






"""simple docstring"""




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_mbart import MBartTokenizer
else:
  A__   :					List[str]							=   None


A__   :					str							=   logging.get_logger(__name__)


A__   :					Any							=   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

A__   :					Union[str, Any]							=   {
    'vocab_file': {
        'facebook/mbart-large-en-ro': (
            'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model'
        ),
        'facebook/mbart-large-cc25': (
            'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-en-ro': 'https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json',
        'facebook/mbart-large-cc25': 'https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json',
    },
}

A__   :					int							=   {
    'facebook/mbart-large-en-ro': 1_0_2_4,
    'facebook/mbart-large-cc25': 1_0_2_4,
}

# fmt: off
A__   :					Dict							=   ['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN']


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		VOCAB_FILES_NAMES
       UpperCamelCase_   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_   =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_   =		['''input_ids''', '''attention_mask''']
       UpperCamelCase_   =		MBartTokenizer

       UpperCamelCase_   =		[]
       UpperCamelCase_   =		[]
       def __init__(     self ,				A_=None ,				A_=None ,				A_="<s>" ,				A_="</s>" ,				A_="</s>" ,				A_="<s>" ,				A_="<unk>" ,				A_="<pad>" ,				A_="<mask>" ,				A_=None ,				A_=None ,				A_=None ,				**A_ ,				)     ->	str:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     AddedToken(A_ ,				lstrip=A_ ,				rstrip=A_       ) if isinstance(A_ ,				A_       ) else mask_token

         super().__init__(
             vocab_file=A_ ,				tokenizer_file=A_ ,				bos_token=A_ ,				eos_token=A_ ,				sep_token=A_ ,				cls_token=A_ ,				unk_token=A_ ,				pad_token=A_ ,				mask_token=A_ ,				src_lang=A_ ,				tgt_lang=A_ ,				additional_special_tokens=A_ ,				**A_ ,				)

         _lowercase:			Dict       =     vocab_file
         _lowercase:			Optional[Any]       =     False if not self.vocab_file else True

         _lowercase:			Tuple       =     FAIRSEQ_LANGUAGE_CODES.copy()

         if additional_special_tokens is not None:
           # Only add those special tokens if they are not already there.
           _additional_special_tokens.extend(
               [t for t in additional_special_tokens if t not in _additional_special_tokens]       )

         self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}       )
         _lowercase:			Tuple       =     {
             lang_code: self.convert_tokens_to_ids(A_       ) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }

         _lowercase:			List[str]       =     src_lang if src_lang is not None else '''en_XX'''
         _lowercase:			Optional[Any]       =     self.convert_tokens_to_ids(self._src_lang       )
         _lowercase:			Dict       =     tgt_lang
         self.set_src_lang_special_tokens(self._src_lang       )
       @property
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         return self._src_lang
       @src_lang.setter
       def 							lowercase_							(     self ,				A_       )     ->	None:





         """simple docstring"""




         _lowercase:			Any       =     new_src_lang
         self.set_src_lang_special_tokens(self._src_lang       )
       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	List[int]:





         """simple docstring"""




         if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	List[int]:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     [self.sep_token_id]
         _lowercase:			List[str]       =     [self.cls_token_id]

         if token_ids_a is None:
           return len(cls + token_ids_a + sep       ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ ,				**A_       )     ->	str:





         """simple docstring"""




         if src_lang is None or tgt_lang is None:
           raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''       )
         _lowercase:			Tuple       =     src_lang
         _lowercase:			Tuple       =     self(A_ ,				add_special_tokens=A_ ,				return_tensors=A_ ,				**A_       )
         _lowercase:			Dict       =     self.convert_tokens_to_ids(A_       )
         _lowercase:			Union[str, Any]       =     tgt_lang_id
         return inputs
       def 							lowercase_							(     self ,				A_ ,				A_ = "en_XX" ,				A_ = None ,				A_ = "ro_RO" ,				**A_ ,				)     ->	BatchEncoding:





         """simple docstring"""




         _lowercase:			int       =     src_lang
         _lowercase:			Tuple       =     tgt_lang
         return super().prepare_seqaseq_batch(A_ ,				A_ ,				**A_       )
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         return self.set_src_lang_special_tokens(self.src_lang       )
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         return self.set_tgt_lang_special_tokens(self.tgt_lang       )
       def 							lowercase_							(     self ,				A_       )     ->	None:





         """simple docstring"""




         _lowercase:			Any       =     self.convert_tokens_to_ids(A_       )
         _lowercase:			Optional[Any]       =     []
         _lowercase:			str       =     [self.eos_token_id, self.cur_lang_code]

         _lowercase:			Optional[int]       =     self.convert_ids_to_tokens(self.prefix_tokens       )
         _lowercase:			Optional[int]       =     self.convert_ids_to_tokens(self.suffix_tokens       )

