import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest


class 						lowercase    (     _a     ):
							lowercase__						:       Any								=							(CMStochasticIterativeScheduler,)
							lowercase__						:       int								=							10




							def 	__snake_case(						self   :					Dict	,							**_UpperCamelCase   :					str      )					->				int:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {
												    "num_train_timesteps": 201,
												    "sigma_min": 0.0_0_2,
												    "sigma_max": 8_0.0,
												}

												config.update(**_A      )
												return config




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       10

												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config()
												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0](**_A      )

												scheduler.set_timesteps(_A      )

												SCREAMING_SNAKE_CASE    			=       scheduler.timesteps[0]
												SCREAMING_SNAKE_CASE    			=       scheduler.timesteps[1]

												SCREAMING_SNAKE_CASE    			=       self.dummy_sample
												SCREAMING_SNAKE_CASE    			=       0.1 * sample

												SCREAMING_SNAKE_CASE    			=       scheduler.step(_A	,							_A	,							_A      ).prev_sample
												SCREAMING_SNAKE_CASE    			=       scheduler.step(_A	,							_A	,							_A      ).prev_sample

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




							def 	__snake_case(						self   :					Tuple      )					->				Tuple:



												'''simple docstring'''

												for timesteps in [10, 50, 100, 1_000]:
																	self.check_over_configs(num_train_timesteps=_A      )




							def 	__snake_case(						self   :					Dict      )					->				int:



												'''simple docstring'''

												for clip_denoised in [True, False]:
																	self.check_over_configs(clip_denoised=_A      )




							def 	__snake_case(						self   :					Optional[int]      )					->				Optional[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config()
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_A      )

												SCREAMING_SNAKE_CASE    			=       1
												scheduler.set_timesteps(_A      )
												SCREAMING_SNAKE_CASE    			=       scheduler.timesteps

												SCREAMING_SNAKE_CASE    			=       torch.manual_seed(0      )

												SCREAMING_SNAKE_CASE    			=       self.dummy_model()
												SCREAMING_SNAKE_CASE    			=       self.dummy_sample_deter * scheduler.init_noise_sigma

												for i, t in enumerate(_A      ):
																	# 1. scale model input
																	SCREAMING_SNAKE_CASE    			=       scheduler.scale_model_input(_A	,							_A      )

																	# 2. predict noise residual
																	SCREAMING_SNAKE_CASE    			=       model(_A	,							_A      )

																	# 3. predict previous sample x_t-1
																	SCREAMING_SNAKE_CASE    			=       scheduler.step(_A	,							_A	,							_A	,							generator=_A      ).prev_sample

																	SCREAMING_SNAKE_CASE    			=       pred_prev_sample

												SCREAMING_SNAKE_CASE    			=       torch.sum(torch.abs(_A      )      )
												SCREAMING_SNAKE_CASE    			=       torch.mean(torch.abs(_A      )      )

												assert abs(result_sum.item() - 1_9_2.7_6_1_4      ) < 1e-2
												assert abs(result_mean.item() - 0.2_5_1_0      ) < 1e-3




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config()
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_A      )

												SCREAMING_SNAKE_CASE    			=       [106, 0]
												scheduler.set_timesteps(timesteps=_A      )
												SCREAMING_SNAKE_CASE    			=       scheduler.timesteps

												SCREAMING_SNAKE_CASE    			=       torch.manual_seed(0      )

												SCREAMING_SNAKE_CASE    			=       self.dummy_model()
												SCREAMING_SNAKE_CASE    			=       self.dummy_sample_deter * scheduler.init_noise_sigma

												for t in timesteps:
																	# 1. scale model input
																	SCREAMING_SNAKE_CASE    			=       scheduler.scale_model_input(_A	,							_A      )

																	# 2. predict noise residual
																	SCREAMING_SNAKE_CASE    			=       model(_A	,							_A      )

																	# 3. predict previous sample x_t-1
																	SCREAMING_SNAKE_CASE    			=       scheduler.step(_A	,							_A	,							_A	,							generator=_A      ).prev_sample

																	SCREAMING_SNAKE_CASE    			=       pred_prev_sample

												SCREAMING_SNAKE_CASE    			=       torch.sum(torch.abs(_A      )      )
												SCREAMING_SNAKE_CASE    			=       torch.mean(torch.abs(_A      )      )

												assert abs(result_sum.item() - 3_4_7.6_3_5_7      ) < 1e-2
												assert abs(result_mean.item() - 0.4_5_2_7      ) < 1e-3




							def 	__snake_case(						self   :					List[Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config()
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_A      )

												SCREAMING_SNAKE_CASE    			=       [39, 30, 12, 15, 0]

												with self.assertRaises(_A	,							msg="`timesteps` must be in descending order."      ):
																	scheduler.set_timesteps(timesteps=_A      )




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



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config()
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_A      )

