
import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class    _snake_case					(  a__       ):
			UpperCamelCase__					   =					(LMSDiscreteScheduler,)
			UpperCamelCase__					   =					10







			def     SCREAMING_SNAKE_CASE					(       self		,     **_a	):
				__magic_name__	:	Union[str, Any]			     =  {
				    "num_train_timesteps": 1_100,
				    "beta_start": 0.00_01,
				    "beta_end": 0.02,
				    "beta_schedule": "linear",
				}

				config.update(**__A	)
				return config







			def     SCREAMING_SNAKE_CASE					(       self	):







				for timesteps in [10, 50, 100, 1_000]:
					self.check_over_configs(num_train_timesteps=__A	)







			def     SCREAMING_SNAKE_CASE					(       self	):







				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     SCREAMING_SNAKE_CASE					(       self	):







				for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=__A	)







			def     SCREAMING_SNAKE_CASE					(       self	):







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







			def     SCREAMING_SNAKE_CASE					(       self	):







				for t in [0, 500, 800]:
					self.check_over_forward(time_step=__A	)







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__	:	Dict			     =  self.scheduler_classes[0]
				__magic_name__	:	List[Any]			     =  self.get_scheduler_config()
				__magic_name__	:	Any			     =  scheduler_class(**__A	)

				scheduler.set_timesteps(self.num_inference_steps	)

				__magic_name__	:	Union[str, Any]			     =  self.dummy_model()
				__magic_name__	:	Dict			     =  self.dummy_sample_deter * scheduler.init_noise_sigma

				for i, t in enumerate(scheduler.timesteps	):
					__magic_name__	:	Tuple			     =  scheduler.scale_model_input(__A		,     __A	)

					__magic_name__	:	Optional[int]			     =  model(__A		,     __A	)

					__magic_name__	:	Dict			     =  scheduler.step(__A		,     __A		,     __A	)
					__magic_name__	:	List[Any]			     =  output.prev_sample

				__magic_name__	:	List[Any]			     =  torch.sum(torch.abs(__A	)	)
				__magic_name__	:	str			     =  torch.mean(torch.abs(__A	)	)

				assert abs(result_sum.item() - 10_06.3_88	) < 1e-2
				assert abs(result_mean.item() - 1.31	) < 1e-3







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__	:	str			     =  self.scheduler_classes[0]
				__magic_name__	:	Optional[int]			     =  self.get_scheduler_config(prediction_type="v_prediction"	)
				__magic_name__	:	Union[str, Any]			     =  scheduler_class(**__A	)

				scheduler.set_timesteps(self.num_inference_steps	)

				__magic_name__	:	int			     =  self.dummy_model()
				__magic_name__	:	str			     =  self.dummy_sample_deter * scheduler.init_noise_sigma

				for i, t in enumerate(scheduler.timesteps	):
					__magic_name__	:	Any			     =  scheduler.scale_model_input(__A		,     __A	)

					__magic_name__	:	Optional[int]			     =  model(__A		,     __A	)

					__magic_name__	:	List[Any]			     =  scheduler.step(__A		,     __A		,     __A	)
					__magic_name__	:	int			     =  output.prev_sample

				__magic_name__	:	List[str]			     =  torch.sum(torch.abs(__A	)	)
				__magic_name__	:	List[str]			     =  torch.mean(torch.abs(__A	)	)

				assert abs(result_sum.item() - 0.00_17	) < 1e-2
				assert abs(result_mean.item() - 2.2676e-06	) < 1e-3







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__	:	Dict			     =  self.scheduler_classes[0]
				__magic_name__	:	Tuple			     =  self.get_scheduler_config()
				__magic_name__	:	Optional[Any]			     =  scheduler_class(**__A	)

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

				__magic_name__	:	List[str]			     =  self.dummy_model()
				__magic_name__	:	Optional[Any]			     =  self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
				__magic_name__	:	Union[str, Any]			     =  sample.to(__A	)

				for i, t in enumerate(scheduler.timesteps	):
					__magic_name__	:	str			     =  scheduler.scale_model_input(__A		,     __A	)

