




import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     DebertaForMaskedLM,
     DebertaForQuestionAnswering,
     DebertaForSequenceClassification,
     DebertaForTokenClassification,
     DebertaModel,
 )
 from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST

class  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE   ):

      """simple docstring"""

      def __init__(     self					: Optional[int] ,       snake_case					: Optional[Any] ,       snake_case					: Optional[Any]=13 ,       snake_case					: Optional[int]=7 ,       snake_case					: List[str]=True ,       snake_case					: Optional[Any]=True ,       snake_case					: Dict=True ,       snake_case					: int=True ,       snake_case					: Tuple=99 ,       snake_case					: str=32 ,       snake_case					: Tuple=5 ,       snake_case					: Tuple=4 ,       snake_case					: List[str]=37 ,       snake_case					: int="gelu" ,       snake_case					: Any=0.1 ,       snake_case					: Optional[Any]=0.1 ,       snake_case					: Any=512 ,       snake_case					: str=16 ,       snake_case					: str=2 ,       snake_case					: Any=0.02 ,       snake_case					: Tuple=False ,       snake_case					: str=True ,       snake_case					: List[str]="None" ,       snake_case					: int=3 ,       snake_case					: Dict=4 ,       snake_case					: int=None ,       ):
          __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  		=						relative_attention
          __UpperCamelCase  		=						position_biased_input
          __UpperCamelCase  		=						pos_att_type
          __UpperCamelCase  		=						scope







      def       snake_case	(     self					: List[Any]				):
          __UpperCamelCase  		=						ids_tensor([self.batch_size, self.seq_length] ,       self.vocab_size				)

          __UpperCamelCase  		=						None
          if self.use_input_mask:
              __UpperCamelCase  		=						ids_tensor([self.batch_size, self.seq_length] ,       vocab_size=2				)

          __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       snake_case	(     self					: Dict				):
          return DebertaConfig(
              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 ,       initializer_range=self.initializer_range ,       relative_attention=self.relative_attention ,       position_biased_input=self.position_biased_input ,       pos_att_type=self.pos_att_type ,       )







      def       snake_case	(     self					: int				):
          __UpperCamelCase  		=						self.get_config()
          __UpperCamelCase  		=						300
          return config







      def       snake_case	(     self					: Dict ,       snake_case					: Optional[Any]				):
          self.parent.assertListEqual(list(result.loss.size()				) ,       []				)







      def       snake_case	(     self					: List[Any] ,       snake_case					: List[str] ,       snake_case					: Union[str, Any] ,       snake_case					: List[Any] ,       snake_case					: Any ,       snake_case					: Optional[int] ,       snake_case					: Optional[Any] ,       snake_case					: Tuple				):
          __UpperCamelCase  		=						DebertaModel(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case ,       attention_mask=snake_case ,       token_type_ids=snake_case				)[0]
          __UpperCamelCase  		=						model(snake_case ,       token_type_ids=snake_case				)[0]
          __UpperCamelCase  		=						model(snake_case				)[0]

          self.parent.assertListEqual(list(sequence_output.size()				) ,       [self.batch_size, self.seq_length, self.hidden_size]				)







      def       snake_case	(     self					: Union[str, Any] ,       snake_case					: Tuple ,       snake_case					: int ,       snake_case					: Dict ,       snake_case					: List[Any] ,       snake_case					: str ,       snake_case					: Dict ,       snake_case					: int				):
          __UpperCamelCase  		=						DebertaForMaskedLM(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case ,       attention_mask=snake_case ,       token_type_ids=snake_case ,       labels=snake_case				)

          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.seq_length, self.vocab_size)				)







      def       snake_case	(     self					: Optional[Any] ,       snake_case					: int ,       snake_case					: Optional[int] ,       snake_case					: int ,       snake_case					: Optional[Any] ,       snake_case					: List[str] ,       snake_case					: Tuple ,       snake_case					: List[Any]				):
          __UpperCamelCase  		=						self.num_labels
          __UpperCamelCase  		=						DebertaForSequenceClassification(snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case ,       attention_mask=snake_case ,       token_type_ids=snake_case ,       labels=snake_case				)
          self.parent.assertListEqual(list(result.logits.size()				) ,       [self.batch_size, self.num_labels]				)
          self.check_loss_output(snake_case				)







      def       snake_case	(     self					: Optional[Any] ,       snake_case					: Optional[int] ,       snake_case					: Tuple ,       snake_case					: List[str] ,       snake_case					: List[str] ,       snake_case					: Tuple ,       snake_case					: Optional[int] ,       snake_case					: str				):
          __UpperCamelCase  		=						self.num_labels
          __UpperCamelCase  		=						DebertaForTokenClassification(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(snake_case ,       attention_mask=snake_case ,       token_type_ids=snake_case ,       labels=snake_case				)
          self.parent.assertEqual(result.logits.shape ,       (self.batch_size, self.seq_length, self.num_labels)				)







      def       snake_case	(     self					: int ,       snake_case					: List[Any] ,       snake_case					: Any ,       snake_case					: List[str] ,       snake_case					: Tuple ,       snake_case					: List[Any] ,       snake_case					: List[Any] ,       snake_case					: List[str]				):
          __UpperCamelCase  		=						DebertaForQuestionAnswering(config=snake_case				)
          model.to(snake_case				)
          model.eval()
          __UpperCamelCase  		=						model(
              snake_case ,       attention_mask=snake_case ,       token_type_ids=snake_case ,       start_positions=snake_case ,       end_positions=snake_case ,       )
          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       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						self.prepare_config_and_inputs()
          (
              (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __UpperCamelCase
          )		,      (
              __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  _lowerCamelCase						(			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE							,			unittest.TestCase   ):

