




"""simple docstring"""

import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


a         =      get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
       from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

a         =      256_047
a         =      256_145





@require_sentencepiece
@require_tokenizers
class     lowercase_	(					__lowerCAmelCase      ,     unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase   :					List[Any]				  =     NllbTokenizer
 UpperCAmelCase   :					Tuple				  =     NllbTokenizerFast
 UpperCAmelCase   :					Any				  =     True
 UpperCAmelCase   :					Tuple				  =     True
 UpperCAmelCase   :					Any				  =     {}

 def       lowerCAmelCase_							(			self	:	List[str]		):
       super().setUp()

       # We have a SentencePiece fixture for testing
       _A        =       NllbTokenizer(_UpperCAmelCase   ,							keep_accents=_UpperCAmelCase		)
       tokenizer.save_pretrained(self.tmpdirname		)

 def       lowerCAmelCase_							(			self	:	Dict		):
       _A        =       NllbTokenizer(_UpperCAmelCase   ,							keep_accents=_UpperCAmelCase		)

       _A        =       tokenizer.tokenize('This is a test'		)
       self.assertListEqual(_UpperCAmelCase   ,							['▁This', '▁is', '▁a', '▁t', 'est']		)

       self.assertListEqual(
           tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)   ,							[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]   ,							)

       _A        =       tokenizer.tokenize('I was born in 92000, and this is falsé.'		)
       self.assertListEqual(
           _UpperCAmelCase   ,							[
               SPIECE_UNDERLINE + 'I',
               SPIECE_UNDERLINE + 'was',
               SPIECE_UNDERLINE + 'b',
               'or',
               'n',
               SPIECE_UNDERLINE + 'in',
               SPIECE_UNDERLINE + '',
               '9',
               '2',
               '0',
               '0',
               '0',
               ',',
               SPIECE_UNDERLINE + 'and',
               SPIECE_UNDERLINE + 'this',
               SPIECE_UNDERLINE + 'is',
               SPIECE_UNDERLINE + 'f',
               'al',
               's',
               'é',
               '.',
           ]   ,							)
       _A        =       tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)
       self.assertListEqual(
           _UpperCAmelCase   ,							[
               value + tokenizer.fairseq_offset
               for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
           ]   ,							)

       _A        =       tokenizer.convert_ids_to_tokens(_UpperCAmelCase		)
       self.assertListEqual(
           _UpperCAmelCase   ,							[
               SPIECE_UNDERLINE + 'I',
               SPIECE_UNDERLINE + 'was',
               SPIECE_UNDERLINE + 'b',
               'or',
               'n',
               SPIECE_UNDERLINE + 'in',
               SPIECE_UNDERLINE + '',
               '<unk>',
               '2',
               '0',
               '0',
               '0',
               ',',
               SPIECE_UNDERLINE + 'and',
               SPIECE_UNDERLINE + 'this',
               SPIECE_UNDERLINE + 'is',
               SPIECE_UNDERLINE + 'f',
               'al',
               's',
               '<unk>',
               '.',
           ]   ,							)

 def       lowerCAmelCase_							(			self	:	Any		):
       _A        =       (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
                   _A        =       self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase   ,							**_UpperCAmelCase		)
                   _A        =       self.tokenizer_class.from_pretrained(_UpperCAmelCase   ,							**_UpperCAmelCase		)

                   _A        =       tempfile.mkdtemp()

                   _A        =       tokenizer_r.save_pretrained(_UpperCAmelCase		)
                   _A        =       tokenizer_p.save_pretrained(_UpperCAmelCase		)

                   # Checks it save with the same files + the tokenizer.json file for the fast one
                   self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files		)		)
                   _A        =       tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f		)
                   self.assertSequenceEqual(_UpperCAmelCase   ,							_UpperCAmelCase		)

                   # Checks everything loads correctly in the same way
                   _A        =       tokenizer_r.from_pretrained(_UpperCAmelCase		)
                   _A        =       tokenizer_p.from_pretrained(_UpperCAmelCase		)

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(_UpperCAmelCase   ,							_UpperCAmelCase		)		)

                   shutil.rmtree(_UpperCAmelCase		)

                   # Save tokenizer rust, legacy_format=True
                   _A        =       tempfile.mkdtemp()

                   _A        =       tokenizer_r.save_pretrained(_UpperCAmelCase   ,							legacy_format=_UpperCAmelCase		)
                   _A        =       tokenizer_p.save_pretrained(_UpperCAmelCase		)

                   # Checks it save with the same files
                   self.assertSequenceEqual(_UpperCAmelCase   ,							_UpperCAmelCase		)

