
"""simple docstring"""




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	snake_case__							:		Dict         =		(DPMSolverMultistepScheduler,)
	snake_case__							:		str         =		(('num_inference_steps', 25),)


	def    _UpperCamelCase							(  self :Dict       ,    **__magic_name__ :int			)			->   Union[str, Any]:

			'''simple docstring'''






			a__   		=     {
			    '''num_train_timesteps''': 1000,
			    '''beta_start''': 0.0_001,
			    '''beta_end''': 0.02,
			    '''beta_schedule''': '''linear''',
			    '''solver_order''': 2,
			    '''prediction_type''': '''epsilon''',
			    '''thresholding''': False,
			    '''sample_max_value''': 1.0,
			    '''algorithm_type''': '''dpmsolver++''',
			    '''solver_type''': '''midpoint''',
			    '''lower_order_final''': False,
			    '''lambda_min_clipped''': -float('''inf'''			),
			    '''variance_type''': None,
			}

			config.update(**__magic_name__			)
			return config


	def    _UpperCamelCase							(  self :List[str]       ,    __magic_name__ :List[str]=0       ,    **__magic_name__ :Tuple			)			->   Any:

			'''simple docstring'''






			a__   		=     dict(self.forward_default_kwargs			)
			a__   		=     kwargs.pop('''num_inference_steps'''       ,    __magic_name__			)
			a__   		=     self.dummy_sample
			a__   		=     0.1 * sample
			a__   		=     [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

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

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


	def    _UpperCamelCase							(  self :str			)			->   List[str]:

			'''simple docstring'''






			pass


	def    _UpperCamelCase							(  self :str       ,    __magic_name__ :Union[str, Any]=0       ,    **__magic_name__ :Any			)			->   List[Any]:

			'''simple docstring'''






			a__   		=     dict(self.forward_default_kwargs			)
			a__   		=     kwargs.pop('''num_inference_steps'''       ,    __magic_name__			)
			a__   		=     self.dummy_sample
			a__   		=     0.1 * sample
			a__   		=     [residual + 0.2, residual + 0.15, residual + 0.10]

			for scheduler_class in self.scheduler_classes:
					a__   		=     self.get_scheduler_config()
					a__   		=     scheduler_class(**__magic_name__			)
					scheduler.set_timesteps(__magic_name__			)

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

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

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

					a__   		=     scheduler.step(__magic_name__       ,    __magic_name__       ,    __magic_name__       ,    **__magic_name__			).prev_sample
					a__   		=     new_scheduler.step(__magic_name__       ,    __magic_name__       ,    __magic_name__       ,    **__magic_name__			).prev_sample

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


	def    _UpperCamelCase							(  self :Optional[Any]       ,    __magic_name__ :Optional[int]=None       ,    **__magic_name__ :Optional[Any]			)			->   str:

			'''simple docstring'''






			if scheduler is None:
					a__   		=     self.scheduler_classes[0]
					a__   		=     self.get_scheduler_config(**__magic_name__			)
					a__   		=     scheduler_class(**__magic_name__			)

			a__   		=     10
			a__   		=     self.dummy_model()
			a__   		=     self.dummy_sample_deter
			scheduler.set_timesteps(__magic_name__			)

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

			return sample


	def    _UpperCamelCase							(  self :Optional[int]			)			->   Tuple:

			'''simple docstring'''






			a__   		=     dict(self.forward_default_kwargs			)

			a__   		=     kwargs.pop('''num_inference_steps'''       ,    __magic_name__			)

			for scheduler_class in self.scheduler_classes:
					a__   		=     self.get_scheduler_config()
					a__   		=     scheduler_class(**__magic_name__			)

					a__   		=     self.dummy_sample
					a__   		=     0.1 * sample

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

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

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

					a__   		=     scheduler.step(__magic_name__       ,    __magic_name__       ,    __magic_name__       ,    **__magic_name__			).prev_sample
					a__   		=     scheduler.step(__magic_name__       ,    __magic_name__       ,    __magic_name__       ,    **__magic_name__			).prev_sample

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


	def    _UpperCamelCase							(  self :Optional[int]			)			->   Dict:

			'''simple docstring'''






			for timesteps in [25, 50, 100, 999, 1000]:
					self.check_over_configs(num_train_timesteps=__magic_name__			)


	def    _UpperCamelCase							(  self :Union[str, Any]			)			->   Optional[int]:

			'''simple docstring'''






			self.check_over_configs(thresholding=__magic_name__			)
			for order in [1, 2, 3]:
					for solver_type in ["midpoint", "heun"]:
							for threshold in [0.5, 1.0, 2.0]:
									for prediction_type in ["epsilon", "sample"]:
											self.check_over_configs(
											    thresholding=__magic_name__       ,    prediction_type=__magic_name__       ,    sample_max_value=__magic_name__       ,    algorithm_type='''dpmsolver++'''       ,    solver_order=__magic_name__       ,    solver_type=__magic_name__       ,    )


	def    _UpperCamelCase							(  self :int			)			->   List[str]:

