
"""simple docstring"""



import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest





class 				a			(					lowercase      ):
      UpperCamelCase							:   Optional[int]           =     (IPNDMScheduler,)
      UpperCamelCase							:   Optional[int]           =     (("""num_inference_steps""", 5_0),)



      def  __snake_case							(   self     ,	**UpperCamelCase_      ):
          UpperCAmelCase__	:      Any     							=					{'num_train_timesteps': 1_000}
          config.update(**UpperCamelCase_      )
          return config



      def  __snake_case							(   self     ,	UpperCamelCase_=0     ,	**UpperCamelCase_      ):
          UpperCAmelCase__	:      List[Any]     							=					dict(self.forward_default_kwargs      )
          UpperCAmelCase__	:      Optional[int]     							=					kwargs.pop('num_inference_steps'     ,	UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[Any]     							=					self.dummy_sample
          UpperCAmelCase__	:      Dict     							=					0.1 * sample
          UpperCAmelCase__	:      List[Any]     							=					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

          for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__	:      List[Any]     							=					self.get_scheduler_config(**UpperCamelCase_      )
              UpperCAmelCase__	:      Optional[int]     							=					scheduler_class(**UpperCamelCase_      )
              scheduler.set_timesteps(UpperCamelCase_      )
              # copy over dummy past residuals
              UpperCAmelCase__	:      Union[str, Any]     							=					dummy_past_residuals[:]

              if time_step is None:
                  UpperCAmelCase__	:      List[str]     							=					scheduler.timesteps[len(scheduler.timesteps      ) // 2]

              with tempfile.TemporaryDirectory() as tmpdirname:
                  scheduler.save_config(UpperCamelCase_      )
                  UpperCAmelCase__	:      Optional[int]     							=					scheduler_class.from_pretrained(UpperCamelCase_      )
                  new_scheduler.set_timesteps(UpperCamelCase_      )
                  # copy over dummy past residuals
                  UpperCAmelCase__	:      Any     							=					dummy_past_residuals[:]

              UpperCAmelCase__	:      List[str]     							=					scheduler.step(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ).prev_sample
              UpperCAmelCase__	:      str     							=					new_scheduler.step(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ).prev_sample

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

              UpperCAmelCase__	:      Dict     							=					scheduler.step(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ).prev_sample
              UpperCAmelCase__	:      Optional[Any]     							=					new_scheduler.step(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ).prev_sample

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



      def  __snake_case							(   self      ):
          pass



      def  __snake_case							(   self     ,	UpperCamelCase_=0     ,	**UpperCamelCase_      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					dict(self.forward_default_kwargs      )
          UpperCAmelCase__	:      Any     							=					kwargs.pop('num_inference_steps'     ,	UpperCamelCase_      )
          UpperCAmelCase__	:      List[str]     							=					self.dummy_sample
          UpperCAmelCase__	:      Optional[int]     							=					0.1 * sample
          UpperCAmelCase__	:      str     							=					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

          for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__	:      str     							=					self.get_scheduler_config()
              UpperCAmelCase__	:      str     							=					scheduler_class(**UpperCamelCase_      )
              scheduler.set_timesteps(UpperCamelCase_      )

              # copy over dummy past residuals (must be after setting timesteps)
              UpperCAmelCase__	:      Union[str, Any]     							=					dummy_past_residuals[:]

              if time_step is None:
                  UpperCAmelCase__	:      int     							=					scheduler.timesteps[len(scheduler.timesteps      ) // 2]

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

                  # copy over dummy past residual (must be after setting timesteps)
                  UpperCAmelCase__	:      List[Any]     							=					dummy_past_residuals[:]

              UpperCAmelCase__	:      Optional[Any]     							=					scheduler.step(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ).prev_sample
              UpperCAmelCase__	:      int     							=					new_scheduler.step(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ).prev_sample