                   # Checks everything loads correctly in the same way
                   _A        =       tokenizer_r.from_pretrained(_UpperCAmelCase		)
                   _A        =       tokenizer_p.from_pretrained(_UpperCAmelCase		)

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(_UpperCAmelCase   ,							_UpperCAmelCase		)		)

                   shutil.rmtree(_UpperCAmelCase		)

                   # Save tokenizer rust, legacy_format=False
                   _A        =       tempfile.mkdtemp()

                   _A        =       tokenizer_r.save_pretrained(_UpperCAmelCase   ,							legacy_format=_UpperCAmelCase		)
                   _A        =       tokenizer_p.save_pretrained(_UpperCAmelCase		)

                   # Checks it saved the tokenizer.json file
                   self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files		)		)

                   # Checks everything loads correctly in the same way
                   _A        =       tokenizer_r.from_pretrained(_UpperCAmelCase		)
                   _A        =       tokenizer_p.from_pretrained(_UpperCAmelCase		)

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                         self.assertTrue(hasattr(_UpperCAmelCase   ,							_UpperCAmelCase		)		)

                   shutil.rmtree(_UpperCAmelCase		)

 @require_torch
 def       lowerCAmelCase_							(			self	:	Any		):
       if not self.test_seqaseq:
             return

       _A        =       self.get_tokenizers()
       for tokenizer in tokenizers:
             with self.subTest(F'''{tokenizer.__class__.__name__}'''		):
                   # Longer text that will definitely require truncation.
                   _A        =       [
                       ' UN Chief Says There Is No Military Solution in Syria',
                       ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'
                       ' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'
                       ' will only worsen the violence and misery for millions of people.',
                   ]
                   _A        =       [
                       'Şeful ONU declară că nu există o soluţie militară în Siria',
                       'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'
                       ' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
                       ' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
                   ]
                   try:
                         _A        =       tokenizer.prepare_seqaseq_batch(
                             src_texts=_UpperCAmelCase   ,							tgt_texts=_UpperCAmelCase   ,							max_length=3   ,							max_target_length=10   ,							return_tensors='pt'   ,							src_lang='eng_Latn'   ,							tgt_lang='ron_Latn'   ,							)
                   except NotImplementedError:
                         return
                   self.assertEqual(batch.input_ids.shape[1]   ,							3		)
                   self.assertEqual(batch.labels.shape[1]   ,							10		)
                   # max_target_length will default to max_length if not specified
                   _A        =       tokenizer.prepare_seqaseq_batch(
                       _UpperCAmelCase   ,							tgt_texts=_UpperCAmelCase   ,							max_length=3   ,							return_tensors='pt'		)
                   self.assertEqual(batch.input_ids.shape[1]   ,							3		)
                   self.assertEqual(batch.labels.shape[1]   ,							3		)

                   _A        =       tokenizer.prepare_seqaseq_batch(
                       src_texts=_UpperCAmelCase   ,							max_length=3   ,							max_target_length=10   ,							return_tensors='pt'		)
                   self.assertEqual(batch_encoder_only.input_ids.shape[1]   ,							3		)
                   self.assertEqual(batch_encoder_only.attention_mask.shape[1]   ,							3		)
                   self.assertNotIn('decoder_input_ids'   ,							_UpperCAmelCase		)

 @unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.'		)
 def       lowerCAmelCase_							(			self	:	Dict		):
       pass



 def       lowerCAmelCase_							(			self	:	Any		):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
                   _A        =       [AddedToken('<special>'   ,							lstrip=_UpperCAmelCase		)]

                   _A        =       self.rust_tokenizer_class.from_pretrained(
                       _UpperCAmelCase   ,							additional_special_tokens=_UpperCAmelCase   ,							**_UpperCAmelCase		)
                   _A        =       tokenizer_r.encode('Hey this is a <special> token'		)

                   _A        =       tokenizer_r.encode('<special>'   ,							add_special_tokens=_UpperCAmelCase		)[0]

                   self.assertTrue(special_token_id in r_output		)

                   if self.test_slow_tokenizer:
                         _A        =       self.rust_tokenizer_class.from_pretrained(
                             _UpperCAmelCase   ,							additional_special_tokens=_UpperCAmelCase   ,							**_UpperCAmelCase   ,							)
                         _A        =       self.tokenizer_class.from_pretrained(
                             _UpperCAmelCase   ,							additional_special_tokens=_UpperCAmelCase   ,							**_UpperCAmelCase		)

                         _A        =       tokenizer_p.encode('Hey this is a <special> token'		)

