
'''simple docstring'''

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__ :			List[str]											=		logging.get_logger(__name__)







def 	A  (   UpperCamelCase_       :  int    ,						UpperCamelCase_       :  Optional[int]    ,						UpperCamelCase_       :  List[str]   )       ->							List[Any]:







			'''simple docstring'''



			return [
			    int(10_00 * (box[0] / width)   ),
			    int(10_00 * (box[1] / height)   ),
			    int(10_00 * (box[2] / width)   ),
			    int(10_00 * (box[3] / height)   ),
			]







def 	A  (   UpperCamelCase_       :  np.ndarray    ,						UpperCamelCase_       :  Optional[str]    ,						UpperCamelCase_       :  Optional[str] = None   )       ->							Union[str, Any]:







			'''simple docstring'''



			lowerCAmelCase__										=				tesseract_config if tesseract_config is not None else ""

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

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

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

			# finally, normalize the bounding boxes
			lowerCAmelCase__										=				[]
			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  A  (						SCREAMING_SNAKE_CASE__     ):
	snake_case__   :int   =  ['pixel_values']





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


				"""simple docstring"""

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

				lowerCAmelCase__										=				do_resize
				lowerCAmelCase__										=				size
				lowerCAmelCase__										=				resample
				lowerCAmelCase__										=				apply_ocr
				lowerCAmelCase__										=				ocr_lang
				lowerCAmelCase__										=				tesseract_config





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


				"""simple docstring"""

				lowerCAmelCase__										=				get_size_dict(__magic_name__				)
				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__										=				(size["height"], size["width"])
				return resize(__magic_name__   ,				size=__magic_name__   ,				resample=__magic_name__   ,				data_format=__magic_name__   ,				**__magic_name__				)







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


				"""simple docstring"""

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

				lowerCAmelCase__										=				make_list_of_images(__magic_name__				)

				if not valid_images(__magic_name__				):
							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__										=				[to_numpy_array(__magic_name__				) for image in images]

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

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

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

				lowerCAmelCase__										=				BatchFeature(data={"pixel_values": images}   ,				tensor_type=__magic_name__				)

				if apply_ocr:
							lowerCAmelCase__										=				words_batch
							lowerCAmelCase__										=				boxes_batch
				return data







import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
						import torch
						from torch import nn

						from transformers import (
						    Adafactor,
						    AdamW,
						    get_constant_schedule,
						    get_constant_schedule_with_warmup,
						    get_cosine_schedule_with_warmup,
						    get_cosine_with_hard_restarts_schedule_with_warmup,
						    get_inverse_sqrt_schedule,
						    get_linear_schedule_with_warmup,
						    get_polynomial_decay_schedule_with_warmup,
						)

def  __snake_case							(	__magic_name__		,    __magic_name__=10						):







				'''simple docstring'''


				lowercase         =      []
				for _ in range(__magic_name__						):
								lrs.append(scheduler.get_lr()[0]						)
								scheduler.step()
				return lrs

def  __snake_case							(	__magic_name__		,    __magic_name__=10						):







				'''simple docstring'''


				lowercase         =      []
				for step in range(__magic_name__						):
								lrs.append(scheduler.get_lr()[0]						)
								scheduler.step()
								if step == num_steps // 2:
												with tempfile.TemporaryDirectory() as tmpdirname:
																lowercase         =      os.path.join(__magic_name__		,    "schedule.bin"						)
																torch.save(scheduler.state_dict()		,    __magic_name__						)

																lowercase         =      torch.load(__magic_name__						)
																scheduler.load_state_dict(__magic_name__						)
				return lrs





@require_torch
class   UpperCamelCase_      (      unittest.TestCase		):







						'''simple docstring'''



						def        SCREAMING_SNAKE_CASE(			self							:Optional[Any]		,    lowerCAmelCase__							:List[str]		,    lowerCAmelCase__							:Tuple		,    lowerCAmelCase__							:Dict     )  ->Tuple:
										self.assertEqual(len(lowerCAmelCase__     )		,    len(lowerCAmelCase__     )     )
										for a, b in zip(lowerCAmelCase__		,    lowerCAmelCase__     ):
														self.assertAlmostEqual(lowerCAmelCase__		,    lowerCAmelCase__		,    delta=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Dict:
										lowercase         =      torch.tensor([0.1, -0.2, -0.1]		,    requires_grad=lowerCAmelCase__     )
										lowercase         =      torch.tensor([0.4, 0.2, -0.5]     )
										lowercase         =      nn.MSELoss()
										# No warmup, constant schedule, no gradient clipping
										lowercase         =      AdamW(params=[w]		,    lr=2E-1		,    weight_decay=0.0     )
										for _ in range(100     ):
														lowercase         =      criterion(lowerCAmelCase__		,    lowerCAmelCase__     )
														loss.backward()
														optimizer.step()
														w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
														w.grad.zero_()
										self.assertListAlmostEqual(w.tolist()		,    [0.4, 0.2, -0.5]		,    tol=1E-2     )





