
'''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_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






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






class    UpperCAmelCase_				:





 def __init__(    self ,       lowercase_ ,       lowercase_ = 0.9_999 ,       lowercase_ = 0.0 ,       lowercase_ = 0 ,       lowercase_ = False ,       lowercase_ = 1.0 ,       lowercase_ = 2 / 3 ,       lowercase_ = None ,       lowercase_ = None ,       **lowercase_ ,       ):

     if isinstance(lowercase_ ,       torch.nn.Module):
         snake_case_ :    str     			=						(
             "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" ,       lowercase_ ,       standard_warn=lowercase_ ,       )
         snake_case_ :    List[Any]     			=						parameters.parameters()

         # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
         snake_case_ :    Optional[int]     			=						True

     if kwargs.get("max_value" ,       lowercase_) is not None:
         snake_case_ :    str     			=						"The `max_value` argument is deprecated. Please use `decay` instead."
         deprecate("max_value" ,       "1.0.0" ,       lowercase_ ,       standard_warn=lowercase_)
         snake_case_ :    Optional[int]     			=						kwargs["max_value"]

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

     snake_case_ :    Optional[Any]     			=						list(lowercase_)
     snake_case_ :    Tuple     			=						[p.clone().detach() for p in parameters]

     if kwargs.get("device" ,       lowercase_) is not None:
         snake_case_ :    List[Any]     			=						"The `device` argument is deprecated. Please use `to` instead."
         deprecate("device" ,       "1.0.0" ,       lowercase_ ,       standard_warn=lowercase_)
         self.to(device=kwargs["device"])

     snake_case_ :    List[str]     			=						None

     snake_case_ :    Tuple     			=						decay
     snake_case_ :    str     			=						min_decay
     snake_case_ :    int     			=						update_after_step
     snake_case_ :    str     			=						use_ema_warmup
     snake_case_ :    List[Any]     			=						inv_gamma
     snake_case_ :    int     			=						power
     snake_case_ :    Any     			=						0
     snake_case_ :    Dict     			=						None  # set in `step()`

     snake_case_ :    Optional[int]     			=						model_cls
     snake_case_ :    int     			=						model_config





 @classmethod
 def        snake_case__		(    cls ,       lowercase_ ,       lowercase_):
     snake_case_			, snake_case_ :    List[str]     			=						model_cls.load_config(lowercase_ ,       return_unused_kwargs=lowercase_)
     snake_case_ :    Optional[int]     			=						model_cls.from_pretrained(lowercase_)

     snake_case_ :    List[Any]     			=						cls(model.parameters() ,       model_cls=lowercase_ ,       model_config=model.config)

     ema_model.load_state_dict(lowercase_)
     return ema_model





 def        snake_case__		(    self ,       lowercase_):
     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__.")

     snake_case_ :    Union[str, Any]     			=						self.model_cls.from_config(self.model_config)
     snake_case_ :    Optional[Any]     			=						self.state_dict()
     state_dict.pop("shadow_params" ,       lowercase_)

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





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    Optional[int]     			=						max(0 ,       optimization_step - self.update_after_step - 1)

     if step <= 0:
         return 0.0

     if self.use_ema_warmup:
         snake_case_ :    Union[str, Any]     			=						1 - (1 + step / self.inv_gamma) ** -self.power
     else:
         snake_case_ :    str     			=						(1 + step) / (10 + step)

     snake_case_ :    List[str]     			=						min(lowercase_ ,       self.decay)
     # make sure decay is not smaller than min_decay
     snake_case_ :    int     			=						max(lowercase_ ,       self.min_decay)
     return cur_decay_value





 @torch.no_grad()
 def        snake_case__		(    self ,       lowercase_):
     if isinstance(lowercase_ ,       torch.nn.Module):
         snake_case_ :    List[Any]     			=						(
             "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" ,       lowercase_ ,       standard_warn=lowercase_ ,       )
         snake_case_ :    str     			=						parameters.parameters()

     snake_case_ :    Union[str, Any]     			=						list(lowercase_)

     self.optimization_step += 1

     # Compute the decay factor for the exponential moving average.
     snake_case_ :    Optional[int]     			=						self.get_decay(self.optimization_step)
     snake_case_ :    Optional[int]     			=						decay
     snake_case_ :    int     			=						1 - decay

     snake_case_ :    List[str]     			=						contextlib.nullcontext
     if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
         import deepspeed

     for s_param, param in zip(self.shadow_params ,       lowercase_):
         if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
             snake_case_ :    Optional[Any]     			=						deepspeed.zero.GatheredParameters(lowercase_ ,       modifier_rank=lowercase_)

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





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    Union[str, Any]     			=						list(lowercase_)
     for s_param, param in zip(self.shadow_params ,       lowercase_):
         param.data.copy_(s_param.to(param.device).data)





 def        snake_case__		(    self ,       lowercase_=None ,       lowercase_=None):
     snake_case_ :    List[str]     			=						[
         p.to(device=lowercase_ ,       dtype=lowercase_) if p.is_floating_point() else p.to(device=lowercase_)
         for p in self.shadow_params
     ]





 def        snake_case__		(    self):
     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        snake_case__		(    self ,       lowercase_):
     snake_case_ :    Any     			=						[param.detach().cpu().clone() for param in parameters]





 def        snake_case__		(    self ,       lowercase_):
     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 ,       lowercase_):
         param.data.copy_(c_param.data)