                         _A        =       tokenizer_cr.encode('Hey this is a <special> token'		)

                         self.assertEqual(_UpperCAmelCase   ,							_UpperCAmelCase		)
                         self.assertEqual(_UpperCAmelCase   ,							_UpperCAmelCase		)
                         self.assertTrue(special_token_id in p_output		)
                         self.assertTrue(special_token_id in cr_output		)





@require_torch
@require_sentencepiece
@require_tokenizers
class     lowercase_	(					unittest.TestCase							):



 '''simple docstring'''
 UpperCAmelCase   :					List[str]				  =     '''facebook/nllb-200-distilled-600M'''
 UpperCAmelCase   :					Tuple				  =     [
     ''' UN Chief Says There Is No Military Solution in Syria''',
     ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
 ]
 UpperCAmelCase   :					List[Any]				  =     [
     '''Şeful ONU declară că nu există o soluţie militară în Siria''',
     '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
     ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
     ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
 ]
 UpperCAmelCase   :					Optional[int]				  =     [
     25_6047,
     1_6297,
     13_4408,
     8165,
     24_8066,
     1_4734,
     950,
     1135,
     10_5721,
     3573,
     83,
     2_7352,
     108,
     4_9486,
     2,
 ]

 @classmethod
 def       lowerCAmelCase_							(			cls	:	List[Any]		):
       _A        =       NllbTokenizer.from_pretrained(
           cls.checkpoint_name   ,							src_lang='eng_Latn'   ,							tgt_lang='ron_Latn'		)
       _A        =       1
       return cls

 def       lowerCAmelCase_							(			self	:	List[Any]		):
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab']   ,							256_001		)
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn']   ,							256_002		)
       self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn']   ,							256_057		)

 def       lowerCAmelCase_							(			self	:	Tuple		):
       _A        =       self.tokenizer.batch_encode_plus(self.src_text		).input_ids[0]
       self.assertListEqual(self.expected_src_tokens   ,							_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Tuple		):
       self.assertIn(_UpperCAmelCase   ,							self.tokenizer.all_special_ids		)
       # fmt: off
       _A        =       [RO_CODE, 4_254, 98_068, 112_923, 39_072, 3_909, 713, 102_767, 26, 17_314, 35_642, 14_683, 33_118, 2_022, 66_987, 2, 256_047]
       # fmt: on

       _A        =       self.tokenizer.decode(_UpperCAmelCase   ,							skip_special_tokens=_UpperCAmelCase		)
       _A        =       self.tokenizer.decode(generated_ids[1:]   ,							skip_special_tokens=_UpperCAmelCase		)
       self.assertEqual(_UpperCAmelCase   ,							_UpperCAmelCase		)
       self.assertNotIn(self.tokenizer.eos_token   ,							_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	str		):
       _A        =       ['this is gunna be a long sentence ' * 20]
       assert isinstance(src_text[0]   ,							_UpperCAmelCase		)
       _A        =       10
       _A        =       self.tokenizer(_UpperCAmelCase   ,							max_length=_UpperCAmelCase   ,							truncation=_UpperCAmelCase		).input_ids[0]
       self.assertEqual(ids[-1]   ,							2		)
       self.assertEqual(ids[0]   ,							_UpperCAmelCase		)
       self.assertEqual(len(_UpperCAmelCase		)   ,							_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Tuple		):
       self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']		)   ,							[256_203, 3]		)

 def       lowerCAmelCase_							(			self	:	Tuple		):
       _A        =       tempfile.mkdtemp()
       _A        =       self.tokenizer.fairseq_tokens_to_ids
       self.tokenizer.save_pretrained(_UpperCAmelCase		)
       _A        =       NllbTokenizer.from_pretrained(_UpperCAmelCase		)
       self.assertDictEqual(new_tok.fairseq_tokens_to_ids   ,							_UpperCAmelCase		)

 @require_torch
 def       lowerCAmelCase_							(			self	:	Optional[int]		):
       _A        =       self.tokenizer(
           self.src_text   ,							text_target=self.tgt_text   ,							padding=_UpperCAmelCase   ,							truncation=_UpperCAmelCase   ,							max_length=len(self.expected_src_tokens		)   ,							return_tensors='pt'   ,							)
       _A        =       shift_tokens_right(
           batch['labels']   ,							self.tokenizer.pad_token_id   ,							self.tokenizer.lang_code_to_id['ron_Latn']		)

       self.assertIsInstance(_UpperCAmelCase   ,							_UpperCAmelCase		)

       self.assertEqual((2, 15)   ,							batch.input_ids.shape		)
       self.assertEqual((2, 15)   ,							batch.attention_mask.shape		)
       _A        =       batch.input_ids.tolist()[0]
       self.assertListEqual(self.expected_src_tokens   ,							_UpperCAmelCase		)
       self.assertEqual(_UpperCAmelCase   ,							batch.decoder_input_ids[0, 0]		)  # EOS
       # Test that special tokens are reset
       self.assertEqual(self.tokenizer.prefix_tokens   ,							[EN_CODE]		)
       self.assertEqual(self.tokenizer.suffix_tokens   ,							[self.tokenizer.eos_token_id]		)

