




'''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

a  :				Dict									=  logging.get_logger(__name__)


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[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 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					List[str]:

					'''simple docstring'''

					snake_case_									=				to_pil_image(SCREAMING_SNAKE_CASE_	)
					snake_case_			,snake_case_									=				pil_image.size
					snake_case_									=				pytesseract.image_to_data(SCREAMING_SNAKE_CASE_,      lang=SCREAMING_SNAKE_CASE_,      output_type='''dict''',      config=SCREAMING_SNAKE_CASE_	)
					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(SCREAMING_SNAKE_CASE_	) if not word.strip()]
					snake_case_									=				[word for idx, word in enumerate(SCREAMING_SNAKE_CASE_	) if idx not in irrelevant_indices]
					snake_case_									=				[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_	) if idx not in irrelevant_indices]
					snake_case_									=				[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_	) if idx not in irrelevant_indices]
					snake_case_									=				[coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_	) if idx not in irrelevant_indices]
					snake_case_									=				[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
					snake_case_									=				[]
					for x, y, w, h in zip(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	):
										snake_case_									=				[x, y, x + w, y + h]
										actual_boxes.append(SCREAMING_SNAKE_CASE_	)

					# finally, normalize the bounding boxes
					snake_case_									=				[]
					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				(   snake_case__     ):
				snake_case_      =		["""pixel_values"""]


				def __init__(		self    :					List[str]       ,				lowercase_    :					Dict = True       ,				lowercase_    :					Tuple = None       ,				lowercase_    :					Union[str, Any] = PILImageResampling.BILINEAR       ,				lowercase_    :					List[str] = True       ,				lowercase_    :					Dict = 1 / 255       ,				lowercase_    :					str = True       ,				lowercase_    :					List[Any] = None       ,				lowercase_    :					List[str] = None       ,				lowercase_    :					List[Any] = True       ,				lowercase_    :					Optional[Any] = None       ,				lowercase_    :					Optional[Any] = ""       ,				**lowercase_    :					Optional[int]       ,				):
									super().__init__(**_A					)
									snake_case_									=				size if size is not None else {'''height''': 224, '''width''': 224}
									snake_case_									=				get_size_dict(_A					)

									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    :					Tuple       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Dict = PILImageResampling.BILINEAR       ,				lowercase_    :					List[str] = None       ,				**lowercase_    :					str       ,				):
									snake_case_									=				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()}"					)
									snake_case_									=				(size['''height'''], size['''width'''])
									return resize(_A       ,				size=_A       ,				resample=_A       ,				data_format=_A       ,				**_A					)


				def   A_ (		self    :					str       ,				lowercase_    :					List[Any]       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					int = None       ,				**lowercase_    :					Tuple       ,				):
									return rescale(_A       ,				scale=_A       ,				data_format=_A       ,				**_A					)


				def   A_ (		self    :					Union[str, Any]       ,				lowercase_    :					Dict       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					Any       ,				lowercase_    :					Optional[Any] = None       ,				**lowercase_    :					Any       ,				):
									return normalize(_A       ,				mean=_A       ,				std=_A       ,				data_format=_A       ,				**_A					)


				def   A_ (		self    :					List[str]       ,				lowercase_    :					int       ,				lowercase_    :					Tuple = None       ,				lowercase_    :					List[Any] = None       ,				lowercase_    :					str=None       ,				lowercase_    :					Tuple = None       ,				lowercase_    :					Tuple = None       ,				lowercase_    :					Optional[int] = None       ,				lowercase_    :					List[str] = None       ,				lowercase_    :					int = None       ,				lowercase_    :					List[str] = None       ,				lowercase_    :					List[Any] = None       ,				lowercase_    :					List[Any] = None       ,				lowercase_    :					Union[str, Any] = None       ,				lowercase_    :					List[str] = ChannelDimension.FIRST       ,				**lowercase_    :					List[str]       ,				):
									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(_A					)
									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(_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.'''					)

									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(_A					) 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(_A       ,				_A       ,				_A					)
																			words_batch.append(_A					)
																			boxes_batch.append(_A					)

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

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

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

									snake_case_									=				[to_channel_dimension_format(_A       ,				_A					) for image in images]

									snake_case_									=				BatchFeature(data={'''pixel_values''': images}       ,				tensor_type=_A					)

									if apply_ocr:
														snake_case_									=				words_batch
														snake_case_									=				boxes_batch
									return data







import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
					import torch.distributed.checkpoint as dist_cp
					from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
					from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
					from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
					from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


UpperCamelCase__    			=			get_logger(__name__)







def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				Optional[Any]      ,      SCREAMING_SNAKE_CASE_							:				str=0     ):
					os.makedirs(SCREAMING_SNAKE_CASE_      ,      exist_ok=SCREAMING_SNAKE_CASE_     )
					with FSDP.state_dict_type(
					    SCREAMING_SNAKE_CASE_      ,      fsdp_plugin.state_dict_type      ,      fsdp_plugin.state_dict_config      ,      fsdp_plugin.optim_state_dict_config     ):
										__lowerCAmelCase				   =       model.state_dict()
										if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
															__lowerCAmelCase				   =       F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
															__lowerCAmelCase				   =       os.path.join(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															if accelerator.process_index == 0:
																				logger.info(F"""Saving model to {output_model_file}"""     )
																				torch.save(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
																				logger.info(F"""Model saved to {output_model_file}"""     )
										elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
															__lowerCAmelCase				   =       (
															    F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
															    if model_index == 0
															    else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
															)
															__lowerCAmelCase				   =       os.path.join(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Saving model to {output_model_file}"""     )
															torch.save(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Model saved to {output_model_file}"""     )
										elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
															__lowerCAmelCase				   =       os.path.join(SCREAMING_SNAKE_CASE_      ,      F"""{MODEL_NAME}_{model_index}"""     )
															os.makedirs(SCREAMING_SNAKE_CASE_      ,      exist_ok=SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Saving model to {ckpt_dir}"""     )
															__lowerCAmelCase				   =       {"model": state_dict}

															dist_cp.save_state_dict(
															    state_dict=SCREAMING_SNAKE_CASE_      ,      storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE_     )      ,      planner=DefaultSavePlanner()      ,      )
															logger.info(F"""Model saved to {ckpt_dir}"""     )







