
import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	List[Any]	 =(DDPMScheduler,)




							def 			A__				(    self       ,**A__):
												lowercase						     =						{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''variance_type''': '''fixed_small''',
												    '''clip_sample''': True,
												}

												config.update(**A__)
												return config




							def 			A__				(    self):
												for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=A__)




							def 			A__				(    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=A__       ,beta_end=A__)




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




							def 			A__				(    self):
												for variance in ["fixed_small", "fixed_large", "other"]:
																	self.check_over_configs(variance_type=A__)




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




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




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




							def 			A__				(    self):
												for t in [0, 5_0_0, 9_9_9]:
																	self.check_over_forward(time_step=A__)




							def 			A__				(    self):
												lowercase						     =						self.scheduler_classes[0]
												lowercase						     =						self.get_scheduler_config()
												lowercase						     =						scheduler_class(**A__)

												assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_8_7) - 0.00979)) < 1E-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_9_9) - 0.02)) < 1E-5




							def 			A__				(    self):
												lowercase						     =						self.scheduler_classes[0]
												lowercase						     =						self.get_scheduler_config()
												lowercase						     =						scheduler_class(**A__)

												lowercase						     =						len(A__)

												lowercase						     =						self.dummy_model()
												lowercase						     =						self.dummy_sample_deter
												lowercase						     =						torch.manual_seed(0)

												for t in reversed(range(A__)):
																	# 1. predict noise residual
																	lowercase						     =						model(A__       ,A__)

																	# 2. predict previous mean of sample x_t-1
																	lowercase						     =						scheduler.step(A__       ,A__       ,A__       ,generator=A__).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	lowercase						     =						pred_prev_sample

												lowercase						     =						torch.sum(torch.abs(A__))
												lowercase						     =						torch.mean(torch.abs(A__))

												assert abs(result_sum.item() - 258.9606) < 1E-2
												assert abs(result_mean.item() - 0.3372) < 1E-3




							def 			A__				(    self):
												lowercase						     =						self.scheduler_classes[0]
												lowercase						     =						self.get_scheduler_config(prediction_type='''v_prediction''')
												lowercase						     =						scheduler_class(**A__)

												lowercase						     =						len(A__)

												lowercase						     =						self.dummy_model()
												lowercase						     =						self.dummy_sample_deter
												lowercase						     =						torch.manual_seed(0)

												for t in reversed(range(A__)):
																	# 1. predict noise residual
																	lowercase						     =						model(A__       ,A__)

																	# 2. predict previous mean of sample x_t-1
																	lowercase						     =						scheduler.step(A__       ,A__       ,A__       ,generator=A__).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	lowercase						     =						pred_prev_sample

												lowercase						     =						torch.sum(torch.abs(A__))
												lowercase						     =						torch.mean(torch.abs(A__))

												assert abs(result_sum.item() - 202.0296) < 1E-2
												assert abs(result_mean.item() - 0.2631) < 1E-3




							def 			A__				(    self):
												lowercase						     =						self.scheduler_classes[0]
												lowercase						     =						self.get_scheduler_config()
												lowercase						     =						scheduler_class(**A__)

												lowercase						     =						[1_0_0, 8_7, 5_0, 1, 0]

												scheduler.set_timesteps(timesteps=A__)

												lowercase						     =						scheduler.timesteps

												for i, timestep in enumerate(A__):
																	if i == len(A__) - 1:
																						lowercase						     =						-1
																	else:
																						lowercase						     =						timesteps[i + 1]

																	lowercase						     =						scheduler.previous_timestep(A__)
																	lowercase						     =						prev_t.item()

																	self.assertEqual(A__       ,A__)




							def 			A__				(    self):
												lowercase						     =						self.scheduler_classes[0]
												lowercase						     =						self.get_scheduler_config()
												lowercase						     =						scheduler_class(**A__)

												lowercase						     =						[1_0_0, 8_7, 5_0, 5_1, 0]

