



'''simple docstring'''
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


snake_case_					:      Dict    			=    get_logger(__name__)


snake_case_					:      Any    			=    Path(__file__).parent / 'model_card_template.md'
snake_case_					:      List[Any]    			=    uuida().hex
snake_case_					:      List[Any]    			=    os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
snake_case_					:      Tuple    			=    os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
snake_case_					:      List[Any]    			=    HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'






def A__       (	UpperCAmelCase_ = None				):
						_UpperCamelCase   :						Any												=  f'diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'
						if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
												return ua + "; telemetry/off"
						if is_torch_available():
												ua += f'; torch/{_torch_version}'
						if is_flax_available():
												ua += f'; jax/{_jax_version}'
												ua += f'; flax/{_flax_version}'
						if is_onnx_available():
												ua += f'; onnxruntime/{_onnxruntime_version}'
						# CI will set this value to True
						if os.environ.get('DIFFUSERS_IS_CI'    ,    ''				).upper() in ENV_VARS_TRUE_VALUES:
												ua += "; is_ci/true"
						if isinstance(UpperCAmelCase_    ,    UpperCAmelCase_				):
												ua += "; " + "; ".join(f'{k}/{v}' for k, v in user_agent.items()				)
						elif isinstance(UpperCAmelCase_    ,    UpperCAmelCase_				):
												ua += "; " + user_agent
						return ua






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_ = None    ,    UpperCAmelCase_ = None				):
						if token is None:
												_UpperCamelCase   :						Optional[Any]												=  HfFolder.get_token()
						if organization is None:
												_UpperCamelCase   :						Union[str, Any]												=  whoami(UpperCAmelCase_				)['name']
												return f'{username}/{model_id}'
						else:
												return f'{organization}/{model_id}'






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						if not is_jinja_available():
												raise ValueError(
												    'Modelcard rendering is based on Jinja templates.'
												    ' Please make sure to have `jinja` installed before using `create_model_card`.'
												    ' To install it, please run `pip install Jinja2`.'				)

						if hasattr(UpperCAmelCase_    ,    'local_rank'				) and args.local_rank not in [-1, 0]:
												return

						_UpperCamelCase   :						Union[str, Any]												=  args.hub_token if hasattr(UpperCAmelCase_    ,    'hub_token'				) else None
						_UpperCamelCase   :						Union[str, Any]												=  get_full_repo_name(UpperCAmelCase_    ,    token=UpperCAmelCase_				)

						_UpperCamelCase   :						Union[str, Any]												=  ModelCard.from_template(
						    card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
						        language='en'    ,    license='apache-2.0'    ,    library_name='diffusers'    ,    tags=[]    ,    datasets=args.dataset_name    ,    metrics=[]    ,    )    ,    template_path=UpperCAmelCase_    ,    model_name=UpperCAmelCase_    ,    repo_name=UpperCAmelCase_    ,    dataset_name=args.dataset_name if hasattr(UpperCAmelCase_    ,    'dataset_name'				) else None    ,    learning_rate=args.learning_rate    ,    train_batch_size=args.train_batch_size    ,    eval_batch_size=args.eval_batch_size    ,    gradient_accumulation_steps=(
						        args.gradient_accumulation_steps if hasattr(UpperCAmelCase_    ,    'gradient_accumulation_steps'				) else None
						    )    ,    adam_betaa=args.adam_betaa if hasattr(UpperCAmelCase_    ,    'adam_beta1'				) else None    ,    adam_betaa=args.adam_betaa if hasattr(UpperCAmelCase_    ,    'adam_beta2'				) else None    ,    adam_weight_decay=args.adam_weight_decay if hasattr(UpperCAmelCase_    ,    'adam_weight_decay'				) else None    ,    adam_epsilon=args.adam_epsilon if hasattr(UpperCAmelCase_    ,    'adam_epsilon'				) else None    ,    lr_scheduler=args.lr_scheduler if hasattr(UpperCAmelCase_    ,    'lr_scheduler'				) else None    ,    lr_warmup_steps=args.lr_warmup_steps if hasattr(UpperCAmelCase_    ,    'lr_warmup_steps'				) else None    ,    ema_inv_gamma=args.ema_inv_gamma if hasattr(UpperCAmelCase_    ,    'ema_inv_gamma'				) else None    ,    ema_power=args.ema_power if hasattr(UpperCAmelCase_    ,    'ema_power'				) else None    ,    ema_max_decay=args.ema_max_decay if hasattr(UpperCAmelCase_    ,    'ema_max_decay'				) else None    ,    mixed_precision=args.mixed_precision    ,    )