					__magic_name__	:	Any			     =  model(__A		,     __A	)

					__magic_name__	:	int			     =  scheduler.step(__A		,     __A		,     __A	)
					__magic_name__	:	List[str]			     =  output.prev_sample

				__magic_name__	:	str			     =  torch.sum(torch.abs(__A	)	)
				__magic_name__	:	Union[str, Any]			     =  torch.mean(torch.abs(__A	)	)

				assert abs(result_sum.item() - 10_06.3_88	) < 1e-2
				assert abs(result_mean.item() - 1.31	) < 1e-3







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__	:	Union[str, Any]			     =  self.scheduler_classes[0]
				__magic_name__	:	Dict			     =  self.get_scheduler_config()
				__magic_name__	:	Dict			     =  scheduler_class(**__A		,     use_karras_sigmas=__A	)

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

				__magic_name__	:	str			     =  self.dummy_model()
				__magic_name__	:	Any			     =  self.dummy_sample_deter.to(__A	) * scheduler.init_noise_sigma
				__magic_name__	:	Union[str, Any]			     =  sample.to(__A	)

				for t in scheduler.timesteps:
					__magic_name__	:	str			     =  scheduler.scale_model_input(__A		,     __A	)

					__magic_name__	:	Optional[int]			     =  model(__A		,     __A	)

					__magic_name__	:	List[str]			     =  scheduler.step(__A		,     __A		,     __A	)
					__magic_name__	:	Dict			     =  output.prev_sample

				__magic_name__	:	List[str]			     =  torch.sum(torch.abs(__A	)	)
				__magic_name__	:	str			     =  torch.mean(torch.abs(__A	)	)

				assert abs(result_sum.item() - 38_12.99_27	) < 2e-2
				assert abs(result_mean.item() - 4.96_48	) < 1e-3


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

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


if is_sentencepiece_available():
				from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
				snake_case				:	Any							   =						None


snake_case				:	Union[str, Any]							   =						logging.get_logger(__name__)

snake_case				:	Any							   =						{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

snake_case				:	Optional[Any]							   =						{
    "vocab_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "xlm-roberta-base": "https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json",
        "xlm-roberta-large": "https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json",
        "xlm-roberta-large-finetuned-conll02-dutch": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll02-spanish": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-english": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json"
        ),
        "xlm-roberta-large-finetuned-conll03-german": (
            "https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json"
        ),
    },
}

snake_case				:	Optional[int]							   =						{
    "xlm-roberta-base": 512,
    "xlm-roberta-large": 512,
    "xlm-roberta-large-finetuned-conll02-dutch": 512,
    "xlm-roberta-large-finetuned-conll02-spanish": 512,
    "xlm-roberta-large-finetuned-conll03-english": 512,
    "xlm-roberta-large-finetuned-conll03-german": 512,
}





class    _snake_case      (  snake_case       ):
			UpperCamelCase__					   =					VOCAB_FILES_NAMES
			UpperCamelCase__					   =					PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__					   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__					   =					['input_ids', 'attention_mask']
			UpperCamelCase__					   =					XLMRobertaTokenizer







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

				super().__init__(
				    _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		,     **_a		,     )

				__magic_name__	:	Optional[int]			     =  vocab_file
				__magic_name__	:	List[str]			     =  False if not self.vocab_file else True







			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):

				if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				__magic_name__	:	Union[str, Any]			     =  [self.cls_token_id]
				__magic_name__	:	str			     =  [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep







			def     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
				__magic_name__	:	List[str]			     =  [self.sep_token_id]
				__magic_name__	:	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     SCREAMING_SNAKE_CASE					(       self		,     _a		,     _a = None	):
				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
				__magic_name__	:	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	):
					copyfile(self.vocab_file		,     _a	)

				return (out_vocab_file,)
