
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 		_UpperCAmelCase (						UpperCamelCase:  int      ,				UpperCamelCase:  List[Any]=1_0	):






	"""simple docstring"""


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






def 		_UpperCAmelCase (						UpperCamelCase:  List[str]      ,				UpperCamelCase:  Optional[Any]=1_0	):






	"""simple docstring"""


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

				__lowerCAmelCase    =				torch.load(UpperCamelCase_	)
				scheduler.load_state_dict(UpperCamelCase_	)
	return lrs

@require_torch
class    a		( unittest.TestCase       ):







							def    UpperCAmelCase__       (    self	:	Any   ,					snake_case__	:	List[str]   ,					snake_case__	:	Optional[Any]   ,					snake_case__	:	Union[str, Any]			):
								"""simple docstring"""







								self.assertEqual(len(snake_case__			)   ,					len(snake_case__			)			)
								for a, b in zip(snake_case__   ,					snake_case__			):
									self.assertAlmostEqual(snake_case__   ,					snake_case__   ,					delta=snake_case__			)







							def    UpperCAmelCase__       (    self	:	Union[str, Any]			):
								"""simple docstring"""







								__lowerCAmelCase    =				torch.tensor([0.1, -0.2, -0.1]   ,					requires_grad=snake_case__			)
								__lowerCAmelCase    =				torch.tensor([0.4, 0.2, -0.5]			)
								__lowerCAmelCase    =				nn.MSELoss()
								# No warmup, constant schedule, no gradient clipping
								__lowerCAmelCase    =				AdamW(params=[w]   ,					lr=2E-1   ,					weight_decay=0.0			)
								for _ in range(100			):
									__lowerCAmelCase    =				criterion(snake_case__   ,					snake_case__			)
									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    UpperCAmelCase__       (    self	:	str			):
								"""simple docstring"""







								__lowerCAmelCase    =				torch.tensor([0.1, -0.2, -0.1]   ,					requires_grad=snake_case__			)
								__lowerCAmelCase    =				torch.tensor([0.4, 0.2, -0.5]			)
								__lowerCAmelCase    =				nn.MSELoss()
								# No warmup, constant schedule, no gradient clipping
								__lowerCAmelCase    =				Adafactor(
								    params=[w]   ,					lr=1E-2   ,					eps=(1E-30, 1E-3)   ,					clip_threshold=1.0   ,					decay_rate=-0.8   ,					betaa=snake_case__   ,					weight_decay=0.0   ,					relative_step=snake_case__   ,					scale_parameter=snake_case__   ,					warmup_init=snake_case__   ,					)
								for _ in range(1_000			):
									__lowerCAmelCase    =				criterion(snake_case__   ,					snake_case__			)
									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    a		( unittest.TestCase       ):
							lowercase_		:    Union[str, Any] 				=	nn.Linear(50   ,						50       ) if is_torch_available() else None
							lowercase_		:    Optional[Any] 				=	AdamW(m.parameters()   ,						lr=10.0       ) if is_torch_available() else None
							lowercase_		:    Dict 				=	10







							def    UpperCAmelCase__       (    self	:	int   ,					snake_case__	:	Any   ,					snake_case__	:	List[str]   ,					snake_case__	:	int   ,					snake_case__	:	Optional[int]=None			):
								"""simple docstring"""







								self.assertEqual(len(snake_case__			)   ,					len(snake_case__			)			)
								for a, b in zip(snake_case__   ,					snake_case__			):
									self.assertAlmostEqual(snake_case__   ,					snake_case__   ,					delta=snake_case__   ,					msg=snake_case__			)







							def    UpperCAmelCase__       (    self	:	Any			):
								"""simple docstring"""







								__lowerCAmelCase    =				{"num_warmup_steps": 2, "num_training_steps": 10}
								# schedulers doct format
								# function: (sched_args_dict, expected_learning_rates)
								__lowerCAmelCase    =				{
								    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, 8.7_5, 7.5, 6.2_5, 5.0, 3.7_5, 2.5, 1.2_5],
								    ),
								    get_cosine_schedule_with_warmup: (
								        {**common_kwargs},
								        [0.0, 5.0, 10.0, 9.6_1, 8.5_3, 6.9_1, 5.0, 3.0_8, 1.4_6, 0.3_8],
								    ),
								    get_cosine_with_hard_restarts_schedule_with_warmup: (
								        {**common_kwargs, "num_cycles": 2},
								        [0.0, 5.0, 10.0, 8.5_3, 5.0, 1.4_6, 10.0, 8.5_3, 5.0, 1.4_6],
								    ),
								    get_polynomial_decay_schedule_with_warmup: (
								        {**common_kwargs, "power": 2.0, "lr_end": 1E-7},
								        [0.0, 5.0, 10.0, 7.6_5_6, 5.6_2_5, 3.9_0_6, 2.5, 1.4_0_6, 0.6_2_5, 0.1_5_6],
								    ),
								    get_inverse_sqrt_schedule: (
								        {"num_warmup_steps": 2},
								        [0.0, 5.0, 10.0, 8.1_6_5, 7.0_7_1, 6.3_2_5, 5.7_7_4, 5.3_4_5, 5.0, 4.7_1_4],
								    ),
								}

