

import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					int      =						(PNDMScheduler,)
 _lowercase     :					List[str]      =						(('''num_inference_steps''', 50),)

 def    __magic_name__       (		self :	str     ,						**A_ :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    {
          "num_train_timesteps": 1_0_0_0,
          "beta_start": 0.0001,
          "beta_end": 0.02,
          "beta_schedule": "linear",
      }

      config.update(**A_       )
      return config

 def    __magic_name__       (		self :	Dict     ,						A_ :	Union[str, Any]=0     ,						**A_ :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    dict(self.forward_default_kwargs       )
      _lowerCAmelCase   :					Union[str, Any]        =    kwargs.pop("num_inference_steps"     ,						A_       )
      _lowerCAmelCase   :					Union[str, Any]        =    self.dummy_sample
      _lowerCAmelCase   :					Optional[Any]        =    0.1 * sample
      _lowerCAmelCase   :					Union[str, Any]        =    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

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

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

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

           _lowerCAmelCase   :					Dict        =    scheduler.step_plms(A_     ,						A_     ,						A_     ,						**A_       ).prev_sample
           _lowerCAmelCase   :					int        =    new_scheduler.step_plms(A_     ,						A_     ,						A_     ,						**A_       ).prev_sample

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

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      pass

 def    __magic_name__       (		self :	str     ,						A_ :	Any=0     ,						**A_ :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    dict(self.forward_default_kwargs       )
      _lowerCAmelCase   :					Tuple        =    kwargs.pop("num_inference_steps"     ,						A_       )
      _lowerCAmelCase   :					int        =    self.dummy_sample
      _lowerCAmelCase   :					int        =    0.1 * sample
      _lowerCAmelCase   :					List[Any]        =    [residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

      for scheduler_class in self.scheduler_classes:
           _lowerCAmelCase   :					Dict        =    self.get_scheduler_config()
           _lowerCAmelCase   :					Optional[int]        =    scheduler_class(**A_       )
           scheduler.set_timesteps(A_       )

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

           with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(A_       )
                _lowerCAmelCase   :					List[str]        =    scheduler_class.from_pretrained(A_       )
                # copy over dummy past residuals
                new_scheduler.set_timesteps(A_       )

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

           _lowerCAmelCase   :					Optional[Any]        =    scheduler.step_prk(A_     ,						A_     ,						A_     ,						**A_       ).prev_sample
           _lowerCAmelCase   :					List[str]        =    new_scheduler.step_prk(A_     ,						A_     ,						A_     ,						**A_       ).prev_sample

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

           _lowerCAmelCase   :					Any        =    scheduler.step_plms(A_     ,						A_     ,						A_     ,						**A_       ).prev_sample
           _lowerCAmelCase   :					List[str]        =    new_scheduler.step_plms(A_     ,						A_     ,						A_     ,						**A_       ).prev_sample

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

 def    __magic_name__       (		self :	Optional[int]     ,						**A_ :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    self.scheduler_classes[0]
      _lowerCAmelCase   :					Tuple        =    self.get_scheduler_config(**A_       )
      _lowerCAmelCase   :					Any        =    scheduler_class(**A_       )

      _lowerCAmelCase   :					List[Any]        =    1_0
      _lowerCAmelCase   :					List[str]        =    self.dummy_model()
      _lowerCAmelCase   :					int        =    self.dummy_sample_deter
      scheduler.set_timesteps(A_       )

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

      for i, t in enumerate(scheduler.plms_timesteps       ):
           _lowerCAmelCase   :					Any        =    model(A_     ,						A_       )
           _lowerCAmelCase   :					Optional[Any]        =    scheduler.step_plms(A_     ,						A_     ,						A_       ).prev_sample

      return sample

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    dict(self.forward_default_kwargs       )

      _lowerCAmelCase   :					Optional[int]        =    kwargs.pop("num_inference_steps"     ,						A_       )

      for scheduler_class in self.scheduler_classes:
           _lowerCAmelCase   :					int        =    self.get_scheduler_config()
           _lowerCAmelCase   :					Tuple        =    scheduler_class(**A_       )

           _lowerCAmelCase   :					List[Any]        =    self.dummy_sample
           _lowerCAmelCase   :					Optional[int]        =    0.1 * sample

           if num_inference_steps is not None and hasattr(A_     ,						"set_timesteps"       ):
                scheduler.set_timesteps(A_       )
           elif num_inference_steps is not None and not hasattr(A_     ,						"set_timesteps"       ):
                _lowerCAmelCase   :					List[str]        =    num_inference_steps