			'''simple docstring'''






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


	def    _UpperCamelCase							(  self :List[str]			)			->   int:

			'''simple docstring'''






			for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
					for solver_type in ["midpoint", "heun"]:
							for order in [1, 2, 3]:
									for prediction_type in ["epsilon", "sample"]:
											if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
													if order == 3:
															continue
											else:
													self.check_over_configs(
													    solver_order=__magic_name__       ,    solver_type=__magic_name__       ,    prediction_type=__magic_name__       ,    algorithm_type=__magic_name__       ,    )
											a__   		=     self.full_loop(
											    solver_order=__magic_name__       ,    solver_type=__magic_name__       ,    prediction_type=__magic_name__       ,    algorithm_type=__magic_name__       ,    )
											assert not torch.isnan(__magic_name__			).any(), "Samples have nan numbers"


	def    _UpperCamelCase							(  self :List[str]			)			->   List[str]:

			'''simple docstring'''






			self.check_over_configs(lower_order_final=__magic_name__			)
			self.check_over_configs(lower_order_final=__magic_name__			)


	def    _UpperCamelCase							(  self :Optional[Any]			)			->   List[Any]:

			'''simple docstring'''






			self.check_over_configs(lambda_min_clipped=-float('''inf'''			)			)
			self.check_over_configs(lambda_min_clipped=-5.1			)


	def    _UpperCamelCase							(  self :Optional[int]			)			->   Union[str, Any]:

			'''simple docstring'''






			self.check_over_configs(variance_type=__magic_name__			)
			self.check_over_configs(variance_type='''learned_range'''			)


	def    _UpperCamelCase							(  self :Optional[int]			)			->   Optional[int]:

			'''simple docstring'''






			for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
					self.check_over_forward(num_inference_steps=__magic_name__       ,    time_step=0			)


	def    _UpperCamelCase							(  self :Union[str, Any]			)			->   List[str]:

			'''simple docstring'''






			a__   		=     self.full_loop()
			a__   		=     torch.mean(torch.abs(__magic_name__			)			)

			assert abs(result_mean.item() - 0.3_301			) < 1e-3


	def    _UpperCamelCase							(  self :Optional[Any]			)			->   Dict:

			'''simple docstring'''






			a__   		=     self.full_loop(thresholding=__magic_name__       ,    dynamic_thresholding_ratio=0.87       ,    sample_max_value=0.5			)
			a__   		=     torch.mean(torch.abs(__magic_name__			)			)

			assert abs(result_mean.item() - 1.1_364			) < 1e-3


	def    _UpperCamelCase							(  self :Any			)			->   Optional[int]:

			'''simple docstring'''






			a__   		=     self.full_loop(prediction_type='''v_prediction'''			)
			a__   		=     torch.mean(torch.abs(__magic_name__			)			)

			assert abs(result_mean.item() - 0.2_251			) < 1e-3


	def    _UpperCamelCase							(  self :Optional[int]			)			->   int:

			'''simple docstring'''






			a__   		=     self.full_loop(prediction_type='''v_prediction'''       ,    use_karras_sigmas=__magic_name__			)
			a__   		=     torch.mean(torch.abs(__magic_name__			)			)

			assert abs(result_mean.item() - 0.2_096			) < 1e-3


	def    _UpperCamelCase							(  self :List[Any]			)			->   str:

			'''simple docstring'''






			a__   		=     DPMSolverMultistepScheduler(**self.get_scheduler_config()			)
			a__   		=     self.full_loop(scheduler=__magic_name__			)
			a__   		=     torch.mean(torch.abs(__magic_name__			)			)

			assert abs(result_mean.item() - 0.3_301			) < 1e-3

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

			a__   		=     self.full_loop(scheduler=__magic_name__			)
			a__   		=     torch.mean(torch.abs(__magic_name__			)			)

			assert abs(result_mean.item() - 0.3_301			) < 1e-3


	def    _UpperCamelCase							(  self :str			)			->   int:

			'''simple docstring'''






			a__   		=     self.scheduler_classes[0]
			a__   		=     self.get_scheduler_config(thresholding=__magic_name__       ,    dynamic_thresholding_ratio=0			)
			a__   		=     scheduler_class(**__magic_name__			)

			a__   		=     10
			a__   		=     self.dummy_model()
			a__   		=     self.dummy_sample_deter.half()
			scheduler.set_timesteps(__magic_name__			)

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

			assert sample.dtype == torch.floataa






	def    _UpperCamelCase							(  self :Dict       ,    **__magic_name__ :int			)			->   Union[str, Any]:

			'''simple docstring'''






			for scheduler_class in self.scheduler_classes:
					a__   		=     self.get_scheduler_config(**__magic_name__			)
					a__   		=     scheduler_class(**__magic_name__			)

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






"""simple docstring"""




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

from tokenizers import processors

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


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation

require_version('''tokenizers>=0.13.3''')

if is_sentencepiece_available():
					from .tokenization_llama import LlamaTokenizer
else:
					__lowerCAmelCase							:  List[str] 				=		None

