






"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


A__   :					str							=   logging.get_logger(__name__)


A__   :					List[Any]							=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
A__   :					int							=   {
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

A__   :					Optional[Any]							=   {
    'facebook/bart-base': 1_0_2_4,
    'facebook/bart-large': 1_0_2_4,
    'facebook/bart-large-mnli': 1_0_2_4,
    'facebook/bart-large-cnn': 1_0_2_4,
    'facebook/bart-large-xsum': 1_0_2_4,
    'yjernite/bart_eli5': 1_0_2_4,
}


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       UpperCamelCase_   =		VOCAB_FILES_NAMES
       UpperCamelCase_   =		PRETRAINED_VOCAB_FILES_MAP
       UpperCamelCase_   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       UpperCamelCase_   =		['''input_ids''', '''attention_mask''']
       UpperCamelCase_   =		BartTokenizer
       def __init__(     self ,				A_=None ,				A_=None ,				A_=None ,				A_="replace" ,				A_="<s>" ,				A_="</s>" ,				A_="</s>" ,				A_="<s>" ,				A_="<unk>" ,				A_="<pad>" ,				A_="<mask>" ,				A_=False ,				A_=True ,				**A_ ,				)     ->	Any:





         """simple docstring"""




         super().__init__(
             A_ ,				A_ ,				tokenizer_file=A_ ,				errors=A_ ,				bos_token=A_ ,				eos_token=A_ ,				sep_token=A_ ,				cls_token=A_ ,				unk_token=A_ ,				pad_token=A_ ,				mask_token=A_ ,				add_prefix_space=A_ ,				trim_offsets=A_ ,				**A_ ,				)

         _lowercase:			str       =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
         if pre_tok_state.get('''add_prefix_space''' ,				A_       ) != add_prefix_space:
           _lowercase:			List[Any]       =     getattr(A_ ,				pre_tok_state.pop('''type'''       )       )
           _lowercase:			List[Any]       =     add_prefix_space
           _lowercase:			Any       =     pre_tok_class(**A_       )

         _lowercase:			int       =     add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         _lowercase:			Dict       =     '''post_processor'''
         _lowercase:			List[str]       =     getattr(self.backend_tokenizer ,				A_ ,				A_       )
         if tokenizer_component_instance:
           _lowercase:			Dict       =     json.loads(tokenizer_component_instance.__getstate__()       )

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
             _lowercase:			str       =     tuple(state['''sep''']       )
           if "cls" in state:
             _lowercase:			List[Any]       =     tuple(state['''cls''']       )

           _lowercase:			Tuple       =     False

           if state.get('''add_prefix_space''' ,				A_       ) != add_prefix_space:
             _lowercase:			List[str]       =     add_prefix_space
             _lowercase:			str       =     True

           if state.get('''trim_offsets''' ,				A_       ) != trim_offsets:
             _lowercase:			Dict       =     trim_offsets
             _lowercase:			int       =     True

           if changes_to_apply:
             _lowercase:			Optional[int]       =     getattr(A_ ,				state.pop('''type'''       )       )
             _lowercase:			Dict       =     component_class(**A_       )
             setattr(self.backend_tokenizer ,				A_ ,				A_       )
       @property
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         if self._mask_token is None:
           if self.verbose:
             logger.error('''Using mask_token, but it is not set yet.'''       )
           return None
         return str(self._mask_token       )
       @mask_token.setter
       def 							lowercase_							(     self ,				A_       )     ->	Dict:





         """simple docstring"""




         _lowercase:			List[Any]       =     AddedToken(A_ ,				lstrip=A_ ,				rstrip=A_       ) if isinstance(A_ ,				A_       ) else value
         _lowercase:			Optional[Any]       =     value
       def 							lowercase_							(     self ,				*A_ ,				**A_       )     ->	BatchEncoding:





         """simple docstring"""




         _lowercase:			int       =     kwargs.get('''is_split_into_words''' ,				A_       )

         if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
               '''to use it with pretokenized inputs.'''       )

         return super()._batch_encode_plus(*A_ ,				**A_       )
       def 							lowercase_							(     self ,				*A_ ,				**A_       )     ->	BatchEncoding:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     kwargs.get('''is_split_into_words''' ,				A_       )

         if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
               '''to use it with pretokenized inputs.'''       )

         return super()._encode_plus(*A_ ,				**A_       )
       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	Tuple[str]:





         """simple docstring"""




         _lowercase:			Dict       =     self._tokenizer.model.save(A_ ,				name=A_       )
         return tuple(A_       )
       def 							lowercase_							(     self ,				A_ ,				A_=None       )     ->	Optional[Any]:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     [self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
           return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



       def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	List[int]:





         """simple docstring"""




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


"""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
