






"""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:
			a :				Tuple      					=		None

a :				int      					=		logging.get_logger(__name__)
a :				Tuple      					=		{"""vocab_file""": """tokenizer.model""", """tokenizer_file""": """tokenizer.json"""}

a					,		a :				Optional[int]      					=		"""[INST]""", """[/INST]"""
a					,		a :				Optional[Any]      					=		"""<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
a :				Dict      					=		"""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     __UpperCAmelCase(   __snake_case							):




						"""simple docstring"""




						__lowerCamelCase				       =					VOCAB_FILES_NAMES
						__lowerCamelCase				       =					LlamaTokenizer
						__lowerCamelCase				       =					'left'
						__lowerCamelCase				       =					['input_ids', 'attention_mask']




						def __init__(   self       ,  snake_case__=None       ,  snake_case__=None       ,  snake_case__=False       ,  snake_case__="<unk>"       ,  snake_case__="<s>"       ,  snake_case__="</s>"       ,  snake_case__=True       ,  snake_case__=False       ,  **snake_case__       ,  ):
												'''simple docstring'''
												super().__init__(
												    vocab_file=snake_case__       ,  tokenizer_file=snake_case__       ,  clean_up_tokenization_spaces=snake_case__       ,  unk_token=snake_case__       ,  bos_token=snake_case__       ,  eos_token=snake_case__       ,  **snake_case__       ,  )
												lowercase__				:      Union[str, Any]=       add_bos_token
												lowercase__				:      Optional[int]=       add_eos_token
												self.update_post_processor()

												lowercase__				:      List[str]=       vocab_file
												lowercase__				:      Optional[int]=       False if not self.vocab_file else True




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      int=       self.bos_token
												lowercase__				:      int=       self.bos_token_id

												lowercase__				:      Optional[Any]=       self.eos_token
												lowercase__				:      Union[str, Any]=       self.eos_token_id

												lowercase__				:      Dict=       F'''{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}'''
												lowercase__				:      Union[str, Any]=       F'''{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}'''

												lowercase__				:      Optional[Any]=       []
												if self.add_bos_token:
																		special_tokens.append((bos, bos_token_id)			)
												if self.add_eos_token:
																		special_tokens.append((eos, eos_token_id)			)
												lowercase__				:      List[Any]=       processors.TemplateProcessing(
												    single=snake_case__       ,  pair=snake_case__       ,  special_tokens=snake_case__			)




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return self._add_eos_token




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return self._add_bos_token




						@add_eos_token.setter
						def 		UpperCAmelCase_       (   self       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      List[str]=       value
												self.update_post_processor()




						@add_bos_token.setter
						def 		UpperCAmelCase_       (   self       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Optional[int]=       value
												self.update_post_processor()




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

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

												return (out_vocab_file,)




						def 		UpperCAmelCase_       (   self       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Dict=       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...)"			)

												lowercase__				:      Tuple=       []
												if len(conversation.past_user_inputs			) > 0:
																		if not conversation.past_user_inputs[0].startswith(snake_case__			) or E_SYS not in conversation.past_user_inputs[0]:
																								lowercase__				:      Optional[Any]=       (
																								    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
																								)
												elif not dialogue[0][1].startswith(snake_case__			) or E_SYS not in dialogue[0][1]:
																		lowercase__				:      int=       (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=snake_case__			)
												        + [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=snake_case__			)
												return dialog_tokens









import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      Tuple          =					(DDIMScheduler,)
      lowerCamelCase_	:      Union[str, Any]          =					(('eta', 0.0), ('num_inference_steps', 50))







      def 	lowerCAmelCase_    (     self							,  **lowerCamelCase   )						->       Dict:
        snake_case_       						=      {
            """num_train_timesteps""": 1000,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """clip_sample""": True,
        }

        config.update(**lowerCamelCase   )
        return config







      def 	lowerCAmelCase_    (     self							,  **lowerCamelCase   )						->       List[str]:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config(**lowerCamelCase   )
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

        snake_case_						,				snake_case_       						=      10, 0.0

        snake_case_       						=      self.dummy_model()
        snake_case_       						=      self.dummy_sample_deter

        scheduler.set_timesteps(lowerCamelCase   )

        for t in scheduler.timesteps:
          snake_case_       						=      model(lowerCamelCase							,  lowerCamelCase   )
          snake_case_       						=      scheduler.step(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   ).prev_sample

        return sample







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        for timesteps in [100, 500, 1000]:
          self.check_over_configs(num_train_timesteps=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        for steps_offset in [0, 1]:
          self.check_over_configs(steps_offset=lowerCamelCase   )

        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config(steps_offset=1   )
        snake_case_       						=      scheduler_class(**lowerCamelCase   )
        scheduler.set_timesteps(5   )
        assert torch.equal(scheduler.timesteps							,  torch.LongTensor([801, 601, 401, 201, 1]   )   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        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=lowerCamelCase							,  beta_end=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Any:
        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for timestep_spacing in ["trailing", "leading"]:
          self.check_over_configs(timestep_spacing=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for rescale_betas_zero_snr in [True, False]:
          self.check_over_configs(rescale_betas_zero_snr=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        self.check_over_configs(thresholding=lowerCamelCase   )
        for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(
                thresholding=lowerCamelCase							,  prediction_type=lowerCamelCase							,  sample_max_value=lowerCamelCase							,  )







      def 	lowerCAmelCase_    (     self   )						->       Dict:
        for t in [1, 10, 49]:
          self.check_over_forward(time_step=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        for t, num_inference_steps in zip([1, 10, 50]							,  [10, 50, 500]   ):
          self.check_over_forward(time_step=lowerCamelCase							,  num_inference_steps=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        for t, eta in zip([1, 10, 49]							,  [0.0, 0.5, 1.0]   ):
          self.check_over_forward(time_step=lowerCamelCase							,  eta=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       int:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config()
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

        assert torch.sum(torch.abs(scheduler._get_variance(0							,  0   ) - 0.0   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(420							,  400   ) - 0.1_4771   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(980							,  960   ) - 0.3_2460   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(0							,  0   ) - 0.0   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(487							,  486   ) - 0.0_0979   )   ) < 1e-5
        assert torch.sum(torch.abs(scheduler._get_variance(999							,  998   ) - 0.02   )   ) < 1e-5







      def 	lowerCAmelCase_    (     self   )						->       Union[str, Any]:
        snake_case_       						=      self.full_loop()

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 172.0067   ) < 1e-2
        assert abs(result_mean.item() - 0.22_3967   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      self.full_loop(prediction_type="""v_prediction"""   )

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 52.5302   ) < 1e-2
        assert abs(result_mean.item() - 0.0684   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        # We specify different beta, so that the first alpha is 0.99
        snake_case_       						=      self.full_loop(set_alpha_to_one=lowerCamelCase							,  beta_start=0.01   )
        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 149.8295   ) < 1e-2
        assert abs(result_mean.item() - 0.1951   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        # We specify different beta, so that the first alpha is 0.99
        snake_case_       						=      self.full_loop(set_alpha_to_one=lowerCamelCase							,  beta_start=0.01   )
        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 149.0784   ) < 1e-2
        assert abs(result_mean.item() - 0.1941   ) < 1e-3