import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler


class 						a__							(   unittest.TestCase		):
			A__				:   int								=			(ScoreSdeVeScheduler,)
			A__				:   int								=			()



			@property
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    int:
								__a							=   4
								__a							=   3
								__a							=   8
								__a							=   8

								__a							=   torch.rand((batch_size, num_channels, height, width)    )

								return sample



			@property
			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Union[str, Any]:
								__a							=   4
								__a							=   3
								__a							=   8
								__a							=   8

								__a							=   batch_size * num_channels * height * width
								__a							=   torch.arange(UpperCAmelCase    )
								__a							=   sample.reshape(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
								__a							=   sample / num_elems
								__a							=   sample.permute(3      ,						0      ,						1      ,						2    )

								return sample



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    str:





								def model(UpperCAmelCase      ,						UpperCAmelCase      ,						*UpperCAmelCase    ):
													return sample * t / (t + 1)

								return model



			def __SCREAMING_SNAKE_CASE							(						self      ,						**UpperCAmelCase    ) ->    Any:
								__a							=   {
								    'num_train_timesteps': 2_0_0_0,
								    'snr': 0.15,
								    'sigma_min': 0.01,
								    'sigma_max': 1_3_4_8,
								    'sampling_eps': 1e-5,
								}

								config.update(**UpperCAmelCase    )
								return config



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase=0      ,						**UpperCAmelCase    ) ->    Tuple:
								__a							=   dict(self.forward_default_kwargs    )

								for scheduler_class in self.scheduler_classes:
													__a							=   self.dummy_sample
													__a							=   0.1 * sample

													__a							=   self.get_scheduler_config(**UpperCAmelCase    )
													__a							=   scheduler_class(**UpperCAmelCase    )

													with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(UpperCAmelCase    )
																		__a							=   scheduler_class.from_pretrained(UpperCAmelCase    )

													__a							=   scheduler.step_pred(
													    UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						generator=torch.manual_seed(0    )      ,						**UpperCAmelCase    ).prev_sample
													__a							=   new_scheduler.step_pred(
													    UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						generator=torch.manual_seed(0    )      ,						**UpperCAmelCase    ).prev_sample

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

													__a							=   scheduler.step_correct(UpperCAmelCase      ,						UpperCAmelCase      ,						generator=torch.manual_seed(0    )      ,						**UpperCAmelCase    ).prev_sample
													__a							=   new_scheduler.step_correct(
													    UpperCAmelCase      ,						UpperCAmelCase      ,						generator=torch.manual_seed(0    )      ,						**UpperCAmelCase    ).prev_sample

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



			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase=0      ,						**UpperCAmelCase    ) ->    str:
								__a							=   dict(self.forward_default_kwargs    )
								kwargs.update(UpperCAmelCase    )

								for scheduler_class in self.scheduler_classes:
													__a							=   self.dummy_sample
													__a							=   0.1 * sample

													__a							=   self.get_scheduler_config()
													__a							=   scheduler_class(**UpperCAmelCase    )

													with tempfile.TemporaryDirectory() as tmpdirname:
																		scheduler.save_config(UpperCAmelCase    )
																		__a							=   scheduler_class.from_pretrained(UpperCAmelCase    )

													__a							=   scheduler.step_pred(
													    UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						generator=torch.manual_seed(0    )      ,						**UpperCAmelCase    ).prev_sample
													__a							=   new_scheduler.step_pred(
													    UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						generator=torch.manual_seed(0    )      ,						**UpperCAmelCase    ).prev_sample

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

													__a							=   scheduler.step_correct(UpperCAmelCase      ,						UpperCAmelCase      ,						generator=torch.manual_seed(0    )      ,						**UpperCAmelCase    ).prev_sample
													__a							=   new_scheduler.step_correct(
													    UpperCAmelCase      ,						UpperCAmelCase      ,						generator=torch.manual_seed(0    )      ,						**UpperCAmelCase    ).prev_sample

