






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							:						Union[str, Any]										=['image_processor', 'tokenizer']
       a							:						List[str]										='LayoutLMv3ImageProcessor'
       a							:						Optional[int]										=('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')





       def __init__(   self    ,     _lowerCamelCase=None    ,     _lowerCamelCase=None    ,     **_lowerCamelCase				):
         UpperCamelCase_:   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.'    ,     lowercase_    ,     )
           UpperCamelCase_:   Any        =							kwargs.pop('feature_extractor'				)

         UpperCamelCase_:   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__(lowercase_    ,     lowercase_				)





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




         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.'				)

         # first, apply the image processor
         UpperCamelCase_:   str        =							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_				):
             UpperCamelCase_:   Union[str, Any]        =							[text]  # add batch dimension (as the image processor always adds a batch dimension)
           UpperCamelCase_:   List[str]        =							features['words']

         UpperCamelCase_:   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
         UpperCamelCase_:   Optional[int]        =							features.pop('pixel_values'				)
         if return_overflowing_tokens is True:
           UpperCamelCase_:   Optional[Any]        =							self.get_overflowing_images(lowercase_    ,     encoded_inputs['overflow_to_sample_mapping']				)
         UpperCamelCase_:   Union[str, Any]        =							images

         return encoded_inputs





       def      _a       (   self    ,     _lowerCamelCase    ,     _lowerCamelCase				):
         UpperCamelCase_:   List[str]        =							[]
         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      _a       (   self    ,     *_lowerCamelCase    ,     **_lowerCamelCase				):
         return self.tokenizer.batch_decode(*lowercase_    ,     **lowercase_				)





       def      _a       (   self    ,     *_lowerCamelCase    ,     **_lowerCamelCase				):
         return self.tokenizer.decode(*lowercase_    ,     **lowercase_				)





       @property
       def      _a       (   self				):
         return ["input_ids", "bbox", "attention_mask", "pixel_values"]





       @property
       def      _a       (   self				):
         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      _a       (   self				):
         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,     lowercase_    ,     )
         return self.image_processor



import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class A    (  UpperCamelCase_	):
      UpperCamelCase__				:    List[str] =(PNDMScheduler,)
      UpperCamelCase__				:    Dict =(('num_inference_steps', 50),)






      def 			lowerCamelCase			(   self		:	Dict     ,  **lowercase_		:	Dict					)   ->		str:
             """simple docstring"""




             _lowerCamelCase			:						List[Any]            ={
                 'num_train_timesteps': 1000,
                 'beta_start': 0.0001,
                 'beta_end': 0.02,
                 'beta_schedule': 'linear',
             }

             config.update(**lowercase_					)
             return config






      def 			lowerCamelCase			(   self		:	Any     ,  lowercase_		:	str=0     ,  **lowercase_		:	str					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[Any]            =dict(self.forward_default_kwargs					)
             _lowerCamelCase			:						Optional[Any]            =kwargs.pop('num_inference_steps'     ,  lowercase_					)
             _lowerCamelCase			:						Tuple            =self.dummy_sample
             _lowerCamelCase			:						int            =0.1 * sample
             _lowerCamelCase			:						int            =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase			:						str            =self.get_scheduler_config(**lowercase_					)
                    _lowerCamelCase			:						str            =scheduler_class(**lowercase_					)
                    scheduler.set_timesteps(lowercase_					)
                    # copy over dummy past residuals
                    _lowerCamelCase			:						Any            =dummy_past_residuals[:]

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(lowercase_					)
                           _lowerCamelCase			:						Optional[Any]            =scheduler_class.from_pretrained(lowercase_					)
                           new_scheduler.set_timesteps(lowercase_					)
                           # copy over dummy past residuals
                           _lowerCamelCase			:						Any            =dummy_past_residuals[:]

                    _lowerCamelCase			:						Union[str, Any]            =scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						List[str]            =new_scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample

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

                    _lowerCamelCase			:						Optional[int]            =scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						Optional[Any]            =new_scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample

