




'''simple docstring'''





import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	(KDPMaAncestralDiscreteScheduler,)
      __lowercase					=	10






      def  A__(self			,	**a__       )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			{
             '''num_train_timesteps''': 1_1_0_0,
             '''beta_start''': 0.00_01,
             '''beta_end''': 0.02,
             '''beta_schedule''': '''linear''',
         }

         config.update(**a__       )
         return config






      def  A__(self       )							-> int:
         for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=a__       )






      def  A__(self       )							-> Tuple:
         for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]			,	[0.00_02, 0.0_02, 0.02]       ):
            self.check_over_configs(beta_start=a__			,	beta_end=a__       )






      def  A__(self       )							-> Tuple:
         for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=a__       )






      def  A__(self       )							-> List[str]:
         if torch_device == "mps":
            return
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.get_scheduler_config()
         SCREAMING_SNAKE_CASE__      :					Any							    =			scheduler_class(**a__       )

         scheduler.set_timesteps(self.num_inference_steps       )

         SCREAMING_SNAKE_CASE__      :					Dict							    =			torch.manual_seed(0       )

         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self.dummy_model()
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			self.dummy_sample_deter * scheduler.init_noise_sigma
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			sample.to(a__       )

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

            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			model(a__			,	a__       )

            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			scheduler.step(a__			,	a__			,	a__			,	generator=a__       )
            SCREAMING_SNAKE_CASE__      :					Dict							    =			output.prev_sample

         SCREAMING_SNAKE_CASE__      :					Dict							    =			torch.sum(torch.abs(a__       )       )
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			torch.mean(torch.abs(a__       )       )

         assert abs(result_sum.item() - 1_38_49.38_77       ) < 1E-2
         assert abs(result_mean.item() - 18.03_31       ) < 5E-3






      def  A__(self       )							-> List[str]:
         for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=a__       )






      def  A__(self       )							-> Tuple:
         if torch_device == "mps":
            return
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			self.get_scheduler_config(prediction_type='''v_prediction'''       )
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			scheduler_class(**a__       )

         scheduler.set_timesteps(self.num_inference_steps       )

         SCREAMING_SNAKE_CASE__      :					Any							    =			self.dummy_model()
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self.dummy_sample_deter * scheduler.init_noise_sigma
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			sample.to(a__       )

         SCREAMING_SNAKE_CASE__      :					str							    =			torch.manual_seed(0       )

         for i, t in enumerate(scheduler.timesteps       ):
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			scheduler.scale_model_input(a__			,	a__       )

            SCREAMING_SNAKE_CASE__      :					Any							    =			model(a__			,	a__       )

            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			scheduler.step(a__			,	a__			,	a__			,	generator=a__       )
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			output.prev_sample

         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			torch.sum(torch.abs(a__       )       )
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			torch.mean(torch.abs(a__       )       )

         assert abs(result_sum.item() - 3_28.99_70       ) < 1E-2
         assert abs(result_mean.item() - 0.42_84       ) < 1E-3






      def  A__(self       )							-> Tuple:
         if torch_device == "mps":
            return
         SCREAMING_SNAKE_CASE__      :					Any							    =			self.scheduler_classes[0]
         SCREAMING_SNAKE_CASE__      :					Any							    =			self.get_scheduler_config()
         SCREAMING_SNAKE_CASE__      :					Any							    =			scheduler_class(**a__       )

         scheduler.set_timesteps(self.num_inference_steps			,	device=a__       )
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			torch.manual_seed(0       )

         SCREAMING_SNAKE_CASE__      :					Dict							    =			self.dummy_model()
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			self.dummy_sample_deter.to(a__       ) * scheduler.init_noise_sigma

         for t in scheduler.timesteps:
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			scheduler.scale_model_input(a__			,	a__       )

            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			model(a__			,	a__       )

            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			scheduler.step(a__			,	a__			,	a__			,	generator=a__       )
            SCREAMING_SNAKE_CASE__      :					str							    =			output.prev_sample

         SCREAMING_SNAKE_CASE__      :					Any							    =			torch.sum(torch.abs(a__       )       )
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			torch.mean(torch.abs(a__       )       )

         assert abs(result_sum.item() - 1_38_49.38_18       ) < 1E-1
         assert abs(result_mean.item() - 18.03_31       ) < 1E-3







'''simple docstring'''





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     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''LayoutLMv2ImageProcessor'''
      __lowercase					=	('''LayoutLMv2Tokenizer''', '''LayoutLMv2TokenizerFast''')






      def __init__(self			,	a__=None			,	a__=None			,	**a__       )							-> Any:
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			None
         if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''			,	a__			,	)
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			kwargs.pop('''feature_extractor'''       )

         SCREAMING_SNAKE_CASE__      :					List[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__(a__			,	a__       )






      def __call__(self			,	a__			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = True			,	a__ = False			,	a__ = False			,	a__ = None			,	a__ = 0			,	a__ = None			,	a__ = None			,	a__ = None			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = False			,	a__ = True			,	a__ = None			,	**a__			,	)							-> BatchEncoding:
         # verify input
         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
         SCREAMING_SNAKE_CASE__      :					str							    =			self.image_processor(images=a__			,	return_tensors=a__       )

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

         SCREAMING_SNAKE_CASE__      :					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=a__			,	add_special_tokens=a__			,	padding=a__			,	truncation=a__			,	max_length=a__			,	stride=a__			,	pad_to_multiple_of=a__			,	return_token_type_ids=a__			,	return_attention_mask=a__			,	return_overflowing_tokens=a__			,	return_special_tokens_mask=a__			,	return_offsets_mapping=a__			,	return_length=a__			,	verbose=a__			,	return_tensors=a__			,	**a__			,	)

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

         return encoded_inputs






      def  A__(self			,	a__			,	a__       )							-> str:
         # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
         SCREAMING_SNAKE_CASE__      :					int							    =			[]
         for sample_idx in overflow_to_sample_mapping:
            images_with_overflow.append(images[sample_idx]       )

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

         return images_with_overflow






      def  A__(self			,	*a__			,	**a__       )							-> List[Any]:
         return self.tokenizer.batch_decode(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Optional[Any]:
         return self.tokenizer.decode(*a__			,	**a__       )






      @property
      def  A__(self       )							-> Tuple:
         return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]






      @property
      def  A__(self       )							-> str:
         warnings.warn(
             '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,	a__			,	)
         return self.image_processor_class






      @property
      def  A__(self       )							-> Dict:
         warnings.warn(
             '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,	a__			,	)
         return self.image_processor