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

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

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

           _lowerCAmelCase   :					Dict        =    scheduler.step_plms(A_     ,						0     ,						A_     ,						**A_       ).prev_sample
           _lowerCAmelCase   :					int        =    scheduler.step_plms(A_     ,						1     ,						A_     ,						**A_       ).prev_sample

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

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      for timesteps in [1_0_0, 1_0_0_0]:
           self.check_over_configs(num_train_timesteps=A_       )

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




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

      _lowerCAmelCase   :					Tuple        =    self.scheduler_classes[0]
      _lowerCAmelCase   :					Optional[Any]        =    self.get_scheduler_config(steps_offset=1       )
      _lowerCAmelCase   :					List[Any]        =    scheduler_class(**A_       )
      scheduler.set_timesteps(1_0       )
      assert torch.equal(
          scheduler.timesteps     ,						torch.LongTensor(
              [9_0_1, 8_5_1, 8_5_1, 8_0_1, 8_0_1, 7_5_1, 7_5_1, 7_0_1, 7_0_1, 6_5_1, 6_5_1, 6_0_1, 6_0_1, 5_0_1, 4_0_1, 3_0_1, 2_0_1, 1_0_1, 1]       )     ,						)

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




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

 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      for t in [1, 5, 1_0]:
           self.check_over_forward(time_step=A_       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      for t, num_inference_steps in zip([1, 5, 1_0]     ,						[1_0, 5_0, 1_0_0]       ):
           self.check_over_forward(num_inference_steps=A_       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    2_7

      for scheduler_class in self.scheduler_classes:
           _lowerCAmelCase   :					Dict        =    self.dummy_sample
           _lowerCAmelCase   :					int        =    0.1 * sample

           _lowerCAmelCase   :					Any        =    self.get_scheduler_config()
           _lowerCAmelCase   :					List[str]        =    scheduler_class(**A_       )

           scheduler.set_timesteps(A_       )

           # 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   :					List[Any]        =    scheduler.step_prk(A_     ,						A_     ,						A_       ).prev_sample

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      with self.assertRaises(A_       ):
           _lowerCAmelCase   :					Tuple        =    self.scheduler_classes[0]
           _lowerCAmelCase   :					Union[str, Any]        =    self.get_scheduler_config()
           _lowerCAmelCase   :					List[Any]        =    scheduler_class(**A_       )

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

 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.full_loop()
      _lowerCAmelCase   :					Dict        =    torch.sum(torch.abs(A_       )       )
      _lowerCAmelCase   :					int        =    torch.mean(torch.abs(A_       )       )

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

 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    self.full_loop(prediction_type="v_prediction"       )
      _lowerCAmelCase   :					int        =    torch.sum(torch.abs(A_       )       )
      _lowerCAmelCase   :					Tuple        =    torch.mean(torch.abs(A_       )       )

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

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    self.full_loop(set_alpha_to_one=A_     ,						beta_start=0.01       )
      _lowerCAmelCase   :					str        =    torch.sum(torch.abs(A_       )       )
      _lowerCAmelCase   :					Optional[Any]        =    torch.mean(torch.abs(A_       )       )

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


 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    self.full_loop(set_alpha_to_one=A_     ,						beta_start=0.01       )
      _lowerCAmelCase   :					str        =    torch.sum(torch.abs(A_       )       )
      _lowerCAmelCase   :					Dict        =    torch.mean(torch.abs(A_       )       )

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









from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    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 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Union[str, Any]:

     """simple docstring"""







     return [
         int(1000 * (box[0] / width)				),
         int(1000 * (box[1] / height)				),
         int(1000 * (box[2] / width)				),
         int(1000 * (box[3] / height)				),
     ]







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = None				)       ->      str:

     """simple docstring"""







     _lowerCAmelCase   :					str        =    tesseract_config if tesseract_config is not None else ""

     # apply OCR
     _lowerCAmelCase   :					List[str]        =    to_pil_image(SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase   :					List[str]        =    pil_image.size
     _lowerCAmelCase   :					Union[str, Any]        =    pytesseract.image_to_data(SCREAMING_SNAKE_CASE		,							lang=SCREAMING_SNAKE_CASE		,							output_type="dict"		,							config=SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[Any]        =    data["text"], data["left"], data["top"], data["width"], data["height"]

