import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






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



      def __init__(					self     :				Any    ,  _UpperCamelCase     :				List[str]=None    ,  _UpperCamelCase     :				int=None    ,  **_UpperCamelCase     :				str)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						None
          if "feature_extractor" in kwargs:
              warnings.warn(
                  """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                  """ instead."""    ,  _UpperCamelCase    ,  )
              _lowerCamelCase     :		Optional[int] 						=						kwargs.pop("""feature_extractor""")

          _lowerCamelCase     :		Dict 						=						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__(_UpperCamelCase    ,  _UpperCamelCase)
          _lowerCamelCase     :		Dict 						=						self.image_processor
          _lowerCamelCase     :		int 						=						False



      def __call__(					self     :				str    ,  *_UpperCamelCase     :				Optional[int]    ,  **_UpperCamelCase     :				Optional[int])				->List[Any]:


          """simple docstring"""


          if self._in_target_context_manager:
              return self.current_processor(*_UpperCamelCase    ,  **_UpperCamelCase)

          _lowerCamelCase     :		Optional[int] 						=						kwargs.pop("""images"""    ,  _UpperCamelCase)
          _lowerCamelCase     :		Any 						=						kwargs.pop("""text"""    ,  _UpperCamelCase)
          if len(_UpperCamelCase) > 0:
              _lowerCamelCase     :		Tuple 						=						args[0]
              _lowerCamelCase     :		List[Any] 						=						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     :		Union[str, Any] 						=						self.image_processor(_UpperCamelCase    ,  *_UpperCamelCase    ,  **_UpperCamelCase)
          if text is not None:
              _lowerCamelCase     :		Optional[int] 						=						self.tokenizer(_UpperCamelCase    ,  **_UpperCamelCase)

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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  *_UpperCamelCase     :				List[str]    ,  **_UpperCamelCase     :				Optional[Any])				->Optional[int]:


          """simple docstring"""


          return self.tokenizer.batch_decode(*_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  *_UpperCamelCase     :				Tuple    ,  **_UpperCamelCase     :				int)				->Union[str, Any]:


          """simple docstring"""


          return self.tokenizer.decode(*_UpperCamelCase    ,  **_UpperCamelCase)



      @contextmanager
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->List[Any]:


          """simple docstring"""


          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     :		Tuple 						=						True
          _lowerCamelCase     :		Optional[Any] 						=						self.tokenizer
          yield
          _lowerCamelCase     :		List[Any] 						=						self.image_processor
          _lowerCamelCase     :		int 						=						False



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				int=False    ,  _UpperCamelCase     :				Optional[int]=None)				->Tuple:


          """simple docstring"""


          if added_vocab is None:
              _lowerCamelCase     :		int 						=						self.tokenizer.get_added_vocab()

          _lowerCamelCase     :		List[str] 						=						{}

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

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

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



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Any:


          """simple docstring"""


          warnings.warn(
              """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""    ,  _UpperCamelCase    ,  )
          return self.image_processor_class




      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Dict:


          """simple docstring"""


          warnings.warn(
              """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,  _UpperCamelCase    ,  )
          return self.image_processor





import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       (EulerDiscreteScheduler,)
      _snake_case    		=       10



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  **_UpperCamelCase     :				Optional[Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						{
              """num_train_timesteps""": 1100,
              """beta_start""": 0.0_0_0_1,
              """beta_end""": 0.0_2,
              """beta_schedule""": """linear""",
          }

          config.update(**_UpperCamelCase)
          return config



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[str]:


          """simple docstring"""


          for timesteps in [10, 50, 100, 1000]:
              self.check_over_configs(num_train_timesteps=_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Dict:


          """simple docstring"""


          for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]    ,  [0.0_0_0_2, 0.0_0_2, 0.0_2]):
              self.check_over_configs(beta_start=_UpperCamelCase    ,  beta_end=_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Dict:


          """simple docstring"""


          for schedule in ["linear", "scaled_linear"]:
              self.check_over_configs(beta_schedule=_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Union[str, Any]:


          """simple docstring"""


          for prediction_type in ["epsilon", "v_prediction"]:
              self.check_over_configs(prediction_type=_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		str 						=						self.get_scheduler_config()
          _lowerCamelCase     :		Any 						=						scheduler_class(**_UpperCamelCase)

          scheduler.set_timesteps(self.num_inference_steps)

