

'''simple docstring'''
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Optional[int]     	=							(DDIMParallelScheduler,)
   _SCREAMING_SNAKE_CASE   :					Any     	=							(("eta", 0.0), ("num_inference_steps", 50))


   def 						_lowerCamelCase       (			self      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				int      					=       {
          "num_train_timesteps": 1000,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
          "clip_sample": True,
      }

      config.update(**_UpperCamelCase  )
      return config


   def 						_lowerCamelCase       (			self      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       self.scheduler_classes[0]
      _lowercase      :				Dict      					=       self.get_scheduler_config(**_UpperCamelCase  )
      _lowercase      :				Dict      					=       scheduler_class(**_UpperCamelCase  )

      _lowercase					,				_lowercase      :				List[Any]      					=       10, 0.0

      _lowercase      :				List[Any]      					=       self.dummy_model()
      _lowercase      :				Union[str, Any]      					=       self.dummy_sample_deter

      scheduler.set_timesteps(_UpperCamelCase  )

      for t in scheduler.timesteps:
         _lowercase      :				str      					=       model(_UpperCamelCase      ,    _UpperCamelCase  )
         _lowercase      :				List[str]      					=       scheduler.step(_UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ).prev_sample

      return sample


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      for timesteps in [100, 500, 1000]:
         self.check_over_configs(num_train_timesteps=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      for steps_offset in [0, 1]:
         self.check_over_configs(steps_offset=_UpperCamelCase  )

      _lowercase      :				Union[str, Any]      					=       self.scheduler_classes[0]
      _lowercase      :				List[str]      					=       self.get_scheduler_config(steps_offset=1  )
      _lowercase      :				List[str]      					=       scheduler_class(**_UpperCamelCase  )
      scheduler.set_timesteps(5  )
      assert torch.equal(scheduler.timesteps      ,    torch.LongTensor([801, 601, 401, 201, 1]  )  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      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=_UpperCamelCase      ,    beta_end=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      for schedule in ["linear", "squaredcos_cap_v2"]:
         self.check_over_configs(beta_schedule=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      for clip_sample in [True, False]:
         self.check_over_configs(clip_sample=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      for timestep_spacing in ["trailing", "leading"]:
         self.check_over_configs(timestep_spacing=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      for rescale_betas_zero_snr in [True, False]:
         self.check_over_configs(rescale_betas_zero_snr=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self.check_over_configs(thresholding=_UpperCamelCase  )
      for threshold in [0.5, 1.0, 2.0]:
         for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(
                thresholding=_UpperCamelCase      ,    prediction_type=_UpperCamelCase      ,    sample_max_value=_UpperCamelCase      ,    )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      for t in [1, 10, 49]:
         self.check_over_forward(time_step=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      for t, num_inference_steps in zip([1, 10, 50]      ,    [10, 50, 500]  ):
         self.check_over_forward(time_step=_UpperCamelCase      ,    num_inference_steps=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      for t, eta in zip([1, 10, 49]      ,    [0.0, 0.5, 1.0]  ):
         self.check_over_forward(time_step=_UpperCamelCase      ,    eta=_UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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

      assert torch.sum(torch.abs(scheduler._get_variance(0      ,    0  ) - 0.0  )  ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(420      ,    400  ) - 0.1_4_7_7_1  )  ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(980      ,    960  ) - 0.3_2_4_6_0  )  ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(0      ,    0  ) - 0.0  )  ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(487      ,    486  ) - 0.0_0_9_7_9  )  ) < 1E-5
      assert torch.sum(torch.abs(scheduler._get_variance(999      ,    998  ) - 0.0_2  )  ) < 1E-5


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





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

      _lowercase					,				_lowercase      :				Optional[Any]      					=       10, 0.0
      scheduler.set_timesteps(_UpperCamelCase  )

      _lowercase      :				Any      					=       self.dummy_model()
      _lowercase      :				str      					=       self.dummy_sample_deter
      _lowercase      :				Any      					=       self.dummy_sample_deter + 0.1
      _lowercase      :				str      					=       self.dummy_sample_deter - 0.1

      _lowercase      :				Union[str, Any]      					=       samplea.shape[0]
      _lowercase      :				int      					=       torch.stack([samplea, samplea, samplea]      ,    dim=0  )
      _lowercase      :				List[Any]      					=       torch.arange(_UpperCamelCase  )[0:3, None].repeat(1      ,    _UpperCamelCase  )

      _lowercase      :				Any      					=       model(samples.flatten(0      ,    1  )      ,    timesteps.flatten(0      ,    1  )  )
      _lowercase      :				Union[str, Any]      					=       scheduler.batch_step_no_noise(_UpperCamelCase      ,    timesteps.flatten(0      ,    1  )      ,    samples.flatten(0      ,    1  )      ,    _UpperCamelCase  )

      _lowercase      :				Optional[Any]      					=       torch.sum(torch.abs(_UpperCamelCase  )  )
      _lowercase      :				int      					=       torch.mean(torch.abs(_UpperCamelCase  )  )

      assert abs(result_sum.item() - 1_1_4_7.7_9_0_4  ) < 1E-2
      assert abs(result_mean.item() - 0.4_9_8_2  ) < 1E-3


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       self.full_loop()

      _lowercase      :				Optional[int]      					=       torch.sum(torch.abs(_UpperCamelCase  )  )
      _lowercase      :				Any      					=       torch.mean(torch.abs(_UpperCamelCase  )  )

      assert abs(result_sum.item() - 1_7_2.0_0_6_7  ) < 1E-2
      assert abs(result_mean.item() - 0.2_2_3_9_6_7  ) < 1E-3


