
'''simple docstring'''







from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_tf_available():
					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
					import torch

					from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

lowercase					:							Tuple   =      logging.get_logger(__name__)


@add_end_docstrings(__lowerCamelCase		)
class 		__UpperCAmelCase   (      __lowerCamelCase		):


							def __init__( self					,     *lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""simple docstring"""






								super().__init__(*UpperCamelCase_					,     **UpperCamelCase_						)
								requires_backends(self					,     'vision'						)
								self.check_model_type(
								    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None						):





								"""simple docstring"""






								_snake_case     		=	{}
								_snake_case     		=	{}

								if prompt is not None:
									_snake_case     		=	prompt

								if generate_kwargs is not None:
									_snake_case     		=	generate_kwargs
								if max_new_tokens is not None:
									if "generate_kwargs" not in forward_kwargs:
										_snake_case     		=	{}
									if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
										raise ValueError(
										    '\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
										    ' please use only one'						)
									_snake_case     		=	max_new_tokens
								return preprocess_params, forward_kwargs, {}


							def __call__( self					,     lowerCAmelCase_					,     **lowerCAmelCase_						):





								"""simple docstring"""






								return super().__call__(UpperCamelCase_					,     **UpperCamelCase_						)


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_=None						):





								"""simple docstring"""






								_snake_case     		=	load_image(UpperCamelCase_						)

								if prompt is not None:
									if not isinstance(UpperCamelCase_					,     UpperCamelCase_						):
										raise ValueError(
										    F'Received an invalid text input, got - {type(UpperCamelCase_						)} - but expected a single string. '
										    'Note also that one single text can be provided for conditional image to text generation.'						)

									_snake_case     		=	self.model.config.model_type

									if model_type == "git":
										_snake_case     		=	self.image_processor(images=UpperCamelCase_					,     return_tensors=self.framework						)
										_snake_case     		=	self.tokenizer(text=UpperCamelCase_					,     add_special_tokens=UpperCamelCase_						).input_ids
										_snake_case     		=	[self.tokenizer.cls_token_id] + input_ids
										_snake_case     		=	torch.tensor(UpperCamelCase_						).unsqueeze(0						)
										model_inputs.update({'input_ids': input_ids}						)

									elif model_type == "pix2struct":
										_snake_case     		=	self.image_processor(images=UpperCamelCase_					,     header_text=UpperCamelCase_					,     return_tensors=self.framework						)

									elif model_type != "vision-encoder-decoder":
										# vision-encoder-decoder does not support conditional generation
										_snake_case     		=	self.image_processor(images=UpperCamelCase_					,     return_tensors=self.framework						)
										_snake_case     		=	self.tokenizer(UpperCamelCase_					,     return_tensors=self.framework						)
										model_inputs.update(UpperCamelCase_						)

									else:
										raise ValueError(F'Model type {model_type} does not support conditional text generation'						)

								else:
									_snake_case     		=	self.image_processor(images=UpperCamelCase_					,     return_tensors=self.framework						)

								if self.model.config.model_type == "git" and prompt is None:
									_snake_case     		=	None

								return model_inputs


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_=None						):





								"""simple docstring"""






								if (
								    "input_ids" in model_inputs
								    and isinstance(model_inputs['input_ids']					,     UpperCamelCase_						)
								    and all(x is None for x in model_inputs['input_ids']						)
								):
									_snake_case     		=	None

								if generate_kwargs is None:
									_snake_case     		=	{}
								# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
								#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
								#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
								#  in the `_prepare_model_inputs` method.
								_snake_case     		=	model_inputs.pop(self.model.main_input_name						)
								_snake_case     		=	self.model.generate(UpperCamelCase_					,     **UpperCamelCase_					,     **UpperCamelCase_						)
								return model_outputs




							def 				lowerCamelCase       ( self					,     lowerCAmelCase_						):





								"""simple docstring"""