         _lowercase:			List[str]       =     processors.TemplateProcessing(
             single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str ,				pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str ,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,				self.prefix_tokens + self.suffix_tokens       )       ) ,				)
       def 							lowercase_							(     self ,				A_       )     ->	None:





         """simple docstring"""




         _lowercase:			List[Any]       =     self.convert_tokens_to_ids(A_       )
         _lowercase:			Optional[Any]       =     []
         _lowercase:			List[Any]       =     [self.eos_token_id, self.cur_lang_code]

         _lowercase:			Optional[int]       =     self.convert_ids_to_tokens(self.prefix_tokens       )
         _lowercase:			Dict       =     self.convert_ids_to_tokens(self.suffix_tokens       )

         _lowercase:			int       =     processors.TemplateProcessing(
             single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str ,				pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str ,				special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,				self.prefix_tokens + self.suffix_tokens       )       ) ,				)



       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	Tuple[str]:





         """simple docstring"""




         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(A_       ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''       )
           return
         _lowercase:			str       =     os.path.join(
             A_ ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

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

         return (out_vocab_file,)


"""simple docstring"""




import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		(KDPMaDiscreteScheduler,)
       UpperCamelCase_   =		10
       def 							lowercase_							(     self ,				**A_       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Tuple       =     {
             '''num_train_timesteps''': 1100,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**A_       )
         return config
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




         for timesteps in [10, 50, 100, 1000]:
           self.check_over_configs(num_train_timesteps=A_       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01] ,				[0.00_02, 0.0_02, 0.02]       ):
           self.check_over_configs(beta_start=A_ ,				beta_end=A_       )
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=A_       )
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=A_       )
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[int]       =     self.scheduler_classes[0]
         _lowercase:			List[str]       =     self.get_scheduler_config(prediction_type='''v_prediction'''       )
         _lowercase:			List[Any]       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps       )

         _lowercase:			List[str]       =     self.dummy_model()
         _lowercase:			Dict       =     self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:			List[Any]       =     sample.to(A_       )

         for i, t in enumerate(scheduler.timesteps       ):
           _lowercase:			Optional[int]       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			Optional[Any]       =     model(A_ ,				A_       )

           _lowercase:			List[str]       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			str       =     output.prev_sample

         _lowercase:			Dict       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Optional[int]       =     torch.mean(torch.abs(A_       )       )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 4.69_34E-07       ) < 1E-2
           assert abs(result_mean.item() - 6.11_12E-10       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07       ) < 1E-2
           assert abs(result_mean.item() - 0.00_02       ) < 1E-3
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:			List[str]       =     self.scheduler_classes[0]
         _lowercase:			Optional[int]       =     self.get_scheduler_config()
         _lowercase:			int       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps       )

         _lowercase:			Tuple       =     self.dummy_model()
         _lowercase:			Tuple       =     self.dummy_sample_deter * scheduler.init_noise_sigma
         _lowercase:			Any       =     sample.to(A_       )

         for i, t in enumerate(scheduler.timesteps       ):
           _lowercase:			Any       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			List[Any]       =     model(A_ ,				A_       )

           _lowercase:			Any       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			List[Any]       =     output.prev_sample

         _lowercase:			Optional[Any]       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Tuple       =     torch.mean(torch.abs(A_       )       )

         if torch_device in ["cpu", "mps"]:
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3



       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         if torch_device == "mps":
           return
         _lowercase:			int       =     self.scheduler_classes[0]
         _lowercase:			Union[str, Any]       =     self.get_scheduler_config()
         _lowercase:			Optional[Any]       =     scheduler_class(**A_       )

         scheduler.set_timesteps(self.num_inference_steps ,				device=A_       )

         _lowercase:			List[str]       =     self.dummy_model()
         _lowercase:			Union[str, Any]       =     self.dummy_sample_deter.to(A_       ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
           _lowercase:			Optional[Any]       =     scheduler.scale_model_input(A_ ,				A_       )

           _lowercase:			int       =     model(A_ ,				A_       )

           _lowercase:			Tuple       =     scheduler.step(A_ ,				A_ ,				A_       )
           _lowercase:			int       =     output.prev_sample

         _lowercase:			Union[str, Any]       =     torch.sum(torch.abs(A_       )       )
         _lowercase:			Dict       =     torch.mean(torch.abs(A_       )       )

         if str(A_       ).startswith('''cpu'''       ):
           # The following sum varies between 148 and 156 on mps. Why?
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
         else:
           # CUDA
           assert abs(result_sum.item() - 20.41_25       ) < 1E-2
           assert abs(result_mean.item() - 0.02_66       ) < 1E-3
