




import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


__SCREAMING_SNAKE_CASE     : Optional[int]      =logging.get_logger(__name__)  # pylint: disable=invalid-name


__SCREAMING_SNAKE_CASE     : Optional[int]      ="""lfs-multipart-upload"""






class       A      (		_lowerCAmelCase		):
     """simple docstring"""

     @staticmethod
     def 			__snake_case (     A__	: ArgumentParser      )					->							str:
           lowerCamelCase__   	=					parser.add_parser(
               'lfs-enable-largefiles'						,	help=(
                   'Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB.'
               )						,	)
           enable_parser.add_argument('path'						,	type=A__						,	help='Local path to repository you want to configure.'      )
           enable_parser.set_defaults(func=lambda A__      : LfsEnableCommand(A__      )      )

           lowerCamelCase__   	=					parser.add_parser(
               A__						,	help=(
                   'Deprecated: use `huggingface-cli` instead. '
                   'Command will get called by git-lfs, do not call it directly.'
               )						,	)
           upload_parser.set_defaults(func=lambda A__      : LfsUploadCommand(A__      )      )






class       A      :
     """simple docstring"""

     def __init__(     self	: Tuple						,	A__	: Any      )					->							Union[str, Any]:
           lowerCamelCase__   	=					args




     def 			__snake_case (     self	: Any      )					->							Any:
           warnings.warn(
               'Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead.'      )
           lowerCamelCase__   	=					os.path.abspath(self.args.path      )
           if not os.path.isdir(A__      ):
                 print('This does not look like a valid git repo.'      )
                 exit(1      )
           subprocess.run(
               'git config lfs.customtransfer.multipart.path transformers-cli'.split()						,	check=A__						,	cwd=A__      )
           subprocess.run(
               F"""git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}""".split()						,	check=A__						,	cwd=A__						,	)
           print('Local repo set up for largefiles'      )

def       lowerCAmelCase_      (	lowerCamelCase:							Dict    )       ->      List[str]:


      '''simple docstring'''




      lowerCamelCase__   	=					json.dumps(lowerCamelCase    ) + '\n'
      sys.stdout.write(lowerCamelCase    )
      sys.stdout.flush()

def       lowerCAmelCase_      (	)       ->      Optional[Dict]:


      '''simple docstring'''




      lowerCamelCase__   	=					json.loads(sys.stdin.readline().strip()    )

      if "terminate" in (msg.get('type'    ), msg.get('event'    )):
            # terminate message received
            return None

      if msg.get('event'    ) not in ("download", "upload"):
            logger.critical('Received unexpected message'    )
            sys.exit(1    )

      return msg






class       A      (		_lowerCAmelCase		):
     """simple docstring"""

     def __init__(     self	: List[str]						,	A__	: str						,	A__	: int						,	A__	: int      )					->							Union[str, Any]:
           lowerCamelCase__   	=					filepath
           lowerCamelCase__   	=					seek_from
           lowerCamelCase__   	=					read_limit
           lowerCamelCase__   	=					0




     def __enter__(     self	: Optional[int]      )					->							int:
           lowerCamelCase__   	=					open(self.filepath						,	'rb'      )
           self.f.seek(self.seek_from      )
           return self




     def __len__(     self	: int      )					->							Optional[int]:
           lowerCamelCase__   	=					os.fstat(self.f.fileno()      ).st_size
           return min(self.read_limit						,	total_length - self.seek_from      )




     def 			__snake_case (     self	: str						,	A__	: Union[str, Any]=-1      )					->							str:
           if self.n_seen >= self.read_limit:
                 return b""
           lowerCamelCase__   	=					self.read_limit - self.n_seen
           lowerCamelCase__   	=					self.f.read(remaining_amount if n < 0 else min(A__						,	A__      )      )
           self.n_seen += len(A__      )
           return data




     def __iter__(     self	: Any      )					->							str:
           yield self.read(n=4 * 1_0_2_4 * 1_0_2_4      )




     def __exit__(     self	: List[Any]						,	*A__	: Optional[Any]      )					->							Union[str, Any]:
           self.f.close()






class       A      :
     """simple docstring"""

     def __init__(     self	: Union[str, Any]						,	A__	: Tuple      )					->							str:
           lowerCamelCase__   	=					args




     def 			__snake_case (     self	: Optional[int]      )					->							List[str]:
           # Immediately after invoking a custom transfer process, git-lfs
           # sends initiation data to the process over stdin.
           # This tells the process useful information about the configuration.
           lowerCamelCase__   	=					json.loads(sys.stdin.readline().strip()      )
           if not (init_msg.get('event'      ) == "init" and init_msg.get('operation'      ) == "upload"):
                 write_msg({'error': {'code': 3_2, 'message': 'Wrong lfs init operation'}}      )
                 sys.exit(1      )

