





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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a__							:							List[str] =logging.get_logger(__name__)

a__							:							Any ='''▁'''

a__							:							Dict ={'''vocab_file''': '''sentencepiece.bpe.model'''}

a__							:							List[Any] ={
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

a__							:							List[Any] ={
    '''facebook/xglm-564M''': 2_048,
}

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					List[Any]					=VOCAB_FILES_NAMES
 SCREAMING_SNAKE_CASE_	:					List[Any]					=PRETRAINED_VOCAB_FILES_MAP
 SCREAMING_SNAKE_CASE_	:					Dict					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 SCREAMING_SNAKE_CASE_	:					Optional[Any]					=["input_ids", "attention_mask"]






 def __init__(       self       :							Dict							,	__A       :							List[Any]							,	__A       :							Dict="<s>"							,	__A       :							Any="</s>"							,	__A       :							Union[str, Any]="</s>"							,	__A       :							Optional[int]="<s>"							,	__A       :							Dict="<unk>"							,	__A       :							Optional[Any]="<pad>"							,	__A       :							Optional[Dict[str, Any]] = None							,	**__A       :							Union[str, Any]							,	):
        __UpperCamelCase       =  {} if sp_model_kwargs is None else sp_model_kwargs

        # Compatibility with the original tokenizer
        __UpperCamelCase       =  7
        __UpperCamelCase       =  [f'''<madeupword{i}>''' for i in range(self.num_madeup_words	)]

        __UpperCamelCase       =  kwargs.get('additional_special_tokens'							,	[]	)
        kwargs["additional_special_tokens"] += [
            word for word in madeup_words if word not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            bos_token=__A							,	eos_token=__A							,	unk_token=__A							,	sep_token=__A							,	cls_token=__A							,	pad_token=__A							,	sp_model_kwargs=self.sp_model_kwargs							,	**__A							,	)

        __UpperCamelCase       =  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(str(__A	)	)
        __UpperCamelCase       =  vocab_file

        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
        # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
        # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
        __UpperCamelCase       =  1

        # Mimic fairseq token-to-id alignment for the first 4 token
        __UpperCamelCase       =  {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

        __UpperCamelCase       =  len(self.sp_model	)
        __UpperCamelCase       =  {f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words	)}
        self.fairseq_tokens_to_ids.update(__A	)

        __UpperCamelCase       =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}






 def __getstate__(       self       :							Tuple	):
        __UpperCamelCase       =  self.__dict__.copy()
        __UpperCamelCase       =  None
        __UpperCamelCase       =  self.sp_model.serialized_model_proto()
        return state






 def __setstate__(       self       :							Any							,	__A       :							Optional[int]	):
        __UpperCamelCase       =  d

        # for backward compatibility
        if not hasattr(self							,	'sp_model_kwargs'	):
               __UpperCamelCase       =  {}

        __UpperCamelCase       =  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							List[int]							,	__A       :							Optional[List[int]] = None	):

        if token_ids_a is None:
               return [self.sep_token_id] + token_ids_a
        __UpperCamelCase       =  [self.sep_token_id]
        return sep + token_ids_a + sep + sep + token_ids_a






 def        _lowerCamelCase					(       self       :							Any							,	__A       :							List[int]							,	__A       :							Optional[List[int]] = None							,	__A       :							bool = False	):

        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=__A							,	token_ids_a=__A							,	already_has_special_tokens=__A	)

        if token_ids_a is None:
               return [1] + ([0] * len(__A	))
        return [1] + ([0] * len(__A	)) + [1, 1] + ([0] * len(__A	))






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							List[int]							,	__A       :							Optional[List[int]] = None	):
        __UpperCamelCase       =  [self.sep_token_id]

        if token_ids_a is None:
               return len(sep + token_ids_a	) * [0]
        return len(sep + token_ids_a + sep + sep + token_ids_a	) * [0]






 @property
 def        _lowerCamelCase					(       self       :							Optional[int]	):
        return len(self.sp_model	) + self.fairseq_offset + self.num_madeup_words






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  {self.convert_ids_to_tokens(__A	): i for i in range(self.vocab_size	)}
        vocab.update(self.added_tokens_encoder	)
        return vocab






 def        _lowerCamelCase					(       self       :							List[Any]							,	__A       :							str	):
        return self.sp_model.encode(__A							,	out_type=__A	)






 def        _lowerCamelCase					(       self       :							int							,	__A       :							Any	):
        if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
        __UpperCamelCase       =  self.sp_model.PieceToId(__A	)

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							List[str]	):
        if index in self.fairseq_ids_to_tokens:
               return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset	)






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							int	):
        __UpperCamelCase       =  ''.join(__A	).replace(__A							,	' '	).strip()
        return out_string






 def        _lowerCamelCase					(       self       :							int							,	__A       :							str							,	__A       :							Optional[str] = None	):
        if not os.path.isdir(__A	):
               logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
               return
        __UpperCamelCase       =  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	) and os.path.isfile(self.vocab_file	):
               copyfile(self.vocab_file							,	__A	)
        elif not os.path.isfile(self.vocab_file	):
               with open(__A							,	'wb'	) as fi:
                      __UpperCamelCase       =  self.sp_model.serialized_model_proto()
                      fi.write(__A	)

        return (out_vocab_file,)





import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest






class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: List[str]					=      (DDIMParallelScheduler,)
    __lowerCamelCase			: int					=      (("eta", 0.0), ("num_inference_steps", 50))






    def 			_lowerCAmelCase					(       self,		**lowerCamelCase__		):
        A :       int          =   {
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """clip_sample""": True,
        }

        config.update(**lowerCamelCase__		)
        return config






    def 			_lowerCAmelCase					(       self,		**lowerCamelCase__		):
        A :       Dict          =   self.scheduler_classes[0]
        A :       str          =   self.get_scheduler_config(**lowerCamelCase__		)
        A :       Tuple          =   scheduler_class(**lowerCamelCase__		)

