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

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


if is_sentencepiece_available():
							from .tokenization_pegasus import PegasusTokenizer
else:
							_lowercase			:  str   							=	None


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


_lowercase			:  Any   							=	"▁"

_lowercase			:  Tuple   							=	{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

_lowercase			:  str   							=	{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

_lowercase			:  Optional[int]   							=	{
    "google/pegasus-xsum": 5_1_2,
}






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					VOCAB_FILES_NAMES
					lowerCAmelCase_					       =					PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_					       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_					       =					PegasusTokenizer
					lowerCAmelCase_					       =					['''input_ids''', '''attention_mask''']






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE="<pad>"    ,							__SCREAMING_SNAKE_CASE="</s>"    ,							__SCREAMING_SNAKE_CASE="<unk>"    ,							__SCREAMING_SNAKE_CASE="<mask_2>"    ,							__SCREAMING_SNAKE_CASE="<mask_1>"    ,							__SCREAMING_SNAKE_CASE=None    ,							__SCREAMING_SNAKE_CASE=1_03    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											lowercase_     :					Any													=	offset

											if additional_special_tokens is not None:
																	if not isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):
																							raise TypeError(
																							    F'''additional_special_tokens should be of type {type(__SCREAMING_SNAKE_CASE		)}, but is'''
																							    F''' {type(__SCREAMING_SNAKE_CASE		)}'''		)

																	lowercase_     :					Dict													=	(
																	    ([mask_token_sent] + additional_special_tokens)
																	    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																	    else additional_special_tokens
																	)
																	# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																	additional_special_tokens_extended += [
																	    F'''<unk_{i}>''' for i in range(len(__SCREAMING_SNAKE_CASE		)    ,							self.offset - 1		)
																	]

																	if len(set(__SCREAMING_SNAKE_CASE		)		) != len(__SCREAMING_SNAKE_CASE		):
																							raise ValueError(
																							    '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
																							    F''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''		)
																	lowercase_     :					List[Any]													=	additional_special_tokens_extended
											else:
																	lowercase_     :					Dict													=	[mask_token_sent] if mask_token_sent is not None else []
																	additional_special_tokens += [F'''<unk_{i}>''' for i in range(2    ,							self.offset		)]

											super().__init__(
											    __SCREAMING_SNAKE_CASE    ,							tokenizer_file=__SCREAMING_SNAKE_CASE    ,							pad_token=__SCREAMING_SNAKE_CASE    ,							eos_token=__SCREAMING_SNAKE_CASE    ,							unk_token=__SCREAMING_SNAKE_CASE    ,							mask_token=__SCREAMING_SNAKE_CASE    ,							mask_token_sent=__SCREAMING_SNAKE_CASE    ,							offset=__SCREAMING_SNAKE_CASE    ,							additional_special_tokens=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE    ,							)
											lowercase_     :					Tuple													=	vocab_file
											lowercase_     :					Dict													=	False if not self.vocab_file else True






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	set(self.all_special_ids		)  # call it once instead of inside list comp
											all_special_ids.remove(self.unk_token_id		)  # <unk> is only sometimes special

											if all_special_ids != set(range(len(self.additional_special_tokens		) + 3		)		):
																	raise ValueError(
																	    '''There should be 3 special tokens: mask_token, pad_token, and eos_token +'''
																	    F''' {len(self.additional_special_tokens		)} additional_special_tokens, but got {all_special_ids}'''		)

											return [1 if x in all_special_ids else 0 for x in seq]






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None    ,							__SCREAMING_SNAKE_CASE = False		):


											"""simple docstring"""





											if already_has_special_tokens:
																	return self._special_token_mask(__SCREAMING_SNAKE_CASE		)
											elif token_ids_a is None:
																	return self._special_token_mask(__SCREAMING_SNAKE_CASE		) + [1]
											else:
																	return self._special_token_mask(token_ids_a + token_ids_a		) + [1]






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=None		):


											"""simple docstring"""





											if token_ids_a is None:
																	return token_ids_a + [self.eos_token_id]
											# We don't expect to process pairs, but leave the pair logic for API consistency
											return token_ids_a + token_ids_a + [self.eos_token_id]






					def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											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(__SCREAMING_SNAKE_CASE		):
																	logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
																	return
											lowercase_     :					Tuple													=	os.path.join(
											    __SCREAMING_SNAKE_CASE    ,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

											if os.path.abspath(self.vocab_file		) != os.path.abspath(__SCREAMING_SNAKE_CASE		):
																	copyfile(self.vocab_file    ,							__SCREAMING_SNAKE_CASE		)

											return (out_vocab_file,)





'''simple docstring'''







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					(DDPMScheduler,)






					def      _snake_case   (       self    ,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	{
											    '''num_train_timesteps''': 10_00,
											    '''beta_start''': 0.0_001,
											    '''beta_end''': 0.02,
											    '''beta_schedule''': '''linear''',
											    '''variance_type''': '''fixed_small''',
											    '''clip_sample''': True,
											}

											config.update(**__SCREAMING_SNAKE_CASE		)
											return config






					def      _snake_case   (       self		):


