code: 
  
  
  '''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
  
  
  __lowercase:		List[str]							=     logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  __lowercase:		Optional[Any]							=     "lfs-multipart-upload"
  
  
  
  
  
  
  
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
  
   @staticmethod
   def 		lowercase_						( a_ :	ArgumentParser					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      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=a_,					help="Local path to repository you want to configure."					)
      enable_parser.set_defaults(func=lambda a_					: LfsEnableCommand(a_					)					)
  
      UpperCamelCase__ 						=						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  UpperCAmelCase :
  
   def __init__( self :	Any,					a_ :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						args
  
   def 		lowercase_						( self :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      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(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        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Dict   )		->					Union[str, Any]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						json.dumps(_UpperCamelCase   ) + "\n"
     sys.stdout.write(_UpperCamelCase   )
     sys.stdout.flush()
  
  
  
  def        SCREAMING_SNAKE_CASE__(		)		->					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  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
  
   def __init__( self :	int,					a_ :	str,					a_ :	int,					a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						filepath
      UpperCamelCase__ 						=						seek_from
      UpperCamelCase__ 						=						read_limit
      UpperCamelCase__ 						=						0
  
   def __enter__( self :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						open(self.filepath,					"rb"					)
      self.f.seek(self.seek_from					)
      return self
  
   def __len__( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						os.fstat(self.f.fileno()					).st_size
      return min(self.read_limit,					total_length - self.seek_from					)
  
   def 		lowercase_						( self :	Union[str, Any],					a_ :	Tuple=-1					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      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(a_,					a_					)					)
      self.n_seen += len(a_					)
      return data
  
   def __iter__( self :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      yield self.read(n=4 * 1024 * 1024					)
  
   def __exit__( self :	str,					*a_ :	Dict					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      self.f.close()
  
  
  
  
  
  
  
  class  UpperCAmelCase :
  
   def __init__( self :	List[str],					a_ :	List[str]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						args
  
   def 		lowercase_						( self :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      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(a_					):
            with FileSlice(a_,					seek_from=i * chunk_size,					read_limit=a_					) as data:
               UpperCamelCase__ 						=						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.
  
         UpperCamelCase__ 						=						requests.post(
             a_,					json={
                 "oid": oid,
                 "parts": parts,
             },					)
         r.raise_for_status()
  
         write_msg({"event": "complete", "oid": oid}					)
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  import unittest
  from functools import partial
  
  import torch
  
  from accelerate import Accelerator, debug_launcher
  from accelerate.state import AcceleratorState, GradientState
  from accelerate.test_utils import require_cpu, require_huggingface_suite
  from accelerate.utils import GradientAccumulationPlugin
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Any=2    ,		_UpperCamelCase      :				Tuple=True    ,		_UpperCamelCase      :				Union[str, Any]=False   )		->					int:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						Accelerator(step_scheduler_with_optimizer=_UpperCamelCase    ,		split_batches=_UpperCamelCase   )
     UpperCamelCase__ 						=						torch.nn.Linear(2    ,		4   )
     UpperCamelCase__ 						=						torch.optim.AdamW(model.parameters()    ,		lr=1.0   )
     UpperCamelCase__ 						=						torch.optim.lr_scheduler.OneCycleLR(_UpperCamelCase    ,		max_lr=0.0_1    ,		steps_per_epoch=2    ,		epochs=1   )
     UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__ 						=						accelerator.prepare(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
  
     # Optimizer has stepped
     scheduler.step()
     if step_scheduler_with_optimizer or (num_processes == 1):
        assert (
            scheduler.scheduler.last_epoch == num_processes
        ), F'Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'
     else:
        assert (
            scheduler.scheduler.last_epoch != num_processes
        ), F'Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[int]=2    ,		_UpperCamelCase      :				List[str]=True    ,		_UpperCamelCase      :				Tuple=False   )		->					Optional[Any]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						Accelerator(step_scheduler_with_optimizer=_UpperCamelCase    ,		split_batches=_UpperCamelCase   )
     UpperCamelCase__ 						=						torch.nn.Linear(2    ,		4   )
     UpperCamelCase__ 						=						torch.optim.AdamW(model.parameters()    ,		lr=1.0   )
     UpperCamelCase__ 						=						torch.optim.lr_scheduler.LambdaLR(_UpperCamelCase    ,		lr_lambda=lambda _UpperCamelCase   : 1 - n / 10   )
     UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__ 						=						accelerator.prepare(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
  
