



import subprocess
from argparse import ArgumentParser
from typing import List, Union

from huggingface_hub.hf_api import HfFolder, create_repo, whoami
from requests.exceptions import HTTPError

from . import BaseTransformersCLICommand

class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





     @staticmethod
     def 						__snake_case     (							UpperCamelCase			:				ArgumentParser					)->str:
      __SCREAMING_SNAKE_CASE							:     int							=						parser.add_parser("login"  ,	help="Log in using the same credentials as on huggingface.co"					)
      login_parser.set_defaults(func=lambda UpperCamelCase					: LoginCommand(UpperCamelCase					)					)
      __SCREAMING_SNAKE_CASE							:     str							=						parser.add_parser("whoami"  ,	help="Find out which huggingface.co account you are logged in as."					)
      whoami_parser.set_defaults(func=lambda UpperCamelCase					: WhoamiCommand(UpperCamelCase					)					)
      __SCREAMING_SNAKE_CASE							:     int							=						parser.add_parser("logout"  ,	help="Log out"					)
      logout_parser.set_defaults(func=lambda UpperCamelCase					: LogoutCommand(UpperCamelCase					)					)

      # new system: git-based repo system
      __SCREAMING_SNAKE_CASE							:     Any							=						parser.add_parser(
          "repo"  ,	help="Deprecated: use `huggingface-cli` instead. Commands to interact with your huggingface.co repos."  ,	)
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						repo_parser.add_subparsers(
          help="Deprecated: use `huggingface-cli` instead. huggingface.co repos related commands"					)
      __SCREAMING_SNAKE_CASE							:     Any							=						repo_subparsers.add_parser(
          "create"  ,	help="Deprecated: use `huggingface-cli` instead. Create a new repo on huggingface.co"					)
      repo_create_parser.add_argument(
          "name"  ,	type=UpperCamelCase  ,	help="Name for your model's repo. Will be namespaced under your username to build the model id."  ,	)
      repo_create_parser.add_argument("--organization"  ,	type=UpperCamelCase  ,	help="Optional: organization namespace."					)
      repo_create_parser.add_argument("-y"  ,	"--yes"  ,	action="store_true"  ,	help="Optional: answer Yes to the prompt"					)
      repo_create_parser.set_defaults(func=lambda UpperCamelCase					: RepoCreateCommand(UpperCamelCase					)					)






class    _SCREAMING_SNAKE_CASE						:
     lowerCAmelCase       =      """\u001b[1m"""
     lowerCAmelCase       =      """\u001b[31m"""
     lowerCAmelCase       =      """\u001b[90m"""
     lowerCAmelCase       =      """\u001b[0m"""





     @classmethod
     def 						__snake_case     (							cls			:				List[str]  ,	UpperCamelCase			:				str					)->Dict:
      return F"""{cls._bold}{s}{cls._reset}"""





     @classmethod
     def 						__snake_case     (							cls			:				Union[str, Any]  ,	UpperCamelCase			:				Union[str, Any]					)->List[str]:
      return F"""{cls._bold}{cls._red}{s}{cls._reset}"""






     @classmethod
     def 						__snake_case     (							cls			:				Optional[Any]  ,	UpperCamelCase			:				str					)->Optional[int]:
      return F"""{cls._gray}{s}{cls._reset}"""






def 						_lowerCAmelCase    (					__lowerCamelCase :  List[List[Union[str, int]]]		,						__lowerCamelCase :  List[str]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     List[Any]							=						[max(len(str(__lowerCamelCase  )  ) for x in col  ) for col in zip(*__lowerCamelCase		,						__lowerCamelCase  )]
 __SCREAMING_SNAKE_CASE							:     int							=						("{{:{}}} " * len(__lowerCamelCase  )).format(*__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Tuple							=						[]
 lines.append(row_format.format(*__lowerCamelCase  )  )
 lines.append(row_format.format(*["-" * w for w in col_widths]  )  )
 for row in rows:
  lines.append(row_format.format(*__lowerCamelCase  )  )
 return "\n".join(__lowerCamelCase  )

