"""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 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  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 				a :


    def __init__(    self		:     Tuple      , __lowerCAmelCase		:     Any      , __lowerCAmelCase		:     Tuple = 0.9_999      , __lowerCAmelCase		:     Any = 0.0      , __lowerCAmelCase		:     int = 0      , __lowerCAmelCase		:     Any = False      , __lowerCAmelCase		:     int = 1.0      , __lowerCAmelCase		:     Dict = 2 / 3      , __lowerCAmelCase		:     Optional[Any] = None      , __lowerCAmelCase		:     Dict = None      , **__lowerCAmelCase		:     Optional[int]      , ):

      if isinstance(_lowerCamelCase      , 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"""      , _lowerCamelCase      , standard_warn=_lowerCamelCase      , )
        _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"""      , _lowerCamelCase    ) is not None:
        _UpperCAmelCase         =					'''The `max_value` argument is deprecated. Please use `decay` instead.'''
        deprecate("""max_value"""      , """1.0.0"""      , _lowerCamelCase      , standard_warn=_lowerCamelCase    )
        _UpperCAmelCase         =					kwargs['''max_value''']

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

      _UpperCAmelCase         =					list(_lowerCamelCase    )
      _UpperCAmelCase         =					[p.clone().detach() for p in parameters]

      if kwargs.get("""device"""      , _lowerCamelCase    ) is not None:
        _UpperCAmelCase         =					'''The `device` argument is deprecated. Please use `to` instead.'''
        deprecate("""device"""      , """1.0.0"""      , _lowerCamelCase      , standard_warn=_lowerCamelCase    )
        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    lowerCAmelCase_     (    cls		:     Optional[Any]      , __lowerCAmelCase		:     Dict      , __lowerCAmelCase		:     int    ):
      _UpperCAmelCase         =					model_cls.load_config(_lowerCamelCase      , return_unused_kwargs=_lowerCamelCase    )
      _UpperCAmelCase         =					model_cls.from_pretrained(_lowerCamelCase    )

      _UpperCAmelCase         =					cls(model.parameters()      , model_cls=_lowerCamelCase      , model_config=model.config    )

      ema_model.load_state_dict(_lowerCamelCase    )
      return ema_model


    def    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     Tuple    ):
      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"""      , _lowerCamelCase    )

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


    def    lowerCAmelCase_     (    self		:     Union[str, Any]      , __lowerCAmelCase		:     int    ):
      _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(_lowerCamelCase      , self.decay    )
      # make sure decay is not smaller than min_decay
      _UpperCAmelCase         =					max(_lowerCamelCase      , self.min_decay    )
      return cur_decay_value


    @torch.no_grad()
    def    lowerCAmelCase_     (    self		:     List[str]      , __lowerCAmelCase		:     Any    ):
      if isinstance(_lowerCamelCase      , 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"""      , _lowerCamelCase      , standard_warn=_lowerCamelCase      , )
        _UpperCAmelCase         =					parameters.parameters()

      _UpperCAmelCase         =					list(_lowerCamelCase    )

      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      , _lowerCamelCase    ):
        if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
          _UpperCAmelCase         =					deepspeed.zero.GatheredParameters(_lowerCamelCase      , modifier_rank=_lowerCamelCase    )

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


    def    lowerCAmelCase_     (    self		:     Optional[int]      , __lowerCAmelCase		:     Union[str, Any]    ):
      _UpperCAmelCase         =					list(_lowerCamelCase    )
      for s_param, param in zip(self.shadow_params      , _lowerCamelCase    ):
        param.data.copy_(s_param.to(param.device    ).data    )


    def    lowerCAmelCase_     (    self		:     int      , __lowerCAmelCase		:     Any=None      , __lowerCAmelCase		:     Union[str, Any]=None    ):
      _UpperCAmelCase         =					[
          p.to(device=_lowerCamelCase      , dtype=_lowerCamelCase    ) if p.is_floating_point() else p.to(device=_lowerCamelCase    )
          for p in self.shadow_params
      ]


    def    lowerCAmelCase_     (    self		:     int    ):
      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    lowerCAmelCase_     (    self		:     Any      , __lowerCAmelCase		:     List[str]    ):
      _UpperCAmelCase         =					[param.detach().cpu().clone() for param in parameters]


    def    lowerCAmelCase_     (    self		:     Dict      , __lowerCAmelCase		:     Optional[int]    ):
      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      , _lowerCamelCase    ):
        param.data.copy_(c_param.data    )

