import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
__UpperCamelCase	: Dict    		=  logging.get_logger(__name__)
def    A			(					_lowercase		):
      SCREAMING_SNAKE_CASE							:					List[str]           =							torch.load(_lowercase	,						map_location='''cpu'''		)
      if "model" in sd.keys():
            SCREAMING_SNAKE_CASE							:					Optional[int]           =							torch.load(_lowercase	,						map_location='''cpu'''		)['''model''']

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

      SCREAMING_SNAKE_CASE							:					Tuple           =							{
          '''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:
                  SCREAMING_SNAKE_CASE							:					Any           =							sd.pop(_lowercase		)

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

                  SCREAMING_SNAKE_CASE							:					int           =							key.replace('''.qkv_proj.'''	,						'''.q_proj.'''		)
                  SCREAMING_SNAKE_CASE							:					int           =							key.replace('''.qkv_proj.'''	,						'''.k_proj.'''		)
                  SCREAMING_SNAKE_CASE							:					Dict           =							key.replace('''.qkv_proj.'''	,						'''.v_proj.'''		)

                  SCREAMING_SNAKE_CASE							:					Dict           =							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
                  SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					List[str]           =							torch.split(_lowercase	,						depth // 3	,						dim=0		)

                  SCREAMING_SNAKE_CASE							:					Any           =							q
                  SCREAMING_SNAKE_CASE							:					Optional[Any]           =							k
                  SCREAMING_SNAKE_CASE							:					int           =							v
                  del sd[key]

      return sd
@torch.no_grad()
def    A			(					_lowercase	,						_lowercase	,						_lowercase=None		):
      SCREAMING_SNAKE_CASE							:					List[Any]           =							load_checkpoint(_lowercase		)

      if config is not None:
            SCREAMING_SNAKE_CASE							:					int           =							OPTConfig.from_pretrained(_lowercase		)
      else:
            SCREAMING_SNAKE_CASE							:					Optional[Any]           =							OPTConfig()

      SCREAMING_SNAKE_CASE							:					Tuple           =							OPTModel(_lowercase		).half().eval()
      model.load_state_dict(_lowercase		)

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


if __name__ == "__main__":
     __UpperCamelCase	: List[Any]    		=  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.')
     __UpperCamelCase	: Optional[Any]    		=  parser.parse_args()
     convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
					from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


__UpperCamelCase	: Optional[Any]    		=  logging.get_logger(__name__)

__UpperCamelCase	: Optional[Any]    		=  {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""deberta-v2"""


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       Any=12_8100					,	UpperCamelCase__			:       Optional[int]=1536					,	UpperCamelCase__			:       Dict=24					,	UpperCamelCase__			:       List[str]=24					,	UpperCamelCase__			:       Tuple=6144					,	UpperCamelCase__			:       List[Any]="gelu"					,	UpperCamelCase__			:       Optional[int]=0.1					,	UpperCamelCase__			:       List[str]=0.1					,	UpperCamelCase__			:       Optional[int]=512					,	UpperCamelCase__			:       Optional[Any]=0					,	UpperCamelCase__			:       Union[str, Any]=0.02					,	UpperCamelCase__			:       List[Any]=1E-7					,	UpperCamelCase__			:       List[str]=False					,	UpperCamelCase__			:       str=-1					,	UpperCamelCase__			:       List[Any]=0					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       Optional[Any]=None					,	UpperCamelCase__			:       Tuple=0					,	UpperCamelCase__			:       str="gelu"					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							hidden_size
												SCREAMING_SNAKE_CASE							:					int           =							num_hidden_layers
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							num_attention_heads
												SCREAMING_SNAKE_CASE							:					List[Any]           =							intermediate_size
												SCREAMING_SNAKE_CASE							:					Any           =							hidden_act
												SCREAMING_SNAKE_CASE							:					Dict           =							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:					str           =							max_position_embeddings
												SCREAMING_SNAKE_CASE							:					Dict           =							type_vocab_size
												SCREAMING_SNAKE_CASE							:					str           =							initializer_range
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							relative_attention
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							max_relative_positions
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							pad_token_id
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							position_biased_input

												# Backwards compatibility
												if type(UpperCamelCase__		) == str:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							[x.strip() for x in pos_att_type.lower().split('''|'''		)]

												SCREAMING_SNAKE_CASE							:					Any           =							pos_att_type
												SCREAMING_SNAKE_CASE							:					int           =							vocab_size
												SCREAMING_SNAKE_CASE							:					Dict           =							layer_norm_eps

												SCREAMING_SNAKE_CASE							:					Tuple           =							kwargs.get('''pooler_hidden_size'''					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Any           =							pooler_dropout
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							pooler_hidden_act






class       lowercase__		(     UpperCamelCase_):


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{0: '''batch''', 1: '''sequence'''}
												if self._config.type_vocab_size > 0:
																		return OrderedDict(
																		    [('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis), ('''token_type_ids''', dynamic_axis)]		)
												else:
																		return OrderedDict([('''input_ids''', dynamic_axis), ('''attention_mask''', dynamic_axis)]		)


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return 12



						def  __A ( self			:       Dict					,	UpperCamelCase__			:       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       Optional["TensorType"] = None					,	UpperCamelCase__			:       int = 3					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       "PreTrainedTokenizerBase" = None					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							super().generate_dummy_inputs(preprocessor=UpperCamelCase__					,	framework=UpperCamelCase__		)
												if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																		del dummy_inputs["token_type_ids"]
												return dummy_inputs





