



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


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


_UpperCamelCase							    =						'lfs-multipart-upload'






class  lowerCamelCase_   (    lowerCamelCase_					):
  """simple docstring"""




  @staticmethod
  def 						_lowercase					(			_a				:     Optional[Any]       ) -> Union[str, Any]:
    __lowerCamelCase	:	Any   	=			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	:	str   	=			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  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     Optional[int]      ,     _a				:     Optional[Any]       ) -> int:
    __lowerCamelCase	:	Any   	=			args




  def 						_lowercase					(			self				:     List[Any]       ) -> int:
    warnings.warn(
        'Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead.'       )
    __lowerCamelCase	:	Any   	=			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 	a_					(							_lowerCAmelCase	)				->				List[Any]:
  __lowerCamelCase	:	Tuple   	=			json.dumps(lowercase__	) + "\n"
  sys.stdout.write(lowercase__	)
  sys.stdout.flush()

def 	a_					(							)				->				int:
  __lowerCamelCase	:	Union[str, Any]   	=			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_   (    lowerCamelCase_					):
  """simple docstring"""




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




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




  def __len__(			self				:     Any       ) -> Tuple:
    __lowerCamelCase	:	int   	=			os.fstat(self.f.fileno()       ).st_size
    return min(self.read_limit      ,     total_length - self.seek_from       )




  def 						_lowercase					(			self				:     str      ,     _a				:     Any=-1       ) -> Tuple:







    if self.n_seen >= self.read_limit:
      return b""
    __lowerCamelCase	:	Any   	=			self.read_limit - self.n_seen
    __lowerCamelCase	:	int   	=			self.f.read(remaining_amount if n < 0 else min(_a      ,     _a       )       )
    self.n_seen += len(_a       )
    return data




  def __iter__(			self				:     Tuple       ) -> Optional[int]:
    yield self.read(n=4 * 1024 * 1024       )




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






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     Tuple      ,     _a				:     Dict       ) -> Optional[int]:
    __lowerCamelCase	:	List[str]   	=			args




  def 						_lowercase					(			self				:     List[str]       ) -> Tuple:
    __lowerCamelCase	:	Tuple   	=			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:
      __lowerCamelCase	:	str   	=			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	:	List[Any]   	=			msg["oid"]
      __lowerCamelCase	:	Any   	=			msg["path"]
      __lowerCamelCase	:	List[Any]   	=			msg["action"]["href"]
      __lowerCamelCase	:	Tuple   	=			msg["action"]["header"]
      __lowerCamelCase	:	Optional[int]   	=			int(header.pop('chunk_size'       )       )
      __lowerCamelCase	:	List[str]   	=			list(header.values()       )

      __lowerCamelCase	:	Union[str, Any]   	=			[]
      for i, presigned_url in enumerate(_a       ):
        with FileSlice(_a      ,     seek_from=i * chunk_size      ,     read_limit=_a       ) as data:
          __lowerCamelCase	:	List[Any]   	=			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	:	str   	=			requests.post(
          _a      ,     json={
              'oid': oid,
              'parts': parts,
          }      ,     )
      r.raise_for_status()

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











'''simple docstring'''





import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()

def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				List[Any]:
  # Initialize PyTorch model
  __lowerCamelCase	:	Union[str, Any]   	=			CanineConfig()
  __lowerCamelCase	:	Optional[Any]   	=			CanineModel(_lowerCAmelCase	)
  model.eval()

  print(F'Building PyTorch model from configuration: {config}'	)

  # Load weights from tf checkpoint
  load_tf_weights_in_canine(_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)

  # Save pytorch-model (weights and configuration)
  print(F'Save PyTorch model to {pytorch_dump_path}'	)
  model.save_pretrained(_lowerCAmelCase	)

  # Save tokenizer files
  __lowerCamelCase	:	str   	=			CanineTokenizer()
  print(F'Save tokenizer files to {pytorch_dump_path}'	)
  tokenizer.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
   _UpperCamelCase							    =						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path',
       default=None,
       type=str,
       required=True,
       help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
   )
   parser.add_argument(
       '--pytorch_dump_path',
       default=None,
       type=str,
       required=True,
       help='Path to a folder where the PyTorch model will be placed.',
   )
   _UpperCamelCase							    =						parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






