




from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
 from ..models.auto import AutoTokenizer







class     A:



 '''simple docstring'''


 def 							a__					(							self   :    int		,				A_   :    Optional[Any]    )  ->      Optional[int]:

       """simple docstring"""






       raise NotImplementedError()





 def 							a__					(							self   :    List[str]    )  ->      List[Any]:

       """simple docstring"""






       raise NotImplementedError()







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 def __init__(							self   :    Optional[Any]		,				A_   :    "AutoTokenizer"		,				A_   :    bool = False		,				**A_   :    str    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       tokenizer
       lowerCamelCase_							=       skip_prompt
       lowerCamelCase_							=       decode_kwargs

       # variables used in the streaming process
       lowerCamelCase_							=       []
       lowerCamelCase_							=       0
       lowerCamelCase_							=       True

 def 							a__					(							self   :    List[Any]		,				A_   :    str    )  ->      Tuple:

       """simple docstring"""






       if len(value.shape    ) > 1 and value.shape[0] > 1:
             raise ValueError('TextStreamer only supports batch size 1'    )
       elif len(value.shape    ) > 1:
             lowerCamelCase_							=       value[0]

       if self.skip_prompt and self.next_tokens_are_prompt:
             lowerCamelCase_							=       False
             return

       # Add the new token to the cache and decodes the entire thing.
       self.token_cache.extend(value.tolist()    )
       lowerCamelCase_							=       self.tokenizer.decode(self.token_cache		,				**self.decode_kwargs    )

       # After the symbol for a new line, we flush the cache.
       if text.endswith('\n'    ):
             lowerCamelCase_							=       text[self.print_len :]
             lowerCamelCase_							=       []
             lowerCamelCase_							=       0
       # If the last token is a CJK character, we print the characters.
       elif len(A_    ) > 0 and self._is_chinese_char(ord(text[-1]    )    ):
             lowerCamelCase_							=       text[self.print_len :]
             self.print_len += len(A_    )
       # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
       # which may change with the subsequent token -- there are probably smarter ways to do this!)
       else:
             lowerCamelCase_							=       text[self.print_len : text.rfind(' '    ) + 1]
             self.print_len += len(A_    )

       self.on_finalized_text(A_    )

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

       """simple docstring"""






       if len(self.token_cache    ) > 0:
             lowerCamelCase_							=       self.tokenizer.decode(self.token_cache		,				**self.decode_kwargs    )
             lowerCamelCase_							=       text[self.print_len :]
             lowerCamelCase_							=       []
             lowerCamelCase_							=       0
       else:
             lowerCamelCase_							=       ''

       lowerCamelCase_							=       True
       self.on_finalized_text(A_		,				stream_end=A_    )

 def 							a__					(							self   :    Optional[int]		,				A_   :    str		,				A_   :    bool = False    )  ->      Optional[int]:

       """simple docstring"""






       print(A_		,				flush=A_		,				end='' if not stream_end else None    )





 def 							a__					(							self   :    str		,				A_   :    Dict    )  ->      Any:

       """simple docstring"""






       if (
           (cp >= 0X4E_00 and cp <= 0X9F_FF)
           or (cp >= 0X34_00 and cp <= 0X4D_BF)  #
           or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF)  #
           or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F)  #
           or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F)  #
           or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF)  #
           or (cp >= 0XF9_00 and cp <= 0XFA_FF)
           or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F)  #
       ):  #
             return True

       return False







class     A(		UpperCamelCase  ):



 '''simple docstring'''


 def __init__(							self   :    int		,				A_   :    "AutoTokenizer"		,				A_   :    bool = False		,				A_   :    Optional[float] = None		,				**A_   :    List[Any]    )  ->      Optional[Any]:

       """simple docstring"""






       super().__init__(A_		,				A_		,				**A_    )
       lowerCamelCase_							=       Queue()
       lowerCamelCase_							=       None
       lowerCamelCase_							=       timeout

 def 							a__					(							self   :    Optional[Any]		,				A_   :    str		,				A_   :    bool = False    )  ->      int:

       """simple docstring"""






       self.text_queue.put(A_		,				timeout=self.timeout    )
       if stream_end:
             self.text_queue.put(self.stop_signal		,				timeout=self.timeout    )

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

       """simple docstring"""






       return self





 def 							a__					(							self   :    Optional[Any]    )  ->      Any:

       """simple docstring"""






       lowerCamelCase_							=       self.text_queue.get(timeout=self.timeout    )
       if value == self.stop_signal:
             raise StopIteration()
       else:
             return value







import argparse
from pathlib import Path

import torch

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


logging.set_verbosity_info()
a			:			List[str]												=					logging.get_logger(__name__)


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







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

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

			__lowercase			       = {
			    '''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:
									__lowercase			       = sd.pop(_UpperCamelCase	)

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

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

									__lowercase			       = 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
									__lowercase       ,			__lowercase       ,			__lowercase			       = torch.split(_UpperCamelCase							,      depth // 3							,      dim=0	)

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

			return sd


@torch.no_grad()
def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=None	):


			'''simple docstring'''







			__lowercase			       = load_checkpoint(_UpperCamelCase	)

			if config is not None:
						__lowercase			       = OPTConfig.from_pretrained(_UpperCamelCase	)
			else:
						__lowercase			       = OPTConfig()

			__lowercase			       = OPTModel(_UpperCamelCase	).half().eval()
			model.load_state_dict(_UpperCamelCase	)

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


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