												SCREAMING_SNAKE_CASE    			=       [39, 30, 12, 1, 0]
												SCREAMING_SNAKE_CASE    			=       len(_A      )

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





							def 	__snake_case(						self   :					Any      )					->				List[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config()
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_A      )

												SCREAMING_SNAKE_CASE    			=       [scheduler.config.num_train_timesteps]

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

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

_lowerCamelCase					:   Union[str, Any]							=		logging.get_logger(__name__)





def 	__lowerCamelCase							(UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							List[Any]      , UpperCAmelCase__     :							Optional[Any]  ):
					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							(UpperCAmelCase__     :							np.ndarray      , UpperCAmelCase__     :							Optional[str]      , UpperCAmelCase__     :							Optional[str] = None  ):
					SCREAMING_SNAKE_CASE    			=       tesseract_config if tesseract_config is not None else ""

					# apply OCR
					SCREAMING_SNAKE_CASE    			=       to_pil_image(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       pil_image.size
					SCREAMING_SNAKE_CASE    			=       pytesseract.image_to_data(UpperCAmelCase__      , lang=UpperCAmelCase__      , output_type="dict"      , config=UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       data["text"], data["left"], data["top"], data["width"], data["height"]

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

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

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

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

					return words, normalized_boxes


class 						lowercase    (     a     ):
							lowercase__						:       Optional[int]								=							["""pixel_values"""]




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



												'''simple docstring'''

												super().__init__(**_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       size if size is not None else {"height": 224, "width": 224}
												SCREAMING_SNAKE_CASE    			=       get_size_dict(_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       do_resize
												SCREAMING_SNAKE_CASE    			=       size
												SCREAMING_SNAKE_CASE    			=       resample
												SCREAMING_SNAKE_CASE    			=       apply_ocr
												SCREAMING_SNAKE_CASE    			=       ocr_lang
												SCREAMING_SNAKE_CASE    			=       tesseract_config




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					np.ndarray	,							_UpperCamelCase   :					Dict[str, int]	,							_UpperCamelCase   :					PILImageResampling = PILImageResampling.BILINEAR	,							_UpperCamelCase   :					Optional[Union[str, ChannelDimension]] = None	,							**_UpperCamelCase   :					Any	,							)					->				np.ndarray:



												'''simple docstring'''

												SCREAMING_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()}"      )
												SCREAMING_SNAKE_CASE    			=       (size["height"], size["width"])
												return resize(_UpperCamelCase	,							size=_UpperCamelCase	,							resample=_UpperCamelCase	,							data_format=_UpperCamelCase	,							**_UpperCamelCase      )





							def 	__snake_case(						self   :					Tuple	,							_UpperCamelCase   :					ImageInput	,							_UpperCamelCase   :					bool = None	,							_UpperCamelCase   :					Dict[str, int] = None	,							_UpperCamelCase   :					PILImageResampling = None	,							_UpperCamelCase   :					bool = None	,							_UpperCamelCase   :					Optional[str] = None	,							_UpperCamelCase   :					Optional[str] = None	,							_UpperCamelCase   :					Optional[Union[str, TensorType]] = None	,							_UpperCamelCase   :					ChannelDimension = ChannelDimension.FIRST	,							**_UpperCamelCase   :					str	,							)					->				PIL.Image.Image:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       do_resize if do_resize is not None else self.do_resize
												SCREAMING_SNAKE_CASE    			=       size if size is not None else self.size
												SCREAMING_SNAKE_CASE    			=       get_size_dict(_UpperCamelCase      )
												SCREAMING_SNAKE_CASE    			=       resample if resample is not None else self.resample
												SCREAMING_SNAKE_CASE    			=       apply_ocr if apply_ocr is not None else self.apply_ocr
												SCREAMING_SNAKE_CASE    			=       ocr_lang if ocr_lang is not None else self.ocr_lang
												SCREAMING_SNAKE_CASE    			=       tesseract_config if tesseract_config is not None else self.tesseract_config

												SCREAMING_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."      )

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

												if apply_ocr:
																	requires_backends(self	,							"pytesseract"      )
																	SCREAMING_SNAKE_CASE    			=       []
																	SCREAMING_SNAKE_CASE    			=       []
																	for image in images:
																						SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       apply_tesseract(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																						words_batch.append(_UpperCamelCase      )
																						boxes_batch.append(_UpperCamelCase      )

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

												# flip color channels from RGB to BGR (as Detectron2 requires this)
												SCREAMING_SNAKE_CASE    			=       [flip_channel_order(_UpperCamelCase      ) for image in images]
												SCREAMING_SNAKE_CASE    			=       [to_channel_dimension_format(_UpperCamelCase	,							_UpperCamelCase      ) for image in images]

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

												if apply_ocr:
																	SCREAMING_SNAKE_CASE    			=       words_batch
																	SCREAMING_SNAKE_CASE    			=       boxes_batch
												return data




