
"""simple docstring"""




import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation

_A     :			int  						=			logging.get_logger(__name__)


_A     :			Any  						=			{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

_A     :			List[str]  						=			{
    """vocab_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"""},
    """merges_file""": {"""facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"""},
    """tokenizer_config_file""": {
        """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"""
    },
}

_A     :			int  						=			{"""facebook/blenderbot-3B""": 1_28}

class 						a__		( a_		):
   __lowerCAmelCase				=							VOCAB_FILES_NAMES
   __lowerCAmelCase				=							PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase				=							["""input_ids""", """attention_mask"""]
   __lowerCAmelCase				=							BlenderbotTokenizer
   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       :							Dict									=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
          if pre_tok_state.get("add_prefix_space"      ,       _a							) != add_prefix_space:
                 lowercase       :							List[str]									=				getattr(_a      ,       pre_tok_state.pop("type"							)							)
                 lowercase       :							str									=				add_prefix_space
                 lowercase       :							int									=				pre_tok_class(**_a							)

          lowercase       :							str									=				add_prefix_space

          lowercase       :							str									=				"post_processor"
          lowercase       :							Dict									=				getattr(self.backend_tokenizer      ,       _a      ,       _a							)
          if tokenizer_component_instance:
                 lowercase       :							Any									=				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       :							str									=				tuple(state["sep"]							)
                 if "cls" in state:
                        lowercase       :							Optional[int]									=				tuple(state["cls"]							)

                 lowercase       :							int									=				False

                 if state.get("add_prefix_space"      ,       _a							) != add_prefix_space:
                        lowercase       :							Optional[Any]									=				add_prefix_space
                        lowercase       :							Optional[Any]									=				True

                 if state.get("trim_offsets"      ,       _a							) != trim_offsets:
                        lowercase       :							Any									=				trim_offsets
                        lowercase       :							Optional[Any]									=				True

                 if changes_to_apply:
                        lowercase       :							int									=				getattr(_a      ,       state.pop("type"							)							)
                        lowercase       :							Union[str, Any]									=				component_class(**_a							)
                        setattr(self.backend_tokenizer      ,       _a      ,       _a							)
   @property
   # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
   def      __magic_name__     (     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      __magic_name__     (     self      ,       _a							):
          lowercase       :							Optional[Any]									=				AddedToken(_a      ,       lstrip=_a      ,       rstrip=_a							) if isinstance(_a      ,       _a							) else value
          lowercase       :							Optional[int]									=				value
   def      __magic_name__     (     self      ,       *_a      ,       **_a							):
          lowercase       :							List[str]									=				kwargs.get("is_split_into_words"      ,       _a							)
          assert self.add_prefix_space or not is_split_into_words, (
              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      __magic_name__     (     self      ,       *_a      ,       **_a							):
          lowercase       :							Union[str, Any]									=				kwargs.get("is_split_into_words"      ,       _a							)

          assert self.add_prefix_space or not is_split_into_words, (
              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      __magic_name__     (     self      ,       _a      ,       _a = None							):
          lowercase       :							Optional[Any]									=				self._tokenizer.model.save(_a      ,       name=_a							)
          return tuple(_a							)
   def      __magic_name__     (     self      ,       _a      ,       _a = None							):
          lowercase       :							Any									=				[self.sep_token_id]
          lowercase       :							Optional[Any]									=				[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]
   def      __magic_name__     (     self      ,       _a      ,       _a = None							):
          return token_ids_a + [self.eos_token_id]

   def      __magic_name__     (     self      ,       _a							):
          lowercase       :							Dict									=				[]
          for is_user, text in conversation.iter_texts():
                 if is_user:
                        # We need to space prefix as it's being done within blenderbot
                        inputs.append(" " + text							)
                 else:
                        # Generated responses should contain them already.
                        inputs.append(_a							)

          lowercase       :							Optional[int]									=				"  ".join(_a							)
          lowercase       :							str									=				self.encode(_a							)
          if len(_a							) > self.model_max_length:
                 lowercase       :							List[str]									=				input_ids[-self.model_max_length :]
                 logger.warning(f"""Trimmed input from conversation as it was longer than {self.model_max_length} tokens."""							)
          return input_ids




