


"""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       __a	(   __snake_case       ):
				lowerCamelCase    :					str 					=['image_processor', 'tokenizer']
				lowerCamelCase    :					List[str] 					='LayoutLMv2ImageProcessor'
				lowerCamelCase    :					Union[str, Any] 					=('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')







				def __init__(   self			,  UpperCAmelCase=None			,  UpperCAmelCase=None			,  **UpperCAmelCase      ):

					'''simple docstring'''






					lowerCAmelCase_			       = 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_			       = kwargs.pop('''feature_extractor'''      )

					lowerCAmelCase_			       = 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			,  UpperCAmelCase			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = True			,  UpperCAmelCase = False			,  UpperCAmelCase = False			,  UpperCAmelCase = None			,  UpperCAmelCase = 0			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = False			,  UpperCAmelCase = False			,  UpperCAmelCase = False			,  UpperCAmelCase = False			,  UpperCAmelCase = True			,  UpperCAmelCase = None			,  **UpperCAmelCase			,  ):

					'''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
					lowerCAmelCase_			       = 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      ):
							lowerCAmelCase_			       = [text]  # add batch dimension (as the image processor always adds a batch dimension)
						lowerCAmelCase_			       = features['''words''']

					lowerCAmelCase_			       = 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
					lowerCAmelCase_			       = features.pop('''pixel_values'''      )
					if return_overflowing_tokens is True:
						lowerCAmelCase_			       = self.get_overflowing_images(UpperCAmelCase			,  encoded_inputs['''overflow_to_sample_mapping''']      )
					lowerCAmelCase_			       = images

					return encoded_inputs







				def     lowerCamelCase_    (   self			,  UpperCAmelCase			,  UpperCAmelCase      ):

					'''simple docstring'''






					lowerCAmelCase_			       = []
					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     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
				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






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







				@property
				def     lowerCamelCase_    (   self      ):

					'''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     lowerCamelCase_    (   self      ):

					'''simple docstring'''






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



"""simple docstring"""



import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class       __a	(   __snake_case       ):
				lowerCamelCase    :					Union[str, Any] 					=(LMSDiscreteScheduler,)
				lowerCamelCase    :					int 					=10







				def     lowerCamelCase_    (   self			,  **UpperCAmelCase      ):

					'''simple docstring'''






					lowerCAmelCase_			       = {
					    '''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     lowerCamelCase_    (   self      ):

					'''simple docstring'''






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







				def     lowerCamelCase_    (   self      ):

					'''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     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for schedule in ["linear", "scaled_linear"]:
						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 t in [0, 500, 800]:
						self.check_over_forward(time_step=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






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

					scheduler.set_timesteps(self.num_inference_steps      )

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma

					for i, t in enumerate(scheduler.timesteps      ):
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

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

					assert abs(result_sum.item() - 1_0_0_6.3_8_8      ) < 1E-2
					assert abs(result_mean.item() - 1.3_1      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config(prediction_type='''v_prediction'''      )
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps      )

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma

					for i, t in enumerate(scheduler.timesteps      ):
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

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

					assert abs(result_sum.item() - 0.0_0_1_7      ) < 1E-2
					assert abs(result_mean.item() - 2.2_6_7_6E-0_6      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






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

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

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
					lowerCAmelCase_			       = sample.to(UpperCAmelCase      )

					for i, t in enumerate(scheduler.timesteps      ):
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

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

					assert abs(result_sum.item() - 1_0_0_6.3_8_8      ) < 1E-2
					assert abs(result_mean.item() - 1.3_1      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config()
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase			,  use_karras_sigmas=UpperCAmelCase      )

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

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter.to(UpperCAmelCase      ) * scheduler.init_noise_sigma
					lowerCAmelCase_			       = sample.to(UpperCAmelCase      )

					for t in scheduler.timesteps:
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

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

					assert abs(result_sum.item() - 3_8_1_2.9_9_2_7      ) < 2E-2
					assert abs(result_mean.item() - 4.9_6_4_8      ) < 1E-3