


'''simple docstring'''






import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					(IPNDMScheduler,)
    lowercase    =					(('num_inference_steps', 50),)

    def __lowercase   (   self    :  Optional[Any]    ,						**lowerCamelCase    :  int		)   ->  Tuple:
      lowerCAmelCase_			:      Dict			=   {"""num_train_timesteps""": 10_00}
      config.update(**lowerCamelCase		)
      return config

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  Tuple=0    ,						**lowerCamelCase    :  Union[str, Any]		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Optional[Any]			=   dict(self.forward_default_kwargs		)
      lowerCAmelCase_			:      Dict			=   kwargs.pop("""num_inference_steps"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      List[str]			=   self.dummy_sample
      lowerCAmelCase_			:      Optional[int]			=   0.1 * sample
      lowerCAmelCase_			:      int			=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      List[str]			=   self.get_scheduler_config(**lowerCamelCase		)
        lowerCAmelCase_			:      int			=   scheduler_class(**lowerCamelCase		)
        scheduler.set_timesteps(lowerCamelCase		)
        # copy over dummy past residuals
        lowerCAmelCase_			:      Optional[int]			=   dummy_past_residuals[:]

        if time_step is None:
          lowerCAmelCase_			:      Optional[Any]			=   scheduler.timesteps[len(scheduler.timesteps		) // 2]

        with tempfile.TemporaryDirectory() as tmpdirname:
          scheduler.save_config(lowerCamelCase		)
          lowerCAmelCase_			:      List[Any]			=   scheduler_class.from_pretrained(lowerCamelCase		)
          new_scheduler.set_timesteps(lowerCamelCase		)
          # copy over dummy past residuals
          lowerCAmelCase_			:      str			=   dummy_past_residuals[:]

        lowerCAmelCase_			:      int			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Dict			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Any			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

    def __lowercase   (   self    :  str		)   ->  Tuple:
      pass

    def __lowercase   (   self    :  Tuple    ,						lowerCamelCase    :  int=0    ,						**lowerCamelCase    :  Any		)   ->  int:
      lowerCAmelCase_			:      Optional[int]			=   dict(self.forward_default_kwargs		)
      lowerCAmelCase_			:      List[str]			=   kwargs.pop("""num_inference_steps"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   self.dummy_sample
      lowerCAmelCase_			:      Optional[int]			=   0.1 * sample
      lowerCAmelCase_			:      List[Any]			=   [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      Optional[int]			=   self.get_scheduler_config()
        lowerCAmelCase_			:      Optional[int]			=   scheduler_class(**lowerCamelCase		)
        scheduler.set_timesteps(lowerCamelCase		)

        # copy over dummy past residuals (must be after setting timesteps)
        lowerCAmelCase_			:      int			=   dummy_past_residuals[:]

        if time_step is None:
          lowerCAmelCase_			:      Optional[Any]			=   scheduler.timesteps[len(scheduler.timesteps		) // 2]

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

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

        lowerCAmelCase_			:      Any			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Optional[Any]			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      str			=   new_scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

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

      lowerCAmelCase_			:      Dict			=   10
      lowerCAmelCase_			:      Optional[int]			=   self.dummy_model()
      lowerCAmelCase_			:      Tuple			=   self.dummy_sample_deter
      scheduler.set_timesteps(lowerCamelCase		)

      for i, t in enumerate(scheduler.timesteps		):
        lowerCAmelCase_			:      Optional[Any]			=   model(lowerCamelCase    ,						lowerCamelCase		)
        lowerCAmelCase_			:      str			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		).prev_sample

      for i, t in enumerate(scheduler.timesteps		):
        lowerCAmelCase_			:      Dict			=   model(lowerCamelCase    ,						lowerCamelCase		)
        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase		).prev_sample

      return sample

    def __lowercase   (   self    :  Tuple		)   ->  Dict:
      lowerCAmelCase_			:      List[Any]			=   dict(self.forward_default_kwargs		)

      lowerCAmelCase_			:      int			=   kwargs.pop("""num_inference_steps"""    ,						lowerCamelCase		)

