

import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							(DDIMParallelScheduler,)
       __SCREAMING_SNAKE_CASE				    =							(("eta", 0.0), ("num_inference_steps", 50))


       def 			a		(		self      , **_UpperCamelCase   )      ->							Optional[Any]:






              """simple docstring"""


              __snake_case       	=							{
                  """num_train_timesteps""": 10_00,
                  """beta_start""": 0.0001,
                  """beta_end""": 0.02,
                  """beta_schedule""": """linear""",
                  """clip_sample""": True,
              }

              config.update(**_UpperCamelCase   )
              return config


       def 			a		(		self      , **_UpperCamelCase   )      ->							Optional[Any]:






              """simple docstring"""


              __snake_case       	=							self.scheduler_classes[0]
              __snake_case       	=							self.get_scheduler_config(**_UpperCamelCase   )
              __snake_case       	=							scheduler_class(**_UpperCamelCase   )

              __snake_case	,    __snake_case       	=							10, 0.0

              __snake_case       	=							self.dummy_model()
              __snake_case       	=							self.dummy_sample_deter

              scheduler.set_timesteps(_UpperCamelCase   )

              for t in scheduler.timesteps:
                     __snake_case       	=							model(_UpperCamelCase      , _UpperCamelCase   )
                     __snake_case       	=							scheduler.step(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   ).prev_sample

              return sample


       def 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


              for timesteps in [1_00, 5_00, 10_00]:
                     self.check_over_configs(num_train_timesteps=_UpperCamelCase   )


       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


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

              __snake_case       	=							self.scheduler_classes[0]
              __snake_case       	=							self.get_scheduler_config(steps_offset=1   )
              __snake_case       	=							scheduler_class(**_UpperCamelCase   )
              scheduler.set_timesteps(5   )
              assert torch.equal(scheduler.timesteps      , torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]   )   )


       def 			a		(		self   )      ->							Optional[Any]:






              """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=_UpperCamelCase      , beta_end=_UpperCamelCase   )


       def 			a		(		self   )      ->							Any:






              """simple docstring"""


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


       def 			a		(		self   )      ->							Any:






              """simple docstring"""


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


       def 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


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


       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              for timestep_spacing in ["trailing", "leading"]:
                     self.check_over_configs(timestep_spacing=_UpperCamelCase   )


       def 			a		(		self   )      ->							str:






              """simple docstring"""


              for rescale_betas_zero_snr in [True, False]:
                     self.check_over_configs(rescale_betas_zero_snr=_UpperCamelCase   )


       def 			a		(		self   )      ->							Optional[Any]:






              """simple docstring"""


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


       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              for t in [1, 10, 49]:
                     self.check_over_forward(time_step=_UpperCamelCase   )


       def 			a		(		self   )      ->							List[str]:






              """simple docstring"""


              for t, num_inference_steps in zip([1, 10, 50]      , [10, 50, 5_00]   ):
                     self.check_over_forward(time_step=_UpperCamelCase      , num_inference_steps=_UpperCamelCase   )


       def 			a		(		self   )      ->							int:






              """simple docstring"""


              for t, eta in zip([1, 10, 49]      , [0.0, 0.5, 1.0]   ):
                     self.check_over_forward(time_step=_UpperCamelCase      , eta=_UpperCamelCase   )


       def 			a		(		self   )      ->							str:






              """simple docstring"""


              __snake_case       	=							self.scheduler_classes[0]
              __snake_case       	=							self.get_scheduler_config()
              __snake_case       	=							scheduler_class(**_UpperCamelCase   )

              assert torch.sum(torch.abs(scheduler._get_variance(0      , 0   ) - 0.0   )   ) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(4_20      , 4_00   ) - 0.1_4771   )   ) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(9_80      , 9_60   ) - 0.3_2460   )   ) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(0      , 0   ) - 0.0   )   ) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(4_87      , 4_86   ) - 0.0_0979   )   ) < 1E-5
              assert torch.sum(torch.abs(scheduler._get_variance(9_99      , 9_98   ) - 0.02   )   ) < 1E-5


       def 			a		(		self   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							self.scheduler_classes[0]
              __snake_case       	=							self.get_scheduler_config()
              __snake_case       	=							scheduler_class(**_UpperCamelCase   )

              __snake_case	,    __snake_case       	=							10, 0.0
              scheduler.set_timesteps(_UpperCamelCase   )

              __snake_case       	=							self.dummy_model()
              __snake_case       	=							self.dummy_sample_deter
              __snake_case       	=							self.dummy_sample_deter + 0.1
              __snake_case       	=							self.dummy_sample_deter - 0.1

              __snake_case       	=							samplea.shape[0]
              __snake_case       	=							torch.stack([samplea, samplea, samplea]      , dim=0   )
              __snake_case       	=							torch.arange(_UpperCamelCase   )[0:3, None].repeat(1      , _UpperCamelCase   )