						_UpperCamelCase   :						Optional[Any]												=  os.path.join(args.output_dir    ,    'README.md'				)
						model_card.save(UpperCAmelCase_				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_ = None				):
						if resolved_file is None or commit_hash is not None:
												return commit_hash
						_UpperCamelCase   :						Tuple												=  str(Path(UpperCAmelCase_				).as_posix()				)
						_UpperCamelCase   :						int												=  re.search(R'snapshots/([^/]+)/'    ,    UpperCAmelCase_				)
						if search is None:
												return None
						_UpperCamelCase   :						Optional[Any]												=  search.groups()[0]
						return commit_hash if REGEX_COMMIT_HASH.match(UpperCAmelCase_				) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
snake_case_					:      Optional[int]    			=    os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
snake_case_					:      Dict    			=    os.path.join(hf_cache_home, 'diffusers')






def A__       (	UpperCAmelCase_ = None    ,    UpperCAmelCase_ = None				):
						if new_cache_dir is None:
												_UpperCamelCase   :						Optional[Any]												=  DIFFUSERS_CACHE
						if old_cache_dir is None:
												_UpperCamelCase   :						Optional[int]												=  old_diffusers_cache

						_UpperCamelCase   :						str												=  Path(UpperCAmelCase_				).expanduser()
						_UpperCamelCase   :						Tuple												=  Path(UpperCAmelCase_				).expanduser()
						for old_blob_path in old_cache_dir.glob('**/blobs/*'				):
												if old_blob_path.is_file() and not old_blob_path.is_symlink():
																		_UpperCamelCase   :						List[Any]												=  new_cache_dir / old_blob_path.relative_to(UpperCAmelCase_				)
																		new_blob_path.parent.mkdir(parents=UpperCAmelCase_    ,    exist_ok=UpperCAmelCase_				)
																		os.replace(UpperCAmelCase_    ,    UpperCAmelCase_				)
																		try:
																								os.symlink(UpperCAmelCase_    ,    UpperCAmelCase_				)
																		except OSError:
																								logger.warning(
																								    'Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded.'				)
						# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


snake_case_					:      Any    			=    os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
					snake_case_					:      List[str]    			=    0
else:
					with open(cache_version_file) as f:
										try:
															snake_case_					:      Optional[Any]    			=    int(f.read())
										except ValueError:
															snake_case_					:      Dict    			=    0

if cache_version < 1:
					snake_case_					:      Union[str, Any]    			=    os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
					if old_cache_is_not_empty:
										logger.warning(
										    'The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your '
										    'existing cached models. This is a one-time operation, you can interrupt it or run it '
										    'later by calling `diffusers.utils.hub_utils.move_cache()`.'
										)
										try:
															move_cache()
										except Exception as e:
															snake_case_					:      Union[str, Any]    			=    '\n'.join(traceback.format_tb(e.__traceback__))
															logger.error(
															    F"""There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease """
															    'file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole '
															    'message and we will do our best to help.'
															)

if cache_version < 1:
					try:
										os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
										with open(cache_version_file, 'w') as f:
															f.write('1')
					except Exception:
										logger.warning(
										    F"""There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure """
										    'the directory exists and can be written to.'
										)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_ = None				):
						if variant is not None:
												_UpperCamelCase   :						str												=  weights_name.split('.'				)
												_UpperCamelCase   :						Any												=  splits[:-1] + [variant] + splits[-1:]
												_UpperCamelCase   :						Any												=  '.'.join(UpperCAmelCase_				)