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				Any=0     ):
					accelerator.wait_for_everyone()
					with FSDP.state_dict_type(
					    SCREAMING_SNAKE_CASE_      ,      fsdp_plugin.state_dict_type      ,      fsdp_plugin.state_dict_config      ,      fsdp_plugin.optim_state_dict_config     ):
										if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
															if type(SCREAMING_SNAKE_CASE_     ) != FSDP and accelerator.process_index != 0:
																				if not fsdp_plugin.sync_module_states:
																									raise ValueError(
																									    "Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
																									    "initializing FSDP object"     )
																				return
															__lowerCAmelCase				   =       F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
															__lowerCAmelCase				   =       os.path.join(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Loading model from {input_model_file}"""     )
															__lowerCAmelCase				   =       torch.load(SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Model loaded from {input_model_file}"""     )
										elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
															__lowerCAmelCase				   =       (
															    F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
															    if model_index == 0
															    else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
															)
															__lowerCAmelCase				   =       os.path.join(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Loading model from {input_model_file}"""     )
															__lowerCAmelCase				   =       torch.load(SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Model loaded from {input_model_file}"""     )
										elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
															__lowerCAmelCase				   =       (
															    os.path.join(SCREAMING_SNAKE_CASE_      ,      F"""{MODEL_NAME}_{model_index}"""     )
															    if F"""{MODEL_NAME}""" not in input_dir
															    else input_dir
															)
															logger.info(F"""Loading model from {ckpt_dir}"""     )
															__lowerCAmelCase				   =       {"model": model.state_dict()}
															dist_cp.load_state_dict(
															    state_dict=SCREAMING_SNAKE_CASE_      ,      storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_     )      ,      planner=DefaultLoadPlanner()      ,      )
															__lowerCAmelCase				   =       state_dict["model"]
															logger.info(F"""Model loaded from {ckpt_dir}"""     )
										model.load_state_dict(SCREAMING_SNAKE_CASE_     )







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				Optional[Any]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				str=0     ):
					os.makedirs(SCREAMING_SNAKE_CASE_      ,      exist_ok=SCREAMING_SNAKE_CASE_     )
					with FSDP.state_dict_type(
					    SCREAMING_SNAKE_CASE_      ,      fsdp_plugin.state_dict_type      ,      fsdp_plugin.state_dict_config      ,      fsdp_plugin.optim_state_dict_config     ):
										__lowerCAmelCase				   =       FSDP.optim_state_dict(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
										if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
															if accelerator.process_index == 0:
																				__lowerCAmelCase				   =       (
																				    F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
																				)
																				__lowerCAmelCase				   =       os.path.join(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
																				logger.info(F"""Saving Optimizer state to {output_optimizer_file}"""     )
																				torch.save(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
																				logger.info(F"""Optimizer state saved in {output_optimizer_file}"""     )
										else:
															__lowerCAmelCase				   =       os.path.join(SCREAMING_SNAKE_CASE_      ,      F"""{OPTIMIZER_NAME}_{optimizer_index}"""     )
															os.makedirs(SCREAMING_SNAKE_CASE_      ,      exist_ok=SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Saving Optimizer state to {ckpt_dir}"""     )
															dist_cp.save_state_dict(
															    state_dict={"optimizer": optim_state}      ,      storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE_     )      ,      planner=DefaultSavePlanner()      ,      )
															logger.info(F"""Optimizer state saved in {ckpt_dir}"""     )







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				Dict=0     ):
					accelerator.wait_for_everyone()
					with FSDP.state_dict_type(
					    SCREAMING_SNAKE_CASE_      ,      fsdp_plugin.state_dict_type      ,      fsdp_plugin.state_dict_config      ,      fsdp_plugin.optim_state_dict_config     ):
										if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
															__lowerCAmelCase				   =       None
															# below check should work but currently it isn't working (mostly opytorch issue),
															# in the meantime disabling it at the cost of excess memory usage
															# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
															__lowerCAmelCase				   =       (
															    F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
															)
															__lowerCAmelCase				   =       os.path.join(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""     )
															__lowerCAmelCase				   =       torch.load(SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""     )
										else:
															__lowerCAmelCase				   =       (
															    os.path.join(SCREAMING_SNAKE_CASE_      ,      F"""{OPTIMIZER_NAME}_{optimizer_index}"""     )
															    if F"""{OPTIMIZER_NAME}""" not in input_dir
															    else input_dir
															)
															logger.info(F"""Loading Optimizer from {ckpt_dir}"""     )
															__lowerCAmelCase				   =       load_sharded_optimizer_state_dict(
															    model_state_dict=model.state_dict()      ,      optimizer_key="optimizer"      ,      storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_     )      ,      )
															__lowerCAmelCase				   =       optim_state["optimizer"]
															logger.info(F"""Optimizer loaded from {ckpt_dir}"""     )
										__lowerCAmelCase				   =       FSDP.optim_state_dict_to_load(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
										optimizer.load_state_dict(SCREAMING_SNAKE_CASE_     )




