


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:
    lowerCAmelCase__      		= None


lowerCAmelCase__      		= logging.get_logger(__name__)


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

lowerCAmelCase__      		= {
    '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',
    },
}

lowerCAmelCase__      		= {
    'facebook/mbart-large-en-ro': 10_24,
    'facebook/mbart-large-cc25': 10_24,
}

# fmt: off
lowerCAmelCase__      		= ['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      lowerCAmelCase__       (				a):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VOCAB_FILES_NAMES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __SCREAMING_SNAKE_CASE  			= PRETRAINED_VOCAB_FILES_MAP
     __SCREAMING_SNAKE_CASE  			= ["input_ids", "attention_mask"]
     __SCREAMING_SNAKE_CASE  			= MBartTokenizer

     __SCREAMING_SNAKE_CASE  			= []
     __SCREAMING_SNAKE_CASE  			= []


     def __init__(						self ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="</s>" ,						__lowerCamelCase="<s>" ,						__lowerCamelCase="<unk>" ,						__lowerCamelCase="<pad>" ,						__lowerCamelCase="<mask>" ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase ,						)			->				Optional[int]:
            # Mask token behave like a normal word, i.e. include the space before it
            _A      :       List[str]				    = AddedToken(__lowerCamelCase ,						lstrip=__lowerCamelCase ,						rstrip=__lowerCamelCase) if isinstance(__lowerCamelCase ,						__lowerCamelCase) else mask_token

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

            _A      :       Union[str, Any]				    = vocab_file
            _A      :       int				    = False if not self.vocab_file else True

            _A      :       Optional[int]				    = 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})
            _A      :       Union[str, Any]				    = {
                lang_code: self.convert_tokens_to_ids(__lowerCamelCase) for lang_code in FAIRSEQ_LANGUAGE_CODES
            }

            _A      :       Optional[int]				    = src_lang if src_lang is not None else "en_XX"
            _A      :       Union[str, Any]				    = self.convert_tokens_to_ids(self._src_lang)
            _A      :       int				    = tgt_lang
            self.set_src_lang_special_tokens(self._src_lang)


     @property
     def  _lowerCamelCase (						self)			->				str:
            return self._src_lang


     @src_lang.setter
     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       Dict				    = new_src_lang
            self.set_src_lang_special_tokens(self._src_lang)


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            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  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				List[int]:
            _A      :       List[str]				    = [self.sep_token_id]
            _A      :       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  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)			->				Dict:
            if src_lang is None or tgt_lang is None:
                   raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
            _A      :       str				    = src_lang
            _A      :       Any				    = self(__lowerCamelCase ,						add_special_tokens=__lowerCamelCase ,						return_tensors=__lowerCamelCase ,						**__lowerCamelCase)
            _A      :       Tuple				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       Dict				    = tgt_lang_id
            return inputs


     def  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = "en_XX" ,						__lowerCamelCase = None ,						__lowerCamelCase = "ro_RO" ,						**__lowerCamelCase ,						)			->				BatchEncoding:
            _A      :       Any				    = src_lang
            _A      :       int				    = tgt_lang
            return super().prepare_seqaseq_batch(__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase)


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


     def  _lowerCamelCase (						self)			->				List[Any]:
            return self.set_tgt_lang_special_tokens(self.tgt_lang)


     def  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       int				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       int				    = []
            _A      :       List[str]				    = [self.eos_token_id, self.cur_lang_code]

            _A      :       Union[str, Any]				    = self.convert_ids_to_tokens(self.prefix_tokens)
            _A      :       str				    = self.convert_ids_to_tokens(self.suffix_tokens)

            _A      :       List[Any]				    = 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  _lowerCamelCase (						self ,						__lowerCamelCase)			->				None:
            _A      :       Optional[int]				    = self.convert_tokens_to_ids(__lowerCamelCase)
            _A      :       List[Any]				    = []
            _A      :       str				    = [self.eos_token_id, self.cur_lang_code]