              __snake_case       	=							model(samples.flatten(0      , 1   )      , timesteps.flatten(0      , 1   )   )
              __snake_case       	=							scheduler.batch_step_no_noise(_UpperCamelCase      , timesteps.flatten(0      , 1   )      , samples.flatten(0      , 1   )      , _UpperCamelCase   )

              __snake_case       	=							torch.sum(torch.abs(_UpperCamelCase   )   )
              __snake_case       	=							torch.mean(torch.abs(_UpperCamelCase   )   )

              assert abs(result_sum.item() - 1147.7904   ) < 1E-2
              assert abs(result_mean.item() - 0.4982   ) < 1E-3


       def 			a		(		self   )      ->							Union[str, Any]:






              """simple docstring"""


              __snake_case       	=							self.full_loop()

              __snake_case       	=							torch.sum(torch.abs(_UpperCamelCase   )   )
              __snake_case       	=							torch.mean(torch.abs(_UpperCamelCase   )   )

              assert abs(result_sum.item() - 172.0067   ) < 1E-2
              assert abs(result_mean.item() - 0.22_3967   ) < 1E-3


       def 			a		(		self   )      ->							Optional[int]:






              """simple docstring"""


              __snake_case       	=							self.full_loop(prediction_type="""v_prediction"""   )

              __snake_case       	=							torch.sum(torch.abs(_UpperCamelCase   )   )
              __snake_case       	=							torch.mean(torch.abs(_UpperCamelCase   )   )

              assert abs(result_sum.item() - 52.5302   ) < 1E-2
              assert abs(result_mean.item() - 0.0684   ) < 1E-3


       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


              __snake_case       	=							self.full_loop(set_alpha_to_one=_UpperCamelCase      , beta_start=0.01   )
              __snake_case       	=							torch.sum(torch.abs(_UpperCamelCase   )   )
              __snake_case       	=							torch.mean(torch.abs(_UpperCamelCase   )   )

              assert abs(result_sum.item() - 149.8295   ) < 1E-2
              assert abs(result_mean.item() - 0.1951   ) < 1E-3






       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							self.full_loop(set_alpha_to_one=_UpperCamelCase      , beta_start=0.01   )
              __snake_case       	=							torch.sum(torch.abs(_UpperCamelCase   )   )
              __snake_case       	=							torch.mean(torch.abs(_UpperCamelCase   )   )

              assert abs(result_sum.item() - 149.0784   ) < 1E-2
              assert abs(result_mean.item() - 0.1941   ) < 1E-3




from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
    import PIL

# soft dependency
if is_pytesseract_available():
    import pytesseract

UpperCamelCase__		 =     logging.get_logger(__name__)







def 						lowerCamelCase__      ( __A    :Optional[int]	,__A    :Any	,__A    :str		):


       """simple docstring"""




       return [
           int(1_0_0_0 * (box[0] / width)		),
           int(1_0_0_0 * (box[1] / height)		),
           int(1_0_0_0 * (box[2] / width)		),
           int(1_0_0_0 * (box[3] / height)		),
       ]







def 						lowerCamelCase__      ( __A    :np.ndarray	,__A    :Optional[str]	,__A    :Optional[str]		):


       """simple docstring"""




       __snake_case       	=							to_pil_image(__A		)
       __snake_case	,    __snake_case       	=							pil_image.size
       __snake_case       	=							pytesseract.image_to_data(__A	,lang=__A	,output_type="""dict"""	,config=__A		)
       __snake_case	,    __snake_case	,    __snake_case	,    __snake_case	,    __snake_case       	=							data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

       # filter empty words and corresponding coordinates
       __snake_case       	=							[idx for idx, word in enumerate(__A		) if not word.strip()]
       __snake_case       	=							[word for idx, word in enumerate(__A		) if idx not in irrelevant_indices]
       __snake_case       	=							[coord for idx, coord in enumerate(__A		) if idx not in irrelevant_indices]
       __snake_case       	=							[coord for idx, coord in enumerate(__A		) if idx not in irrelevant_indices]
       __snake_case       	=							[coord for idx, coord in enumerate(__A		) if idx not in irrelevant_indices]
       __snake_case       	=							[coord for idx, coord in enumerate(__A		) if idx not in irrelevant_indices]

       # turn coordinates into (left, top, left+width, top+height) format
       __snake_case       	=							[]
       for x, y, w, h in zip(__A	,__A	,__A	,__A		):
              __snake_case       	=							[x, y, x + w, y + h]
              actual_boxes.append(__A		)

       # finally, normalize the bounding boxes
       __snake_case       	=							[]
       for box in actual_boxes:
              normalized_boxes.append(normalize_box(__A	,__A	,__A		)		)

       assert len(__A		) == len(__A		), "Not as many words as there are bounding boxes"

       return words, normalized_boxes




class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							["pixel_values"]


       def __init__(		self      , _UpperCamelCase = True      , _UpperCamelCase = None      , _UpperCamelCase = PILImageResampling.BILINEAR      , _UpperCamelCase = True      , _UpperCamelCase = 1 / 2_55      , _UpperCamelCase = True      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = True      , _UpperCamelCase = None      , _UpperCamelCase = ""      , **_UpperCamelCase      , )      ->							None:






