





'''simple docstring'''






import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
						import transformers


def 					_a   (__SCREAMING_SNAKE_CASE   ):


		"""simple docstring"""

		random.seed(lowerCAmelCase__   )
		np.random.seed(lowerCAmelCase__   )
		torch.manual_seed(lowerCAmelCase__   )
		torch.cuda.manual_seed_all(lowerCAmelCase__   )
		# ^^ safe to call this function even if cuda is not available







class 		UpperCAmelCase   :


		"""simple docstring"""







		def __init__(				self    :					Any							,	UpperCamelCase__    :					Iterable[torch.nn.Parameter]							,	UpperCamelCase__    :					float = 0.9999							,	UpperCamelCase__    :					float = 0.0							,	UpperCamelCase__    :					int = 0							,	UpperCamelCase__    :					bool = False							,	UpperCamelCase__    :					Union[float, int] = 1.0							,	UpperCamelCase__    :					Union[float, int] = 2 / 3							,	UpperCamelCase__    :					Optional[Any] = None							,	UpperCamelCase__    :					Dict[str, Any] = None							,	**UpperCamelCase__    :					int							,	)     ->						Dict:

				if isinstance(snake_case__							,	torch.nn.Module  ):
						_UpperCamelCase       			=(
						    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '''
						    '''Please pass the parameters of the module instead.'''
						)
						deprecate(
						    '''passing a `torch.nn.Module` to `ExponentialMovingAverage`'''							,	'''1.0.0'''							,	snake_case__							,	standard_warn=snake_case__							,	)
						_UpperCamelCase       			=parameters.parameters()

						# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
						_UpperCamelCase       			=True

				if kwargs.get('''max_value'''							,	snake_case__  ) is not None:
						_UpperCamelCase       			='''The `max_value` argument is deprecated. Please use `decay` instead.'''
						deprecate('''max_value'''							,	'''1.0.0'''							,	snake_case__							,	standard_warn=snake_case__  )
						_UpperCamelCase       			=kwargs['''max_value''']

				if kwargs.get('''min_value'''							,	snake_case__  ) is not None:
						_UpperCamelCase       			='''The `min_value` argument is deprecated. Please use `min_decay` instead.'''
						deprecate('''min_value'''							,	'''1.0.0'''							,	snake_case__							,	standard_warn=snake_case__  )
						_UpperCamelCase       			=kwargs['''min_value''']

				_UpperCamelCase       			=list(snake_case__  )
				_UpperCamelCase       			=[p.clone().detach() for p in parameters]

				if kwargs.get('''device'''							,	snake_case__  ) is not None:
						_UpperCamelCase       			='''The `device` argument is deprecated. Please use `to` instead.'''
						deprecate('''device'''							,	'''1.0.0'''							,	snake_case__							,	standard_warn=snake_case__  )
						self.to(device=kwargs['''device''']  )

				_UpperCamelCase       			=None

				_UpperCamelCase       			=decay
				_UpperCamelCase       			=min_decay
				_UpperCamelCase       			=update_after_step
				_UpperCamelCase       			=use_ema_warmup
				_UpperCamelCase       			=inv_gamma
				_UpperCamelCase       			=power
				_UpperCamelCase       			=0
				_UpperCamelCase       			=None  # set in `step()`

				_UpperCamelCase       			=model_cls
				_UpperCamelCase       			=model_config


		@classmethod
		def   UpperCamelCase__      (				cls    :					Dict							,	UpperCamelCase__    :					List[Any]							,	UpperCamelCase__    :					Any  )     ->						str:
				_UpperCamelCase			,     _UpperCamelCase       			=model_cls.load_config(snake_case__							,	return_unused_kwargs=snake_case__  )
				_UpperCamelCase       			=model_cls.from_pretrained(snake_case__  )

				_UpperCamelCase       			=cls(model.parameters()							,	model_cls=snake_case__							,	model_config=model.config  )

				ema_model.load_state_dict(snake_case__  )
				return ema_model


		def   UpperCamelCase__      (				self    :					Optional[Any]							,	UpperCamelCase__    :					List[Any]  )     ->						Any:
				if self.model_cls is None:
						raise ValueError('''`save_pretrained` can only be used if `model_cls` was defined at __init__.'''  )

				if self.model_config is None:
						raise ValueError('''`save_pretrained` can only be used if `model_config` was defined at __init__.'''  )

				_UpperCamelCase       			=self.model_cls.from_config(self.model_config  )
				_UpperCamelCase       			=self.state_dict()
				state_dict.pop('''shadow_params'''							,	snake_case__  )

				model.register_to_config(**snake_case__  )
				self.copy_to(model.parameters()  )
				model.save_pretrained(snake_case__  )


		def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					int  )     ->						List[Any]:
				_UpperCamelCase       			=max(0							,	optimization_step - self.update_after_step - 1  )

				if step <= 0:
						return 0.0

				if self.use_ema_warmup:
						_UpperCamelCase       			=1 - (1 + step / self.inv_gamma) ** -self.power
				else:
						_UpperCamelCase       			=(1 + step) / (10 + step)

				_UpperCamelCase       			=min(snake_case__							,	self.decay  )
				# make sure decay is not smaller than min_decay
				_UpperCamelCase       			=max(snake_case__							,	self.min_decay  )
				return cur_decay_value


		@torch.no_grad()
		def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					Iterable[torch.nn.Parameter]  )     ->						Union[str, Any]:
				if isinstance(snake_case__							,	torch.nn.Module  ):
						_UpperCamelCase       			=(
						    '''Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '''
						    '''Please pass the parameters of the module instead.'''
						)
						deprecate(
						    '''passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'''							,	'''1.0.0'''							,	snake_case__							,	standard_warn=snake_case__							,	)
						_UpperCamelCase       			=parameters.parameters()

				_UpperCamelCase       			=list(snake_case__  )

				self.optimization_step += 1

				# Compute the decay factor for the exponential moving average.
				_UpperCamelCase       			=self.get_decay(self.optimization_step  )
				_UpperCamelCase       			=decay
				_UpperCamelCase       			=1 - decay

				_UpperCamelCase       			=contextlib.nullcontext
				if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
						import deepspeed

				for s_param, param in zip(self.shadow_params							,	snake_case__  ):
						if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
								_UpperCamelCase       			=deepspeed.zero.GatheredParameters(snake_case__							,	modifier_rank=snake_case__  )

						with context_manager():
								if param.requires_grad:
										s_param.sub_(one_minus_decay * (s_param - param)  )
								else:
										s_param.copy_(snake_case__  )


		def   UpperCamelCase__      (				self    :					List[Any]							,	UpperCamelCase__    :					Iterable[torch.nn.Parameter]  )     ->						Tuple:
				_UpperCamelCase       			=list(snake_case__  )
				for s_param, param in zip(self.shadow_params							,	snake_case__  ):
						param.data.copy_(s_param.to(param.device  ).data  )


		def   UpperCamelCase__      (				self    :					List[str]							,	UpperCamelCase__    :					int=None							,	UpperCamelCase__    :					int=None  )     ->						Optional[int]:
				_UpperCamelCase       			=[
				    p.to(device=snake_case__							,	dtype=snake_case__  ) if p.is_floating_point() else p.to(device=snake_case__  )
				    for p in self.shadow_params
				]


		def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						str:
				return {
				    "decay": self.decay,
				    "min_decay": self.min_decay,
				    "optimization_step": self.optimization_step,
				    "update_after_step": self.update_after_step,
				    "use_ema_warmup": self.use_ema_warmup,
				    "inv_gamma": self.inv_gamma,
				    "power": self.power,
				    "shadow_params": self.shadow_params,
				}


		def   UpperCamelCase__      (				self    :					Any							,	UpperCamelCase__    :					Iterable[torch.nn.Parameter]  )     ->						str:
				_UpperCamelCase       			=[param.detach().cpu().clone() for param in parameters]


		def   UpperCamelCase__      (				self    :					str							,	UpperCamelCase__    :					Iterable[torch.nn.Parameter]  )     ->						Any:
				if self.temp_stored_params is None:
						raise RuntimeError('''This ExponentialMovingAverage has no `store()`ed weights ''' '''to `restore()`'''  )
				for c_param, param in zip(self.temp_stored_params							,	snake_case__  ):
						param.data.copy_(c_param.data  )

				# Better memory-wise.
				_UpperCamelCase       			=None




		def   UpperCamelCase__      (				self    :					Union[str, Any]							,	UpperCamelCase__    :					dict  )     ->						Dict:
				_UpperCamelCase       			=copy.deepcopy(snake_case__  )

				_UpperCamelCase       			=state_dict.get('''decay'''							,	self.decay  )
				if self.decay < 0.0 or self.decay > 1.0:
						raise ValueError('''Decay must be between 0 and 1'''  )

				_UpperCamelCase       			=state_dict.get('''min_decay'''							,	self.min_decay  )
				if not isinstance(self.min_decay							,	snake_case__  ):
						raise ValueError('''Invalid min_decay'''  )

				_UpperCamelCase       			=state_dict.get('''optimization_step'''							,	self.optimization_step  )
				if not isinstance(self.optimization_step							,	snake_case__  ):
						raise ValueError('''Invalid optimization_step'''  )

				_UpperCamelCase       			=state_dict.get('''update_after_step'''							,	self.update_after_step  )
				if not isinstance(self.update_after_step							,	snake_case__  ):
						raise ValueError('''Invalid update_after_step'''  )

				_UpperCamelCase       			=state_dict.get('''use_ema_warmup'''							,	self.use_ema_warmup  )
				if not isinstance(self.use_ema_warmup							,	snake_case__  ):
						raise ValueError('''Invalid use_ema_warmup'''  )