"""simple docstring"""




import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest

class 						a__		( a_		):
   __lowerCAmelCase				=							(DDPMScheduler,)
   def      __magic_name__     (     self      ,       **_a							):
          lowercase       :							Dict									=				{
              "num_train_timesteps": 1_000,
              "beta_start": 0.0_0_0_1,
              "beta_end": 0.0_2,
              "beta_schedule": "linear",
              "variance_type": "fixed_small",
              "clip_sample": True,
          }

          config.update(**_a							)
          return config
   def      __magic_name__     (     self							):
          for timesteps in [1, 5, 100, 1_000]:
                 self.check_over_configs(num_train_timesteps=_a							)
   def      __magic_name__     (     self							):
          for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]      ,       [0.0_0_2, 0.0_2, 0.2, 2]							):
                 self.check_over_configs(beta_start=_a      ,       beta_end=_a							)
   def      __magic_name__     (     self							):
          for schedule in ["linear", "squaredcos_cap_v2"]:
                 self.check_over_configs(beta_schedule=_a							)
   def      __magic_name__     (     self							):
          for variance in ["fixed_small", "fixed_large", "other"]:
                 self.check_over_configs(variance_type=_a							)
   def      __magic_name__     (     self							):
          for clip_sample in [True, False]:
                 self.check_over_configs(clip_sample=_a							)
   def      __magic_name__     (     self							):
          self.check_over_configs(thresholding=_a							)
          for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample", "v_prediction"]:
                        self.check_over_configs(
                            thresholding=_a      ,       prediction_type=_a      ,       sample_max_value=_a      ,       )
   def      __magic_name__     (     self							):
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
                 self.check_over_configs(prediction_type=_a							)
   def      __magic_name__     (     self							):
          for t in [0, 500, 999]:
                 self.check_over_forward(time_step=_a							)
   def      __magic_name__     (     self							):
          lowercase       :							Union[str, Any]									=				self.scheduler_classes[0]
          lowercase       :							Any									=				self.get_scheduler_config()
          lowercase       :							Dict									=				scheduler_class(**_a							)

          assert torch.sum(torch.abs(scheduler._get_variance(0							) - 0.0							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(487							) - 0.0_0_9_7_9							)							) < 1E-5
          assert torch.sum(torch.abs(scheduler._get_variance(999							) - 0.0_2							)							) < 1E-5
   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Dict									=				scheduler_class(**_a							)

          lowercase       :							Dict									=				len(_a							)

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

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

                 # 2. predict previous mean of sample x_t-1
                 lowercase       :							str									=				scheduler.step(_a      ,       _a      ,       _a      ,       generator=_a							).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       :							Dict									=				torch.sum(torch.abs(_a							)							)
          lowercase       :							List[Any]									=				torch.mean(torch.abs(_a							)							)

          assert abs(result_sum.item() - 2_5_8.9_6_0_6							) < 1E-2
          assert abs(result_mean.item() - 0.3_3_7_2							) < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							Optional[int]									=				self.scheduler_classes[0]
          lowercase       :							Any									=				self.get_scheduler_config(prediction_type="v_prediction"							)
          lowercase       :							int									=				scheduler_class(**_a							)

          lowercase       :							str									=				len(_a							)

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

          for t in reversed(range(_a							)							):
                 # 1. predict noise residual
                 lowercase       :							Union[str, Any]									=				model(_a      ,       _a							)

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

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

          lowercase       :							str									=				torch.sum(torch.abs(_a							)							)
          lowercase       :							Tuple									=				torch.mean(torch.abs(_a							)							)

          assert abs(result_sum.item() - 2_0_2.0_2_9_6							) < 1E-2
          assert abs(result_mean.item() - 0.2_6_3_1							) < 1E-3
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Tuple									=				scheduler_class(**_a							)

          lowercase       :							List[str]									=				[100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=_a							)

          lowercase       :							Union[str, Any]									=				scheduler.timesteps

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

                 lowercase       :							Optional[int]									=				scheduler.previous_timestep(_a							)
                 lowercase       :							Union[str, Any]									=				prev_t.item()

                 self.assertEqual(_a      ,       _a							)
   def      __magic_name__     (     self							):
          lowercase       :							str									=				self.scheduler_classes[0]
          lowercase       :							List[str]									=				self.get_scheduler_config()
          lowercase       :							List[Any]									=				scheduler_class(**_a							)

          lowercase       :							Optional[int]									=				[100, 87, 50, 51, 0]

          with self.assertRaises(_a      ,       msg="`custom_timesteps` must be in descending order."							):
                 scheduler.set_timesteps(timesteps=_a							)
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				self.scheduler_classes[0]
          lowercase       :							Union[str, Any]									=				self.get_scheduler_config()
          lowercase       :							Any									=				scheduler_class(**_a							)

          lowercase       :							int									=				[100, 87, 50, 1, 0]
          lowercase       :							Any									=				len(_a							)

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

   def      __magic_name__     (     self							):
          lowercase       :							str									=				self.scheduler_classes[0]
          lowercase       :							Tuple									=				self.get_scheduler_config()
          lowercase       :							Optional[int]									=				scheduler_class(**_a							)

          lowercase       :							List[str]									=				[scheduler.config.num_train_timesteps]

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