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

            _A      :       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  _lowerCamelCase (						self ,						__lowerCamelCase ,						__lowerCamelCase = None)			->				Tuple[str]:
            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(__lowerCamelCase):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory.")
                   return
            _A      :       int				    = os.path.join(
                __lowerCamelCase ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

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

            return (out_vocab_file,)







import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	(DPMSolverSinglestepScheduler,)
 __SCREAMING_SNAKE_CASE							       =	(('''num_inference_steps''', 25),)



 def 				UpperCamelCase			(							self,**__lowerCamelCase ):
        A__											=						{
            '''num_train_timesteps''': 1000,
            '''beta_start''': 0.0001,
            '''beta_end''': 0.02,
            '''beta_schedule''': '''linear''',
            '''solver_order''': 2,
            '''prediction_type''': '''epsilon''',
            '''thresholding''': False,
            '''sample_max_value''': 1.0,
            '''algorithm_type''': '''dpmsolver++''',
            '''solver_type''': '''midpoint''',
            '''lambda_min_clipped''': -float('''inf''' ),
            '''variance_type''': None,
        }

        config.update(**__lowerCamelCase )
        return config



 def 				UpperCamelCase			(							self,__lowerCamelCase=0,**__lowerCamelCase ):
        A__											=						dict(self.forward_default_kwargs )
        A__											=						kwargs.pop('''num_inference_steps''',__lowerCamelCase )
        A__											=						self.dummy_sample
        A__											=						0.1 * sample
        A__											=						[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               A__											=						self.get_scheduler_config(**__lowerCamelCase )
               A__											=						scheduler_class(**__lowerCamelCase )
               scheduler.set_timesteps(__lowerCamelCase )
               # copy over dummy past residuals
               A__											=						dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__lowerCamelCase )
                      A__											=						scheduler_class.from_pretrained(__lowerCamelCase )
                      new_scheduler.set_timesteps(__lowerCamelCase )
                      # copy over dummy past residuals
                      A__											=						dummy_past_residuals[: new_scheduler.config.solver_order]

               A__					,	A__											=						sample, sample
               for t in range(__lowerCamelCase,time_step + scheduler.config.solver_order + 1 ):
                      A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample
                      A__											=						new_scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample

                      assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"



 def 				UpperCamelCase			(							self ):
        pass



 def 				UpperCamelCase			(							self,__lowerCamelCase=0,**__lowerCamelCase ):
        A__											=						dict(self.forward_default_kwargs )
        A__											=						kwargs.pop('''num_inference_steps''',__lowerCamelCase )
        A__											=						self.dummy_sample
        A__											=						0.1 * sample
        A__											=						[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               A__											=						self.get_scheduler_config()
               A__											=						scheduler_class(**__lowerCamelCase )
               scheduler.set_timesteps(__lowerCamelCase )

               # copy over dummy past residuals (must be after setting timesteps)
               A__											=						dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__lowerCamelCase )
                      A__											=						scheduler_class.from_pretrained(__lowerCamelCase )
                      # copy over dummy past residuals
                      new_scheduler.set_timesteps(__lowerCamelCase )

                      # copy over dummy past residual (must be after setting timesteps)
                      A__											=						dummy_past_residuals[: new_scheduler.config.solver_order]

               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample
               A__											=						new_scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,**__lowerCamelCase ).prev_sample

               assert torch.sum(torch.abs(output - new_output ) ) < 1E-5, "Scheduler outputs are not identical"



 def 				UpperCamelCase			(							self,__lowerCamelCase=None,**__lowerCamelCase ):
        if scheduler is None:
               A__											=						self.scheduler_classes[0]
               A__											=						self.get_scheduler_config(**__lowerCamelCase )
               A__											=						scheduler_class(**__lowerCamelCase )