class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				int  ,	UpperCamelCase			:				int					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						args






class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





     def 						__snake_case     (							self			:				Optional[int]					)->Optional[Any]:
      print(
          ANSI.red(
              "ERROR! `huggingface-cli login` uses an outdated login mechanism "
              "that is not compatible with the Hugging Face Hub backend anymore. "
              "Please use `huggingface-cli login instead."					)					)






class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





     def 						__snake_case     (							self			:				Optional[int]					)->Dict:
      print(
          ANSI.red(
              "WARNING! `transformers-cli whoami` is deprecated and will be removed in v5. Please use "
              "`huggingface-cli whoami` instead."					)					)
      __SCREAMING_SNAKE_CASE							:     Dict							=						HfFolder.get_token()
      if token is None:
       print("Not logged in"					)
       exit()
      try:
       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[int]							=						whoami(UpperCamelCase					)
       print(UpperCamelCase					)
       if orgs:
        print(ANSI.bold("orgs: "					)  ,	",".join(UpperCamelCase					)					)
      except HTTPError as e:
       print(UpperCamelCase					)
       print(ANSI.red(e.response.text					)					)
       exit(1					)






class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





     def 						__snake_case     (							self			:				Any					)->Optional[Any]:
      print(
          ANSI.red(
              "ERROR! `transformers-cli logout` uses an outdated logout mechanism "
              "that is not compatible with the Hugging Face Hub backend anymore. "
              "Please use `huggingface-cli logout instead."					)					)






class    _SCREAMING_SNAKE_CASE						(UpperCamelCase			):





     def 						__snake_case     (							self			:				Dict					)->int:
      print(
          ANSI.red(
              "WARNING! Managing repositories through transformers-cli is deprecated. "
              "Please use `huggingface-cli` instead."					)					)
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						HfFolder.get_token()
      if token is None:
       print("Not logged in"					)
       exit(1					)
      try:
       __SCREAMING_SNAKE_CASE							:     Tuple							=						subprocess.check_output(["git", "--version"]					).decode("utf-8"					)
       print(ANSI.gray(stdout.strip()					)					)
      except FileNotFoundError:
       print("Looks like you do not have git installed, please install."					)

      try:
       __SCREAMING_SNAKE_CASE							:     Tuple							=						subprocess.check_output(["git-lfs", "--version"]					).decode("utf-8"					)
       print(ANSI.gray(stdout.strip()					)					)
      except FileNotFoundError:
       print(
           ANSI.red(
               "Looks like you do not have git-lfs installed, please install."
               " You can install from https://git-lfs.github.com/."
               " Then run `git lfs install` (you only have to do this once)."					)					)
      print(""					)

      __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     Optional[int]							=						whoami(UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     int							=						self.args.organization if self.args.organization is not None else user
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						F"""{namespace}/{self.args.name}"""
      print(F"""You are about to create {ANSI.bold(UpperCamelCase					)}"""					)

      if not self.args.yes:
       __SCREAMING_SNAKE_CASE							:     List[Any]							=						input("Proceed? [Y/n] "					).lower()
       if not (choice == "" or choice == "y" or choice == "yes"):
        print("Abort"					)
        exit()
      try:
       __SCREAMING_SNAKE_CASE							:     List[str]							=						create_repo(UpperCamelCase  ,	name=self.args.name  ,	organization=self.args.organization					)
      except HTTPError as e:
       print(UpperCamelCase					)
       print(ANSI.red(e.response.text					)					)
       exit(1					)
      print("\nYour repo now lives at:"					)
      print(F"""  {ANSI.bold(UpperCamelCase					)}"""					)
      print("\nYou can clone it locally with the command below, and commit/push as usual."					)
      print(F"""\n  git clone {url}"""					)
      print(""					)