											"""simple docstring"""





											for timesteps in [1, 5, 1_00, 10_00]:
																	self.check_over_configs(num_train_timesteps=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]    ,							[0.002, 0.02, 0.2, 2]		):
																	self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE    ,							beta_end=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





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






					def      _snake_case   (       self		):


											"""simple docstring"""





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






					def      _snake_case   (       self		):


											"""simple docstring"""





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






					def      _snake_case   (       self		):


											"""simple docstring"""





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






					def      _snake_case   (       self		):


											"""simple docstring"""





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






					def      _snake_case   (       self		):


											"""simple docstring"""





											for t in [0, 5_00, 9_99]:
																	self.check_over_forward(time_step=__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[str]													=	self.scheduler_classes[0]
											lowercase_     :					int													=	self.get_scheduler_config()
											lowercase_     :					str													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(4_87		) - 0.00_979		)		) < 1E-5
											assert torch.sum(torch.abs(scheduler._get_variance(9_99		) - 0.02		)		) < 1E-5






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Union[str, Any]													=	self.scheduler_classes[0]
											lowercase_     :					Any													=	self.get_scheduler_config()
											lowercase_     :					Optional[int]													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Any													=	len(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	self.dummy_model()
											lowercase_     :					Any													=	self.dummy_sample_deter
											lowercase_     :					List[str]													=	torch.manual_seed(0		)

											for t in reversed(range(__SCREAMING_SNAKE_CASE		)		):
																	# 1. predict noise residual
																	lowercase_     :					Optional[int]													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

																	# 2. predict previous mean of sample x_t-1
																	lowercase_     :					Union[str, Any]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE		).prev_sample

																	# if t > 0:
																	#     noise = self.dummy_sample_deter
																	#     variance = scheduler.get_variance(t) ** (0.5) * noise
																	#
																	# sample = pred_prev_sample + variance
																	lowercase_     :					List[str]													=	pred_prev_sample

											lowercase_     :					str													=	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Tuple													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_sum.item() - 258.9_606		) < 1E-2
											assert abs(result_mean.item() - 0.3_372		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.scheduler_classes[0]
											lowercase_     :					Tuple													=	self.get_scheduler_config(prediction_type='''v_prediction'''		)
											lowercase_     :					Any													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	len(__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	self.dummy_model()
											lowercase_     :					int													=	self.dummy_sample_deter
											lowercase_     :					str													=	torch.manual_seed(0		)

											for t in reversed(range(__SCREAMING_SNAKE_CASE		)		):
																	# 1. predict noise residual
																	lowercase_     :					Optional[Any]													=	model(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)

																	# 2. predict previous mean of sample x_t-1
																	lowercase_     :					Optional[Any]													=	scheduler.step(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							generator=__SCREAMING_SNAKE_CASE		).prev_sample

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

											lowercase_     :					str													=	torch.sum(torch.abs(__SCREAMING_SNAKE_CASE		)		)
											lowercase_     :					Tuple													=	torch.mean(torch.abs(__SCREAMING_SNAKE_CASE		)		)

											assert abs(result_sum.item() - 202.0_296		) < 1E-2
											assert abs(result_mean.item() - 0.2_631		) < 1E-3






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[int]													=	self.scheduler_classes[0]
											lowercase_     :					int													=	self.get_scheduler_config()
											lowercase_     :					int													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					int													=	[1_00, 87, 50, 1, 0]

											scheduler.set_timesteps(timesteps=__SCREAMING_SNAKE_CASE		)

											lowercase_     :					List[str]													=	scheduler.timesteps

											for i, timestep in enumerate(__SCREAMING_SNAKE_CASE		):
																	if i == len(__SCREAMING_SNAKE_CASE		) - 1:
																							lowercase_     :					str													=	-1
																	else:
																							lowercase_     :					Any													=	timesteps[i + 1]

																	lowercase_     :					Optional[Any]													=	scheduler.previous_timestep(__SCREAMING_SNAKE_CASE		)
																	lowercase_     :					int													=	prev_t.item()

																	self.assertEqual(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Tuple													=	self.scheduler_classes[0]
											lowercase_     :					List[Any]													=	self.get_scheduler_config()
											lowercase_     :					str													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Optional[int]													=	[1_00, 87, 50, 51, 0]

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






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					Optional[Any]													=	self.scheduler_classes[0]
											lowercase_     :					str													=	self.get_scheduler_config()
											lowercase_     :					Tuple													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					Tuple													=	[1_00, 87, 50, 1, 0]
											lowercase_     :					List[str]													=	len(__SCREAMING_SNAKE_CASE		)

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






					def      _snake_case   (       self		):


											"""simple docstring"""





											lowercase_     :					List[Any]													=	self.scheduler_classes[0]
											lowercase_     :					Optional[int]													=	self.get_scheduler_config()
											lowercase_     :					Any													=	scheduler_class(**__SCREAMING_SNAKE_CASE		)

											lowercase_     :					str													=	[scheduler.config.num_train_timesteps]

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



