'''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


UpperCamelCase__  :						Dict					=      logging.get_logger(__name__)

UpperCamelCase__  :						Tuple					=      '▁'

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

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

UpperCamelCase__  :						Any					=      {
    'facebook/xglm-564M': 2_048,
}


class 					_lowercase						(   lowerCAmelCase ):
      '''simple docstring'''


      UpperCAmelCase_   :   Dict   				= VOCAB_FILES_NAMES
      UpperCAmelCase_   :   List[str]   				= PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_   :   List[str]   				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_   :   Optional[Any]   				= ['''input_ids''', '''attention_mask''']




      def __init__(					self       ,lowerCamelCase_       ,lowerCamelCase_="<s>"       ,lowerCamelCase_="</s>"       ,lowerCamelCase_="</s>"       ,lowerCamelCase_="<s>"       ,lowerCamelCase_="<unk>"       ,lowerCamelCase_="<pad>"       ,lowerCamelCase_ = None       ,**lowerCamelCase_       ,)      ->      None:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[int] 					=	{} if sp_model_kwargs is None else sp_model_kwargs

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

         UpperCAmelCase__				:					Any 					=	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__				:					List[Any] 					=	spm.SentencePieceProcessor(**self.sp_model_kwargs				)
         self.sp_model.Load(str(__a				)				)
         UpperCAmelCase__				:					List[Any] 					=	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__				:					Optional[Any] 					=	1

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

         UpperCAmelCase__				:					Dict 					=	len(self.sp_model				)
         UpperCAmelCase__				:					List[str] 					=	{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__				:					int 					=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}




      def __getstate__(					self				)      ->      Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Optional[Any] 					=	self.__dict__.copy()
         UpperCAmelCase__				:					Dict 					=	None
         UpperCAmelCase__				:					Union[str, Any] 					=	self.sp_model.serialized_model_proto()
         return state




      def __setstate__(					self       ,lowerCamelCase_				)      ->      Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	d

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

         UpperCAmelCase__				:					Union[str, Any] 					=	spm.SentencePieceProcessor(**self.sp_model_kwargs				)
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_ = None				)      ->      List[int]:



         '''simple docstring'''



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




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_ = None       ,lowerCamelCase_ = False				)      ->      List[int]:



         '''simple docstring'''



         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       ,lowerCamelCase_       ,lowerCamelCase_ = None				)      ->      List[int]:



         '''simple docstring'''



         UpperCAmelCase__				:					str 					=	[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				)      ->      str:



         '''simple docstring'''



         return len(self.sp_model				) + self.fairseq_offset + self.num_madeup_words




      def 		lowerCAmelCase__       (					self				)      ->      List[str]:



         '''simple docstring'''



         UpperCAmelCase__				:					Dict 					=	{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       ,lowerCamelCase_				)      ->      List[str]:



         '''simple docstring'''



         return self.sp_model.encode(__a       ,out_type=__a				)




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_				)      ->      Union[str, Any]:



         '''simple docstring'''



         if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
         UpperCAmelCase__				:					Any 					=	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       ,lowerCamelCase_				)      ->      Tuple:



         '''simple docstring'''



         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       ,lowerCamelCase_				)      ->      int:



         '''simple docstring'''



         UpperCAmelCase__				:					Tuple 					=	''''''.join(__a				).replace(__a       ,''' '''				).strip()
         return out_string




      def 		lowerCAmelCase__       (					self       ,lowerCamelCase_       ,lowerCamelCase_ = None				)      ->      Tuple[str]:



         '''simple docstring'''



         if not os.path.isdir(__a				):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
            return
         UpperCAmelCase__				:					List[Any] 					=	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__				:					Tuple 					=	self.sp_model.serialized_model_proto()
               fi.write(__a				)

         return (out_vocab_file,)






"""simple docstring"""





import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest






@require_torchsde
class        _UpperCAmelCase(       lowerCamelCase      ):
		lowercase__         = (DPMSolverSDEScheduler,)
		lowercase__         = 10



		def 		UpperCAmelCase				(   self							,						**__a)			->			int:






							'''simple docstring'''
							_UpperCamelCase									=    {
							    '''num_train_timesteps''': 11_00,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''noise_sampler_seed''': 0,
							}

							config.update(**__a)
							return config



		def 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							for timesteps in [10, 50, 1_00, 10_00]:
												self.check_over_configs(num_train_timesteps=__a)



		def 		UpperCAmelCase				(   self)			->			Dict:






							'''simple docstring'''
							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]							,						[0.0002, 0.002, 0.02]):
												self.check_over_configs(beta_start=__a							,						beta_end=__a)



		def 		UpperCAmelCase				(   self)			->			Optional[int]:






