

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest

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







       __SCREAMING_SNAKE_CASE				    =							(PNDMScheduler,)
       __SCREAMING_SNAKE_CASE				    =							(("num_inference_steps", 50),)


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






              """simple docstring"""


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

              config.update(**_UpperCamelCase   )
              return config


       def 			a		(		self      , _UpperCamelCase=0      , **_UpperCamelCase   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							dict(self.forward_default_kwargs   )
              __snake_case       	=							kwargs.pop("""num_inference_steps"""      , _UpperCamelCase   )
              __snake_case       	=							self.dummy_sample
              __snake_case       	=							0.1 * sample
              __snake_case       	=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

              for scheduler_class in self.scheduler_classes:
                     __snake_case       	=							self.get_scheduler_config(**_UpperCamelCase   )
                     __snake_case       	=							scheduler_class(**_UpperCamelCase   )
                     scheduler.set_timesteps(_UpperCamelCase   )
                     # copy over dummy past residuals
                     __snake_case       	=							dummy_past_residuals[:]

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

                     __snake_case       	=							scheduler.step_prk(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , **_UpperCamelCase   ).prev_sample
                     __snake_case       	=							new_scheduler.step_prk(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , **_UpperCamelCase   ).prev_sample

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

                     __snake_case       	=							scheduler.step_plms(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , **_UpperCamelCase   ).prev_sample
                     __snake_case       	=							new_scheduler.step_plms(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , **_UpperCamelCase   ).prev_sample

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


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






              """simple docstring"""


              pass


       def 			a		(		self      , _UpperCamelCase=0      , **_UpperCamelCase   )      ->							List[str]:






              """simple docstring"""


              __snake_case       	=							dict(self.forward_default_kwargs   )
              __snake_case       	=							kwargs.pop("""num_inference_steps"""      , _UpperCamelCase   )
              __snake_case       	=							self.dummy_sample
              __snake_case       	=							0.1 * sample
              __snake_case       	=							[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

              for scheduler_class in self.scheduler_classes:
                     __snake_case       	=							self.get_scheduler_config()
                     __snake_case       	=							scheduler_class(**_UpperCamelCase   )
                     scheduler.set_timesteps(_UpperCamelCase   )

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

                     with tempfile.TemporaryDirectory() as tmpdirname:
                            scheduler.save_config(_UpperCamelCase   )
                            __snake_case       	=							scheduler_class.from_pretrained(_UpperCamelCase   )
                            # copy over dummy past residuals
                            new_scheduler.set_timesteps(_UpperCamelCase   )

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

                     __snake_case       	=							scheduler.step_prk(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , **_UpperCamelCase   ).prev_sample
                     __snake_case       	=							new_scheduler.step_prk(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , **_UpperCamelCase   ).prev_sample

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

                     __snake_case       	=							scheduler.step_plms(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , **_UpperCamelCase   ).prev_sample
                     __snake_case       	=							new_scheduler.step_plms(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , **_UpperCamelCase   ).prev_sample

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


       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       	=							10
              __snake_case       	=							self.dummy_model()
              __snake_case       	=							self.dummy_sample_deter
              scheduler.set_timesteps(_UpperCamelCase   )

              for i, t in enumerate(scheduler.prk_timesteps   ):
                     __snake_case       	=							model(_UpperCamelCase      , _UpperCamelCase   )
                     __snake_case       	=							scheduler.step_prk(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   ).prev_sample

              for i, t in enumerate(scheduler.plms_timesteps   ):
                     __snake_case       	=							model(_UpperCamelCase      , _UpperCamelCase   )
                     __snake_case       	=							scheduler.step_plms(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   ).prev_sample

              return sample


       def 			a		(		self   )      ->							Any:






              """simple docstring"""


              __snake_case       	=							dict(self.forward_default_kwargs   )

              __snake_case       	=							kwargs.pop("""num_inference_steps"""      , _UpperCamelCase   )

              for scheduler_class in self.scheduler_classes:
                     __snake_case       	=							self.get_scheduler_config()
                     __snake_case       	=							scheduler_class(**_UpperCamelCase   )

                     __snake_case       	=							self.dummy_sample
                     __snake_case       	=							0.1 * sample

                     if num_inference_steps is not None and hasattr(_UpperCamelCase      , """set_timesteps"""   ):
                            scheduler.set_timesteps(_UpperCamelCase   )
                     elif num_inference_steps is not None and not hasattr(_UpperCamelCase      , """set_timesteps"""   ):
                            __snake_case       	=							num_inference_steps

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

                     __snake_case       	=							scheduler.step_prk(_UpperCamelCase      , 0      , _UpperCamelCase      , **_UpperCamelCase   ).prev_sample
                     __snake_case       	=							scheduler.step_prk(_UpperCamelCase      , 1      , _UpperCamelCase      , **_UpperCamelCase   ).prev_sample

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

                     __snake_case       	=							scheduler.step_plms(_UpperCamelCase      , 0      , _UpperCamelCase      , **_UpperCamelCase   ).prev_sample
                     __snake_case       	=							scheduler.step_plms(_UpperCamelCase      , 1      , _UpperCamelCase      , **_UpperCamelCase   ).prev_sample

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


       def 			a		(		self   )      ->							int:






              """simple docstring"""


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


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






              """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(10   )
              assert torch.equal(
                  scheduler.timesteps      , torch.LongTensor(
                      [9_01, 8_51, 8_51, 8_01, 8_01, 7_51, 7_51, 7_01, 7_01, 6_51, 6_51, 6_01, 6_01, 5_01, 4_01, 3_01, 2_01, 1_01, 1]   )      , )


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






              """simple docstring"""


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


       def 			a		(		self   )      ->							str:






              """simple docstring"""


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


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






              """simple docstring"""


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


       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


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


       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


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


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






              """simple docstring"""


              __snake_case       	=							27

              for scheduler_class in self.scheduler_classes:
                     __snake_case       	=							self.dummy_sample
                     __snake_case       	=							0.1 * sample

                     __snake_case       	=							self.get_scheduler_config()
                     __snake_case       	=							scheduler_class(**_UpperCamelCase   )

                     scheduler.set_timesteps(_UpperCamelCase   )

                     # 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]   ):
                            __snake_case       	=							scheduler.step_prk(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase   ).prev_sample


       def 			a		(		self   )      ->							Tuple:






              """simple docstring"""


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

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


       def 			a		(		self   )      ->							List[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() - 198.1318   ) < 1E-2
              assert abs(result_mean.item() - 0.2580   ) < 1E-3


       def 			a		(		self   )      ->							Dict:






              """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() - 67.3986   ) < 1E-2
              assert abs(result_mean.item() - 0.0878   ) < 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() - 230.0399   ) < 1E-2
              assert abs(result_mean.item() - 0.2995   ) < 1E-3






       def 			a		(		self   )      ->							int:






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