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

              UpperCAmelCase__	:      Optional[int]     							=					scheduler.step(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ).prev_sample
              UpperCAmelCase__	:      Dict     							=					new_scheduler.step(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ).prev_sample

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



      def  __snake_case							(   self     ,	**UpperCamelCase_      ):
          UpperCAmelCase__	:      List[str]     							=					self.scheduler_classes[0]
          UpperCAmelCase__	:      str     							=					self.get_scheduler_config(**UpperCamelCase_      )
          UpperCAmelCase__	:      int     							=					scheduler_class(**UpperCamelCase_      )

          UpperCAmelCase__	:      Tuple     							=					10
          UpperCAmelCase__	:      Any     							=					self.dummy_model()
          UpperCAmelCase__	:      Tuple     							=					self.dummy_sample_deter
          scheduler.set_timesteps(UpperCamelCase_      )

          for i, t in enumerate(scheduler.timesteps      ):
              UpperCAmelCase__	:      List[Any]     							=					model(UpperCamelCase_     ,	UpperCamelCase_      )
              UpperCAmelCase__	:      Optional[int]     							=					scheduler.step(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ).prev_sample

          for i, t in enumerate(scheduler.timesteps      ):
              UpperCAmelCase__	:      List[str]     							=					model(UpperCamelCase_     ,	UpperCamelCase_      )
              UpperCAmelCase__	:      Union[str, Any]     							=					scheduler.step(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ).prev_sample

          return sample



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					dict(self.forward_default_kwargs      )

          UpperCAmelCase__	:      List[str]     							=					kwargs.pop('num_inference_steps'     ,	UpperCamelCase_      )

          for scheduler_class in self.scheduler_classes:
              UpperCAmelCase__	:      Optional[int]     							=					self.get_scheduler_config()
              UpperCAmelCase__	:      Optional[Any]     							=					scheduler_class(**UpperCamelCase_      )

              UpperCAmelCase__	:      List[str]     							=					self.dummy_sample
              UpperCAmelCase__	:      List[Any]     							=					0.1 * sample

              if num_inference_steps is not None and hasattr(UpperCamelCase_     ,	'set_timesteps'      ):
                  scheduler.set_timesteps(UpperCamelCase_      )
              elif num_inference_steps is not None and not hasattr(UpperCamelCase_     ,	'set_timesteps'      ):
                  UpperCAmelCase__	:      Optional[Any]     							=					num_inference_steps

              # copy over dummy past residuals (must be done after set_timesteps)
              UpperCAmelCase__	:      str     							=					[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
              UpperCAmelCase__	:      Tuple     							=					dummy_past_residuals[:]

              UpperCAmelCase__	:      List[Any]     							=					scheduler.timesteps[5]
              UpperCAmelCase__	:      List[Any]     							=					scheduler.timesteps[6]

              UpperCAmelCase__	:      Any     							=					scheduler.step(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ).prev_sample
              UpperCAmelCase__	:      Any     							=					scheduler.step(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ).prev_sample

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

              UpperCAmelCase__	:      int     							=					scheduler.step(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ).prev_sample
              UpperCAmelCase__	:      int     							=					scheduler.step(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      ).prev_sample

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



      def  __snake_case							(   self      ):
          for timesteps in [100, 1_000]:
              self.check_over_configs(num_train_timesteps=UpperCamelCase_     ,	time_step=UpperCamelCase_      )



      def  __snake_case							(   self      ):
          for t, num_inference_steps in zip([1, 5, 10]     ,	[10, 50, 100]      ):
              self.check_over_forward(num_inference_steps=UpperCamelCase_     ,	time_step=UpperCamelCase_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					self.full_loop()
          UpperCAmelCase__	:      List[Any]     							=					torch.mean(torch.abs(UpperCamelCase_      )      )

          assert abs(result_mean.item() - 2_540_529      ) < 10



"""simple docstring"""



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


UpperCamelCase__          =	logging.get_logger(__name__)

UpperCamelCase__          =	{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__          =	{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