           # The transfer process should use the information it needs from the
           # initiation structure, and also perform any one-off setup tasks it
           # needs to do. It should then respond on stdout with a simple empty
           # confirmation structure, as follows:
           write_msg({}      )

           # After the initiation exchange, git-lfs will send any number of
           # transfer requests to the stdin of the transfer process, in a serial sequence.
           while True:
                 lowerCamelCase__   	=					read_msg()
                 if msg is None:
                       # When all transfers have been processed, git-lfs will send
                       # a terminate event to the stdin of the transfer process.
                       # On receiving this message the transfer process should
                       # clean up and terminate. No response is expected.
                       sys.exit(0      )

                 lowerCamelCase__   	=					msg['oid']
                 lowerCamelCase__   	=					msg['path']
                 lowerCamelCase__   	=					msg['action']['href']
                 lowerCamelCase__   	=					msg['action']['header']
                 lowerCamelCase__   	=					int(header.pop('chunk_size'      )      )
                 lowerCamelCase__   	=					list(header.values()      )

                 lowerCamelCase__   	=					[]
                 for i, presigned_url in enumerate(A__      ):
                       with FileSlice(A__						,	seek_from=i * chunk_size						,	read_limit=A__      ) as data:
                             lowerCamelCase__   	=					requests.put(A__						,	data=A__      )
                             r.raise_for_status()
                             parts.append(
                                 {
                                     'etag': r.headers.get('etag'      ),
                                     'partNumber': i + 1,
                                 }      )
                             # In order to support progress reporting while data is uploading / downloading,
                             # the transfer process should post messages to stdout
                             write_msg(
                                 {
                                     'event': 'progress',
                                     'oid': oid,
                                     'bytesSoFar': (i + 1) * chunk_size,
                                     'bytesSinceLast': chunk_size,
                                 }      )
                             # Not precise but that's ok.

                 lowerCamelCase__   	=					requests.post(
                     A__						,	json={
                         'oid': oid,
                         'parts': parts,
                     }						,	)
                 r.raise_for_status()

                 write_msg({'event': 'complete', 'oid': oid}      )





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig

def       lowerCAmelCase_      (	lowerCamelCase:							str  ,					lowerCamelCase:							List[str]  ,					lowerCamelCase:							List[Any]=0    )       ->      Dict:


      '''simple docstring'''




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

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

def       lowerCAmelCase_      (	lowerCamelCase:							Tuple  ,					lowerCamelCase:							Dict  ,					lowerCamelCase:							List[str]  ,					lowerCamelCase:							Any    )       ->      Optional[int]:


      '''simple docstring'''




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

def       lowerCAmelCase_      (	lowerCamelCase:							List[str]  ,					lowerCamelCase:							int    )       ->      int:


      '''simple docstring'''




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

      lowerCamelCase__   	=					sd_megatron['model.language_model.embedding.word_embeddings.weight'][:vocab_size, :]
      lowerCamelCase__   	=					{
          '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'],
      }

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

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.input_layernorm.weight"""]
            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.input_layernorm.bias"""]

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.self_attention.query_key_value.weight"""]
            lowerCamelCase__   	=					fix_query_key_value_ordering(lowerCamelCase  ,					3  ,					lowerCamelCase  ,					lowerCamelCase    )
            lowerCamelCase__   	=					vala.transpose(0  ,					1    ).contiguous()

            lowerCamelCase__   	=					sd_megatron[f"""{pf}{i}.self_attention.query_key_value.bias"""]
            lowerCamelCase__   	=					fix_query_key_value_ordering(lowerCamelCase  ,					3  ,					lowerCamelCase  ,					lowerCamelCase    )
            lowerCamelCase__   	=					vala

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

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

      return sd_hf

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[int]  ,					lowerCamelCase:							List[Any]    )       ->      Tuple:


      '''simple docstring'''




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

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

      return config_hf

def       lowerCAmelCase_      (	lowerCamelCase:							Optional[Any]    )       ->      Dict:


      '''simple docstring'''




      print(lowerCamelCase    )

      lowerCamelCase__   	=					args.checkpoint_path
      lowerCamelCase__   	=					args.save_path
      if isfile(lowerCamelCase    ):
            raise FileNotFoundError(f"""ERROR! could not find file {checkpoint_path}"""    )

      # Load the model.
      lowerCamelCase__   	=					torch.load(lowerCamelCase  ,					map_location='cpu'    )

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

      lowerCamelCase__   	=					checkpoint['state_dict']

      # Convert.
      print('Converting'    )
      lowerCamelCase__   	=					convert_megatron_checkpoint(lowerCamelCase  ,					lowerCamelCase    )

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

      lowerCamelCase__   	=					'GPTSw3Tokenizer'

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

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


if __name__ == "__main__":
      __SCREAMING_SNAKE_CASE     : Optional[int]      =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""")
      __SCREAMING_SNAKE_CASE     : Tuple      =parser.parse_args()
      main(_args)