     # Better memory-wise.
     snake_case_ :    List[str]     			=						None





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    List[str]     			=						copy.deepcopy(lowercase_)

     snake_case_ :    Tuple     			=						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")

     snake_case_ :    Optional[Any]     			=						state_dict.get("min_decay" ,       self.min_decay)
     if not isinstance(self.min_decay ,       lowercase_):
         raise ValueError("Invalid min_decay")

     snake_case_ :    Optional[Any]     			=						state_dict.get("optimization_step" ,       self.optimization_step)
     if not isinstance(self.optimization_step ,       lowercase_):
         raise ValueError("Invalid optimization_step")

     snake_case_ :    Dict     			=						state_dict.get("update_after_step" ,       self.update_after_step)
     if not isinstance(self.update_after_step ,       lowercase_):
         raise ValueError("Invalid update_after_step")

     snake_case_ :    Any     			=						state_dict.get("use_ema_warmup" ,       self.use_ema_warmup)
     if not isinstance(self.use_ema_warmup ,       lowercase_):
         raise ValueError("Invalid use_ema_warmup")

     snake_case_ :    List[Any]     			=						state_dict.get("inv_gamma" ,       self.inv_gamma)
     if not isinstance(self.inv_gamma ,       (float, int)):
         raise ValueError("Invalid inv_gamma")

     snake_case_ :    Optional[int]     			=						state_dict.get("power" ,       self.power)
     if not isinstance(self.power ,       (float, int)):
         raise ValueError("Invalid power")

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






'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_pegasus import PegasusTokenizer
else:
  a_											=						None


a_											=						logging.get_logger(__name__)


a_											=						"▁"

a_											=						{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

a_											=						{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

a_											=						{
    "google/pegasus-xsum": 512,
}






class    UpperCAmelCase_				(	snake_case__					):
 UpperCAmelCase_		    =						VOCAB_FILES_NAMES
 UpperCAmelCase_		    =						PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase_		    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase_		    =						PegasusTokenizer
 UpperCAmelCase_		    =						["""input_ids""", """attention_mask"""]





 def __init__(    self ,       lowercase_=None ,       lowercase_=None ,       lowercase_="<pad>" ,       lowercase_="</s>" ,       lowercase_="<unk>" ,       lowercase_="<mask_2>" ,       lowercase_="<mask_1>" ,       lowercase_=None ,       lowercase_=1_03 ,       **lowercase_ ,       ):
     snake_case_ :    List[Any]     			=						offset

     if additional_special_tokens is not None:
         if not isinstance(lowercase_ ,       lowercase_):
             raise TypeError(
                 F'additional_special_tokens should be of type {type(lowercase_)}, but is'
                 F' {type(lowercase_)}')

         snake_case_ :    Tuple     			=						(
             ([mask_token_sent] + additional_special_tokens)
             if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
             else additional_special_tokens
         )
         # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
         additional_special_tokens_extended += [
             F'<unk_{i}>' for i in range(len(lowercase_) ,       self.offset - 1)
         ]

         if len(set(lowercase_)) != len(lowercase_):
             raise ValueError(
                 "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                 F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.')
         snake_case_ :    Optional[Any]     			=						additional_special_tokens_extended
     else:
         snake_case_ :    Tuple     			=						[mask_token_sent] if mask_token_sent is not None else []
         additional_special_tokens += [F'<unk_{i}>' for i in range(2 ,       self.offset)]

     super().__init__(
         lowercase_ ,       tokenizer_file=lowercase_ ,       pad_token=lowercase_ ,       eos_token=lowercase_ ,       unk_token=lowercase_ ,       mask_token=lowercase_ ,       mask_token_sent=lowercase_ ,       offset=lowercase_ ,       additional_special_tokens=lowercase_ ,       **lowercase_ ,       )
     snake_case_ :    int     			=						vocab_file
     snake_case_ :    str     			=						False if not self.vocab_file else True





 def        snake_case__		(    self ,       lowercase_):
     snake_case_ :    Union[str, Any]     			=						set(self.all_special_ids)  # call it once instead of inside list comp
     all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

     if all_special_ids != set(range(len(self.additional_special_tokens) + 3)):
         raise ValueError(
             "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
             F' {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}')

     return [1 if x in all_special_ids else 0 for x in seq]





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None ,       lowercase_ = False):
     if already_has_special_tokens:
         return self._special_token_mask(lowercase_)
     elif token_ids_a is None:
         return self._special_token_mask(lowercase_) + [1]
     else:
         return self._special_token_mask(token_ids_a + token_ids_a) + [1]





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_=None):
     if token_ids_a is None:
         return token_ids_a + [self.eos_token_id]
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return token_ids_a + token_ids_a + [self.eos_token_id]





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ = None):
     if not self.can_save_slow_tokenizer:
         raise ValueError(
             "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
             "tokenizer.")

     if not os.path.isdir(lowercase_):
         logger.error(F'Vocabulary path ({save_directory}) should be a directory')
         return
     snake_case_ :    Dict     			=						os.path.join(
         lowercase_ ,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

     if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase_):
         copyfile(self.vocab_file ,       lowercase_)

     return (out_vocab_file,)




