
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_barthez import BarthezTokenizer
else:
							lowercase__	:Tuple										=					None

lowercase__	:str										=					logging.get_logger(__name__)

lowercase__	:List[str]										=					{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowercase__	:Optional[int]										=					{
    "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"
        ),
    },
    "tokenizer_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"
        ),
    },
}

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

lowercase__	:Tuple										=					"▁"







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




							def __init__(       self     ,A__=None     ,A__=None     ,A__="<s>"     ,A__="</s>"     ,A__="</s>"     ,A__="<s>"     ,A__="<unk>"     ,A__="<pad>"     ,A__="<mask>"     ,**A__     ,):
								lowercase					=		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     ,unk_token=_a     ,sep_token=_a     ,cls_token=_a     ,pad_token=_a     ,mask_token=_a     ,**_a     ,)

								lowercase					=		vocab_file
								lowercase					=		False if not self.vocab_file else True




							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):
								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]





							def 				A__   (       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
								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):
									copyfile(self.vocab_file     ,_a)

								return (out_vocab_file,)






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : List[str]       =(UniPCMultistepScheduler,)
							lowercase_ : Tuple       =(('''num_inference_steps''', 25),)




							def 				A__   (       self     ,**A__):
												lowercase					=		{
												    '''num_train_timesteps''': 1_0_0_0,
												    '''beta_start''': 0.0001,
												    '''beta_end''': 0.02,
												    '''beta_schedule''': '''linear''',
												    '''solver_order''': 2,
												    '''solver_type''': '''bh2''',
												}

												config.update(**A__)
												return config




							def 				A__   (       self     ,A__=0     ,**A__):
												lowercase					=		dict(self.forward_default_kwargs)
												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)
												lowercase					=		self.dummy_sample
												lowercase					=		0.1 * sample
												lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config(**A__)
																	lowercase					=		scheduler_class(**A__)
																	scheduler.set_timesteps(A__)
																	# copy over dummy past residuals
																	lowercase					=		dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(A__)
																						lowercase					=		scheduler_class.from_pretrained(A__)
																						new_scheduler.set_timesteps(A__)
																						# copy over dummy past residuals
																						lowercase					=		dummy_past_residuals[: new_scheduler.config.solver_order]

																	lowercase			,		lowercase					=		sample, sample
																	for t in range(A__     ,time_step + scheduler.config.solver_order + 1):
																						lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
																						lowercase					=		new_scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample

																						assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"




							def 				A__   (       self     ,A__=0     ,**A__):
												lowercase					=		dict(self.forward_default_kwargs)
												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)
												lowercase					=		self.dummy_sample
												lowercase					=		0.1 * sample
												lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config()
																	lowercase					=		scheduler_class(**A__)
																	scheduler.set_timesteps(A__)

																	# copy over dummy past residuals (must be after setting timesteps)
																	lowercase					=		dummy_past_residuals[: scheduler.config.solver_order]

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						scheduler.save_config(A__)
																						lowercase					=		scheduler_class.from_pretrained(A__)
																						# copy over dummy past residuals
																						new_scheduler.set_timesteps(A__)

																						# copy over dummy past residual (must be after setting timesteps)
																						lowercase					=		dummy_past_residuals[: new_scheduler.config.solver_order]

																	lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
																	lowercase					=		new_scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample

																	assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"




							def 				A__   (       self     ,A__=None     ,**A__):
												if scheduler is None:
																	lowercase					=		self.scheduler_classes[0]
																	lowercase					=		self.get_scheduler_config(**A__)
																	lowercase					=		scheduler_class(**A__)

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

												lowercase					=		1_0
												lowercase					=		self.dummy_model()
												lowercase					=		self.dummy_sample_deter
												scheduler.set_timesteps(A__)

												for i, t in enumerate(scheduler.timesteps):
																	lowercase					=		model(A__     ,A__)
																	lowercase					=		scheduler.step(A__     ,A__     ,A__).prev_sample

												return sample




							def 				A__   (       self):
												lowercase					=		dict(self.forward_default_kwargs)

												lowercase					=		kwargs.pop('''num_inference_steps'''     ,A__)