				_UpperCamelCase       			=state_dict.get('''inv_gamma'''							,	self.inv_gamma  )
				if not isinstance(self.inv_gamma							,	(float, int)  ):
						raise ValueError('''Invalid inv_gamma'''  )

				_UpperCamelCase       			=state_dict.get('''power'''							,	self.power  )
				if not isinstance(self.power							,	(float, int)  ):
						raise ValueError('''Invalid power'''  )

				_UpperCamelCase       			=state_dict.get('''shadow_params'''							,	snake_case__  )
				if shadow_params is not None:
						_UpperCamelCase       			=shadow_params
						if not isinstance(self.shadow_params							,	snake_case__  ):
								raise ValueError('''shadow_params must be a list'''  )
						if not all(isinstance(snake_case__							,	torch.Tensor  ) for p in self.shadow_params  ):
								raise ValueError('''shadow_params must all be Tensors'''  )













'''simple docstring'''






import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  return EnvironmentCommand()


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  return EnvironmentCommand(args.accelerate_config_file   )







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  @staticmethod
  def   UpperCamelCase__      (				UpperCamelCase__    :					ArgumentParser  )     ->						Union[str, Any]:
    _UpperCamelCase       			=parser.add_parser('''env'''  )
    download_parser.set_defaults(func=UpperCamelCase__  )
    download_parser.add_argument(
        '''--accelerate-config_file'''							,	default=UpperCamelCase__							,	help='''The accelerate config file to use for the default values in the launching script.'''							,	)
    download_parser.set_defaults(func=UpperCamelCase__  )


  def __init__(				self    :					Tuple							,	UpperCamelCase__    :					Dict							,	*UpperCamelCase__    :					str  )     ->						None:
    _UpperCamelCase       			=accelerate_config_file


  def   UpperCamelCase__      (				self    :					str  )     ->						int:
    _UpperCamelCase       			='''not installed'''
    if is_safetensors_available():
      import safetensors

      _UpperCamelCase       			=safetensors.__version__
    elif importlib.util.find_spec('''safetensors'''  ) is not None:
      import safetensors

      _UpperCamelCase       			=F'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			=_UpperCamelCase       			='''not found'''
    if is_accelerate_available():
      import accelerate
      from accelerate.commands.config import default_config_file, load_config_from_file

      _UpperCamelCase       			=accelerate.__version__
      # Get the default from the config file.
      if self._accelerate_config_file is not None or os.path.isfile(UpperCamelCase__  ):
        _UpperCamelCase       			=load_config_from_file(self._accelerate_config_file  ).to_dict()

      _UpperCamelCase       			=(
          '''\n'''.join([F'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()]  )
          if isinstance(UpperCamelCase__							,	UpperCamelCase__  )
          else F'''\t{accelerate_config}'''
      )

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''NA'''
    if is_torch_available():
      import torch

      _UpperCamelCase       			=torch.__version__
      _UpperCamelCase       			=torch.cuda.is_available()

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''NA'''
    if is_tf_available():
      import tensorflow as tf

      _UpperCamelCase       			=tf.__version__
      try:
        # deprecated in v2.1
        _UpperCamelCase       			=tf.test.is_gpu_available()
      except AttributeError:
        # returns list of devices, convert to bool
        _UpperCamelCase       			=bool(tf.config.list_physical_devices('''GPU'''  )  )

    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''not installed'''
    _UpperCamelCase       			='''NA'''
    if is_flax_available():
      import flax
      import jax
      import jaxlib

      _UpperCamelCase       			=flax.__version__
      _UpperCamelCase       			=jax.__version__
      _UpperCamelCase       			=jaxlib.__version__
      _UpperCamelCase       			=jax.lib.xla_bridge.get_backend().platform

    _UpperCamelCase       			={
        '''`transformers` version''': version,
        '''Platform''': platform.platform(),
        '''Python version''': platform.python_version(),
        '''Huggingface_hub version''': huggingface_hub.__version__,
        '''Safetensors version''': F'''{safetensors_version}''',
        '''Accelerate version''': F'''{accelerate_version}''',
        '''Accelerate config''': F'''{accelerate_config_str}''',
        '''PyTorch version (GPU?)''': F'''{pt_version} ({pt_cuda_available})''',
        '''Tensorflow version (GPU?)''': F'''{tf_version} ({tf_cuda_available})''',
        '''Flax version (CPU?/GPU?/TPU?)''': F'''{flax_version} ({jax_backend})''',
        '''Jax version''': F'''{jax_version}''',
        '''JaxLib version''': F'''{jaxlib_version}''',
        '''Using GPU in script?''': '''<fill in>''',
        '''Using distributed or parallel set-up in script?''': '''<fill in>''',
    }

    print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n'''  )
    print(self.format_dict(UpperCamelCase__  )  )

    return info




  @staticmethod
  def   UpperCamelCase__      (				UpperCamelCase__    :					Optional[Any]  )     ->						Dict:
    return "\n".join([F'''- {prop}: {val}''' for prop, val in d.items()]  ) + "\n"






