



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

class    __UpperCamelCase			:

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE = 0.99_99		,				SCREAMING_SNAKE_CASE = 0.0		,				SCREAMING_SNAKE_CASE = 0		,				SCREAMING_SNAKE_CASE = False		,				SCREAMING_SNAKE_CASE = 1.0		,				SCREAMING_SNAKE_CASE = 2 / 3		,				SCREAMING_SNAKE_CASE = None		,				SCREAMING_SNAKE_CASE = None		,				**SCREAMING_SNAKE_CASE		,				) ->			Optional[int]:

  if isinstance(SCREAMING_SNAKE_CASE		,				torch.nn.Module      ):
   a__												= (
       '''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'''		,				SCREAMING_SNAKE_CASE		,				standard_warn=SCREAMING_SNAKE_CASE		,				)
   a__												= parameters.parameters()

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

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

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

  a__												= list(SCREAMING_SNAKE_CASE      )
  a__												= [p.clone().detach() for p in parameters]

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

  a__												= None

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

  a__												= model_cls
  a__												= model_config

 @classmethod
 def 						_UpperCAmelCase       (	cls		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			"EMAModel":
  a__							,			a__												= model_cls.load_config(SCREAMING_SNAKE_CASE		,				return_unused_kwargs=SCREAMING_SNAKE_CASE      )
  a__												= model_cls.from_pretrained(SCREAMING_SNAKE_CASE      )

  a__												= cls(model.parameters()		,				model_cls=SCREAMING_SNAKE_CASE		,				model_config=model.config      )

  ema_model.load_state_dict(SCREAMING_SNAKE_CASE      )
  return ema_model

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			int:
  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__.'''      )

  a__												= self.model_cls.from_config(self.model_config      )
  a__												= self.state_dict()
  state_dict.pop('''shadow_params'''		,				SCREAMING_SNAKE_CASE      )

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

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			float:
  a__												= max(0		,				optimization_step - self.update_after_step - 1      )

  if step <= 0:
   return 0.0

  if self.use_ema_warmup:
   a__												= 1 - (1 + step / self.inv_gamma) ** -self.power
  else:
   a__												= (1 + step) / (1_0 + step)

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

 @torch.no_grad()
 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			Dict:
  if isinstance(SCREAMING_SNAKE_CASE		,				torch.nn.Module      ):
   a__												= (
       '''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'''		,				SCREAMING_SNAKE_CASE		,				standard_warn=SCREAMING_SNAKE_CASE		,				)
   a__												= parameters.parameters()

  a__												= list(SCREAMING_SNAKE_CASE      )

  self.optimization_step += 1

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

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

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

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

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			None:
  a__												= list(SCREAMING_SNAKE_CASE      )
  for s_param, param in zip(self.shadow_params		,				SCREAMING_SNAKE_CASE      ):
   param.data.copy_(s_param.to(param.device      ).data      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None      ) ->			None:
  a__												= [
      p.to(device=SCREAMING_SNAKE_CASE		,				dtype=SCREAMING_SNAKE_CASE      ) if p.is_floating_point() else p.to(device=SCREAMING_SNAKE_CASE      )
      for p in self.shadow_params
  ]

 def 						_UpperCAmelCase       (	self      ) ->			dict:
  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		,				SCREAMING_SNAKE_CASE      ) ->			None:
  a__												= [param.detach().cpu().clone() for param in parameters]

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			None:
  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		,				SCREAMING_SNAKE_CASE      ):
   param.data.copy_(c_param.data      )

  # Better memory-wise.
  a__												= None



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			None:
  a__												= copy.deepcopy(SCREAMING_SNAKE_CASE      )

  a__												= 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'''      )

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

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

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

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

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

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

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










import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


a_      :      int    =     '\\n    Text data.\n    Second line of data.'

a_      :      Optional[Any]    =     'file'






@pytest.fixture(scope='''session'''       )
def     __a					(		__UpperCAmelCase       ):
 a__												= tmp_path_factory.mktemp('''data'''       ) / (FILE_PATH + '''.zstd''')
 a__												= bytes(__UpperCAmelCase							,  '''utf-8'''       )
 with zstd.open(__UpperCAmelCase							,  '''wb'''       ) as f:
  f.write(__UpperCAmelCase       )
 return path






@pytest.fixture
def     __a					(		__UpperCAmelCase       ):
 with open(os.path.join(tmpfs.local_root_dir							,  __UpperCAmelCase       )							,  '''w'''       ) as f:
  f.write(__UpperCAmelCase       )
 return FILE_PATH