							'''simple docstring'''
							for schedule in ["linear", "scaled_linear"]:
												self.check_over_configs(beta_schedule=__a)



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							for prediction_type in ["epsilon", "v_prediction"]:
												self.check_over_configs(prediction_type=__a)



		def 		UpperCAmelCase				(   self)			->			Union[str, Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

							scheduler.set_timesteps(self.num_inference_steps)

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter * scheduler.init_noise_sigma
							_UpperCamelCase									=    sample.to(__a)

							for i, t in enumerate(scheduler.timesteps):
												_UpperCamelCase									=    scheduler.scale_model_input(__a							,						__a)

												_UpperCamelCase									=    model(__a							,						__a)

												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a)
												_UpperCamelCase									=    output.prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							if torch_device in ["mps"]:
												assert abs(result_sum.item() - 167.47_8210_4492_1875) < 1e-2
												assert abs(result_mean.item() - 0.2178_7059_6456_5277) < 1e-3
							elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 171.59_3521_1181_6406) < 1e-2
												assert abs(result_mean.item() - 0.2_2342_9068_9229_9652) < 1e-3
							else:
												assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2
												assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3



		def 		UpperCAmelCase				(   self)			->			List[str]:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config(prediction_type='''v_prediction''')
							_UpperCamelCase									=    scheduler_class(**__a)

							scheduler.set_timesteps(self.num_inference_steps)

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter * scheduler.init_noise_sigma
							_UpperCamelCase									=    sample.to(__a)

							for i, t in enumerate(scheduler.timesteps):
												_UpperCamelCase									=    scheduler.scale_model_input(__a							,						__a)

												_UpperCamelCase									=    model(__a							,						__a)

												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a)
												_UpperCamelCase									=    output.prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							if torch_device in ["mps"]:
												assert abs(result_sum.item() - 124.77_1492_0043_9453) < 1e-2
												assert abs(result_mean.item() - 0.1_6226_2890_1481_6284) < 1e-3
							elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 128.1_6633_6059_5703) < 1e-2
												assert abs(result_mean.item() - 0.1_6688_3260_0116_7297) < 1e-3
							else:
												assert abs(result_sum.item() - 119.8_4875_4882_8125) < 1e-2
												assert abs(result_mean.item() - 0.1560_5306_6253_6621) < 1e-3



		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a)

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

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
												_UpperCamelCase									=    scheduler.scale_model_input(__a							,						__a)

												_UpperCamelCase									=    model(__a							,						__a)

												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a)
												_UpperCamelCase									=    output.prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							if torch_device in ["mps"]:
												assert abs(result_sum.item() - 167.46_9573_9746_0938) < 1e-2
												assert abs(result_mean.item() - 0.2_1805_9346_0798_2635) < 1e-3
							elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 171.59_3536_3769_5312) < 1e-2
												assert abs(result_mean.item() - 0.2_2342_9083_8241_5771) < 1e-3
							else:
												assert abs(result_sum.item() - 162.52_3834_2285_1562) < 1e-2
												assert abs(result_mean.item() - 0.211_6195_7085_1326) < 1e-3




		def 		UpperCAmelCase				(   self)			->			Any:






							'''simple docstring'''
							_UpperCamelCase									=    self.scheduler_classes[0]
							_UpperCamelCase									=    self.get_scheduler_config()
							_UpperCamelCase									=    scheduler_class(**__a							,						use_karras_sigmas=__a)

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

							_UpperCamelCase									=    self.dummy_model()
							_UpperCamelCase									=    self.dummy_sample_deter.to(__a) * scheduler.init_noise_sigma
							_UpperCamelCase									=    sample.to(__a)

							for t in scheduler.timesteps:
												_UpperCamelCase									=    scheduler.scale_model_input(__a							,						__a)

												_UpperCamelCase									=    model(__a							,						__a)

												_UpperCamelCase									=    scheduler.step(__a							,						__a							,						__a)
												_UpperCamelCase									=    output.prev_sample

							_UpperCamelCase									=    torch.sum(torch.abs(__a))
							_UpperCamelCase									=    torch.mean(torch.abs(__a))

							if torch_device in ["mps"]:
												assert abs(result_sum.item() - 176.66_9741_3574_2188) < 1e-2
												assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
							elif torch_device in ["cuda"]:
												assert abs(result_sum.item() - 177.63_6535_6445_3125) < 1e-2
												assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2
							else:
												assert abs(result_sum.item() - 170.3_1352_2338_8672) < 1e-2
												assert abs(result_mean.item() - 0.2_3003_8727_3098_1811) < 1e-2






