


'''simple docstring'''

import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest





class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				(DDPMScheduler,)






     def  a_					(			self     ,							**__UpperCamelCase		)       ->    Optional[int]:
          _a						=		{
              "num_train_timesteps": 1_000,
              "beta_start": 0.00_01,
              "beta_end": 0.02,
              "beta_schedule": "linear",
              "variance_type": "fixed_small",
              "clip_sample": True,
          }

          config.update(**__A		)
          return config






     def  a_					(			self		)       ->    Tuple:
          for timesteps in [1, 5, 100, 1_000]:
               self.check_over_configs(num_train_timesteps=__A		)






     def  a_					(			self		)       ->    Optional[Any]:
          for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]     ,							[0.0_02, 0.02, 0.2, 2]		):
               self.check_over_configs(beta_start=__A     ,							beta_end=__A		)






     def  a_					(			self		)       ->    Union[str, Any]:
          for schedule in ["linear", "squaredcos_cap_v2"]:
               self.check_over_configs(beta_schedule=__A		)






     def  a_					(			self		)       ->    Dict:
          for variance in ["fixed_small", "fixed_large", "other"]:
               self.check_over_configs(variance_type=__A		)






     def  a_					(			self		)       ->    List[str]:
          for clip_sample in [True, False]:
               self.check_over_configs(clip_sample=__A		)






     def  a_					(			self		)       ->    Union[str, Any]:
          self.check_over_configs(thresholding=__A		)
          for threshold in [0.5, 1.0, 2.0]:
               for prediction_type in ["epsilon", "sample", "v_prediction"]:
                    self.check_over_configs(
                        thresholding=__A     ,							prediction_type=__A     ,							sample_max_value=__A     ,							)






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






     def  a_					(			self		)       ->    Optional[int]:
          for t in [0, 500, 999]:
               self.check_over_forward(time_step=__A		)






     def  a_					(			self		)       ->    Union[str, Any]:
          _a						=		self.scheduler_classes[0]
          _a						=		self.get_scheduler_config()
          _a						=		scheduler_class(**__A		)

          assert torch.sum(torch.abs(scheduler._get_variance(0		) - 0.0		)		) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.0_09_79		)		) < 1e-5
          assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.02		)		) < 1e-5






     def  a_					(			self		)       ->    str:
          _a						=		self.scheduler_classes[0]
          _a						=		self.get_scheduler_config()
          _a						=		scheduler_class(**__A		)

          _a						=		len(__A		)

          _a						=		self.dummy_model()
          _a						=		self.dummy_sample_deter
          _a						=		torch.manual_seed(0		)

          for t in reversed(range(__A		)		):
               # 1. predict noise residual
               _a						=		model(__A     ,							__A		)

               # 2. predict previous mean of sample x_t-1
               _a						=		scheduler.step(__A     ,							__A     ,							__A     ,							generator=__A		).prev_sample

               # if t > 0:
               #     noise = self.dummy_sample_deter
               #     variance = scheduler.get_variance(t) ** (0.5) * noise
               #
               # sample = pred_prev_sample + variance
               _a						=		pred_prev_sample

          _a						=		torch.sum(torch.abs(__A		)		)
          _a						=		torch.mean(torch.abs(__A		)		)

          assert abs(result_sum.item() - 2_58.96_06		) < 1e-2
          assert abs(result_mean.item() - 0.33_72		) < 1e-3






     def  a_					(			self		)       ->    str:
          _a						=		self.scheduler_classes[0]
          _a						=		self.get_scheduler_config(prediction_type="v_prediction"		)
          _a						=		scheduler_class(**__A		)

          _a						=		len(__A		)

          _a						=		self.dummy_model()
          _a						=		self.dummy_sample_deter
          _a						=		torch.manual_seed(0		)

          for t in reversed(range(__A		)		):
               # 1. predict noise residual
               _a						=		model(__A     ,							__A		)

               # 2. predict previous mean of sample x_t-1
               _a						=		scheduler.step(__A     ,							__A     ,							__A     ,							generator=__A		).prev_sample

