
'''simple docstring'''



import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
     from transformers.pipelines.conversational import Conversation

UpperCamelCase__ :						Dict    							=					logging.get_logger(__name__)

UpperCamelCase__ :						Any    							=					{"vocab_file": "tokenizer.model"}

UpperCamelCase__ :						Union[str, Any]    							=					{
    "vocab_file": {
        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model",
    },
    "tokenizer_file": {
        "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json",
    },
}
UpperCamelCase__ :						Optional[int]    							=					{
    "hf-internal-testing/llama-tokenizer": 2_048,
}
UpperCamelCase__ :						str    							=					"▁"

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

# fmt: off
UpperCamelCase__ :						Tuple    							=					"You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don't know the answer to a question, please don't share false information."

class 				_a			(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE =					PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE =					['input_ids', 'attention_mask']






       def __init__(							self    ,							A__    ,							A__="<unk>"    ,							A__="<s>"    ,							A__="</s>"    ,							A__=None    ,							A__ = None    ,							A__=True    ,							A__=False    ,							A__=False    ,							A__=True    ,							**A__    ,							)      ->     Optional[Any]:
          _SCREAMING_SNAKE_CASE       	=			{} if sp_model_kwargs is None else sp_model_kwargs
          _SCREAMING_SNAKE_CASE       	=			AddedToken(A__    ,							lstrip=A__    ,							rstrip=A__ ) if isinstance(A__    ,							A__ ) else bos_token
          _SCREAMING_SNAKE_CASE       	=			AddedToken(A__    ,							lstrip=A__    ,							rstrip=A__ ) if isinstance(A__    ,							A__ ) else eos_token
          _SCREAMING_SNAKE_CASE       	=			AddedToken(A__    ,							lstrip=A__    ,							rstrip=A__ ) if isinstance(A__    ,							A__ ) else unk_token
          _SCREAMING_SNAKE_CASE       	=			AddedToken(A__    ,							lstrip=A__    ,							rstrip=A__ ) if isinstance(A__    ,							A__ ) else pad_token
          super().__init__(
              bos_token=A__    ,							eos_token=A__    ,							unk_token=A__    ,							pad_token=A__    ,							add_bos_token=A__    ,							add_eos_token=A__    ,							sp_model_kwargs=self.sp_model_kwargs    ,							clean_up_tokenization_spaces=A__    ,							legacy=A__    ,							**A__    ,							)
          if legacy:
             logger.warning_once(
                 F"You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"
                 """ read the related pull request available at https://github.com/huggingface/transformers/pull/24565""" )
          _SCREAMING_SNAKE_CASE       	=			legacy
          _SCREAMING_SNAKE_CASE       	=			vocab_file
          _SCREAMING_SNAKE_CASE       	=			add_bos_token
          _SCREAMING_SNAKE_CASE       	=			add_eos_token
          _SCREAMING_SNAKE_CASE       	=			spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(A__ )






       def __getstate__(							self )      ->     List[str]:
          _SCREAMING_SNAKE_CASE       	=			self.__dict__.copy()
          _SCREAMING_SNAKE_CASE       	=			None
          _SCREAMING_SNAKE_CASE       	=			self.sp_model.serialized_model_proto()
          return state






       def __setstate__(							self    ,							A__ )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			d
          _SCREAMING_SNAKE_CASE       	=			spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto )






       @property
       def     UpperCamelCase			(							self )      ->     Tuple:
          return self.sp_model.get_piece_size()






       def     UpperCamelCase			(							self )      ->     Any:
          _SCREAMING_SNAKE_CASE       	=			{self.convert_ids_to_tokens(A__ ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab






       def     UpperCamelCase			(							self    ,							A__    ,							**A__ )      ->     List[str]:
          # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
          # the beginning of the text
          if not self.legacy:
             _SCREAMING_SNAKE_CASE       	=			SPIECE_UNDERLINE + text.replace(A__    ,							""" """ )
          return super().tokenize(A__    ,							**A__ )






       def     UpperCamelCase			(							self    ,							A__ )      ->     Union[str, Any]:
          if not self.legacy:
             _SCREAMING_SNAKE_CASE       	=			text.startswith(A__ )
             if is_first:
                _SCREAMING_SNAKE_CASE       	=			text[1:]