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->List[Any]:
										lowercase         =      torch.tensor([0.1, -0.2, -0.1]		,    requires_grad=lowerCAmelCase__     )
										lowercase         =      torch.tensor([0.4, 0.2, -0.5]     )
										lowercase         =      nn.MSELoss()
										# No warmup, constant schedule, no gradient clipping
										lowercase         =      Adafactor(
										    params=[w]		,    lr=1E-2		,    eps=(1E-3_0, 1E-3)		,    clip_threshold=1.0		,    decay_rate=-0.8		,    betaa=lowerCAmelCase__		,    weight_decay=0.0		,    relative_step=lowerCAmelCase__		,    scale_parameter=lowerCAmelCase__		,    warmup_init=lowerCAmelCase__		,    )
										for _ in range(1000     ):
														lowercase         =      criterion(lowerCAmelCase__		,    lowerCAmelCase__     )
														loss.backward()
														optimizer.step()
														w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
														w.grad.zero_()
										self.assertListAlmostEqual(w.tolist()		,    [0.4, 0.2, -0.5]		,    tol=1E-2     )





@require_torch
class   UpperCamelCase_      (      unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase      :  Any				   =					nn.Linear(50		,    50		) if is_torch_available() else None
						UpperCamelCase      :  Union[str, Any]				   =					AdamW(m.parameters()		,    lr=10.0		) if is_torch_available() else None
						UpperCamelCase      :  int				   =					10





						def        SCREAMING_SNAKE_CASE(			self							:Any		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:Optional[Any]		,    lowerCAmelCase__							:Any		,    lowerCAmelCase__							:Any=None     )  ->Dict:
										self.assertEqual(len(lowerCAmelCase__     )		,    len(lowerCAmelCase__     )     )
										for a, b in zip(lowerCAmelCase__		,    lowerCAmelCase__     ):
														self.assertAlmostEqual(lowerCAmelCase__		,    lowerCAmelCase__		,    delta=lowerCAmelCase__		,    msg=lowerCAmelCase__     )





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->str:
										lowercase         =      {"num_warmup_steps": 2, "num_training_steps": 10}
										# schedulers doct format
										# function: (sched_args_dict, expected_learning_rates)
										lowercase         =      {
										    get_constant_schedule: ({}, [10.0] * self.num_steps),
										    get_constant_schedule_with_warmup: (
										        {"num_warmup_steps": 4},
										        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
										    ),
										    get_linear_schedule_with_warmup: (
										        {**common_kwargs},
										        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
										    ),
										    get_cosine_schedule_with_warmup: (
										        {**common_kwargs},
										        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
										    ),
										    get_cosine_with_hard_restarts_schedule_with_warmup: (
										        {**common_kwargs, "num_cycles": 2},
										        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
										    ),
										    get_polynomial_decay_schedule_with_warmup: (
										        {**common_kwargs, "power": 2.0, "lr_end": 1E-7},
										        [0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
										    ),
										    get_inverse_sqrt_schedule: (
										        {"num_warmup_steps": 2},
										        [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
										    ),
										}

										for scheduler_func, data in scheds.items():
														lowercase     ,	lowercase         =      data

														lowercase         =      scheduler_func(self.optimizer		,    **lowerCAmelCase__     )
														self.assertEqual(len([scheduler.get_lr()[0]]     )		,    1     )
														lowercase         =      unwrap_schedule(lowerCAmelCase__		,    self.num_steps     )
														self.assertListAlmostEqual(
														    lowerCAmelCase__		,    lowerCAmelCase__		,    tol=1E-2		,    msg=F'''failed for {scheduler_func} in normal scheduler'''		,    )

														lowercase         =      scheduler_func(self.optimizer		,    **lowerCAmelCase__     )
														if scheduler_func.__name__ != "get_constant_schedule":
																		LambdaScheduleWrapper.wrap_scheduler(lowerCAmelCase__     )  # wrap to test picklability of the schedule
														lowercase         =      unwrap_and_save_reload_schedule(lowerCAmelCase__		,    self.num_steps     )
														self.assertListEqual(lowerCAmelCase__		,    lowerCAmelCase__		,    msg=F'''failed for {scheduler_func} in save and reload'''     )





class   UpperCamelCase_      :







						'''simple docstring'''



						def __init__(			self							:List[str]		,    lowerCAmelCase__							:int     )  ->Optional[int]:
										lowercase         =      fn





						def __call__(			self							:Optional[Any]		,    *lowerCAmelCase__							:int		,    **lowerCAmelCase__							:List[str]     )  ->List[str]:
										return self.fn(*lowerCAmelCase__		,    **lowerCAmelCase__     )





						@classmethod
						def        SCREAMING_SNAKE_CASE(			self							:List[Any]		,    lowerCAmelCase__							:Optional[Any]     )  ->str:
										lowercase         =      list(map(self		,    scheduler.lr_lambdas     )     )