								_snake_case     		=	[]
								for output_ids in model_outputs:
									_snake_case     		=	{
									    'generated_text': self.tokenizer.decode(
									        UpperCamelCase_					,     skip_special_tokens=UpperCamelCase_					,     )
									}
									records.append(UpperCamelCase_						)
								return records



'''simple docstring'''







import glob
import os
import random
from string import ascii_lowercase, digits

import cva
lowercase					:							int   =      ""
lowercase					:							Union[str, Any]   =      ""
lowercase					:							Tuple   =      ""
lowercase					:							Optional[int]   =      1  # (0 is vertical, 1 is horizontal)







def        SCREAMING_SNAKE_CASE__			(     ) ->			None:
	_snake_case		,				_snake_case     		=	get_dataset(__A    ,       __A			)
	print('Processing...'			)
	_snake_case		,				_snake_case		,				_snake_case     		=	update_image_and_anno(__A    ,       __A    ,       __A			)

	for index, image in enumerate(__A			):
		# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
		_snake_case     		=	random_chars(32			)
		_snake_case     		=	paths[index].split(os.sep			)[-1].rsplit('.'    ,       1			)[0]
		_snake_case     		=	F'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
		cva.imwrite(F'/{file_root}.jpg'    ,       __A    ,       [cva.IMWRITE_JPEG_QUALITY, 85]			)
		print(F'Success {index+1}/{len(__A			)} with {file_name}'			)
		_snake_case     		=	[]
		for anno in new_annos[index]:
			_snake_case     		=	F'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
			annos_list.append(__A			)
		with open(F'/{file_root}.txt'    ,       'w'			) as outfile:
			outfile.write('\n'.join(line for line in annos_list			)			)







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A			) ->			tuple[list, list]:
	_snake_case     		=	[]
	_snake_case     		=	[]
	for label_file in glob.glob(os.path.join(__A    ,       '*.txt'			)			):
		_snake_case     		=	label_file.split(os.sep			)[-1].rsplit('.'    ,       1			)[0]
		with open(__A			) as in_file:
			_snake_case     		=	in_file.readlines()
		_snake_case     		=	os.path.join(__A    ,       F'{label_name}.jpg'			)

		_snake_case     		=	[]
		for obj_list in obj_lists:
			_snake_case     		=	obj_list.rstrip('\n'			).split(' '			)
			boxes.append(
			    [
			        int(obj[0]			),
			        float(obj[1]			),
			        float(obj[2]			),
			        float(obj[3]			),
			        float(obj[4]			),
			    ]			)
		if not boxes:
			continue
		img_paths.append(__A			)
		labels.append(__A			)
	return img_paths, labels







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A = 1			) ->			tuple[list, list, list]:
	_snake_case     		=	[]
	_snake_case     		=	[]
	_snake_case     		=	[]
	for idx in range(len(__A			)			):
		_snake_case     		=	[]
		_snake_case     		=	img_list[idx]
		path_list.append(__A			)
		_snake_case     		=	anno_list[idx]
		_snake_case     		=	cva.imread(__A			)
		if flip_type == 1:
			_snake_case     		=	cva.flip(__A    ,       __A			)
			for bbox in img_annos:
				_snake_case     		=	1 - bbox[1]
				new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
		elif flip_type == 0:
			_snake_case     		=	cva.flip(__A    ,       __A			)
			for bbox in img_annos:
				_snake_case     		=	1 - bbox[2]
				new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
		new_annos_lists.append(__A			)
		new_imgs_list.append(__A			)
	return new_imgs_list, new_annos_lists, path_list







def        SCREAMING_SNAKE_CASE__			(     __A = 32			) ->			str:
	assert number_char > 1, "The number of character should greater than 1"
	_snake_case     		=	ascii_lowercase + digits
	return "".join(random.choice(__A			) for _ in range(__A			)			)


if __name__ == "__main__":
					main()
					print("DONE ✅")