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

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

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

class    a		:







							def __init__(    self	:	Union[str, Any]   ,					snake_case__	:	Optional[Any]			):
								"""simple docstring"""







								__lowerCAmelCase    =				fn







							def __call__(    self	:	Any   ,					*snake_case__	:	int   ,					**snake_case__	:	int			):
								"""simple docstring"""







								return self.fn(*snake_case__   ,					**snake_case__			)







							@classmethod
							def    UpperCAmelCase__       (    self	:	Dict   ,					snake_case__	:	Tuple			):
								"""simple docstring"""







								__lowerCAmelCase    =				list(map(self   ,					scheduler.lr_lambdas			)			)










'''simple docstring'''
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

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







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							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       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		to_pil_image(UpperCamelCase_ )
							A_    ,					A_			    =		pil_image.size
							A_			    =		pytesseract.image_to_data(UpperCamelCase_						,  lang=UpperCamelCase_						,  output_type="dict"						,  config=UpperCamelCase_ )
							A_    ,					A_    ,					A_    ,					A_    ,					A_			    =		data["text"], data["left"], data["top"], data["width"], data["height"]

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

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

							# finally, normalize the bounding boxes
							A_			    =		[]
							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 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Any   						=["pixel_values"]






						def __init__(  self							:      List[Any] ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BILINEAR ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      float = 1 / 2_55 ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      bool = True ,  lowerCAmelCase							:      Optional[str] = None ,  lowerCAmelCase							:      Optional[str] = "" ,  **lowerCAmelCase							:      int ,  ):
													super().__init__(**lowerCAmelCase	)
													A_			    =		size if size is not None else {"height": 2_24, "width": 2_24}
													A_			    =		get_size_dict(lowerCAmelCase	)

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






						def 				_UpperCAmelCase       (  self							:      List[str] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Dict[str, int] ,  lowerCAmelCase							:      PILImageResampling = PILImageResampling.BILINEAR ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      List[str] ,  ):
													A_			    =		get_size_dict(lowerCAmelCase	)
													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()}"	)
													A_			    =		(size["height"], size["width"])
													return resize(lowerCAmelCase ,  size=lowerCAmelCase ,  resample=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      List[Any] ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[int, float] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      Tuple ,  ):
													return rescale(lowerCAmelCase ,  scale=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      int ,  lowerCAmelCase							:      np.ndarray ,  lowerCAmelCase							:      Union[float, Iterable[float]] ,  lowerCAmelCase							:      Union[float, Iterable[float]] ,  lowerCAmelCase							:      Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase							:      int ,  ):
													return normalize(lowerCAmelCase ,  mean=lowerCAmelCase ,  std=lowerCAmelCase ,  data_format=lowerCAmelCase ,  **lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      ImageInput ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      Dict[str, int] = None ,  lowerCAmelCase							:      Any=None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      float = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      Union[float, Iterable[float]] = None ,  lowerCAmelCase							:      bool = None ,  lowerCAmelCase							:      Optional[str] = None ,  lowerCAmelCase							:      Optional[str] = None ,  lowerCAmelCase							:      Optional[Union[str, TensorType]] = None ,  lowerCAmelCase							:      ChannelDimension = ChannelDimension.FIRST ,  **lowerCAmelCase							:      int ,  ):
													A_			    =		do_resize if do_resize is not None else self.do_resize
													A_			    =		size if size is not None else self.size
													A_			    =		get_size_dict(lowerCAmelCase	)
													A_			    =		resample if resample is not None else self.resample
													A_			    =		do_rescale if do_rescale is not None else self.do_rescale
													A_			    =		rescale_factor if rescale_factor is not None else self.rescale_factor
													A_			    =		do_normalize if do_normalize is not None else self.do_normalize
													A_			    =		image_mean if image_mean is not None else self.image_mean
													A_			    =		image_std if image_std is not None else self.image_std
													A_			    =		apply_ocr if apply_ocr is not None else self.apply_ocr
													A_			    =		ocr_lang if ocr_lang is not None else self.ocr_lang
													A_			    =		tesseract_config if tesseract_config is not None else self.tesseract_config

													A_			    =		make_list_of_images(lowerCAmelCase	)

													if not valid_images(lowerCAmelCase	):
																				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.
													A_			    =		[to_numpy_array(lowerCAmelCase	) for image in images]

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

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

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

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

													A_			    =		[to_channel_dimension_format(lowerCAmelCase ,  lowerCAmelCase	) for image in images]

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

													if apply_ocr:
																				A_			    =		words_batch
																				A_			    =		boxes_batch
													return data





