
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


lowercase__	:Optional[int]										=					logging.get_logger(__name__)


lowercase__	:int										=					{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
lowercase__	:List[str]										=					{
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

lowercase__	:Optional[Any]										=					{
    "facebook/bart-base": 1024,
    "facebook/bart-large": 1024,
    "facebook/bart-large-mnli": 1024,
    "facebook/bart-large-cnn": 1024,
    "facebook/bart-large-xsum": 1024,
    "yjernite/bart_eli5": 1024,
}







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




							def __init__(       self     ,A__=None     ,A__=None     ,A__=None     ,A__="replace"     ,A__="<s>"     ,A__="</s>"     ,A__="</s>"     ,A__="<s>"     ,A__="<unk>"     ,A__="<pad>"     ,A__="<mask>"     ,A__=False     ,A__=True     ,**A__     ,):
												super().__init__(
												    A__     ,A__     ,tokenizer_file=A__     ,errors=A__     ,bos_token=A__     ,eos_token=A__     ,sep_token=A__     ,cls_token=A__     ,unk_token=A__     ,pad_token=A__     ,mask_token=A__     ,add_prefix_space=A__     ,trim_offsets=A__     ,**A__     ,)

												lowercase					=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
												if pre_tok_state.get('''add_prefix_space'''     ,A__) != add_prefix_space:
																	lowercase					=		getattr(A__     ,pre_tok_state.pop('''type'''))
																	lowercase					=		add_prefix_space
																	lowercase					=		pre_tok_class(**A__)

												lowercase					=		add_prefix_space

												# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
												lowercase					=		'''post_processor'''
												lowercase					=		getattr(self.backend_tokenizer     ,A__     ,A__)
												if tokenizer_component_instance:
																	lowercase					=		json.loads(tokenizer_component_instance.__getstate__())

																	# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																	if "sep" in state:
																						lowercase					=		tuple(state['''sep'''])
																	if "cls" in state:
																						lowercase					=		tuple(state['''cls'''])

																	lowercase					=		False

																	if state.get('''add_prefix_space'''     ,A__) != add_prefix_space:
																						lowercase					=		add_prefix_space
																						lowercase					=		True

																	if state.get('''trim_offsets'''     ,A__) != trim_offsets:
																						lowercase					=		trim_offsets
																						lowercase					=		True

																	if changes_to_apply:
																						lowercase					=		getattr(A__     ,state.pop('''type'''))
																						lowercase					=		component_class(**A__)
																						setattr(self.backend_tokenizer     ,A__     ,A__)




							@property
							def 				A__   (       self):
												if self._mask_token is None:
																	if self.verbose:
																						logger.error('''Using mask_token, but it is not set yet.''')
																	return None
												return str(self._mask_token)




							@mask_token.setter
							def 				A__   (       self     ,A__):
												lowercase					=		AddedToken(A__     ,lstrip=A__     ,rstrip=A__) if isinstance(A__     ,A__) else value
												lowercase					=		value




							def 				A__   (       self     ,*A__     ,**A__):
												lowercase					=		kwargs.get('''is_split_into_words'''     ,A__)

												if is_split_into_words and not self.add_prefix_space:
																	raise ValueError(
																	    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
																	    '''to use it with pretokenized inputs.''')

												return super()._batch_encode_plus(*A__     ,**A__)




							def 				A__   (       self     ,*A__     ,**A__):
												lowercase					=		kwargs.get('''is_split_into_words'''     ,A__)

												if is_split_into_words and not self.add_prefix_space:
																	raise ValueError(
																	    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
																	    '''to use it with pretokenized inputs.''')

												return super()._encode_plus(*A__     ,**A__)




							def 				A__   (       self     ,A__     ,A__ = None):
												lowercase					=		self._tokenizer.model.save(A__     ,name=A__)
												return tuple(A__)




							def 				A__   (       self     ,A__     ,A__=None):
												lowercase					=		[self.bos_token_id] + token_ids_a + [self.eos_token_id]
												if token_ids_a is None:
																	return output

												return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





							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]






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