						return weights_name






def A__       (	UpperCAmelCase_    ,    *,
    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_=None    ,    ):
						_UpperCamelCase   :						int												=  str(UpperCAmelCase_				)
						if os.path.isfile(UpperCAmelCase_				):
												return pretrained_model_name_or_path
						elif os.path.isdir(UpperCAmelCase_				):
												if os.path.isfile(os.path.join(UpperCAmelCase_    ,    UpperCAmelCase_				)				):
																		# Load from a PyTorch checkpoint
																		_UpperCamelCase   :						str												=  os.path.join(UpperCAmelCase_    ,    UpperCAmelCase_				)
																		return model_file
												elif subfolder is not None and os.path.isfile(
												    os.path.join(UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				)				):
																		_UpperCamelCase   :						Tuple												=  os.path.join(UpperCAmelCase_    ,    UpperCAmelCase_    ,    UpperCAmelCase_				)
																		return model_file
												else:
																		raise EnvironmentError(
																		    f'Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.'				)
						else:
												# 1. First check if deprecated way of loading from branches is used
												if (
												    revision in DEPRECATED_REVISION_ARGS
												    and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
												    and version.parse(version.parse(UpperCAmelCase_				).base_version				) >= version.parse('0.20.0'				)
												):
																		try:
																								_UpperCamelCase   :						Union[str, Any]												=  hf_hub_download(
																								    UpperCAmelCase_    ,    filename=_add_variant(UpperCAmelCase_    ,    UpperCAmelCase_				)    ,    cache_dir=UpperCAmelCase_    ,    force_download=UpperCAmelCase_    ,    proxies=UpperCAmelCase_    ,    resume_download=UpperCAmelCase_    ,    local_files_only=UpperCAmelCase_    ,    use_auth_token=UpperCAmelCase_    ,    user_agent=UpperCAmelCase_    ,    subfolder=UpperCAmelCase_    ,    revision=revision or commit_hash    ,    )
																								warnings.warn(
																								    f'Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.'    ,    UpperCAmelCase_    ,    )
																								return model_file
																		except:  # noqa: E722
																								warnings.warn(
																								    f'You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(UpperCAmelCase_    ,    UpperCAmelCase_				)} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(UpperCAmelCase_    ,    UpperCAmelCase_				)}\' so that the correct variant file can be added.'    ,    UpperCAmelCase_    ,    )
												try:
																		# 2. Load model file as usual
																		_UpperCamelCase   :						Tuple												=  hf_hub_download(
																		    UpperCAmelCase_    ,    filename=UpperCAmelCase_    ,    cache_dir=UpperCAmelCase_    ,    force_download=UpperCAmelCase_    ,    proxies=UpperCAmelCase_    ,    resume_download=UpperCAmelCase_    ,    local_files_only=UpperCAmelCase_    ,    use_auth_token=UpperCAmelCase_    ,    user_agent=UpperCAmelCase_    ,    subfolder=UpperCAmelCase_    ,    revision=revision or commit_hash    ,    )
																		return model_file

												except RepositoryNotFoundError:
																		raise EnvironmentError(
																		    f'{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '
																		    'listed on \'https://huggingface.co/models\'\nIf this is a private repository, make sure to pass a '
																		    'token having permission to this repo with `use_auth_token` or log in with `huggingface-cli '
																		    'login`.'				)
												except RevisionNotFoundError:
																		raise EnvironmentError(
																		    f'{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '
																		    'this model name. Check the model page at '
																		    f'\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.'				)
												except EntryNotFoundError:
																		raise EnvironmentError(
																		    f'{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.'				)
												except HTTPError as err:
																		raise EnvironmentError(
																		    f'There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}'				)
												except ValueError:
																		raise EnvironmentError(
																		    f'We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'
																		    f' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'
																		    f' directory containing a file named {weights_name} or'
																		    ' \nCheckout your internet connection or see how to run the library in'
																		    ' offline mode at \'https://huggingface.co/docs/diffusers/installation#offline-mode\'.'				)
												except EnvironmentError:
																		raise EnvironmentError(
																		    f'Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '
																		    '\'https://huggingface.co/models\', make sure you don\'t have a local directory with the same name. '
																		    f'Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '
																		    f'containing a file named {weights_name}'				)







'''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

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_ = None				):
						_UpperCamelCase   :						Optional[int]												=  tesseract_config if tesseract_config is not None else ''

						# apply OCR
						_UpperCamelCase   :						int												=  to_pil_image(UpperCAmelCase_				)
						_UpperCamelCase							,     _UpperCamelCase   :						Dict												=  pil_image.size
						_UpperCamelCase   :						Tuple												=  pytesseract.image_to_data(UpperCAmelCase_    ,    lang=UpperCAmelCase_    ,    output_type='dict'    ,    config=UpperCAmelCase_				)
						_UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase							,     _UpperCamelCase   :						int												=  data['text'], data['left'], data['top'], data['width'], data['height']