        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(**__lowerCamelCase )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__											=						10
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        scheduler.set_timesteps(__lowerCamelCase )

        for i, t in enumerate(scheduler.timesteps ):
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        return sample



 def 				UpperCamelCase			(							self ):
        A__											=						DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
        A__											=						50
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter
        scheduler.set_timesteps(__lowerCamelCase )

        # make sure that the first t is uneven
        for i, t in enumerate(scheduler.timesteps[3:] ):
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2574 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        for timesteps in [25, 50, 100, 999, 1000]:
               self.check_over_configs(num_train_timesteps=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        # make sure that iterating over schedulers with same config names gives same results
        # for defaults
        A__											=						DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
        A__											=						self.full_loop(scheduler=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2791 ) < 1E-3

        A__											=						DEISMultistepScheduler.from_config(scheduler.config )
        A__											=						DPMSolverMultistepScheduler.from_config(scheduler.config )
        A__											=						UniPCMultistepScheduler.from_config(scheduler.config )
        A__											=						DPMSolverSinglestepScheduler.from_config(scheduler.config )

        A__											=						self.full_loop(scheduler=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2791 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(thresholding=__lowerCamelCase )
        for order in [1, 2, 3]:
               for solver_type in ["midpoint", "heun"]:
                      for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                    self.check_over_configs(
                                        thresholding=__lowerCamelCase,prediction_type=__lowerCamelCase,sample_max_value=__lowerCamelCase,algorithm_type='''dpmsolver++''',solver_order=__lowerCamelCase,solver_type=__lowerCamelCase,)



 def 				UpperCamelCase			(							self ):
        for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        for algorithm_type in ["dpmsolver", "dpmsolver++"]:
               for solver_type in ["midpoint", "heun"]:
                      for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                    self.check_over_configs(
                                        solver_order=__lowerCamelCase,solver_type=__lowerCamelCase,prediction_type=__lowerCamelCase,algorithm_type=__lowerCamelCase,)
                                    A__											=						self.full_loop(
                                        solver_order=__lowerCamelCase,solver_type=__lowerCamelCase,prediction_type=__lowerCamelCase,algorithm_type=__lowerCamelCase,)
                                    assert not torch.isnan(__lowerCamelCase ).any(), "Samples have nan numbers"



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(lower_order_final=__lowerCamelCase )
        self.check_over_configs(lower_order_final=__lowerCamelCase )



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(lambda_min_clipped=-float('''inf''' ) )
        self.check_over_configs(lambda_min_clipped=-5.1 )



 def 				UpperCamelCase			(							self ):
        self.check_over_configs(variance_type=__lowerCamelCase )
        self.check_over_configs(variance_type='''learned_range''' )



 def 				UpperCamelCase			(							self ):
        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
               self.check_over_forward(num_inference_steps=__lowerCamelCase,time_step=0 )



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop()
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2791 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(use_karras_sigmas=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.2248 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''' )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.1453 ) < 1E-3



 def 				UpperCamelCase			(							self ):
        A__											=						self.full_loop(prediction_type='''v_prediction''',use_karras_sigmas=__lowerCamelCase )
        A__											=						torch.mean(torch.abs(__lowerCamelCase ) )

        assert abs(result_mean.item() - 0.0649 ) < 1E-3




 def 				UpperCamelCase			(							self ):
        A__											=						self.scheduler_classes[0]
        A__											=						self.get_scheduler_config(thresholding=__lowerCamelCase,dynamic_thresholding_ratio=0 )
        A__											=						scheduler_class(**__lowerCamelCase )

        A__											=						10
        A__											=						self.dummy_model()
        A__											=						self.dummy_sample_deter.half()
        scheduler.set_timesteps(__lowerCamelCase )

        for i, t in enumerate(scheduler.timesteps ):
               A__											=						model(__lowerCamelCase,__lowerCamelCase )
               A__											=						scheduler.step(__lowerCamelCase,__lowerCamelCase,__lowerCamelCase ).prev_sample

        assert sample.dtype == torch.floataa





