


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 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    ["image_processor", "tokenizer"]
     __magic_name__    			=    "LayoutLMv3ImageProcessor"
     __magic_name__    			=    ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")







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

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       None
            if "feature_extractor" in kwargs:
                   warnings.warn(
                       'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                       ' instead.'      ,	snake_case__      ,	)
                   _lowerCAmelCase    :							int   			=       kwargs.pop('feature_extractor'   )

            _lowerCAmelCase    :							Tuple   			=       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__(snake_case__      ,	snake_case__   )







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

            '''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.'   )

            # first, apply the image processor
            _lowerCAmelCase    :							Optional[Any]   			=       self.image_processor(images=snake_case__      ,	return_tensors=snake_case__   )

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

            _lowerCAmelCase    :							List[str]   			=       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=snake_case__      ,	add_special_tokens=snake_case__      ,	padding=snake_case__      ,	truncation=snake_case__      ,	max_length=snake_case__      ,	stride=snake_case__      ,	pad_to_multiple_of=snake_case__      ,	return_token_type_ids=snake_case__      ,	return_attention_mask=snake_case__      ,	return_overflowing_tokens=snake_case__      ,	return_special_tokens_mask=snake_case__      ,	return_offsets_mapping=snake_case__      ,	return_length=snake_case__      ,	verbose=snake_case__      ,	return_tensors=snake_case__      ,	**snake_case__      ,	)

            # add pixel values
            _lowerCAmelCase    :							Dict   			=       features.pop('pixel_values'   )
            if return_overflowing_tokens is True:
                   _lowerCAmelCase    :							Any   			=       self.get_overflowing_images(snake_case__      ,	encoded_inputs['overflow_to_sample_mapping']   )
            _lowerCAmelCase    :							Any   			=       images

            return encoded_inputs







     def a	( self      ,	snake_case__      ,	snake_case__   ):

            '''simple docstring'''

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

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

            return images_with_overflow







     def a	( self      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

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







     def a	( self      ,	*snake_case__      ,	**snake_case__   ):

            '''simple docstring'''

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







     @property
     def a	( self   ):

            '''simple docstring'''

            return ["input_ids", "bbox", "attention_mask", "pixel_values"]







     @property
     def a	( self   ):

            '''simple docstring'''

            warnings.warn(
                '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'      ,	snake_case__      ,	)
            return self.image_processor_class







     @property
     def a	( self   ):

            '''simple docstring'''

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





'''simple docstring'''







import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






     __magic_name__    			=    (DDPMScheduler,)







     def a	( self      ,	**snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       {
                'num_train_timesteps': 1000,
                'beta_start': 0.0001,
                'beta_end': 0.02,
                'beta_schedule': 'linear',
                'variance_type': 'fixed_small',
                'clip_sample': True,
            }

            config.update(**snake_case__   )
            return config







     def a	( self   ):

            '''simple docstring'''

            for timesteps in [1, 5, 100, 1000]:
                   self.check_over_configs(num_train_timesteps=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]      ,	[0.002, 0.02, 0.2, 2]   ):
                   self.check_over_configs(beta_start=snake_case__      ,	beta_end=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            for variance in ["fixed_small", "fixed_large", "other"]:
                   self.check_over_configs(variance_type=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            for clip_sample in [True, False]:
                   self.check_over_configs(clip_sample=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            self.check_over_configs(thresholding=snake_case__   )
            for threshold in [0.5, 1.0, 2.0]:
                   for prediction_type in ["epsilon", "sample", "v_prediction"]:
                          self.check_over_configs(
                              thresholding=snake_case__      ,	prediction_type=snake_case__      ,	sample_max_value=snake_case__      ,	)







     def a	( self   ):

            '''simple docstring'''

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







     def a	( self   ):

            '''simple docstring'''

            for t in [0, 500, 999]:
                   self.check_over_forward(time_step=snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[Any]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

            assert torch.sum(torch.abs(scheduler._get_variance(0   ) - 0.0   )   ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(487   ) - 0.0_0979   )   ) < 1E-5
            assert torch.sum(torch.abs(scheduler._get_variance(999   ) - 0.02   )   ) < 1E-5







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[Any]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       len(snake_case__   )

            _lowerCAmelCase    :							str   			=       self.dummy_model()
            _lowerCAmelCase    :							str   			=       self.dummy_sample_deter
            _lowerCAmelCase    :							Union[str, Any]   			=       torch.manual_seed(0   )

            for t in reversed(range(snake_case__   )   ):
                   # 1. predict noise residual
                   _lowerCAmelCase    :							List[Any]   			=       model(snake_case__      ,	snake_case__   )

                   # 2. predict previous mean of sample x_t-1
                   _lowerCAmelCase    :							Any   			=       scheduler.step(snake_case__      ,	snake_case__      ,	snake_case__      ,	generator=snake_case__   ).prev_sample

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

            _lowerCAmelCase    :							Dict   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							List[str]   			=       torch.mean(torch.abs(snake_case__   )   )

            assert abs(result_sum.item() - 258.9606   ) < 1E-2
            assert abs(result_mean.item() - 0.3372   ) < 1E-3







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Any   			=       self.get_scheduler_config(prediction_type='v_prediction'   )
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       len(snake_case__   )

            _lowerCAmelCase    :							Any   			=       self.dummy_model()
            _lowerCAmelCase    :							Tuple   			=       self.dummy_sample_deter
            _lowerCAmelCase    :							Optional[int]   			=       torch.manual_seed(0   )

            for t in reversed(range(snake_case__   )   ):
                   # 1. predict noise residual
                   _lowerCAmelCase    :							Union[str, Any]   			=       model(snake_case__      ,	snake_case__   )

                   # 2. predict previous mean of sample x_t-1
                   _lowerCAmelCase    :							Dict   			=       scheduler.step(snake_case__      ,	snake_case__      ,	snake_case__      ,	generator=snake_case__   ).prev_sample

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

            _lowerCAmelCase    :							Any   			=       torch.sum(torch.abs(snake_case__   )   )
            _lowerCAmelCase    :							Dict   			=       torch.mean(torch.abs(snake_case__   )   )

            assert abs(result_sum.item() - 202.0296   ) < 1E-2
            assert abs(result_mean.item() - 0.2631   ) < 1E-3







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Optional[int]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Union[str, Any]   			=       [100, 87, 50, 1, 0]

            scheduler.set_timesteps(timesteps=snake_case__   )

            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler.timesteps

            for i, timestep in enumerate(snake_case__   ):
                   if i == len(snake_case__   ) - 1:
                          _lowerCAmelCase    :							str   			=       -1
                   else:
                          _lowerCAmelCase    :							Optional[Any]   			=       timesteps[i + 1]

                   _lowerCAmelCase    :							int   			=       scheduler.previous_timestep(snake_case__   )
                   _lowerCAmelCase    :							int   			=       prev_t.item()

                   self.assertEqual(snake_case__      ,	snake_case__   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							Tuple   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							List[Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       [100, 87, 50, 51, 0]

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							List[str]   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Union[str, Any]   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Optional[int]   			=       [100, 87, 50, 1, 0]
            _lowerCAmelCase    :							int   			=       len(snake_case__   )

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







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[int]   			=       self.scheduler_classes[0]
            _lowerCAmelCase    :							int   			=       self.get_scheduler_config()
            _lowerCAmelCase    :							Any   			=       scheduler_class(**snake_case__   )

            _lowerCAmelCase    :							Any   			=       [scheduler.config.num_train_timesteps]

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