      for scheduler_class in self.scheduler_classes:
        lowerCAmelCase_			:      Tuple			=   self.get_scheduler_config()
        lowerCAmelCase_			:      List[str]			=   scheduler_class(**lowerCamelCase		)

        lowerCAmelCase_			:      int			=   self.dummy_sample
        lowerCAmelCase_			:      Tuple			=   0.1 * sample

        if num_inference_steps is not None and hasattr(lowerCamelCase    ,						"""set_timesteps"""		):
          scheduler.set_timesteps(lowerCamelCase		)
        elif num_inference_steps is not None and not hasattr(lowerCamelCase    ,						"""set_timesteps"""		):
          lowerCAmelCase_			:      Dict			=   num_inference_steps

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

        lowerCAmelCase_			:      List[str]			=   scheduler.timesteps[5]
        lowerCAmelCase_			:      str			=   scheduler.timesteps[6]

        lowerCAmelCase_			:      Dict			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      List[str]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

        lowerCAmelCase_			:      Any			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample
        lowerCAmelCase_			:      Optional[int]			=   scheduler.step(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						**lowerCamelCase		).prev_sample

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

    def __lowercase   (   self    :  Any		)   ->  int:
      for timesteps in [1_00, 10_00]:
        self.check_over_configs(num_train_timesteps=lowerCamelCase    ,						time_step=lowerCamelCase		)

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





    def __lowercase   (   self    :  int		)   ->  List[Any]:
      lowerCAmelCase_			:      List[Any]			=   self.full_loop()
      lowerCAmelCase_			:      Any			=   torch.mean(torch.abs(lowerCamelCase		)		)

      assert abs(result_mean.item() - 2_54_05_29		) < 10







'''simple docstring'''






import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					['image_processor', 'tokenizer']
    lowercase    =					'AutoImageProcessor'
    lowercase    =					'AutoTokenizer'

    def __init__(   self    :  int    ,						lowerCamelCase    :  List[str]=None    ,						lowerCamelCase    :  Union[str, Any]=None    ,						**lowerCamelCase    :  str		)   ->  Tuple:
      lowerCAmelCase_			:      str			=   None
      if "feature_extractor" in kwargs:
        warnings.warn(
            """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
            """ instead."""    ,						lowerCamelCase    ,						)
        lowerCAmelCase_			:      Tuple			=   kwargs.pop("""feature_extractor"""		)

      lowerCAmelCase_			:      Tuple			=   image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
        raise ValueError("""You need to specify an `image_processor`."""		)
      if tokenizer is None:
        raise ValueError("""You need to specify a `tokenizer`."""		)

      super().__init__(lowerCamelCase    ,						lowerCamelCase		)
      lowerCAmelCase_			:      Optional[int]			=   self.image_processor
      lowerCAmelCase_			:      Any			=   False

    def __call__(   self    :  List[Any]    ,						*lowerCamelCase    :  str    ,						**lowerCamelCase    :  Tuple		)   ->  Union[str, Any]:
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*lowerCamelCase    ,						**lowerCamelCase		)

      lowerCAmelCase_			:      Any			=   kwargs.pop("""images"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      Dict			=   kwargs.pop("""text"""    ,						lowerCamelCase		)
      if len(lowerCamelCase		) > 0:
        lowerCAmelCase_			:      str			=   args[0]
        lowerCAmelCase_			:      Dict			=   args[1:]

      if images is None and text is None:
        raise ValueError("""You need to specify either an `images` or `text` input to process."""		)

      if images is not None:
        lowerCAmelCase_			:      Any			=   self.image_processor(lowerCamelCase    ,						*lowerCamelCase    ,						**lowerCamelCase		)
      if text is not None:
        lowerCAmelCase_			:      str			=   self.tokenizer(lowerCamelCase    ,						**lowerCamelCase		)

      if text is None:
        return inputs
      elif images is None:
        return encodings
      else:
        lowerCAmelCase_			:      Dict			=   encodings["""input_ids"""]
        return inputs

    def __lowercase   (   self    :  str    ,						*lowerCamelCase    :  Union[str, Any]    ,						**lowerCamelCase    :  Union[str, Any]		)   ->  List[str]:
      return self.tokenizer.batch_decode(*lowerCamelCase    ,						**lowerCamelCase		)

    def __lowercase   (   self    :  List[Any]    ,						*lowerCamelCase    :  Any    ,						**lowerCamelCase    :  Union[str, Any]		)   ->  List[str]:
      return self.tokenizer.decode(*lowerCamelCase    ,						**lowerCamelCase		)