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



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:





								for timesteps in [1_0, 1_0_0, 1_0_0_0]:
													self.check_over_configs(num_train_timesteps=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[Any]:





								for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]      ,						[1, 1_0_0, 1_0_0_0]    ):
													self.check_over_configs(sigma_min=UpperCAmelCase      ,						sigma_max=UpperCAmelCase    )



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





								for t in [0.1, 0.5, 0.75]:
													self.check_over_forward(time_step=UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								__a							=   dict(self.forward_default_kwargs    )

								__a							=   self.scheduler_classes[0]
								__a							=   self.get_scheduler_config()
								__a							=   scheduler_class(**UpperCAmelCase    )

								__a							=   3

								__a							=   self.dummy_model()
								__a							=   self.dummy_sample_deter

								scheduler.set_sigmas(UpperCAmelCase    )
								scheduler.set_timesteps(UpperCAmelCase    )
								__a							=   torch.manual_seed(0    )

								for i, t in enumerate(scheduler.timesteps    ):
													__a							=   scheduler.sigmas[i]

													for _ in range(scheduler.config.correct_steps    ):
																		with torch.no_grad():
																							__a							=   model(UpperCAmelCase      ,						UpperCAmelCase    )
																		__a							=   scheduler.step_correct(UpperCAmelCase      ,						UpperCAmelCase      ,						generator=UpperCAmelCase      ,						**UpperCAmelCase    ).prev_sample

													with torch.no_grad():
																		__a							=   model(UpperCAmelCase      ,						UpperCAmelCase    )

													__a							=   scheduler.step_pred(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						generator=UpperCAmelCase      ,						**UpperCAmelCase    )
													__a							=   output.prev_sample, output.prev_sample_mean

								__a							=   torch.sum(torch.abs(UpperCAmelCase    )    )
								__a							=   torch.mean(torch.abs(UpperCAmelCase    )    )

								assert np.isclose(result_sum.item()      ,						1_4_3_7_2_7_5_8_5_2_8.0    )
								assert np.isclose(result_mean.item()      ,						1_8_7_1_4_5_3_0.0    )







			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[int]:
								__a							=   dict(self.forward_default_kwargs    )

								__a							=   kwargs.pop('num_inference_steps'      ,						UpperCAmelCase    )

								for scheduler_class in self.scheduler_classes:
													__a							=   self.get_scheduler_config()
													__a							=   scheduler_class(**UpperCAmelCase    )

													__a							=   self.dummy_sample
													__a							=   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'    ):
																		__a							=   num_inference_steps

													__a							=   scheduler.step_pred(UpperCAmelCase      ,						0      ,						UpperCAmelCase      ,						generator=torch.manual_seed(0    )      ,						**UpperCAmelCase    ).prev_sample
													__a							=   scheduler.step_pred(UpperCAmelCase      ,						1      ,						UpperCAmelCase      ,						generator=torch.manual_seed(0    )      ,						**UpperCAmelCase    ).prev_sample

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











import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				List[str]					    =["""image_processor""", """tokenizer"""]
  a_							:				Optional[Any]					    ="""LayoutLMv2ImageProcessor"""
  a_							:				Any					    =("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")







  def __init__(					self					:    Union[str, Any]					, UpperCamelCase					:    str=None					, UpperCamelCase					:    List[Any]=None					, **UpperCamelCase					:    List[str]							):


    '''simple docstring'''





    _snake_case      : Dict														=		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					, )
      _snake_case      : Dict														=		kwargs.pop('feature_extractor'							)