						# filter empty words and corresponding coordinates
						_UpperCamelCase   :						Optional[Any]												=  [idx for idx, word in enumerate(UpperCAmelCase_				) if not word.strip()]
						_UpperCamelCase   :						Any												=  [word for idx, word in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
						_UpperCamelCase   :						Any												=  [coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
						_UpperCamelCase   :						Union[str, Any]												=  [coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
						_UpperCamelCase   :						Union[str, Any]												=  [coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]
						_UpperCamelCase   :						List[str]												=  [coord for idx, coord in enumerate(UpperCAmelCase_				) if idx not in irrelevant_indices]

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

						# finally, normalize the bounding boxes
						_UpperCamelCase   :						List[Any]												=  []
						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__      (				lowercase  ):
	lowercase__    							=     ["""pixel_values"""]





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







							'''simple docstring'''
							super().__init__(**lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  size if size is not None else {'height': 224, 'width': 224}
							_UpperCamelCase   :						Union[str, Any]												=  get_size_dict(lowerCamelCase__					)

							_UpperCamelCase   :						Optional[int]												=  do_resize
							_UpperCamelCase   :						str												=  size
							_UpperCamelCase   :						Any												=  resample
							_UpperCamelCase   :						List[str]												=  apply_ocr
							_UpperCamelCase   :						int												=  ocr_lang
							_UpperCamelCase   :						Union[str, Any]												=  tesseract_config





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







							'''simple docstring'''
							_UpperCamelCase   :						Optional[int]												=  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()}'					)
							_UpperCamelCase   :						List[str]												=  (size['height'], size['width'])
							return resize(lowerCamelCase__					,size=lowerCamelCase__					,resample=lowerCamelCase__					,data_format=lowerCamelCase__					,**lowerCamelCase__					)





	def       UpperCamelCase_    (					self   :							List[str]					,lowerCamelCase__   :							ImageInput					,lowerCamelCase__   :							bool = None					,lowerCamelCase__   :							Dict[str, int] = None					,lowerCamelCase__   :							PILImageResampling = None					,lowerCamelCase__   :							bool = None					,lowerCamelCase__   :							Optional[str] = None					,lowerCamelCase__   :							Optional[str] = None					,lowerCamelCase__   :							Optional[Union[str, TensorType]] = None					,lowerCamelCase__   :							ChannelDimension = ChannelDimension.FIRST					,**lowerCamelCase__   :							List[Any]					,):







							'''simple docstring'''
							_UpperCamelCase   :						Optional[Any]												=  do_resize if do_resize is not None else self.do_resize
							_UpperCamelCase   :						Tuple												=  size if size is not None else self.size
							_UpperCamelCase   :						Optional[int]												=  get_size_dict(lowerCamelCase__					)
							_UpperCamelCase   :						Optional[Any]												=  resample if resample is not None else self.resample
							_UpperCamelCase   :						Union[str, Any]												=  apply_ocr if apply_ocr is not None else self.apply_ocr
							_UpperCamelCase   :						Union[str, Any]												=  ocr_lang if ocr_lang is not None else self.ocr_lang
							_UpperCamelCase   :						int												=  tesseract_config if tesseract_config is not None else self.tesseract_config

							_UpperCamelCase   :						Dict												=  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.'					)

							# All transformations expect numpy arrays.
							_UpperCamelCase   :						Union[str, Any]												=  [to_numpy_array(lowerCamelCase__					) for image in images]

							if apply_ocr:
													requires_backends(self					,'pytesseract'					)
													_UpperCamelCase   :						Tuple												=  []
													_UpperCamelCase   :						Tuple												=  []
													for image in images:
																			_UpperCamelCase							,     _UpperCamelCase   :						Optional[Any]												=  apply_tesseract(lowerCamelCase__					,lowerCamelCase__					,lowerCamelCase__					)
																			words_batch.append(lowerCamelCase__					)
																			boxes_batch.append(lowerCamelCase__					)

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

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

							_UpperCamelCase   :						Union[str, Any]												=  BatchFeature(data={'pixel_values': images}					,tensor_type=lowerCamelCase__					)

							if apply_ocr:
													_UpperCamelCase   :						Dict												=  words_batch
													_UpperCamelCase   :						str												=  boxes_batch
							return data