      """simple docstring"""

      lowerCAmelCase__			: Tuple    =					(
          (
              DebertaModel,
              DebertaForMaskedLM,
              DebertaForSequenceClassification,
              DebertaForTokenClassification,
              DebertaForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      lowerCAmelCase__			: Union[str, Any]    =					(
          {
              "feature-extraction": DebertaModel,
              "fill-mask": DebertaForMaskedLM,
              "question-answering": DebertaForQuestionAnswering,
              "text-classification": DebertaForSequenceClassification,
              "token-classification": DebertaForTokenClassification,
              "zero-shot": DebertaForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )

      lowerCAmelCase__			: str    =					True
      lowerCAmelCase__			: Optional[int]    =					False
      lowerCAmelCase__			: str    =					False
      lowerCAmelCase__			: List[str]    =					False
      lowerCAmelCase__			: Optional[int]    =					False







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						DebertaModelTester(self				)
          __UpperCamelCase  		=						ConfigTester(self ,       config_class=snake_case ,       hidden_size=37				)







      def       snake_case	(     self					: Optional[Any]				):
          self.config_tester.run_common_tests()







      def       snake_case	(     self					: List[Any]				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_model(*snake_case				)







      def       snake_case	(     self					: Tuple				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_sequence_classification(*snake_case				)







      def       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_masked_lm(*snake_case				)







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_question_answering(*snake_case				)







      def       snake_case	(     self					: int				):
          __UpperCamelCase  		=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_token_classification(*snake_case				)







      @slow
      def       snake_case	(     self					: List[Any]				):
          for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase  		=						DebertaModel.from_pretrained(snake_case				)
              self.assertIsNotNone(snake_case				)







@require_torch
@require_sentencepiece
@require_tokenizers
class  _lowerCamelCase						(			unittest.TestCase   ):

      """simple docstring"""

      @unittest.skip(reason='''Model not available yet'''				)
      def       snake_case	(     self					: Optional[int]				):
          pass







      @slow
      def       snake_case	(     self					: Tuple				):
          __UpperCamelCase  		=						DebertaModel.from_pretrained('''microsoft/deberta-base'''				)

          __UpperCamelCase  		=						torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				)
          __UpperCamelCase  		=						torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
          with torch.no_grad():
              __UpperCamelCase  		=						model(snake_case ,       attention_mask=snake_case				)[0]
          # compare the actual values for a slice.
          __UpperCamelCase  		=						torch.tensor(
              [[[-0.59_86, -0.80_55, -0.84_62], [1.44_84, -0.93_48, -0.80_59], [0.31_23, 0.00_32, -1.41_31]]]				)
          self.assertTrue(torch.allclose(output[:, 1:4, 1:4] ,       snake_case ,       atol=1E-4				) ,       F"{output[:, 1:4, 1:4]}"				)












import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
a_   		=     logging.get_logger(__name__)




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	str:

    """simple docstring"""


    __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )
    if "model" in sd.keys():
        __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )['''model''']

    # pop unnecessary weights
    __UpperCamelCase  		=						[
        '''decoder.version''',
        '''decoder.output_projection.weight''',
    ]
    for key in keys_to_delete:
        if key in sd:
            sd.pop(lowercase_      )

    __UpperCamelCase  		=						{
        '''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:
            __UpperCamelCase  		=						sd.pop(lowercase_      )

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

            __UpperCamelCase  		=						key.replace('''.qkv_proj.'''					,     '''.q_proj.'''      )
            __UpperCamelCase  		=						key.replace('''.qkv_proj.'''					,     '''.k_proj.'''      )
            __UpperCamelCase  		=						key.replace('''.qkv_proj.'''					,     '''.v_proj.'''      )

            __UpperCamelCase  		=						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
            __UpperCamelCase		,      __UpperCamelCase		,      __UpperCamelCase  		=						torch.split(lowercase_					,     depth // 3					,     dim=0      )

            __UpperCamelCase  		=						q
            __UpperCamelCase  		=						k
            __UpperCamelCase  		=						v
            del sd[key]

    return sd




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_=None      )       ->	Tuple:

    """simple docstring"""


    __UpperCamelCase  		=						load_checkpoint(lowercase_      )

    if config is not None:
        __UpperCamelCase  		=						OPTConfig.from_pretrained(lowercase_      )
    else:
        __UpperCamelCase  		=						OPTConfig()

    __UpperCamelCase  		=						OPTModel(lowercase_      ).half().eval()
    model.load_state_dict(lowercase_      )

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


if __name__ == "__main__":
 a_   		=     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.")
 a_   		=     parser.parse_args()
 convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)