    _snake_case      : Optional[Any]														=		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							)







  def __call__(					self					:    int					, UpperCamelCase					:    Tuple					, UpperCamelCase					:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					, UpperCamelCase					:    Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None					, UpperCamelCase					:    Union[List[List[int]], List[List[List[int]]]] = None					, UpperCamelCase					:    Optional[Union[List[int], List[List[int]]]] = None					, UpperCamelCase					:    bool = True					, UpperCamelCase					:    Union[bool, str, PaddingStrategy] = False					, UpperCamelCase					:    Union[bool, str, TruncationStrategy] = False					, UpperCamelCase					:    Optional[int] = None					, UpperCamelCase					:    int = 0					, UpperCamelCase					:    Optional[int] = None					, UpperCamelCase					:    Optional[bool] = None					, UpperCamelCase					:    Optional[bool] = None					, UpperCamelCase					:    bool = False					, UpperCamelCase					:    bool = False					, UpperCamelCase					:    bool = False					, UpperCamelCase					:    bool = False					, UpperCamelCase					:    bool = True					, UpperCamelCase					:    Optional[Union[str, TensorType]] = None					, **UpperCamelCase					:    Optional[Any]					, ):


    '''simple docstring'''





    if self.image_processor.apply_ocr and (boxes is not None):
      raise ValueError(
          'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'							)

    if self.image_processor.apply_ocr and (word_labels is not None):
      raise ValueError(
          'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'							)

    if return_overflowing_tokens is True and return_offsets_mapping is False:
      raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'							)

    # first, apply the image processor
    _snake_case      : Optional[Any]														=		self.image_processor(images=UpperCamelCase					, return_tensors=UpperCamelCase							)

    # second, apply the tokenizer
    if text is not None and self.image_processor.apply_ocr and text_pair is None:
      if isinstance(UpperCamelCase					, UpperCamelCase							):
        _snake_case      : List[Any]														=		[text]  # add batch dimension (as the image processor always adds a batch dimension)
      _snake_case      : Dict														=		features['words']

    _snake_case      : Optional[int]														=		self.tokenizer(
        text=text if text is not None else features['words']					, text_pair=text_pair if text_pair is not None else None					, boxes=boxes if boxes is not None else features['boxes']					, word_labels=UpperCamelCase					, add_special_tokens=UpperCamelCase					, padding=UpperCamelCase					, truncation=UpperCamelCase					, max_length=UpperCamelCase					, stride=UpperCamelCase					, pad_to_multiple_of=UpperCamelCase					, return_token_type_ids=UpperCamelCase					, return_attention_mask=UpperCamelCase					, return_overflowing_tokens=UpperCamelCase					, return_special_tokens_mask=UpperCamelCase					, return_offsets_mapping=UpperCamelCase					, return_length=UpperCamelCase					, verbose=UpperCamelCase					, return_tensors=UpperCamelCase					, **UpperCamelCase					, )

    # add pixel values
    _snake_case      : Optional[int]														=		features.pop('pixel_values'							)
    if return_overflowing_tokens is True:
      _snake_case      : Any														=		self.get_overflowing_images(UpperCamelCase					, encoded_inputs['overflow_to_sample_mapping']							)
    _snake_case      : int														=		images

    return encoded_inputs







  def UpperCamelCase_       (					self					:    List[Any]					, UpperCamelCase					:    List[str]					, UpperCamelCase					:    List[str]							):


    '''simple docstring'''





    _snake_case      : Optional[Any]														=		[]
    for sample_idx in overflow_to_sample_mapping:
      images_with_overflow.append(images[sample_idx]							)

    if len(UpperCamelCase							) != len(UpperCamelCase							):
      raise ValueError(
          'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
          f""" {len(UpperCamelCase							)} and {len(UpperCamelCase							)}"""							)

    return images_with_overflow







  def UpperCamelCase_       (					self					:    List[Any]					, *UpperCamelCase					:    Any					, **UpperCamelCase					:    int							):


    '''simple docstring'''





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







  def UpperCamelCase_       (					self					:    Any					, *UpperCamelCase					:    Optional[int]					, **UpperCamelCase					:    Any							):


    '''simple docstring'''





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







  @property
  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]







  @property
  def UpperCamelCase_       (					self					:    Dict							):


    '''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 UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





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