"""simple docstring"""

import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def 					__UpperCamelCase (     a__:			List[Any]			,						a__:			int			,						a__:			Dict=0      )					->      Any:




					"""simple docstring"""



					if name is None:
										__lowercase        =      None
					else:
										__lowercase        =      '''.''' * max(0			,						spaces - 2      ) + '''# {:''' + str(50 - spaces      ) + '''s}'''
										__lowercase        =      fmt.format(a__      )

					# Print and recurse (if needed).
					if isinstance(a__			,						a__      ):
										if msg is not None:
															print(a__      )
										for k in val.keys():
															recursive_print(a__			,						val[k]			,						spaces + 2      )
					elif isinstance(a__			,						torch.Tensor      ):
										print(a__			,						''':'''			,						val.size()      )
					else:
										print(a__			,						''':'''			,						a__      )





def 					__UpperCamelCase (     a__:			Optional[int]			,						a__:			Tuple			,						a__:			List[Any]			,						a__:			List[str]      )					->      str:




					"""simple docstring"""



					__lowercase        =      param.size()
					# other versions store [num_heads * num_splits * hidden_size, :]
					__lowercase        =      (num_heads, num_splits, hidden_size) + input_shape[1:]
					__lowercase        =      param.view(*a__      )
					__lowercase        =      param.transpose(0			,						1      ).contiguous()
					__lowercase        =      param.view(*a__      )
					return param





def 					__UpperCamelCase (     a__:			Tuple			,						a__:			Optional[int]      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      config.n_positions
					__lowercase        =      config.n_layer
					__lowercase        =      config.vocab_size
					__lowercase        =      config.n_head
					__lowercase        =      config.n_embd // config.n_head

					__lowercase        =      sd_megatron['''model.language_model.embedding.word_embeddings.weight'''][:vocab_size, :]
					__lowercase        =      {
					    '''transformer.wte.weight''': word_embeddings,
					    '''transformer.wpe.weight''': sd_megatron['''model.language_model.embedding.position_embeddings.weight'''],
					    '''transformer.ln_f.weight''': sd_megatron['''model.language_model.encoder.final_layernorm.weight'''],
					    '''transformer.ln_f.bias''': sd_megatron['''model.language_model.encoder.final_layernorm.bias'''],
					}

					__lowercase        =      '''model.language_model.encoder.layers.'''
					for i in range(a__      ):
										__lowercase        =      torch.tril(torch.ones((n_positions, n_positions)			,						dtype=torch.bool      )      )
										__lowercase        =      causal_mask.view(1			,						1			,						a__			,						a__      )
										__lowercase        =      causal_mask
										__lowercase        =      torch.tensor(-1e4			,						dtype=torch.bfloataa      )

										__lowercase        =      sd_megatron[f'''{pf}{i}.input_layernorm.weight''']
										__lowercase        =      sd_megatron[f'''{pf}{i}.input_layernorm.bias''']

										__lowercase        =      sd_megatron[f'''{pf}{i}.self_attention.query_key_value.weight''']
										__lowercase        =      fix_query_key_value_ordering(a__			,						3			,						a__			,						a__      )
										__lowercase        =      vala.transpose(0			,						1      ).contiguous()

										__lowercase        =      sd_megatron[f'''{pf}{i}.self_attention.query_key_value.bias''']
										__lowercase        =      fix_query_key_value_ordering(a__			,						3			,						a__			,						a__      )
										__lowercase        =      vala

										__lowercase        =      sd_megatron[f'''{pf}{i}.self_attention.dense.weight'''].transpose(
										    0			,						1      )
										__lowercase        =      sd_megatron[f'''{pf}{i}.self_attention.dense.bias''']
										__lowercase        =      sd_megatron[f'''{pf}{i}.post_attention_layernorm.weight''']
										__lowercase        =      sd_megatron[f'''{pf}{i}.post_attention_layernorm.bias''']
										__lowercase        =      sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.weight'''].transpose(0			,						1      )
										__lowercase        =      sd_megatron[f'''{pf}{i}.mlp.dense_h_to_4h.bias''']
										__lowercase        =      sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.weight'''].transpose(
										    0			,						1      )
										__lowercase        =      sd_megatron[f'''{pf}{i}.mlp.dense_4h_to_h.bias''']