          _SCREAMING_SNAKE_CASE       	=			self.sp_model.encode(A__    ,							out_type=A__ )

          if not self.legacy and not is_first and not text.startswith(""" """ ) and tokens[0].startswith(A__ ):
             _SCREAMING_SNAKE_CASE       	=			([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
          return tokens






       def     UpperCamelCase			(							self    ,							A__ )      ->     Union[str, Any]:
          return self.sp_model.piece_to_id(A__ )






       def     UpperCamelCase			(							self    ,							A__ )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			self.sp_model.IdToPiece(A__ )
          return token






       def     UpperCamelCase			(							self    ,							A__ )      ->     Union[str, Any]:
          _SCREAMING_SNAKE_CASE       	=			[]
          _SCREAMING_SNAKE_CASE       	=			""""""
          _SCREAMING_SNAKE_CASE       	=			False
          for i, token in enumerate(A__ ):
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                if not prev_is_special and i != 0:
                   out_string += " "
                out_string += self.sp_model.decode(A__ ) + token
                _SCREAMING_SNAKE_CASE       	=			True
                _SCREAMING_SNAKE_CASE       	=			[]
             else:
                current_sub_tokens.append(A__ )
                _SCREAMING_SNAKE_CASE       	=			False
          out_string += self.sp_model.decode(A__ )
          return out_string






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = None )      ->     Tuple[str]:
          if not os.path.isdir(A__ ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
             return
          _SCREAMING_SNAKE_CASE       	=			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__ ) and os.path.isfile(self.vocab_file ):
             copyfile(self.vocab_file    ,							A__ )
          elif not os.path.isfile(self.vocab_file ):
             with open(A__    ,							"""wb""" ) as fi:
                _SCREAMING_SNAKE_CASE       	=			self.sp_model.serialized_model_proto()
                fi.write(A__ )

          return (out_vocab_file,)






       def     UpperCamelCase			(							self    ,							A__    ,							A__=None )      ->     int:
          _SCREAMING_SNAKE_CASE       	=			[self.bos_token_id] if self.add_bos_token else []
          _SCREAMING_SNAKE_CASE       	=			[self.eos_token_id] if self.add_eos_token else []

          _SCREAMING_SNAKE_CASE       	=			bos_token_id + token_ids_a + eos_token_id

          if token_ids_a is not None:
             _SCREAMING_SNAKE_CASE       	=			output + bos_token_id + token_ids_a + eos_token_id

          return output






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = None    ,							A__ = False )      ->     List[int]:
          if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=A__    ,							token_ids_a=A__    ,							already_has_special_tokens=A__ )

          _SCREAMING_SNAKE_CASE       	=			[1] if self.add_bos_token else []
          _SCREAMING_SNAKE_CASE       	=			[1] if self.add_eos_token else []

          if token_ids_a is None:
             return bos_token_id + ([0] * len(A__ )) + eos_token_id
          return (
              bos_token_id
              + ([0] * len(A__ ))
              + eos_token_id
              + bos_token_id
              + ([0] * len(A__ ))
              + eos_token_id
          )






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = None )      ->     List[int]:
          _SCREAMING_SNAKE_CASE       	=			[self.bos_token_id] if self.add_bos_token else []
          _SCREAMING_SNAKE_CASE       	=			[self.eos_token_id] if self.add_eos_token else []

          _SCREAMING_SNAKE_CASE       	=			[0] * len(bos_token_id + token_ids_a + eos_token_id )

          if token_ids_a is not None:
             output += [1] * len(bos_token_id + token_ids_a + eos_token_id )

          return output






       def     UpperCamelCase			(							self    ,							A__ )      ->     List[int]:
          _SCREAMING_SNAKE_CASE       	=			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...)""" )

          _SCREAMING_SNAKE_CASE       	=			[]
          if len(conversation.past_user_inputs ) > 0:
             if not conversation.past_user_inputs[0].startswith(A__ ) or E_SYS not in conversation.past_user_inputs[0]:
                _SCREAMING_SNAKE_CASE       	=			(
                    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                )
          elif not dialogue[0][1].startswith(A__ ) or E_SYS not in dialogue[0][1]:
             _SCREAMING_SNAKE_CASE       	=			(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=A__ )
                  + [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=A__ )
          return dialog_tokens








