






"""simple docstring"""





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


A__       :			str=						logging.get_logger(__name__)  # pylint: disable=invalid-name


A__       :			Union[str, Any]=						"""lfs-multipart-upload"""
class  __lowerCamelCase     (	_a	):

   @staticmethod
   def     SCREAMING_SNAKE_CASE__			(       snake_case_   )		->							Optional[int]:
       UpperCamelCase__								=     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=snake_case_			,					help='Local path to repository you want to configure.'   )
       enable_parser.set_defaults(func=lambda snake_case_   : LfsEnableCommand(snake_case_   )   )

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







class  __lowerCamelCase     :

   def __init__(       self			,					snake_case_   )		->							Tuple:
       UpperCamelCase__								=     args







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







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						Union[str, Any]:
    """simple docstring"""

    UpperCamelCase__								=     json.dumps(SCREAMING_SNAKE_CASE       ) + '\n'
    sys.stdout.write(SCREAMING_SNAKE_CASE       )
    sys.stdout.flush()







def 	lowerCAmelCase_(			)       ->						Optional[Dict]:
    """simple docstring"""

    UpperCamelCase__								=     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  __lowerCamelCase     (	_a	):

   def __init__(       self			,					snake_case_			,					snake_case_			,					snake_case_   )		->							Tuple:
       UpperCamelCase__								=     filepath
       UpperCamelCase__								=     seek_from
       UpperCamelCase__								=     read_limit
       UpperCamelCase__								=     0

   def __enter__(       self   )		->							Dict:
       UpperCamelCase__								=     open(self.filepath			,					'rb'   )
       self.f.seek(self.seek_from   )
       return self

   def __len__(       self   )		->							Union[str, Any]:
       UpperCamelCase__								=     os.fstat(self.f.fileno()   ).st_size
       return min(self.read_limit			,					total_length - self.seek_from   )

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_=-1   )		->							Tuple:
       if self.n_seen >= self.read_limit:
           return b""
       UpperCamelCase__								=     self.read_limit - self.n_seen
       UpperCamelCase__								=     self.f.read(remaining_amount if n < 0 else min(snake_case_			,					snake_case_   )   )
       self.n_seen += len(snake_case_   )
       return data

   def __iter__(       self   )		->							List[str]:
       yield self.read(n=4 * 1024 * 1024   )







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







class  __lowerCamelCase     :

   def __init__(       self			,					snake_case_   )		->							str:
       UpperCamelCase__								=     args







   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       # 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.
       UpperCamelCase__								=     json.loads(sys.stdin.readline().strip()   )
       if not (init_msg.get('event'   ) == "init" and init_msg.get('operation'   ) == "upload"):
           write_msg({'error': {'code': 32, '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:
           UpperCamelCase__								=     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   )

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

           UpperCamelCase__								=     []
           for i, presigned_url in enumerate(snake_case_   ):
               with FileSlice(snake_case_			,					seek_from=i * chunk_size			,					read_limit=snake_case_   ) as data:
                   UpperCamelCase__								=     requests.put(snake_case_			,					data=snake_case_   )
                   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.

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

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












"""simple docstring"""





import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE=0       )       ->						str:
    """simple docstring"""

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

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







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Tuple:
    """simple docstring"""

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







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Any:
    """simple docstring"""

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

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

    UpperCamelCase__								=     'model.language_model.encoder.layers.'
    for i in range(SCREAMING_SNAKE_CASE       ):
        UpperCamelCase__								=     torch.tril(torch.ones((n_positions, n_positions)			,						dtype=torch.bool       )       )
        UpperCamelCase__								=     causal_mask.view(1			,						1			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     causal_mask
        UpperCamelCase__								=     torch.tensor(-1E4			,						dtype=torch.bfloataa       )

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.input_layernorm.weight']
        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.input_layernorm.bias']

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.query_key_value.weight']
        UpperCamelCase__								=     fix_query_key_value_ordering(SCREAMING_SNAKE_CASE			,						3			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     vala.transpose(0			,						1       ).contiguous()

        UpperCamelCase__								=     sd_megatron[F'{pf}{i}.self_attention.query_key_value.bias']
        UpperCamelCase__								=     fix_query_key_value_ordering(SCREAMING_SNAKE_CASE			,						3			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )
        UpperCamelCase__								=     vala

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

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

    return sd_hf







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						int:
    """simple docstring"""

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

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

    return config_hf







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						str:
    """simple docstring"""

    print(SCREAMING_SNAKE_CASE       )

    UpperCamelCase__								=     args.checkpoint_path
    UpperCamelCase__								=     args.save_path
    if isfile(SCREAMING_SNAKE_CASE       ):
        raise FileNotFoundError(F'ERROR! could not find file {checkpoint_path}'       )

    # Load the model.
    UpperCamelCase__								=     torch.load(SCREAMING_SNAKE_CASE			,						map_location='cpu'       )

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

    UpperCamelCase__								=     checkpoint['state_dict']

    # Convert.
    print('Converting'       )
    UpperCamelCase__								=     convert_megatron_checkpoint(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )

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

    UpperCamelCase__								=     'GPTSw3Tokenizer'

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

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


if __name__ == "__main__":
  A__       :			Optional[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__       :			Any=						parser.parse_args()
  main(_args)




