

'''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__			(				_UpperCAmelCase      ):
    _A			    =							["image_processor", "tokenizer"]
    _A			    =							"LayoutLMv2ImageProcessor"
    _A			    =							("LayoutLMv2Tokenizer", "LayoutLMv2TokenizerFast")




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






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			str     =       None
          if "feature_extractor" in kwargs:
                warnings.warn(
                    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                    " instead."				,     lowercase__				,     )
                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       kwargs.pop("feature_extractor"			)

          SCREAMING_SNAKE_CASE_      :			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__(lowercase__				,     lowercase__			)




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






          """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
          SCREAMING_SNAKE_CASE_      :			int     =       self.image_processor(images=lowercase__				,     return_tensors=lowercase__			)

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

          SCREAMING_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=lowercase__				,     add_special_tokens=lowercase__				,     padding=lowercase__				,     truncation=lowercase__				,     max_length=lowercase__				,     stride=lowercase__				,     pad_to_multiple_of=lowercase__				,     return_token_type_ids=lowercase__				,     return_attention_mask=lowercase__				,     return_overflowing_tokens=lowercase__				,     return_special_tokens_mask=lowercase__				,     return_offsets_mapping=lowercase__				,     return_length=lowercase__				,     verbose=lowercase__				,     return_tensors=lowercase__				,     **lowercase__				,     )

          # add pixel values
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       features.pop("pixel_values"			)
          if return_overflowing_tokens is True:
                SCREAMING_SNAKE_CASE_      :			List[str]     =       self.get_overflowing_images(lowercase__				,     encoded_inputs["overflow_to_sample_mapping"]			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       images

          return encoded_inputs




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__			):






          """simple docstring"""






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

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

          return images_with_overflow




    def   __lowerCamelCase      (					self				,     *lowercase__				,     **lowercase__			):






          """simple docstring"""






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




    def   __lowerCamelCase      (					self				,     *lowercase__				,     **lowercase__			):






          """simple docstring"""






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




    @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."				,     lowercase__				,     )
          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."				,     lowercase__				,     )
          return self.image_processor




'''simple docstring'''






import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							(HeunDiscreteScheduler,)
    _A			    =							10




    def   __lowerCamelCase      (					self				,     **lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			List[Any]     =       {
              "num_train_timesteps": 1100,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
          }

          config.update(**lowercase__			)
          return config




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]				,     [0.0002, 0.002, 0.02]			):
                self.check_over_configs(beta_start=lowercase__				,     beta_end=lowercase__			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          for schedule in ["linear", "scaled_linear", "exp"]:
                self.check_over_configs(beta_schedule=lowercase__			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          for clip_sample_range in [1.0, 2.0, 3.0]:
                self.check_over_configs(clip_sample_range=lowercase__				,     clip_sample=lowercase__			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Tuple     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_      :			List[str]     =       scheduler_class(**lowercase__			)

          scheduler.set_timesteps(self.num_inference_steps			)

          SCREAMING_SNAKE_CASE_      :			int     =       self.dummy_model()
          SCREAMING_SNAKE_CASE_      :			Dict     =       self.dummy_sample_deter * scheduler.init_noise_sigma
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       sample.to(lowercase__			)

          for i, t in enumerate(scheduler.timesteps			):
                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Dict     =       model(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			str     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__			)
                SCREAMING_SNAKE_CASE_      :			Dict     =       output.prev_sample

          SCREAMING_SNAKE_CASE_      :			List[Any]     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			List[str]     =       torch.mean(torch.abs(lowercase__			)			)

          if torch_device in ["cpu", "mps"]:
                assert abs(result_sum.item() - 0.1233			) < 1e-2
                assert abs(result_mean.item() - 0.0002			) < 1e-3
          else:
                # CUDA
                assert abs(result_sum.item() - 0.1233			) < 1e-2
                assert abs(result_mean.item() - 0.0002			) < 1e-3




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.get_scheduler_config(prediction_type="v_prediction"			)
          SCREAMING_SNAKE_CASE_      :			Any     =       scheduler_class(**lowercase__			)

          scheduler.set_timesteps(self.num_inference_steps			)

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

          for i, t in enumerate(scheduler.timesteps			):
                SCREAMING_SNAKE_CASE_      :			Tuple     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			int     =       model(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__			)
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       output.prev_sample

          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			int     =       torch.mean(torch.abs(lowercase__			)			)

          if torch_device in ["cpu", "mps"]:
                assert abs(result_sum.item() - 4.69_34e-07			) < 1e-2
                assert abs(result_mean.item() - 6.11_12e-10			) < 1e-3
          else:
                # CUDA
                assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72e-07			) < 1e-2
                assert abs(result_mean.item() - 0.0002			) < 1e-3




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       scheduler_class(**lowercase__			)

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

          SCREAMING_SNAKE_CASE_      :			Any     =       self.dummy_model()
          SCREAMING_SNAKE_CASE_      :			str     =       self.dummy_sample_deter.to(lowercase__			) * scheduler.init_noise_sigma

          for t in scheduler.timesteps:
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Dict     =       model(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__			)
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       output.prev_sample

          SCREAMING_SNAKE_CASE_      :			str     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       torch.mean(torch.abs(lowercase__			)			)

          if str(lowercase__			).startswith("cpu"			):
                # The following sum varies between 148 and 156 on mps. Why?
                assert abs(result_sum.item() - 0.1233			) < 1e-2
                assert abs(result_mean.item() - 0.0002			) < 1e-3
          elif str(lowercase__			).startswith("mps"			):
                # Larger tolerance on mps
                assert abs(result_mean.item() - 0.0002			) < 1e-2
          else:
                # CUDA
                assert abs(result_sum.item() - 0.1233			) < 1e-2
                assert abs(result_mean.item() - 0.0002			) < 1e-3







    def   __lowerCamelCase      (					self			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :			int     =       self.get_scheduler_config()
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       scheduler_class(**lowercase__				,     use_karras_sigmas=lowercase__			)

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

          SCREAMING_SNAKE_CASE_      :			List[str]     =       self.dummy_model()
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       self.dummy_sample_deter.to(lowercase__			) * scheduler.init_noise_sigma
          SCREAMING_SNAKE_CASE_      :			Any     =       sample.to(lowercase__			)

          for t in scheduler.timesteps:
                SCREAMING_SNAKE_CASE_      :			Optional[int]     =       scheduler.scale_model_input(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			List[str]     =       model(lowercase__				,     lowercase__			)

                SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       scheduler.step(lowercase__				,     lowercase__				,     lowercase__			)
                SCREAMING_SNAKE_CASE_      :			Tuple     =       output.prev_sample

          SCREAMING_SNAKE_CASE_      :			Any     =       torch.sum(torch.abs(lowercase__			)			)
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       torch.mean(torch.abs(lowercase__			)			)

          assert abs(result_sum.item() - 0.00015			) < 1e-2
          assert abs(result_mean.item() - 1.98_69_55_45_35_03_46_95e-07			) < 1e-2