      # Better memory-wise.
      _UpperCAmelCase         =					None


    def    lowerCAmelCase_     (    self		:     List[Any]      , __lowerCAmelCase		:     Optional[int]    ):
      _UpperCAmelCase         =					copy.deepcopy(_lowerCamelCase    )

      _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      , _lowerCamelCase    ):
        raise ValueError("""Invalid min_decay"""    )

      _UpperCAmelCase         =					state_dict.get("""optimization_step"""      , self.optimization_step    )
      if not isinstance(self.optimization_step      , _lowerCamelCase    ):
        raise ValueError("""Invalid optimization_step"""    )

      _UpperCAmelCase         =					state_dict.get("""update_after_step"""      , self.update_after_step    )
      if not isinstance(self.update_after_step      , _lowerCamelCase    ):
        raise ValueError("""Invalid update_after_step"""    )

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




from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url







class 		_snake_case  (      _snake_case							):
 SCREAMING_SNAKE_CASE__      =       ''
 SCREAMING_SNAKE_CASE__      =       'hf-legacy'  # "hf://"" is reserved for hffs




 def __init__(		self					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):
       super().__init__(self					,  **_lowerCamelCase    )
       a						:Union[str, Any] 							=       repo_info
       a						:int 							=       token
       a						:int 							=       None




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       if self.dir_cache is None:
             a						:Dict 							=       {}
             for hf_file in self.repo_info.siblings:
                   # TODO(QL): add sizes
                   a						:List[Any] 							=       {
                       '''name''': hf_file.rfilename,
                       '''size''': None,
                       '''type''': '''file''',
                   }
                   self.dir_cache.update(
                       {
                           str(_lowerCamelCase    ): {'''name''': str(_lowerCamelCase    ), '''size''': None, '''type''': '''directory'''}
                           for d in list(PurePosixPath(hf_file.rfilename    ).parents    )[:-1]
                       }    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase = "rb"					,  **_lowerCamelCase					,  ):
       if not isinstance(self.repo_info					,  _lowerCamelCase    ):
             raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}'''    )
       a						:Optional[int] 							=       hf_hub_url(self.repo_info.id					,  _lowerCamelCase					,  revision=self.repo_info.sha    )
       return fsspec.open(
           _lowerCamelCase					,  mode=_lowerCamelCase					,  headers=get_authentication_headers_for_url(_lowerCamelCase					,  use_auth_token=self.token    )					,  client_kwargs={'''trust_env''': True}					,  ).open()




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  **_lowerCamelCase    ):
       self._get_dirs()
       a						:Union[str, Any] 							=       self._strip_protocol(_lowerCamelCase    )
       if path in self.dir_cache:
             return self.dir_cache[path]
       else:
             raise FileNotFoundError(_lowerCamelCase    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=False					,  **_lowerCamelCase    ):
       self._get_dirs()
       a						:str 							=       PurePosixPath(path.strip('''/'''    )    )
       a						:Tuple 							=       {}
       for p, f in self.dir_cache.items():
             a						:Optional[int] 							=       PurePosixPath(p.strip('''/'''    )    )
             a						:str 							=       p.parent
             if root == path:
                   a						:List[str] 							=       f
       a						:Any 							=       list(paths.values()    )
       if detail:
             return out
       else:
             return sorted(f['''name'''] for f in out    )