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Optional[Any]      					=       self.full_loop(prediction_type="v_prediction"  )

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

      assert abs(result_sum.item() - 5_2.5_3_0_2  ) < 1E-2
      assert abs(result_mean.item() - 0.0_6_8_4  ) < 1E-3


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       self.full_loop(set_alpha_to_one=_UpperCamelCase      ,    beta_start=0.0_1  )
      _lowercase      :				Any      					=       torch.sum(torch.abs(_UpperCamelCase  )  )
      _lowercase      :				str      					=       torch.mean(torch.abs(_UpperCamelCase  )  )

      assert abs(result_sum.item() - 1_4_9.8_2_9_5  ) < 1E-2
      assert abs(result_mean.item() - 0.1_9_5_1  ) < 1E-3






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				str      					=       self.full_loop(set_alpha_to_one=_UpperCamelCase      ,    beta_start=0.0_1  )
      _lowercase      :				List[str]      					=       torch.sum(torch.abs(_UpperCamelCase  )  )
      _lowercase      :				Optional[Any]      					=       torch.mean(torch.abs(_UpperCamelCase  )  )

      assert abs(result_sum.item() - 1_4_9.0_7_8_4  ) < 1E-2
      assert abs(result_mean.item() - 0.1_9_4_1  ) < 1E-3









'''simple docstring'''
import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer


class        a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE   :					Any     	=							['image_processor', 'tokenizer']
   _SCREAMING_SNAKE_CASE   :					Dict     	=							'BlipImageProcessor'
   _SCREAMING_SNAKE_CASE   :					Dict     	=							'AutoTokenizer'


   def __init__(			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase  ):





      """simple docstring"""





      super().__init__(_UpperCamelCase      ,    _UpperCamelCase  )

      # add QFormer tokenizer
      _lowercase      :				List[Any]      					=       qformer_tokenizer


   def __call__(			self      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = True      ,    _UpperCamelCase = False      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = 0      ,    _UpperCamelCase = None      ,    _UpperCamelCase = None      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = False      ,    _UpperCamelCase = True      ,    _UpperCamelCase = None      ,    **_UpperCamelCase      ,    ):





      """simple docstring"""





      if images is None and text is None:
         raise ValueError("You have to specify at least images or text."  )

      _lowercase      :				str      					=       BatchFeature()

      if text is not None:
         _lowercase      :				Dict      					=       self.tokenizer(
             text=_UpperCamelCase      ,    add_special_tokens=_UpperCamelCase      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=_UpperCamelCase      ,    stride=_UpperCamelCase      ,    pad_to_multiple_of=_UpperCamelCase      ,    return_attention_mask=_UpperCamelCase      ,    return_overflowing_tokens=_UpperCamelCase      ,    return_special_tokens_mask=_UpperCamelCase      ,    return_offsets_mapping=_UpperCamelCase      ,    return_token_type_ids=_UpperCamelCase      ,    return_length=_UpperCamelCase      ,    verbose=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase      ,    )
         encoding.update(_UpperCamelCase  )
         _lowercase      :				Dict      					=       self.qformer_tokenizer(
             text=_UpperCamelCase      ,    add_special_tokens=_UpperCamelCase      ,    padding=_UpperCamelCase      ,    truncation=_UpperCamelCase      ,    max_length=_UpperCamelCase      ,    stride=_UpperCamelCase      ,    pad_to_multiple_of=_UpperCamelCase      ,    return_attention_mask=_UpperCamelCase      ,    return_overflowing_tokens=_UpperCamelCase      ,    return_special_tokens_mask=_UpperCamelCase      ,    return_offsets_mapping=_UpperCamelCase      ,    return_token_type_ids=_UpperCamelCase      ,    return_length=_UpperCamelCase      ,    verbose=_UpperCamelCase      ,    return_tensors=_UpperCamelCase      ,    **_UpperCamelCase      ,    )
         _lowercase      :				Union[str, Any]      					=       qformer_text_encoding.pop("input_ids"  )
         _lowercase      :				List[Any]      					=       qformer_text_encoding.pop("attention_mask"  )

      if images is not None:
         _lowercase      :				List[Any]      					=       self.image_processor(_UpperCamelCase      ,    return_tensors=_UpperCamelCase  )
         encoding.update(_UpperCamelCase  )

      return encoding


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





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


   def 						_lowerCamelCase       (			self      ,    *_UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





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


   @property
   # Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Dict      					=       self.tokenizer.model_input_names
      _lowercase      :				Optional[Any]      					=       self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      if os.path.isfile(_UpperCamelCase  ):
         raise ValueError(f'''Provided path ({save_directory}) should be a directory, not a file'''  )
      os.makedirs(_UpperCamelCase      ,    exist_ok=_UpperCamelCase  )
      _lowercase      :				Union[str, Any]      					=       os.path.join(_UpperCamelCase      ,    "qformer_tokenizer"  )
      self.qformer_tokenizer.save_pretrained(_UpperCamelCase  )
      return super().save_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )






   @classmethod
   def 						_lowerCamelCase       (			cls      ,    _UpperCamelCase      ,    **_UpperCamelCase  ):





      """simple docstring"""





      _lowercase      :				List[Any]      					=       AutoTokenizer.from_pretrained(_UpperCamelCase      ,    subfolder="qformer_tokenizer"  )
      _lowercase      :				Optional[Any]      					=       cls._get_arguments_from_pretrained(_UpperCamelCase      ,    **_UpperCamelCase  )
      args.append(_UpperCamelCase  )
      return cls(*_UpperCamelCase  )