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






      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             pass






      def 			lowerCamelCase			(   self		:	Union[str, Any]     ,  lowercase_		:	List[str]=0     ,  **lowercase_		:	int					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						Any            =dict(self.forward_default_kwargs					)
             _lowerCamelCase			:						Dict            =kwargs.pop('num_inference_steps'     ,  lowercase_					)
             _lowerCamelCase			:						Union[str, Any]            =self.dummy_sample
             _lowerCamelCase			:						Optional[int]            =0.1 * sample
             _lowerCamelCase			:						List[str]            =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase			:						Any            =self.get_scheduler_config()
                    _lowerCamelCase			:						int            =scheduler_class(**lowercase_					)
                    scheduler.set_timesteps(lowercase_					)

                    # copy over dummy past residuals (must be after setting timesteps)
                    _lowerCamelCase			:						Any            =dummy_past_residuals[:]

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           scheduler.save_config(lowercase_					)
                           _lowerCamelCase			:						Optional[Any]            =scheduler_class.from_pretrained(lowercase_					)
                           # copy over dummy past residuals
                           new_scheduler.set_timesteps(lowercase_					)

                           # copy over dummy past residual (must be after setting timesteps)
                           _lowerCamelCase			:						Optional[int]            =dummy_past_residuals[:]

                    _lowerCamelCase			:						List[str]            =scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						str            =new_scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample

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

                    _lowerCamelCase			:						Optional[Any]            =scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						List[str]            =new_scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_     ,  **lowercase_					).prev_sample

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






      def 			lowerCamelCase			(   self		:	Optional[Any]     ,  **lowercase_		:	Any					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =self.scheduler_classes[0]
             _lowerCamelCase			:						Optional[Any]            =self.get_scheduler_config(**lowercase_					)
             _lowerCamelCase			:						Any            =scheduler_class(**lowercase_					)

             _lowerCamelCase			:						Union[str, Any]            =10
             _lowerCamelCase			:						str            =self.dummy_model()
             _lowerCamelCase			:						Union[str, Any]            =self.dummy_sample_deter
             scheduler.set_timesteps(lowercase_					)

             for i, t in enumerate(scheduler.prk_timesteps					):
                    _lowerCamelCase			:						Union[str, Any]            =model(lowercase_     ,  lowercase_					)
                    _lowerCamelCase			:						Any            =scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_					).prev_sample

             for i, t in enumerate(scheduler.plms_timesteps					):
                    _lowerCamelCase			:						List[Any]            =model(lowercase_     ,  lowercase_					)
                    _lowerCamelCase			:						int            =scheduler.step_plms(lowercase_     ,  lowercase_     ,  lowercase_					).prev_sample

             return sample






      def 			lowerCamelCase			(   self		:	Dict					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Any            =dict(self.forward_default_kwargs					)

             _lowerCamelCase			:						Optional[int]            =kwargs.pop('num_inference_steps'     ,  lowercase_					)

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase			:						List[str]            =self.get_scheduler_config()
                    _lowerCamelCase			:						List[Any]            =scheduler_class(**lowercase_					)

                    _lowerCamelCase			:						Union[str, Any]            =self.dummy_sample
                    _lowerCamelCase			:						Optional[int]            =0.1 * sample

                    if num_inference_steps is not None and hasattr(lowercase_     ,  'set_timesteps'					):
                           scheduler.set_timesteps(lowercase_					)
                    elif num_inference_steps is not None and not hasattr(lowercase_     ,  'set_timesteps'					):
                           _lowerCamelCase			:						Tuple            =num_inference_steps

                    # copy over dummy past residuals (must be done after set_timesteps)
                    _lowerCamelCase			:						List[Any]            =[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
                    _lowerCamelCase			:						Union[str, Any]            =dummy_past_residuals[:]

                    _lowerCamelCase			:						List[Any]            =scheduler.step_prk(lowercase_     ,  0     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						str            =scheduler.step_prk(lowercase_     ,  1     ,  lowercase_     ,  **lowercase_					).prev_sample

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

                    _lowerCamelCase			:						Tuple            =scheduler.step_plms(lowercase_     ,  0     ,  lowercase_     ,  **lowercase_					).prev_sample
                    _lowerCamelCase			:						Optional[Any]            =scheduler.step_plms(lowercase_     ,  1     ,  lowercase_     ,  **lowercase_					).prev_sample

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






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Any:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Optional[int]					)   ->		Optional[Any]:
             """simple docstring"""




             for steps_offset in [0, 1]:
                    self.check_over_configs(steps_offset=lowercase_					)