 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       _A        =       self.tokenizer(self.src_text   ,							padding=_UpperCAmelCase   ,							truncation=_UpperCAmelCase   ,							max_length=3   ,							return_tensors='pt'		)
       _A        =       self.tokenizer(
           text_target=self.tgt_text   ,							padding=_UpperCAmelCase   ,							truncation=_UpperCAmelCase   ,							max_length=10   ,							return_tensors='pt'		)
       _A        =       targets['input_ids']
       _A        =       shift_tokens_right(
           _UpperCAmelCase   ,							self.tokenizer.pad_token_id   ,							decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]   ,							)

       self.assertEqual(batch.input_ids.shape[1]   ,							3		)
       self.assertEqual(batch.decoder_input_ids.shape[1]   ,							10		)

 @require_torch
 def       lowerCAmelCase_							(			self	:	Dict		):
       _A        =       self.tokenizer._build_translation_inputs(
           'A test'   ,							return_tensors='pt'   ,							src_lang='eng_Latn'   ,							tgt_lang='fra_Latn'		)

       self.assertEqual(
           nested_simplify(_UpperCAmelCase		)   ,							{
               # A, test, EOS, en_XX
               'input_ids': [[256_047, 70, 7_356, 2]],
               'attention_mask': [[1, 1, 1, 1]],
               # ar_AR
               'forced_bos_token_id': 256_057,
           }   ,							)



 @require_torch
 def       lowerCAmelCase_							(			self	:	int		):
       _A        =       True
       _A        =       self.tokenizer(
           'UN Chief says there is no military solution in Syria'   ,							src_lang='eng_Latn'   ,							tgt_lang='fra_Latn'		)
       self.assertEqual(
           inputs.input_ids   ,							[16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2, 256_047]		)

       _A        =       False
       _A        =       self.tokenizer(
           'UN Chief says there is no military solution in Syria'   ,							src_lang='eng_Latn'   ,							tgt_lang='fra_Latn'		)
       self.assertEqual(
           inputs.input_ids   ,							[256_047, 16_297, 134_408, 25_653, 6_370, 248, 254, 103_929, 94_995, 108, 49_486, 2]		)







"""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 						_snake_case					( _snake_case							: int							)   -> Any:






      '''simple docstring'''


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







class     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	Tuple   ,							_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	:	Optional[int]   ,							):

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

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

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

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

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

       ema_model.load_state_dict(_UpperCAmelCase		)
       return ema_model

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	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__.'		)

       _A        =       self.model_cls.from_config(self.model_config		)
       _A        =       self.state_dict()
       state_dict.pop('shadow_params'   ,							_UpperCAmelCase		)

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

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	int		):
       _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) / (10 + step)

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

 @torch.no_grad()
 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
       if isinstance(_UpperCAmelCase   ,							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'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase   ,							)
             _A        =       parameters.parameters()

       _A        =       list(_UpperCAmelCase		)

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

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

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
       _A        =       list(_UpperCAmelCase		)
       for s_param, param in zip(self.shadow_params   ,							_UpperCAmelCase		):
             param.data.copy_(s_param.to(param.device		).data		)

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	Optional[int]=None   ,							_UpperCAmelCase	:	Dict=None		):
       _A        =       [
           p.to(device=_UpperCAmelCase   ,							dtype=_UpperCAmelCase		) if p.is_floating_point() else p.to(device=_UpperCAmelCase		)
           for p in self.shadow_params
       ]

 def       lowerCAmelCase_							(			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       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
       _A        =       [param.detach().cpu().clone() for param in parameters]

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
       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   ,							_UpperCAmelCase		):
             param.data.copy_(c_param.data		)

       # Better memory-wise.
       _A        =       None



 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	dict		):
       _A        =       copy.deepcopy(_UpperCAmelCase		)

       _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   ,							_UpperCAmelCase		):
             raise ValueError('Invalid min_decay'		)

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

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

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