UpperCamelCase__          =	{
    'junnyu/roformer_chinese_small': 15_36,
    'junnyu/roformer_chinese_base': 15_36,
    'junnyu/roformer_chinese_char_small': 5_12,
    'junnyu/roformer_chinese_char_base': 5_12,
    'junnyu/roformer_small_discriminator': 1_28,
    'junnyu/roformer_small_generator': 1_28,
}


UpperCamelCase__          =	{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}





class 				a			(					lowercase      ):
      UpperCamelCase							:   int           =     VOCAB_FILES_NAMES
      UpperCamelCase							:   Dict           =     PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase							:   Union[str, Any]           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase							:   Tuple           =     PRETRAINED_INIT_CONFIGURATION
      UpperCamelCase							:   Optional[int]           =     RoFormerTokenizer



      def __init__(   self     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	UpperCamelCase_=True     ,	UpperCamelCase_="[UNK]"     ,	UpperCamelCase_="[SEP]"     ,	UpperCamelCase_="[PAD]"     ,	UpperCamelCase_="[CLS]"     ,	UpperCamelCase_="[MASK]"     ,	UpperCamelCase_=True     ,	UpperCamelCase_=None     ,	**UpperCamelCase_     ,	):
          super().__init__(
              UpperCamelCase_     ,	tokenizer_file=UpperCamelCase_     ,	do_lower_case=UpperCamelCase_     ,	unk_token=UpperCamelCase_     ,	sep_token=UpperCamelCase_     ,	pad_token=UpperCamelCase_     ,	cls_token=UpperCamelCase_     ,	mask_token=UpperCamelCase_     ,	tokenize_chinese_chars=UpperCamelCase_     ,	strip_accents=UpperCamelCase_     ,	**UpperCamelCase_     ,	)

          UpperCAmelCase__	:      Union[str, Any]     							=					json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
          if (
              pre_tok_state.get('lowercase'     ,	UpperCamelCase_      ) != do_lower_case
              or pre_tok_state.get('strip_accents'     ,	UpperCamelCase_      ) != strip_accents
          ):
              UpperCAmelCase__	:      Any     							=					getattr(UpperCamelCase_     ,	pre_tok_state.pop('type'      )      )
              UpperCAmelCase__	:      str     							=					do_lower_case
              UpperCAmelCase__	:      Union[str, Any]     							=					strip_accents
              UpperCAmelCase__	:      Dict     							=					pre_tok_class(**UpperCamelCase_      )

          UpperCAmelCase__	:      Union[str, Any]     							=					do_lower_case



      def __getstate__(   self      ):
          UpperCAmelCase__	:      int     							=					self.__dict__.copy()
          UpperCAmelCase__	:      int     							=					BertPreTokenizer()
          return state



      def __setstate__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      Union[str, Any]     							=					d
          UpperCAmelCase__	:      List[str]     							=					self.__dict__['_tokenizer'].get_vocab()
          UpperCAmelCase__	:      List[Any]     							=					PreTokenizer.custom(JiebaPreTokenizer(UpperCamelCase_      )      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=None      ):
          UpperCAmelCase__	:      List[Any]     							=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
              output += token_ids_a + [self.sep_token_id]

          return output



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None      ):
          UpperCAmelCase__	:      int     							=					[self.sep_token_id]
          UpperCAmelCase__	:      Dict     							=					[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      ) * [0] + len(token_ids_a + sep      ) * [1]



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None      ):
          UpperCAmelCase__	:      Any     							=					self._tokenizer.model.save(UpperCamelCase_     ,	name=UpperCamelCase_      )
          return tuple(UpperCamelCase_      )



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=None     ,	UpperCamelCase_=None     ,	UpperCamelCase_=False     ,	**UpperCamelCase_     ,	):
          UpperCAmelCase__	:      int     							=					BertPreTokenizer()
          return super().save_pretrained(UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	**UpperCamelCase_      )