     # filter empty words and corresponding coordinates
     _lowerCAmelCase   :					Dict        =    [idx for idx, word in enumerate(SCREAMING_SNAKE_CASE				) if not word.strip()]
     _lowerCAmelCase   :					Any        =    [word for idx, word in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Dict        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[Any]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[Any]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]
     _lowerCAmelCase   :					Optional[int]        =    [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE				) if idx not in irrelevant_indices]

     # turn coordinates into (left, top, left+width, top+height) format
     _lowerCAmelCase   :					Union[str, Any]        =    []
     for x, y, w, h in zip(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Tuple        =    [x, y, x + w, y + h]
          actual_boxes.append(SCREAMING_SNAKE_CASE				)

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

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

     return words, normalized_boxes





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						['''pixel_values''']

 def __init__(		self :	Tuple     ,						A_ :	bool = True     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	bool = True     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[str] = ""     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      super().__init__(**A_       )
      _lowerCAmelCase   :					int        =    size if size is not None else {"height": 2_2_4, "width": 2_2_4}
      _lowerCAmelCase   :					Any        =    get_size_dict(A_       )

      _lowerCAmelCase   :					Any        =    do_resize
      _lowerCAmelCase   :					Any        =    size
      _lowerCAmelCase   :					Dict        =    resample
      _lowerCAmelCase   :					Optional[int]        =    apply_ocr
      _lowerCAmelCase   :					Dict        =    ocr_lang
      _lowerCAmelCase   :					Optional[int]        =    tesseract_config

 def    __magic_name__       (		self :	Dict     ,						A_ :	np.ndarray     ,						A_ :	Dict[str, int]     ,						A_ :	PILImageResampling = PILImageResampling.BILINEAR     ,						A_ :	Optional[Union[str, ChannelDimension]] = None     ,						**A_ :	List[str]     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    get_size_dict(A_       )
      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()}'''       )
      _lowerCAmelCase   :					Dict        =    (size["height"], size["width"])
      return resize(A_     ,						size=A_     ,						resample=A_     ,						data_format=A_     ,						**A_       )


 def    __magic_name__       (		self :	Dict     ,						A_ :	ImageInput     ,						A_ :	bool = None     ,						A_ :	Dict[str, int] = None     ,						A_ :	PILImageResampling = None     ,						A_ :	bool = None     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[Union[str, TensorType]] = None     ,						A_ :	ChannelDimension = ChannelDimension.FIRST     ,						**A_ :	Dict     ,						):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    do_resize if do_resize is not None else self.do_resize
      _lowerCAmelCase   :					str        =    size if size is not None else self.size
      _lowerCAmelCase   :					List[Any]        =    get_size_dict(A_       )
      _lowerCAmelCase   :					Tuple        =    resample if resample is not None else self.resample
      _lowerCAmelCase   :					Union[str, Any]        =    apply_ocr if apply_ocr is not None else self.apply_ocr
      _lowerCAmelCase   :					Tuple        =    ocr_lang if ocr_lang is not None else self.ocr_lang
      _lowerCAmelCase   :					str        =    tesseract_config if tesseract_config is not None else self.tesseract_config

      _lowerCAmelCase   :					Union[str, Any]        =    make_list_of_images(A_       )

      if not valid_images(A_       ):
           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."       )

      # All transformations expect numpy arrays.
      _lowerCAmelCase   :					Dict        =    [to_numpy_array(A_       ) for image in images]

      if apply_ocr:
           requires_backends(self     ,						"pytesseract"       )
           _lowerCAmelCase   :					Any        =    []
           _lowerCAmelCase   :					List[Any]        =    []
           for image in images:
                _lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[int]        =    apply_tesseract(A_     ,						A_     ,						A_       )
                words_batch.append(A_       )
                boxes_batch.append(A_       )

      if do_resize:
           _lowerCAmelCase   :					Union[str, Any]        =    [self.resize(image=A_     ,						size=A_     ,						resample=A_       ) for image in images]

      # flip color channels from RGB to BGR (as Detectron2 requires this)
      _lowerCAmelCase   :					Optional[int]        =    [flip_channel_order(A_       ) for image in images]
      _lowerCAmelCase   :					List[Any]        =    [to_channel_dimension_format(A_     ,						A_       ) for image in images]

      _lowerCAmelCase   :					Tuple        =    BatchFeature(data={"pixel_values": images}     ,						tensor_type=A_       )

      if apply_ocr:
           _lowerCAmelCase   :					Tuple        =    words_batch
           _lowerCAmelCase   :					Any        =    boxes_batch
      return data