												with self.assertRaises(A__       ,msg='''`custom_timesteps` must be in descending order.'''):
																	scheduler.set_timesteps(timesteps=A__)




							def 			A__				(    self):
												lowercase						     =						self.scheduler_classes[0]
												lowercase						     =						self.get_scheduler_config()
												lowercase						     =						scheduler_class(**A__)

												lowercase						     =						[1_0_0, 8_7, 5_0, 1, 0]
												lowercase						     =						len(A__)

												with self.assertRaises(A__       ,msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.'''):
																	scheduler.set_timesteps(num_inference_steps=A__       ,timesteps=A__)





							def 			A__				(    self):
												lowercase						     =						self.scheduler_classes[0]
												lowercase						     =						self.get_scheduler_config()
												lowercase						     =						scheduler_class(**A__)

												lowercase						     =						[scheduler.config.num_train_timesteps]

												with self.assertRaises(
												    A__       ,msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''       ,):
																	scheduler.set_timesteps(timesteps=A__)






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowercase__		:str			  =   logging.get_logger(__name__)

lowercase__		:Any			  =   {"vocab_file": "sentencepiece.bpe.model"}

lowercase__		:Tuple			  =   {
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

lowercase__		:str			  =   {
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

lowercase__		:int			  =   "▁"







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	Union[str, Any]	 =VOCAB_FILES_NAMES
							lowercase_	:	List[Any]	 =PRETRAINED_VOCAB_FILES_MAP
							lowercase_	:	Tuple	 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_	:	str	 =['''input_ids''', '''attention_mask''']




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

												lowercase						     =						{} if sp_model_kwargs is None else sp_model_kwargs

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

												lowercase						     =						vocab_file
												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(str(A__))

												lowercase						     =						{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

												lowercase						     =						len(self.sp_model) - 1
												lowercase						     =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}




							def 			A__				(    self       ,A__       ,A__ = None):

												if token_ids_a is None:
																	return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
												lowercase						     =						[self.cls_token_id]
												lowercase						     =						[self.sep_token_id]
												return cls + token_ids_a + sep + sep + token_ids_a + sep




							def 			A__				(    self       ,A__       ,A__ = None       ,A__ = 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__)) + [1]
												return [1] + ([0] * len(A__)) + [1, 1] + ([0] * len(A__)) + [1]




							def 			A__				(    self       ,A__       ,A__ = None):
												lowercase						     =						[self.sep_token_id]
												lowercase						     =						[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]




							@property
							def 			A__				(    self):
												return len(self.sp_model)




							def 			A__				(    self):
												lowercase						     =						{self.convert_ids_to_tokens(A__): i for i in range(self.vocab_size)}
												vocab.update(self.added_tokens_encoder)
												return vocab




							def 			A__				(    self       ,A__):
												return self.sp_model.encode(A__       ,out_type=A__)




							def 			A__				(    self       ,A__):
												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												lowercase						     =						self.sp_model.PieceToId(A__)

												return spm_id if spm_id else self.unk_token_id




							def 			A__				(    self       ,A__):
												if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(A__)




							def 			A__				(    self       ,A__):
												lowercase						     =						[]
												lowercase						     =						''''''
												lowercase						     =						False
												for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																						if not prev_is_special:
																											out_string += " "
																						out_string += self.sp_model.decode(A__) + token
																						lowercase						     =						True
																						lowercase						     =						[]
																	else:
																						current_sub_tokens.append(A__)
																						lowercase						     =						False
												out_string += self.sp_model.decode(A__)
												return out_string.strip()




							def __getstate__(    self):
												lowercase						     =						self.__dict__.copy()
												lowercase						     =						None
												return state




							def __setstate__(    self       ,A__):
												lowercase						     =						d

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

												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(self.vocab_file)





							def 			A__				(    self       ,A__       ,A__ = None):
												if not os.path.isdir(A__):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return
												lowercase						     =						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:
																						lowercase						     =						self.sp_model.serialized_model_proto()
																						fi.write(A__)

												return (out_vocab_file,)