@pytest.mark.parametrize('''compression_format'''							,  ['''gzip''', '''xz''', '''zstd''']       )
def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= {'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_path}
 a__												= input_paths[compression_format]
 a__												= tmp_path / '''cache'''
 a__												= DownloadConfig(cache_dir=__UpperCAmelCase							,  extract_compressed_file=__UpperCAmelCase       )
 a__												= cached_path(__UpperCAmelCase							,  download_config=__UpperCAmelCase       )
 with open(__UpperCAmelCase       ) as f:
  a__												= f.read()
 with open(__UpperCAmelCase       ) as f:
  a__												= f.read()
 assert extracted_file_content == expected_file_content






@pytest.mark.parametrize('''default_extracted'''							,  [True, False]       )
@pytest.mark.parametrize('''default_cache_dir'''							,  [True, False]       )
def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= '''custom_cache'''
 a__												= '''custom_extracted_dir'''
 a__												= tmp_path / '''custom_extracted_path'''
 if default_extracted:
  a__												= ('''downloads''' if default_cache_dir else custom_cache_dir, '''extracted''')
 else:
  monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_DIR'''							,  __UpperCAmelCase       )
  monkeypatch.setattr('''datasets.config.EXTRACTED_DATASETS_PATH'''							,  str(__UpperCAmelCase       )       )
  a__												= custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

 a__												= xz_file
 a__												= (
     DownloadConfig(extract_compressed_file=__UpperCAmelCase       )
     if default_cache_dir
     else DownloadConfig(cache_dir=tmp_path / custom_cache_dir							,  extract_compressed_file=__UpperCAmelCase       )
 )
 a__												= cached_path(__UpperCAmelCase							,  download_config=__UpperCAmelCase       )
 assert Path(__UpperCAmelCase       ).parent.parts[-2:] == expected






def     __a					(		__UpperCAmelCase       ):
 # absolute path
 a__												= str(Path(__UpperCAmelCase       ).resolve()       )
 assert cached_path(__UpperCAmelCase       ) == text_file
 # relative path
 a__												= str(Path(__UpperCAmelCase       ).resolve().relative_to(Path(os.getcwd()       )       )       )
 assert cached_path(__UpperCAmelCase       ) == text_file






def     __a					(		__UpperCAmelCase       ):
 # absolute path
 a__												= str(tmp_path.resolve() / '''__missing_file__.txt'''       )
 with pytest.raises(__UpperCAmelCase       ):
  cached_path(__UpperCAmelCase       )
 # relative path
 a__												= '''./__missing_file__.txt'''
 with pytest.raises(__UpperCAmelCase       ):
  cached_path(__UpperCAmelCase       )






def     __a					(		__UpperCAmelCase       ):
 a__												= get_from_cache(f"tmp://{tmpfs_file}"       )
 with open(__UpperCAmelCase       ) as f:
  a__												= f.read()
 assert output_file_content == FILE_CONTENT






@patch('''datasets.config.HF_DATASETS_OFFLINE'''							,  __UpperCAmelCase       )
def     __a					(		):
 with pytest.raises(__UpperCAmelCase       ):
  cached_path('''https://huggingface.co'''       )






@patch('''datasets.config.HF_DATASETS_OFFLINE'''							,  __UpperCAmelCase       )
def     __a					(		__UpperCAmelCase       ):
 a__												= tmp_path_factory.mktemp('''data'''       ) / '''file.html'''
 with pytest.raises(__UpperCAmelCase       ):
  http_get('''https://huggingface.co'''							,  temp_file=__UpperCAmelCase       )
 with pytest.raises(__UpperCAmelCase       ):
  http_head('''https://huggingface.co'''       )






@patch('''datasets.config.HF_DATASETS_OFFLINE'''							,  __UpperCAmelCase       )
def     __a					(		__UpperCAmelCase       ):
 a__												= tmp_path_factory.mktemp('''data'''       ) / '''file.html'''
 with pytest.raises(__UpperCAmelCase       ):
  ftp_get('''ftp://huggingface.co'''							,  temp_file=__UpperCAmelCase       )
 with pytest.raises(__UpperCAmelCase       ):
  ftp_head('''ftp://huggingface.co'''       )






@patch('''datasets.config.HF_DATASETS_OFFLINE'''							,  __UpperCAmelCase       )
def     __a					(		__UpperCAmelCase       ):
 a__												= tmp_path_factory.mktemp('''data'''       ) / '''file.html'''
 with pytest.raises(__UpperCAmelCase       ):
  fsspec_get('''s3://huggingface.co'''							,  temp_file=__UpperCAmelCase       )
 with pytest.raises(__UpperCAmelCase       ):
  fsspec_head('''s3://huggingface.co'''       )





