





"""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_     :      Dict								=logging.get_logger(__name__)  # pylint: disable=invalid-name


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







class 	__a   (  lowerCAmelCase_		):



     @staticmethod
     def 						snake_case_			(       a__   ):
            _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=_SCREAMING_SNAKE_CASE					,     help='Local path to repository you want to configure.'   )
            enable_parser.set_defaults(func=lambda a__   : LfsEnableCommand(_SCREAMING_SNAKE_CASE   )   )

            _lowerCamelCase    	=					parser.add_parser(
                _SCREAMING_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 a__   : LfsUploadCommand(_SCREAMING_SNAKE_CASE   )   )







class 	__a   :



     def __init__(       self					,     a__   ):
            _lowerCamelCase    	=					args



     def 						snake_case_			(       self   ):
            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(_SCREAMING_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=_SCREAMING_SNAKE_CASE					,     cwd=_SCREAMING_SNAKE_CASE   )
            subprocess.run(
                F'git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'.split()					,     check=_SCREAMING_SNAKE_CASE					,     cwd=_SCREAMING_SNAKE_CASE					,     )
            print('Local repo set up for largefiles'   )





def SCREAMING_SNAKE_CASE_   (       snake_case   :					str		)->		Optional[Any]:
       _lowerCamelCase    	=					json.dumps(UpperCamelCase_		) + '\n'
       sys.stdout.write(UpperCamelCase_		)
       sys.stdout.flush()





def SCREAMING_SNAKE_CASE_   (       )->		Optional[Dict]:
       _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_		):



     def __init__(       self					,     a__					,     a__					,     a__   ):
            _lowerCamelCase    	=					filepath
            _lowerCamelCase    	=					seek_from
            _lowerCamelCase    	=					read_limit
            _lowerCamelCase    	=					0



     def __enter__(       self   ):
            _lowerCamelCase    	=					open(self.filepath					,     'rb'   )
            self.f.seek(self.seek_from   )
            return self



     def __len__(       self   ):
            _lowerCamelCase    	=					os.fstat(self.f.fileno()   ).st_size
            return min(self.read_limit					,     total_length - self.seek_from   )



     def 						snake_case_			(       self					,     a__=-1   ):


            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(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE   )   )
            self.n_seen += len(_SCREAMING_SNAKE_CASE   )
            return data



     def __iter__(       self   ):
            yield self.read(n=4 * 10_24 * 10_24   )



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







class 	__a   :



     def __init__(       self					,     a__   ):
            _lowerCamelCase    	=					args



     def 						snake_case_			(       self   ):
            _lowerCamelCase    	=					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    	=					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(_SCREAMING_SNAKE_CASE   ):
                          with FileSlice(_SCREAMING_SNAKE_CASE					,     seek_from=i * chunk_size					,     read_limit=_SCREAMING_SNAKE_CASE   ) as data:
                                 _lowerCamelCase    	=					requests.put(_SCREAMING_SNAKE_CASE					,     data=_SCREAMING_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.

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

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













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        lowerCAmelCase_       (							UpperCamelCase_=2    ,						UpperCamelCase_=True    ,						UpperCamelCase_=False     )   ->						str:
       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=0.01    ,						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        lowerCAmelCase_       (							UpperCamelCase_=2    ,						UpperCamelCase_=True    ,						UpperCamelCase_=False     )   ->						List[Any]:
       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        lowerCAmelCase_       (							UpperCamelCase_ = 2     )   ->						int:
       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=10.0     )
              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:       Dict						)				->       Any:




              """simple docstring"""


              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      num_processes=1						)    ,      num_processes=1						)
              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      num_processes=1    ,      split_batches=_SCREAMING_SNAKE_CASE						)    ,      num_processes=1						)

       def lowercase       (						self:       Dict						)				->       Dict:




              """simple docstring"""


              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      num_processes=1						)    ,      num_processes=1						)
              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      num_processes=1    ,      split_batches=_SCREAMING_SNAKE_CASE						)    ,      num_processes=1						)

       def lowercase       (						self:       Dict						)				->       List[str]:




              """simple docstring"""


              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      num_processes=1    ,      step_scheduler_with_optimizer=_SCREAMING_SNAKE_CASE						)    ,      num_processes=1						)

       def lowercase       (						self:       Any						)				->       int:




              """simple docstring"""


              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      num_processes=1    ,      step_scheduler_with_optimizer=_SCREAMING_SNAKE_CASE						)    ,      num_processes=1						)

       def lowercase       (						self:       Optional[int]						)				->       List[str]:




              """simple docstring"""


              AcceleratorState._reset_state(_SCREAMING_SNAKE_CASE						)
              debug_launcher(_SCREAMING_SNAKE_CASE						)
              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      num_processes=1    ,      split_batches=_SCREAMING_SNAKE_CASE						)    ,      num_processes=1						)

       def lowercase       (						self:       Tuple						)				->       Optional[int]:




              """simple docstring"""


              AcceleratorState._reset_state(_SCREAMING_SNAKE_CASE						)
              debug_launcher(_SCREAMING_SNAKE_CASE						)
              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      num_processes=1    ,      split_batches=_SCREAMING_SNAKE_CASE						)    ,      num_processes=1						)

       def lowercase       (						self:       int						)				->       Tuple:




              """simple docstring"""


              AcceleratorState._reset_state(_SCREAMING_SNAKE_CASE						)
              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      step_scheduler_with_optimizer=_SCREAMING_SNAKE_CASE						)						)

       def lowercase       (						self:       Dict						)				->       Dict:




              """simple docstring"""


              AcceleratorState._reset_state(_SCREAMING_SNAKE_CASE						)
              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      step_scheduler_with_optimizer=_SCREAMING_SNAKE_CASE						)						)



       @require_huggingface_suite
       def lowercase       (						self:       Dict						)				->       Optional[Any]:




              """simple docstring"""


              AcceleratorState._reset_state(_SCREAMING_SNAKE_CASE						)
              debug_launcher(partial(_SCREAMING_SNAKE_CASE    ,      num_processes=1						)						)
              debug_launcher(_SCREAMING_SNAKE_CASE						)

