


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    :				List[str]       =					logging.get_logger(__name__)  # pylint: disable=invalid-name


_SCREAMING_SNAKE_CASE    :				Tuple       =					"lfs-multipart-upload"

class 						A__      (					snake_case__			):







    """simple docstring"""

    @staticmethod
    def 	a_    ( __snake_case					):
         snake_case					=       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					)					)

         snake_case					=       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 						A__      :







    """simple docstring"""

    def __init__( self    ,      __snake_case					):
         snake_case					=       args




    def 	a_    ( self					):
         warnings.warn(
             '''Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead.'''					)
         snake_case					=       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     UpperCAmelCase__		(UpperCamelCase_       ):



     """simple docstring"""






     snake_case					=       json.dumps(UpperCamelCase_       ) + '''\n'''
     sys.stdout.write(UpperCamelCase_       )
     sys.stdout.flush()
def     UpperCAmelCase__		():



     """simple docstring"""






     snake_case					=       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__      (					snake_case__			):







    """simple docstring"""

    def __init__( self    ,      __snake_case    ,      __snake_case    ,      __snake_case					):
         snake_case					=       filepath
         snake_case					=       seek_from
         snake_case					=       read_limit
         snake_case					=       0
    def __enter__( self					):
         snake_case					=       open(self.filepath    ,      '''rb'''					)
         self.f.seek(self.seek_from					)
         return self
    def __len__( self					):
         snake_case					=       os.fstat(self.f.fileno()					).st_size
         return min(self.read_limit    ,      total_length - self.seek_from					)
    def 	a_    ( self    ,      __snake_case=-1					):
         if self.n_seen >= self.read_limit:
              return b""
         snake_case					=       self.read_limit - self.n_seen
         snake_case					=       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					):
         yield self.read(n=4 * 1_0_2_4 * 1_0_2_4					)




    def __exit__( self    ,      *__snake_case					):
         self.f.close()






class 						A__      :







    """simple docstring"""

    def __init__( self    ,      __snake_case					):
         snake_case					=       args




    def 	a_    ( self					):
         # 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.
         snake_case					=       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:
              snake_case					=       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					)

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

              snake_case					=       []
              for i, presigned_url in enumerate(__snake_case					):
                   with FileSlice(__snake_case    ,      seek_from=i * chunk_size    ,      read_limit=__snake_case					) as data:
                        snake_case					=       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.

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

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








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE    :				Optional[int]       =					{
    "configuration_layoutlm": ["LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "LayoutLMConfig", "LayoutLMOnnxConfig"],
    "tokenization_layoutlm": ["LayoutLMTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				str       =					["LayoutLMTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				List[str]       =					[
     "LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LayoutLMForMaskedLM",
     "LayoutLMForSequenceClassification",
     "LayoutLMForTokenClassification",
     "LayoutLMForQuestionAnswering",
     "LayoutLMModel",
     "LayoutLMPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					[
     "TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFLayoutLMForMaskedLM",
     "TFLayoutLMForSequenceClassification",
     "TFLayoutLMForTokenClassification",
     "TFLayoutLMForQuestionAnswering",
     "TFLayoutLMMainLayer",
     "TFLayoutLMModel",
     "TFLayoutLMPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
 from .tokenization_layoutlm import LayoutLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_layoutlm import (
      LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      LayoutLMForMaskedLM,
      LayoutLMForQuestionAnswering,
      LayoutLMForSequenceClassification,
      LayoutLMForTokenClassification,
      LayoutLMModel,
      LayoutLMPreTrainedModel,
  )
 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_layoutlm import (
      TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFLayoutLMForMaskedLM,
      TFLayoutLMForQuestionAnswering,
      TFLayoutLMForSequenceClassification,
      TFLayoutLMForTokenClassification,
      TFLayoutLMMainLayer,
      TFLayoutLMModel,
      TFLayoutLMPreTrainedModel,
  )

else:
 import sys

 _SCREAMING_SNAKE_CASE    :				Optional[int]       =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