    @contextmanager
    def __lowercase   (   self    :  str		)   ->  Union[str, Any]:
      warnings.warn(
          """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
          """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
          """your images inputs, or in a separate call."""		)
      lowerCAmelCase_			:      List[str]			=   True
      lowerCAmelCase_			:      Optional[Any]			=   self.tokenizer
      yield
      lowerCAmelCase_			:      List[Any]			=   self.image_processor
      lowerCAmelCase_			:      List[str]			=   False

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  List[str]    ,						lowerCamelCase    :  str=False    ,						lowerCamelCase    :  List[Any]=None		)   ->  Optional[int]:
      if added_vocab is None:
        lowerCAmelCase_			:      str			=   self.tokenizer.get_added_vocab()

      lowerCAmelCase_			:      Union[str, Any]			=   {}

      while tokens:
        lowerCAmelCase_			:      Dict			=   re.search(R"""<s_(.*?)>"""    ,						lowerCamelCase    ,						re.IGNORECASE		)
        if start_token is None:
          break
        lowerCAmelCase_			:      Tuple			=   start_token.group(1		)
        lowerCAmelCase_			:      Tuple			=   re.search(RF'</s_{key}>'    ,						lowerCamelCase    ,						re.IGNORECASE		)
        lowerCAmelCase_			:      Tuple			=   start_token.group()
        if end_token is None:
          lowerCAmelCase_			:      str			=   tokens.replace(lowerCamelCase    ,						""""""		)
        else:
          lowerCAmelCase_			:      List[str]			=   end_token.group()
          lowerCAmelCase_			:      Dict			=   re.escape(lowerCamelCase		)
          lowerCAmelCase_			:      int			=   re.escape(lowerCamelCase		)
          lowerCAmelCase_			:      Dict			=   re.search(F'{start_token_escaped}(.*?){end_token_escaped}'    ,						lowerCamelCase    ,						re.IGNORECASE		)
          if content is not None:
            lowerCAmelCase_			:      str			=   content.group(1		).strip()
            if r"<s_" in content and r"</s_" in content:  # non-leaf node
              lowerCAmelCase_			:      str			=   self.tokenajson(lowerCamelCase    ,						is_inner_value=lowerCamelCase    ,						added_vocab=lowerCamelCase		)
              if value:
                if len(lowerCamelCase		) == 1:
                  lowerCAmelCase_			:      List[Any]			=   value[0]
                lowerCAmelCase_			:      Optional[Any]			=   value
            else:  # leaf nodes
              lowerCAmelCase_			:      List[str]			=   []
              for leaf in content.split(R"""<sep/>"""		):
                lowerCAmelCase_			:      Union[str, Any]			=   leaf.strip()
                if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                  lowerCAmelCase_			:      Any			=   leaf[1:-2]  # for categorical special tokens
                output[key].append(lowerCamelCase		)
              if len(output[key]		) == 1:
                lowerCAmelCase_			:      Optional[Any]			=   output[key][0]

          lowerCAmelCase_			:      List[Any]			=   tokens[tokens.find(lowerCamelCase		) + len(lowerCamelCase		) :].strip()
          if tokens[:6] == r"<sep/>":  # non-leaf nodes
            return [output] + self.tokenajson(tokens[6:]    ,						is_inner_value=lowerCamelCase    ,						added_vocab=lowerCamelCase		)

      if len(lowerCamelCase		):
        return [output] if is_inner_value else output
      else:
        return [] if is_inner_value else {"text_sequence": tokens}

    @property
    def __lowercase   (   self    :  Dict		)   ->  int:
      warnings.warn(
          """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""    ,						lowerCamelCase    ,						)
      return self.image_processor_class





    @property
    def __lowercase   (   self    :  Dict		)   ->  Optional[Any]:
      warnings.warn(
          """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,						lowerCamelCase    ,						)
      return self.image_processor