          _lowerCamelCase     :		str 						=						torch.manual_seed(0)

          _lowerCamelCase     :		str 						=						self.dummy_model()
          _lowerCamelCase     :		List[Any] 						=						self.dummy_sample_deter * scheduler.init_noise_sigma
          _lowerCamelCase     :		int 						=						sample.to(_UpperCamelCase)

          for i, t in enumerate(scheduler.timesteps):
              _lowerCamelCase     :		Optional[int] 						=						scheduler.scale_model_input(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[str] 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		str 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		Dict 						=						output.prev_sample

          _lowerCamelCase     :		Any 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		Any 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 1_0.0_8_0_7) < 1E-2
          assert abs(result_mean.item() - 0.0_1_3_1) < 1E-3



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		Optional[Any] 						=						self.get_scheduler_config(prediction_type="""v_prediction""")
          _lowerCamelCase     :		int 						=						scheduler_class(**_UpperCamelCase)

          scheduler.set_timesteps(self.num_inference_steps)

          _lowerCamelCase     :		Any 						=						torch.manual_seed(0)

          _lowerCamelCase     :		int 						=						self.dummy_model()
          _lowerCamelCase     :		int 						=						self.dummy_sample_deter * scheduler.init_noise_sigma
          _lowerCamelCase     :		Dict 						=						sample.to(_UpperCamelCase)

          for i, t in enumerate(scheduler.timesteps):
              _lowerCamelCase     :		Optional[int] 						=						scheduler.scale_model_input(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		str 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[Any] 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		Tuple 						=						output.prev_sample

          _lowerCamelCase     :		Union[str, Any] 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		Optional[int] 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 0.0_0_0_2) < 1E-2
          assert abs(result_mean.item() - 2.2_6_7_6E-0_6) < 1E-3



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		int 						=						self.get_scheduler_config()
          _lowerCamelCase     :		List[Any] 						=						scheduler_class(**_UpperCamelCase)

          scheduler.set_timesteps(self.num_inference_steps    ,  device=_UpperCamelCase)

          _lowerCamelCase     :		Optional[Any] 						=						torch.manual_seed(0)

          _lowerCamelCase     :		Tuple 						=						self.dummy_model()
          _lowerCamelCase     :		Optional[Any] 						=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          _lowerCamelCase     :		Tuple 						=						sample.to(_UpperCamelCase)

          for t in scheduler.timesteps:
              _lowerCamelCase     :		List[Any] 						=						scheduler.scale_model_input(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[str] 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		Any 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		List[Any] 						=						output.prev_sample

          _lowerCamelCase     :		Any 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		List[Any] 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 1_0.0_8_0_7) < 1E-2
          assert abs(result_mean.item() - 0.0_1_3_1) < 1E-3




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.scheduler_classes[0]
          _lowerCamelCase     :		Optional[int] 						=						self.get_scheduler_config()
          _lowerCamelCase     :		int 						=						scheduler_class(**_UpperCamelCase    ,  use_karras_sigmas=_UpperCamelCase)

          scheduler.set_timesteps(self.num_inference_steps    ,  device=_UpperCamelCase)

          _lowerCamelCase     :		int 						=						torch.manual_seed(0)

          _lowerCamelCase     :		Tuple 						=						self.dummy_model()
          _lowerCamelCase     :		str 						=						self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
          _lowerCamelCase     :		Optional[int] 						=						sample.to(_UpperCamelCase)

          for t in scheduler.timesteps:
              _lowerCamelCase     :		Tuple 						=						scheduler.scale_model_input(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		Any 						=						model(_UpperCamelCase    ,  _UpperCamelCase)

              _lowerCamelCase     :		List[str] 						=						scheduler.step(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase    ,  generator=_UpperCamelCase)
              _lowerCamelCase     :		int 						=						output.prev_sample

          _lowerCamelCase     :		Tuple 						=						torch.sum(torch.abs(_UpperCamelCase))
          _lowerCamelCase     :		List[str] 						=						torch.mean(torch.abs(_UpperCamelCase))

          assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9) < 1E-2
          assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3) < 1E-3