               # if t > 0:
               #     noise = self.dummy_sample_deter
               #     variance = scheduler.get_variance(t) ** (0.5) * noise
               #
               # sample = pred_prev_sample + variance
               _a						=		pred_prev_sample

          _a						=		torch.sum(torch.abs(__A		)		)
          _a						=		torch.mean(torch.abs(__A		)		)

          assert abs(result_sum.item() - 2_02.02_96		) < 1e-2
          assert abs(result_mean.item() - 0.26_31		) < 1e-3






     def  a_					(			self		)       ->    Any:
          _a						=		self.scheduler_classes[0]
          _a						=		self.get_scheduler_config()
          _a						=		scheduler_class(**__A		)

          _a						=		[100, 87, 50, 1, 0]

          scheduler.set_timesteps(timesteps=__A		)

          _a						=		scheduler.timesteps

          for i, timestep in enumerate(__A		):
               if i == len(__A		) - 1:
                    _a						=		-1
               else:
                    _a						=		timesteps[i + 1]

               _a						=		scheduler.previous_timestep(__A		)
               _a						=		prev_t.item()

               self.assertEqual(__A     ,							__A		)






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		self.scheduler_classes[0]
          _a						=		self.get_scheduler_config()
          _a						=		scheduler_class(**__A		)

          _a						=		[100, 87, 50, 51, 0]

          with self.assertRaises(__A     ,							msg="`custom_timesteps` must be in descending order."		):
               scheduler.set_timesteps(timesteps=__A		)






     def  a_					(			self		)       ->    List[str]:
          _a						=		self.scheduler_classes[0]
          _a						=		self.get_scheduler_config()
          _a						=		scheduler_class(**__A		)

          _a						=		[100, 87, 50, 1, 0]
          _a						=		len(__A		)

          with self.assertRaises(__A     ,							msg="Can only pass one of `num_inference_steps` or `custom_timesteps`."		):
               scheduler.set_timesteps(num_inference_steps=__A     ,							timesteps=__A		)






     def  a_					(			self		)       ->    int:
          _a						=		self.scheduler_classes[0]
          _a						=		self.get_scheduler_config()
          _a						=		scheduler_class(**__A		)

          _a						=		[scheduler.config.num_train_timesteps]

          with self.assertRaises(
              __A     ,							msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"     ,							):
               scheduler.set_timesteps(timesteps=__A		)





'''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 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				['''image_processor''', '''tokenizer''']
     UpperCAmelCase 					=				'''LayoutLMv2ImageProcessor'''
     UpperCAmelCase 					=				('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')






     def __init__(			self     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							**__UpperCamelCase		)       ->    str:
          if "feature_extractor" in kwargs:
               warnings.warn(
                   "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                   " instead."     ,							__UpperCamelCase     ,							)
               _a						=		kwargs.pop("feature_extractor"		)

          _a						=		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 = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = 0     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = False     ,							__UpperCamelCase = True     ,							__UpperCamelCase = None     ,							**__UpperCamelCase     ,							)       ->    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
          _a						=		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		):
                    _a						=		[text]  # add batch dimension (as the image processor always adds a batch dimension)
               _a						=		features["words"]

          _a						=		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
          _a						=		features.pop("pixel_values"		)
          if return_overflowing_tokens is True:
               _a						=		self.get_overflowing_images(__UpperCamelCase     ,							encoded_inputs["overflow_to_sample_mapping"]		)
          _a						=		images

          return encoded_inputs






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Dict:
          # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
          _a						=		[]
          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  a_					(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    Tuple:
          return self.tokenizer.batch_decode(*__UpperCamelCase     ,							**__UpperCamelCase		)






     def  a_					(			self     ,							*__UpperCamelCase     ,							**__UpperCamelCase		)       ->    List[Any]:
          return self.tokenizer.decode(*__UpperCamelCase     ,							**__UpperCamelCase		)






     @property
     def  a_					(			self		)       ->    int:
          return ["input_ids", "bbox", "attention_mask", "image"]






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