             _lowerCamelCase			:						Optional[int]            =self.scheduler_classes[0]
             _lowerCamelCase			:						Dict            =self.get_scheduler_config(steps_offset=1					)
             _lowerCamelCase			:						List[Any]            =scheduler_class(**lowercase_					)
             scheduler.set_timesteps(10					)
             assert torch.equal(
                 scheduler.timesteps     ,  torch.LongTensor(
                     [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1]					)     ,  )






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		str:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Union[str, Any]:
             """simple docstring"""




             for schedule in ["linear", "squaredcos_cap_v2"]:
                    self.check_over_configs(beta_schedule=lowercase_					)






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Any:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Union[str, Any]:
             """simple docstring"""




             for t in [1, 5, 10]:
                    self.check_over_forward(time_step=lowercase_					)






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		List[Any]:
             """simple docstring"""




             for t, num_inference_steps in zip([1, 5, 10]     ,  [10, 50, 100]					):
                    self.check_over_forward(num_inference_steps=lowercase_					)






      def 			lowerCamelCase			(   self		:	int					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Optional[int]            =27

             for scheduler_class in self.scheduler_classes:
                    _lowerCamelCase			:						Dict            =self.dummy_sample
                    _lowerCamelCase			:						List[Any]            =0.1 * sample

                    _lowerCamelCase			:						List[Any]            =self.get_scheduler_config()
                    _lowerCamelCase			:						List[str]            =scheduler_class(**lowercase_					)

                    scheduler.set_timesteps(lowercase_					)

                    # before power of 3 fix, would error on first step, so we only need to do two
                    for i, t in enumerate(scheduler.prk_timesteps[:2]					):
                           _lowerCamelCase			:						Tuple            =scheduler.step_prk(lowercase_     ,  lowercase_     ,  lowercase_					).prev_sample






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		Optional[int]:
             """simple docstring"""




             with self.assertRaises(lowercase_					):
                    _lowerCamelCase			:						Dict            =self.scheduler_classes[0]
                    _lowerCamelCase			:						Optional[Any]            =self.get_scheduler_config()
                    _lowerCamelCase			:						List[Any]            =scheduler_class(**lowercase_					)

                    scheduler.step_plms(self.dummy_sample     ,  1     ,  self.dummy_sample					).prev_sample






      def 			lowerCamelCase			(   self		:	List[str]					)   ->		List[str]:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =self.full_loop()
             _lowerCamelCase			:						Optional[Any]            =torch.sum(torch.abs(lowercase_					)					)
             _lowerCamelCase			:						Union[str, Any]            =torch.mean(torch.abs(lowercase_					)					)

             assert abs(result_sum.item() - 198.1318					) < 1E-2
             assert abs(result_mean.item() - 0.2580					) < 1E-3






      def 			lowerCamelCase			(   self		:	Any					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						int            =self.full_loop(prediction_type='v_prediction'					)
             _lowerCamelCase			:						Dict            =torch.sum(torch.abs(lowercase_					)					)
             _lowerCamelCase			:						str            =torch.mean(torch.abs(lowercase_					)					)

             assert abs(result_sum.item() - 67.3986					) < 1E-2
             assert abs(result_mean.item() - 0.0878					) < 1E-3






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase			:						Tuple            =self.full_loop(set_alpha_to_one=lowercase_     ,  beta_start=0.01					)
             _lowerCamelCase			:						Optional[Any]            =torch.sum(torch.abs(lowercase_					)					)
             _lowerCamelCase			:						Union[str, Any]            =torch.mean(torch.abs(lowercase_					)					)

             assert abs(result_sum.item() - 230.0399					) < 1E-2
             assert abs(result_mean.item() - 0.2995					) < 1E-3






      def 			lowerCamelCase			(   self		:	Optional[Any]					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						str            =self.full_loop(set_alpha_to_one=lowercase_     ,  beta_start=0.01					)
             _lowerCamelCase			:						Union[str, Any]            =torch.sum(torch.abs(lowercase_					)					)
             _lowerCamelCase			:						str            =torch.mean(torch.abs(lowercase_					)					)

             assert abs(result_sum.item() - 186.9482					) < 1E-2
             assert abs(result_mean.item() - 0.2434					) < 1E-3


