
        type=str,
        help=(
            """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
            """ https://huggingface.co/models?other=opt_metasq"""
        ),
    )
    parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
    parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
    lowercase      :       Any									=  parser.parse_args()
    convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