              """simple docstring"""


              super().__init__(**_UpperCamelCase   )
              __snake_case       	=							size if size is not None else {"""height""": 2_24, """width""": 2_24}
              __snake_case       	=							get_size_dict(_UpperCamelCase   )

              __snake_case       	=							do_resize
              __snake_case       	=							size
              __snake_case       	=							resample
              __snake_case       	=							do_rescale
              __snake_case       	=							rescale_value
              __snake_case       	=							do_normalize
              __snake_case       	=							image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
              __snake_case       	=							image_std if image_std is not None else IMAGENET_STANDARD_STD
              __snake_case       	=							apply_ocr
              __snake_case       	=							ocr_lang
              __snake_case       	=							tesseract_config


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase = PILImageResampling.BILINEAR      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							np.ndarray:






              """simple docstring"""


              __snake_case       	=							get_size_dict(_UpperCamelCase   )
              if "height" not in size or "width" not in size:
                     raise ValueError(F'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'   )
              __snake_case       	=							(size["""height"""], size["""width"""])
              return resize(_UpperCamelCase      , size=_UpperCamelCase      , resample=_UpperCamelCase      , data_format=_UpperCamelCase      , **_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							np.ndarray:






              """simple docstring"""


              return rescale(_UpperCamelCase      , scale=_UpperCamelCase      , data_format=_UpperCamelCase      , **_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							np.ndarray:






              """simple docstring"""


              return normalize(_UpperCamelCase      , mean=_UpperCamelCase      , std=_UpperCamelCase      , data_format=_UpperCamelCase      , **_UpperCamelCase   )






       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase=None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = None      , _UpperCamelCase = ChannelDimension.FIRST      , **_UpperCamelCase      , )      ->							PIL.Image.Image:






              """simple docstring"""


              __snake_case       	=							do_resize if do_resize is not None else self.do_resize
              __snake_case       	=							size if size is not None else self.size
              __snake_case       	=							get_size_dict(_UpperCamelCase   )
              __snake_case       	=							resample if resample is not None else self.resample
              __snake_case       	=							do_rescale if do_rescale is not None else self.do_rescale
              __snake_case       	=							rescale_factor if rescale_factor is not None else self.rescale_factor
              __snake_case       	=							do_normalize if do_normalize is not None else self.do_normalize
              __snake_case       	=							image_mean if image_mean is not None else self.image_mean
              __snake_case       	=							image_std if image_std is not None else self.image_std
              __snake_case       	=							apply_ocr if apply_ocr is not None else self.apply_ocr
              __snake_case       	=							ocr_lang if ocr_lang is not None else self.ocr_lang
              __snake_case       	=							tesseract_config if tesseract_config is not None else self.tesseract_config

              __snake_case       	=							make_list_of_images(_UpperCamelCase   )

              if not valid_images(_UpperCamelCase   ):
                     raise ValueError(
                         """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                         """torch.Tensor, tf.Tensor or jax.ndarray."""   )

              if do_resize and size is None:
                     raise ValueError("""Size must be specified if do_resize is True."""   )

              if do_rescale and rescale_factor is None:
                     raise ValueError("""Rescale factor must be specified if do_rescale is True."""   )

              if do_normalize and (image_mean is None or image_std is None):
                     raise ValueError("""If do_normalize is True, image_mean and image_std must be specified."""   )

              # All transformations expect numpy arrays.
              __snake_case       	=							[to_numpy_array(_UpperCamelCase   ) for image in images]

              # Tesseract OCR to get words + normalized bounding boxes
              if apply_ocr:
                     requires_backends(self      , """pytesseract"""   )
                     __snake_case       	=							[]
                     __snake_case       	=							[]
                     for image in images:
                            __snake_case	,    __snake_case       	=							apply_tesseract(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   )
                            words_batch.append(_UpperCamelCase   )
                            boxes_batch.append(_UpperCamelCase   )

              if do_resize:
                     __snake_case       	=							[self.resize(image=_UpperCamelCase      , size=_UpperCamelCase      , resample=_UpperCamelCase   ) for image in images]

              if do_rescale:
                     __snake_case       	=							[self.rescale(image=_UpperCamelCase      , scale=_UpperCamelCase   ) for image in images]

              if do_normalize:
                     __snake_case       	=							[self.normalize(image=_UpperCamelCase      , mean=_UpperCamelCase      , std=_UpperCamelCase   ) for image in images]

              __snake_case       	=							[to_channel_dimension_format(_UpperCamelCase      , _UpperCamelCase   ) for image in images]

              __snake_case       	=							BatchFeature(data={"""pixel_values""": images}      , tensor_type=_UpperCamelCase   )

              if apply_ocr:
                     __snake_case       	=							words_batch
                     __snake_case       	=							boxes_batch
              return data