'''simple docstring'''



import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class 				_a			(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					(DDIMScheduler,)
       SCREAMING_SNAKE_CASE =					(('eta', 0.0), ('num_inference_steps', 50))






       def     UpperCamelCase			(							self    ,							**A__ )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			{
              """num_train_timesteps""": 10_00,
              """beta_start""": 0.0001,
              """beta_end""": 0.02,
              """beta_schedule""": """linear""",
              """clip_sample""": True,
          }

          config.update(**A__ )
          return config






       def     UpperCamelCase			(							self    ,							**A__ )      ->     List[str]:
          _SCREAMING_SNAKE_CASE       	=			self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE       	=			self.get_scheduler_config(**A__ )
          _SCREAMING_SNAKE_CASE       	=			scheduler_class(**A__ )

          _SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE       	=			10, 0.0

          _SCREAMING_SNAKE_CASE       	=			self.dummy_model()
          _SCREAMING_SNAKE_CASE       	=			self.dummy_sample_deter

          scheduler.set_timesteps(A__ )

          for t in scheduler.timesteps:
             _SCREAMING_SNAKE_CASE       	=			model(A__    ,							A__ )
             _SCREAMING_SNAKE_CASE       	=			scheduler.step(A__    ,							A__    ,							A__    ,							A__ ).prev_sample

          return sample






       def     UpperCamelCase			(							self )      ->     str:
          for timesteps in [1_00, 5_00, 10_00]:
             self.check_over_configs(num_train_timesteps=A__ )






       def     UpperCamelCase			(							self )      ->     int:
          for steps_offset in [0, 1]:
             self.check_over_configs(steps_offset=A__ )

          _SCREAMING_SNAKE_CASE       	=			self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE       	=			self.get_scheduler_config(steps_offset=1 )
          _SCREAMING_SNAKE_CASE       	=			scheduler_class(**A__ )
          scheduler.set_timesteps(5 )
          assert torch.equal(scheduler.timesteps    ,							torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1] ) )






       def     UpperCamelCase			(							self )      ->     Tuple:
          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=A__    ,							beta_end=A__ )






       def     UpperCamelCase			(							self )      ->     Optional[Any]:
          for schedule in ["linear", "squaredcos_cap_v2"]:
             self.check_over_configs(beta_schedule=A__ )






       def     UpperCamelCase			(							self )      ->     List[str]:
          for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=A__ )






       def     UpperCamelCase			(							self )      ->     Tuple:
          for clip_sample in [True, False]:
             self.check_over_configs(clip_sample=A__ )






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






       def     UpperCamelCase			(							self )      ->     int:
          for rescale_betas_zero_snr in [True, False]:
             self.check_over_configs(rescale_betas_zero_snr=A__ )






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






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          for t in [1, 10, 49]:
             self.check_over_forward(time_step=A__ )






       def     UpperCamelCase			(							self )      ->     Optional[Any]:
          for t, num_inference_steps in zip([1, 10, 50]    ,							[10, 50, 5_00] ):
             self.check_over_forward(time_step=A__    ,							num_inference_steps=A__ )






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






       def     UpperCamelCase			(							self )      ->     Optional[int]:
          _SCREAMING_SNAKE_CASE       	=			self.scheduler_classes[0]
          _SCREAMING_SNAKE_CASE       	=			self.get_scheduler_config()
          _SCREAMING_SNAKE_CASE       	=			scheduler_class(**A__ )

          assert torch.sum(torch.abs(scheduler._get_variance(0    ,							0 ) - 0.0 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(4_20    ,							4_00 ) - 0.1_4771 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_80    ,							9_60 ) - 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(4_87    ,							4_86 ) - 0.0_0979 ) ) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(9_99    ,							9_98 ) - 0.02 ) ) < 1E-5






       def     UpperCamelCase			(							self )      ->     List[Any]:
          _SCREAMING_SNAKE_CASE       	=			self.full_loop()

          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

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






       def     UpperCamelCase			(							self )      ->     str:
          _SCREAMING_SNAKE_CASE       	=			self.full_loop(prediction_type="""v_prediction""" )

          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

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






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

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






       def     UpperCamelCase			(							self )      ->     Any:
          # We specify different beta, so that the first alpha is 0.99
          _SCREAMING_SNAKE_CASE       	=			self.full_loop(set_alpha_to_one=A__    ,							beta_start=0.01 )
          _SCREAMING_SNAKE_CASE       	=			torch.sum(torch.abs(A__ ) )
          _SCREAMING_SNAKE_CASE       	=			torch.mean(torch.abs(A__ ) )

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