												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config()
																	lowercase					=		scheduler_class(**A__)

																	lowercase					=		self.dummy_sample
																	lowercase					=		0.1 * sample

																	if num_inference_steps is not None and hasattr(A__     ,'''set_timesteps'''):
																						scheduler.set_timesteps(A__)
																	elif num_inference_steps is not None and not hasattr(A__     ,'''set_timesteps'''):
																						lowercase					=		num_inference_steps

																	# copy over dummy past residuals (must be done after set_timesteps)
																	lowercase					=		[residual + 0.2, residual + 0.15, residual + 0.10]
																	lowercase					=		dummy_past_residuals[: scheduler.config.solver_order]

																	lowercase					=		scheduler.timesteps[5]
																	lowercase					=		scheduler.timesteps[6]

																	lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample
																	lowercase					=		scheduler.step(A__     ,A__     ,A__     ,**A__).prev_sample

																	self.assertEqual(output_a.shape     ,sample.shape)
																	self.assertEqual(output_a.shape     ,output_a.shape)




							def 				A__   (       self):
												# make sure that iterating over schedulers with same config names gives same results
												# for defaults
												lowercase					=		UniPCMultistepScheduler(**self.get_scheduler_config())
												lowercase					=		self.full_loop(scheduler=A__)
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.2464) < 1E-3

												lowercase					=		DPMSolverSinglestepScheduler.from_config(scheduler.config)
												lowercase					=		DEISMultistepScheduler.from_config(scheduler.config)
												lowercase					=		DPMSolverMultistepScheduler.from_config(scheduler.config)
												lowercase					=		UniPCMultistepScheduler.from_config(scheduler.config)

												lowercase					=		self.full_loop(scheduler=A__)
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.2464) < 1E-3




							def 				A__   (       self):
												for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_configs(num_train_timesteps=A__)




							def 				A__   (       self):
												self.check_over_configs(thresholding=A__)
												for order in [1, 2, 3]:
																	for solver_type in ["bh1", "bh2"]:
																						for threshold in [0.5, 1.0, 2.0]:
																											for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    thresholding=A__     ,prediction_type=A__     ,sample_max_value=A__     ,solver_order=A__     ,solver_type=A__     ,)




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




							def 				A__   (       self):
												for solver_type in ["bh1", "bh2"]:
																	for order in [1, 2, 3]:
																						for prediction_type in ["epsilon", "sample"]:
																											self.check_over_configs(
																											    solver_order=A__     ,solver_type=A__     ,prediction_type=A__     ,)
																											lowercase					=		self.full_loop(
																											    solver_order=A__     ,solver_type=A__     ,prediction_type=A__     ,)
																											assert not torch.isnan(A__).any(), "Samples have nan numbers"




							def 				A__   (       self):
												self.check_over_configs(lower_order_final=A__)
												self.check_over_configs(lower_order_final=A__)




							def 				A__   (       self):
												for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
																	self.check_over_forward(num_inference_steps=A__     ,time_step=0)




							def 				A__   (       self):
												lowercase					=		self.full_loop()
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.2464) < 1E-3




							def 				A__   (       self):
												lowercase					=		self.full_loop(prediction_type='''v_prediction''')
												lowercase					=		torch.mean(torch.abs(A__))

												assert abs(result_mean.item() - 0.1014) < 1E-3




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

												lowercase					=		1_0
												lowercase					=		self.dummy_model()
												lowercase					=		self.dummy_sample_deter.half()
												scheduler.set_timesteps(A__)

												for i, t in enumerate(scheduler.timesteps):
																	lowercase					=		model(A__     ,A__)
																	lowercase					=		scheduler.step(A__     ,A__     ,A__).prev_sample

												assert sample.dtype == torch.floataa





							def 				A__   (       self     ,**A__):
												for scheduler_class in self.scheduler_classes:
																	lowercase					=		self.get_scheduler_config(**A__)
																	lowercase					=		scheduler_class(**A__)

																	scheduler.set_timesteps(scheduler.config.num_train_timesteps)
																	assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps




