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    UpperCAmelCase__(						lowerCamelCase_=2							,	lowerCamelCase_=True							,	lowerCamelCase_=False  ):



			"""simple docstring"""





			_snake_case              =      Accelerator(step_scheduler_with_optimizer=lowerCamelCase_							,	split_batches=lowerCamelCase_  )
			_snake_case              =      torch.nn.Linear(2							,	4  )
			_snake_case              =      torch.optim.AdamW(model.parameters()							,	lr=1.0  )
			_snake_case              =      torch.optim.lr_scheduler.OneCycleLR(lowerCamelCase_							,	max_lr=0.0_1							,	steps_per_epoch=2							,	epochs=1  )
			_snake_case       , _snake_case       , _snake_case              =      accelerator.prepare(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  )

			# 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    UpperCAmelCase__(						lowerCamelCase_=2							,	lowerCamelCase_=True							,	lowerCamelCase_=False  ):



			"""simple docstring"""





			_snake_case              =      Accelerator(step_scheduler_with_optimizer=lowerCamelCase_							,	split_batches=lowerCamelCase_  )
			_snake_case              =      torch.nn.Linear(2							,	4  )
			_snake_case              =      torch.optim.AdamW(model.parameters()							,	lr=1.0  )
			_snake_case              =      torch.optim.lr_scheduler.LambdaLR(lowerCamelCase_							,	lr_lambda=lambda lowerCamelCase_  : 1 - n / 10  )
			_snake_case       , _snake_case       , _snake_case              =      accelerator.prepare(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  )

			# Optimizer has stepped
			_snake_case              =      False
			scheduler.step()
			_snake_case              =      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
			_snake_case              =      True
			scheduler.step()
			if not step_scheduler_with_optimizer:
						_snake_case              =      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    UpperCAmelCase__(						lowerCamelCase_ = 2  ):



			"""simple docstring"""





			from transformers import get_linear_schedule_with_warmup

			_snake_case              =      [1, 2, 4]
			for num_steps in steps:
						_snake_case              =      GradientAccumulationPlugin(num_steps=lowerCamelCase_							,	adjust_scheduler=num_steps > 1  )
						_snake_case              =      Accelerator(gradient_accumulation_plugin=lowerCamelCase_  )
						_snake_case              =      torch.nn.Linear(2							,	4  )
						_snake_case              =      torch.optim.AdamW(model.parameters()							,	lr=10.0  )
						_snake_case              =      get_linear_schedule_with_warmup(optimizer=lowerCamelCase_							,	num_warmup_steps=0							,	num_training_steps=20  )

						_snake_case       , _snake_case       , _snake_case              =      accelerator.prepare(lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  )

						for i in range(10 * num_steps  ):
									with accelerator.accumulate(lowerCamelCase_  ):
												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 	_SCREAMING_SNAKE_CASE							(     unittest.TestCase							):






		"""simple docstring"""







		def 				_a				(			self    :				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 				_a				(			self    :				List[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 				_a				(			self    :				Optional[Any]   ):






					"""simple docstring"""
					debug_launcher(partial(__SCREAMING_SNAKE_CASE  ,					num_processes=1  ,					step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE   )  ,					num_processes=1   )



		def 				_a				(			self    :				Optional[int]   ):






					"""simple docstring"""
					debug_launcher(partial(__SCREAMING_SNAKE_CASE  ,					num_processes=1  ,					step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE   )  ,					num_processes=1   )



		def 				_a				(			self    :				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 				_a				(			self    :				Tuple   ):






					"""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 				_a				(			self    :				str   ):






					"""simple docstring"""
					AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE   )
					debug_launcher(partial(__SCREAMING_SNAKE_CASE  ,					step_scheduler_with_optimizer=__SCREAMING_SNAKE_CASE   )   )



		def 				_a				(			self    :				List[Any]   ):






					"""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 				_a				(			self    :				int   ):






					"""simple docstring"""
					AcceleratorState._reset_state(__SCREAMING_SNAKE_CASE   )
					debug_launcher(partial(__SCREAMING_SNAKE_CASE  ,					num_processes=1   )   )
					debug_launcher(__SCREAMING_SNAKE_CASE   )


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:						Dict    	=       '''lfs-multipart-upload'''

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		@staticmethod
		def 				_a				(			__SCREAMING_SNAKE_CASE    :				ArgumentParser   ):






					"""simple docstring"""
					_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=__SCREAMING_SNAKE_CASE  ,					help="Local path to repository you want to configure."   )
					enable_parser.set_defaults(func=lambda __SCREAMING_SNAKE_CASE   : LfsEnableCommand(__SCREAMING_SNAKE_CASE   )   )

					_snake_case              =      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 __SCREAMING_SNAKE_CASE   : LfsUploadCommand(__SCREAMING_SNAKE_CASE   )   )

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def __init__(			self    :				Union[str, Any]  ,					__SCREAMING_SNAKE_CASE    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      args







		def 				_a				(			self    :				Optional[int]   ):






					"""simple docstring"""
					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(__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    UpperCAmelCase__(						lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      json.dumps(lowerCamelCase_  ) + "\n"
			sys.stdout.write(lowerCamelCase_  )
			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 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		def __init__(			self    :				Optional[int]  ,					__SCREAMING_SNAKE_CASE    :				str  ,					__SCREAMING_SNAKE_CASE    :				int  ,					__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					_snake_case              =      filepath
					_snake_case              =      seek_from
					_snake_case              =      read_limit
					_snake_case              =      0



		def __enter__(			self    :				Optional[int]   ):






					"""simple docstring"""
					_snake_case              =      open(self.filepath  ,					"rb"   )
					self.f.seek(self.seek_from   )
					return self



		def __len__(			self    :				List[str]   ):






					"""simple docstring"""
					_snake_case              =      os.fstat(self.f.fileno()   ).st_size
					return min(self.read_limit  ,					total_length - self.seek_from   )



		def 				_a				(			self    :				Dict  ,					__SCREAMING_SNAKE_CASE    :				Any=-1   ):






					"""simple docstring"""
					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(__SCREAMING_SNAKE_CASE  ,					__SCREAMING_SNAKE_CASE   )   )
					self.n_seen += len(__SCREAMING_SNAKE_CASE   )
					return data



		def __iter__(			self    :				str   ):






					"""simple docstring"""
					yield self.read(n=4 * 1_024 * 1_024   )







		def __exit__(			self    :				Dict  ,					*__SCREAMING_SNAKE_CASE    :				int   ):






					"""simple docstring"""
					self.f.close()

class 	_SCREAMING_SNAKE_CASE							:






		"""simple docstring"""







		def __init__(			self    :				str  ,					__SCREAMING_SNAKE_CASE    :				str   ):






					"""simple docstring"""
					_snake_case              =      args







		def 				_a				(			self    :				Optional[int]   ):






					"""simple docstring"""
					# 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": 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:
								_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(__SCREAMING_SNAKE_CASE   ):
											with FileSlice(__SCREAMING_SNAKE_CASE  ,					seek_from=i * chunk_size  ,					read_limit=__SCREAMING_SNAKE_CASE   ) as data:
														_snake_case              =      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.

								_snake_case              =      requests.post(
								    __SCREAMING_SNAKE_CASE  ,					json={
								        "oid": oid,
								        "parts": parts,
								    }  ,					)
								r.raise_for_status()

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