        A     ,							A :       Tuple          =   10, 0.0

        A :       Optional[int]          =   self.dummy_model()
        A :       List[str]          =   self.dummy_sample_deter

        scheduler.set_timesteps(lowerCamelCase__		)

        for t in scheduler.timesteps:
            A :       Optional[int]          =   model(lowerCamelCase__,		lowerCamelCase__		)
            A :       str          =   scheduler.step(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		).prev_sample

        return sample






    def 			_lowerCAmelCase					(       self		):
        for timesteps in [100, 500, 1000]:
            self.check_over_configs(num_train_timesteps=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=lowerCamelCase__		)

        A :       Tuple          =   self.scheduler_classes[0]
        A :       Optional[Any]          =   self.get_scheduler_config(steps_offset=1		)
        A :       List[Any]          =   scheduler_class(**lowerCamelCase__		)
        scheduler.set_timesteps(5		)
        assert torch.equal(scheduler.timesteps,		torch.LongTensor([801, 601, 401, 201, 1]		)		)






    def 			_lowerCAmelCase					(       self		):
        for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1],		[0.002, 0.02, 0.2, 2]		):
            self.check_over_configs(beta_start=lowerCamelCase__,		beta_end=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=lowerCamelCase__		)






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






    def 			_lowerCAmelCase					(       self		):
        for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for timestep_spacing in ["trailing", "leading"]:
            self.check_over_configs(timestep_spacing=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        self.check_over_configs(thresholding=lowerCamelCase__		)
        for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(
                    thresholding=lowerCamelCase__,		prediction_type=lowerCamelCase__,		sample_max_value=lowerCamelCase__,		)






    def 			_lowerCAmelCase					(       self		):
        for t in [1, 10, 49]:
            self.check_over_forward(time_step=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for t, num_inference_steps in zip([1, 10, 50],		[10, 50, 500]		):
            self.check_over_forward(time_step=lowerCamelCase__,		num_inference_steps=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        for t, eta in zip([1, 10, 49],		[0.0, 0.5, 1.0]		):
            self.check_over_forward(time_step=lowerCamelCase__,		eta=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       List[str]          =   self.scheduler_classes[0]
        A :       List[str]          =   self.get_scheduler_config()
        A :       Dict          =   scheduler_class(**lowerCamelCase__		)

        assert torch.sum(torch.abs(scheduler._get_variance(0,		0		) - 0.0		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(420,		400		) - 0.1_4771		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(980,		960		) - 0.3_2460		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(0,		0		) - 0.0		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487,		486		) - 0.0_0979		)		) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999,		998		) - 0.02		)		) < 1e-5






    def 			_lowerCAmelCase					(       self		):
        A :       int          =   self.scheduler_classes[0]
        A :       Any          =   self.get_scheduler_config()
        A :       Union[str, Any]          =   scheduler_class(**lowerCamelCase__		)

        A     ,							A :       List[str]          =   10, 0.0
        scheduler.set_timesteps(lowerCamelCase__		)

        A :       Any          =   self.dummy_model()
        A :       Dict          =   self.dummy_sample_deter
        A :       Dict          =   self.dummy_sample_deter + 0.1
        A :       Tuple          =   self.dummy_sample_deter - 0.1

        A :       Tuple          =   samplea.shape[0]
        A :       List[Any]          =   torch.stack([samplea, samplea, samplea],		dim=0		)
        A :       str          =   torch.arange(lowerCamelCase__		)[0:3, None].repeat(1,		lowerCamelCase__		)

        A :       Any          =   model(samples.flatten(0,		1		),		timesteps.flatten(0,		1		)		)
        A :       Any          =   scheduler.batch_step_no_noise(lowerCamelCase__,		timesteps.flatten(0,		1		),		samples.flatten(0,		1		),		lowerCamelCase__		)

        A :       List[Any]          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       Optional[Any]          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 1147.7904		) < 1e-2
        assert abs(result_mean.item() - 0.4982		) < 1e-3






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   self.full_loop()

        A :       str          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       Optional[Any]          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 172.0067		) < 1e-2
        assert abs(result_mean.item() - 0.22_3967		) < 1e-3






    def 			_lowerCAmelCase					(       self		):
        A :       str          =   self.full_loop(prediction_type="""v_prediction"""		)

        A :       Any          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       Optional[Any]          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 52.5302		) < 1e-2
        assert abs(result_mean.item() - 0.0684		) < 1e-3






    def 			_lowerCAmelCase					(       self		):
        # We specify different beta, so that the first alpha is 0.99
        A :       Any          =   self.full_loop(set_alpha_to_one=lowerCamelCase__,		beta_start=0.01		)
        A :       Dict          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       str          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 149.8295		) < 1e-2
        assert abs(result_mean.item() - 0.1951		) < 1e-3






    def 			_lowerCAmelCase					(       self		):
        # We specify different beta, so that the first alpha is 0.99
        A :       int          =   self.full_loop(set_alpha_to_one=lowerCamelCase__,		beta_start=0.01		)
        A :       List[Any]          =   torch.sum(torch.abs(lowerCamelCase__		)		)
        A :       List[Any]          =   torch.mean(torch.abs(lowerCamelCase__		)		)

        assert abs(result_sum.item() - 149.0784		) < 1e-2
        assert abs(result_mean.item() - 0.1941		) < 1e-3