					# For LM head, transformers' wants the matrix to weight embeddings.
					__lowercase        =      word_embeddings

					return sd_hf





def 					__UpperCamelCase (     a__:			Any			,						a__:			List[Any]      )					->      List[Any]:




					"""simple docstring"""



					__lowercase        =      6_40_00
					__lowercase        =      config_megatron['''encoder_seq_length''']
					__lowercase        =      config_megatron['''hidden_size''']
					__lowercase        =      config_megatron['''num_layers''']
					__lowercase        =      config_megatron['''num_attention_heads''']
					__lowercase        =      config_megatron['''ffn_hidden_size''']
					__lowercase        =      '''gelu'''
					__lowercase        =      0.1
					__lowercase        =      0.1
					__lowercase        =      0.1
					__lowercase        =      config_megatron['''layernorm_epsilon''']  # 1e-5
					__lowercase        =      config_megatron['''init_method_std''']  # 0.02
					__lowercase        =      config_megatron['''apply_query_key_layer_scaling''']  # True
					__lowercase        =      True
					__lowercase        =      True

					# This identifies the 6.7B (7B) model which uses a different tokenizer
					if config_megatron["hidden_size"] == 40_96:
										__lowercase        =      1  # <|endoftext|>
										__lowercase        =      1  # <|endoftext|>
										__lowercase        =      0  # <unk>
					else:
										__lowercase        =      2  # <s>
										__lowercase        =      3  # <|endoftext|>
										__lowercase        =      0  # <pad>

					return config_hf





def 					__UpperCamelCase (     a__:			Dict      )					->      Union[str, Any]:




					"""simple docstring"""



					print(a__      )

					__lowercase        =      args.checkpoint_path
					__lowercase        =      args.save_path
					if isfile(a__      ):
										raise FileNotFoundError(f'''ERROR! could not find file {checkpoint_path}'''      )

					# Load the model.
					__lowercase        =      torch.load(a__			,						map_location='''cpu'''      )

					# Load the config.
					__lowercase        =      checkpoint['''hyper_parameters''']['''cfg''']
					__lowercase        =      GPTaConfig()
					__lowercase        =      copy_config(config_hf=a__			,						config_megatron=a__      )
					__lowercase        =      ['''GPT2LMHeadModel''']

					__lowercase        =      checkpoint['''state_dict''']

					# Convert.
					print('''Converting'''      )
					__lowercase        =      convert_megatron_checkpoint(a__			,						a__      )

					# Print the structure of converted state dict.
					if args.print_checkpoint_structure:
										recursive_print(a__			,						a__      )

					__lowercase        =      '''GPTSw3Tokenizer'''

					# Store the config to file.
					print('''Saving config'''      )
					config_hf.save_pretrained(a__      )

					# Store the state_dict to file.
					__lowercase        =      os.path.join(a__			,						'''pytorch_model.bin'''      )
					print(f'''Saving checkpoint to "{output_checkpoint_file}"'''      )
					torch.save(a__			,						a__      )


if __name__ == "__main__":
							a__			:						Union[str, Any]							=			argparse.ArgumentParser()
							parser.add_argument(
							    """--checkpoint_path""",
							    type=str,
							    required=True,
							    help="""e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000""",
							)
							parser.add_argument("""--save_path""", type=str, required=True, help="""e.g. /home/user/gpt-sw3/hf""")
							parser.add_argument("""--print-checkpoint-structure""", action="""store_true""")
							a__			:						Dict							=			parser.parse_args()
							main(_args)