__lowerCAmelCase							:  Optional[int] 				=		logging.get_logger(__name__)
__lowerCAmelCase							:  int 				=		{'''vocab_file''': '''tokenizer.model''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCAmelCase				,__lowerCAmelCase							:  Any 				=		'''[INST]''', '''[/INST]'''
__lowerCAmelCase				,__lowerCAmelCase							:  Tuple 				=		'''<<SYS>>\n''', '''\n<</SYS>>\n\n'''

# fmt: off
__lowerCAmelCase							:  List[str] 				=		'''You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don\'t know the answer to a question, please don\'t share false information.'''
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	snake_case__							:		List[str]         =		VOCAB_FILES_NAMES
	snake_case__							:		Optional[Any]         =		LlamaTokenizer
	snake_case__							:		Union[str, Any]         =		'left'
	snake_case__							:		Any         =		['input_ids', 'attention_mask']


	def __init__(  self :Optional[Any]       ,    __magic_name__ :Optional[Any]=None       ,    __magic_name__ :Tuple=None       ,    __magic_name__ :List[Any]=False       ,    __magic_name__ :List[str]="<unk>"       ,    __magic_name__ :List[str]="<s>"       ,    __magic_name__ :List[str]="</s>"       ,    __magic_name__ :Any=True       ,    __magic_name__ :Union[str, Any]=False       ,    **__magic_name__ :Optional[int]       ,    )			->   List[Any]:

			'''simple docstring'''






			super().__init__(
			    vocab_file=__magic_name__       ,    tokenizer_file=__magic_name__       ,    clean_up_tokenization_spaces=__magic_name__       ,    unk_token=__magic_name__       ,    bos_token=__magic_name__       ,    eos_token=__magic_name__       ,    **__magic_name__       ,    )
			a__   		=     add_bos_token
			a__   		=     add_eos_token
			self.update_post_processor()

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


	def    _UpperCamelCase							(  self :List[str]			)			->   Optional[int]:

			'''simple docstring'''






			a__   		=     self.bos_token
			a__   		=     self.bos_token_id

			a__   		=     self.eos_token
			a__   		=     self.eos_token_id

			a__   		=     F"{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"
			a__   		=     F"{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"

			a__   		=     []
			if self.add_bos_token:
					special_tokens.append((bos, bos_token_id)			)
			if self.add_eos_token:
					special_tokens.append((eos, eos_token_id)			)
			a__   		=     processors.TemplateProcessing(
			    single=__magic_name__       ,    pair=__magic_name__       ,    special_tokens=__magic_name__			)


	@property
	def    _UpperCamelCase							(  self :Optional[int]			)			->   Any:

			'''simple docstring'''






			return self._add_eos_token


	@property
	def    _UpperCamelCase							(  self :Optional[int]			)			->   Optional[Any]:

			'''simple docstring'''






			return self._add_bos_token


	@add_eos_token.setter
	def    _UpperCamelCase							(  self :int       ,    __magic_name__ :int			)			->   int:

			'''simple docstring'''






			a__   		=     value
			self.update_post_processor()


	@add_bos_token.setter
	def    _UpperCamelCase							(  self :Any       ,    __magic_name__ :str			)			->   Optional[int]:

			'''simple docstring'''






			a__   		=     value
			self.update_post_processor()


	def    _UpperCamelCase							(  self :Dict       ,    __magic_name__ :str       ,    __magic_name__ :Optional[str] = None			)			->   Tuple[str]:

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

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

			return (out_vocab_file,)






	def    _UpperCamelCase							(  self :int       ,    __magic_name__ :"Conversation"			)			->   List[Any]:

			'''simple docstring'''






			a__   		=     list(conversation.iter_texts()			)
			if not all([is_user for is_user, msg in dialogue[::2]]			) or not all(
			    [not is_user for is_user, msg in dialogue[1::2]]			):
					raise ValueError(
					    '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''			)

			a__   		=     []
			if len(conversation.past_user_inputs			) > 0:
					if not conversation.past_user_inputs[0].startswith(__magic_name__			) or E_SYS not in conversation.past_user_inputs[0]:
							a__   		=     (
							    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
							)
			elif not dialogue[0][1].startswith(__magic_name__			) or E_SYS not in dialogue[0][1]:
					a__   		=     (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

			dialog_tokens += sum(
			    [
			        [self.bos_token_id]
			        + self.encode(
			            F"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} "       ,    add_special_tokens=__magic_name__			)
			        + [self.eos_token_id]
			        for prompt, answer in zip(dialogue[::2]       ,    dialogue[1::2]			)
			    ]       ,    []       ,    )
			if not (dialogue[-1][0]):
					raise ValueError(F"Last message must be from user, got {dialogue[-1]['role']}"			)
			dialog_tokens += [self.bos_token_id] + self.encode(
			    F"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"       ,    add_special_tokens=__magic_name__			)
			return dialog_tokens