     # Optimizer has stepped
     UpperCamelCase__ 						=						False
     scheduler.step()
     UpperCamelCase__ 						=						1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
     assert (
         scheduler.get_last_lr()[0] == expected_lr
     ), F'Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'
  
     # Optimizer has not stepped
     UpperCamelCase__ 						=						True
     scheduler.step()
     if not step_scheduler_with_optimizer:
        UpperCamelCase__ 						=						1 - 2 / 10
     assert (
         scheduler.get_last_lr()[0] == expected_lr
     ), F'Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				int = 2   )		->					Optional[int]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     from transformers import get_linear_schedule_with_warmup
  
     UpperCamelCase__ 						=						[1, 2, 4]
     for num_steps in steps:
        UpperCamelCase__ 						=						GradientAccumulationPlugin(num_steps=_UpperCamelCase    ,		adjust_scheduler=num_steps > 1   )
        UpperCamelCase__ 						=						Accelerator(gradient_accumulation_plugin=_UpperCamelCase   )
        UpperCamelCase__ 						=						torch.nn.Linear(2    ,		4   )
        UpperCamelCase__ 						=						torch.optim.AdamW(model.parameters()    ,		lr=1_0.0   )
        UpperCamelCase__ 						=						get_linear_schedule_with_warmup(optimizer=_UpperCamelCase    ,		num_warmup_steps=0    ,		num_training_steps=20   )
  
        UpperCamelCase__						,		UpperCamelCase__						,		UpperCamelCase__ 						=						accelerator.prepare(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
  
        for i in range(10 * num_steps   ):
           with accelerator.accumulate(_UpperCamelCase   ):
              optimizer.step()
              scheduler.step()
  
           if i == (10 * num_steps - 2):
              assert (
                  scheduler.get_last_lr()[0] != 0
              ), F'Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'
        assert (
            scheduler.get_last_lr()[0] == 0
        ), F'Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'
        GradientState._reset_state()
  
  
  
  
  
  
  
  @require_cpu
  class  UpperCAmelCase (       unittest.TestCase):
  
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      debug_launcher(partial(a_,					num_processes=1					),					num_processes=1					)
      debug_launcher(partial(a_,					num_processes=1,					split_batches=a_					),					num_processes=1					)
  
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      debug_launcher(partial(a_,					num_processes=1					),					num_processes=1					)
      debug_launcher(partial(a_,					num_processes=1,					split_batches=a_					),					num_processes=1					)
  
   def 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      debug_launcher(partial(a_,					num_processes=1,					step_scheduler_with_optimizer=a_					),					num_processes=1					)
  
   def 		lowercase_						( self :	List[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      debug_launcher(partial(a_,					num_processes=1,					step_scheduler_with_optimizer=a_					),					num_processes=1					)
  
   def 		lowercase_						( self :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      AcceleratorState._reset_state(a_					)
      debug_launcher(a_					)
      debug_launcher(partial(a_,					num_processes=1,					split_batches=a_					),					num_processes=1					)
  
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      AcceleratorState._reset_state(a_					)
      debug_launcher(a_					)
      debug_launcher(partial(a_,					num_processes=1,					split_batches=a_					),					num_processes=1					)
  
   def 		lowercase_						( self :	Optional[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      AcceleratorState._reset_state(a_					)
      debug_launcher(partial(a_,					step_scheduler_with_optimizer=a_					)					)
  
   def 		lowercase_						( self :	List[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      AcceleratorState._reset_state(a_					)
      debug_launcher(partial(a_,					step_scheduler_with_optimizer=a_					)					)
  
   @require_huggingface_suite
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      AcceleratorState._reset_state(a_					)
      debug_launcher(partial(a_,					num_processes=1					)					)
      debug_launcher(a_					)