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

import cva
import numpy as np

# Parrameters
_lowerCamelCase					:   Optional[int]							=		(7_20, 12_80)  # Height, Width
_lowerCamelCase					:   str							=		(0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowerCamelCase					:   List[Any]							=		1 / 1_00
_lowerCamelCase					:   int							=		''''''
_lowerCamelCase					:   List[Any]							=		''''''
_lowerCamelCase					:   Dict							=		''''''
_lowerCamelCase					:   Optional[Any]							=		2_50





def 	__lowerCamelCase							():
					SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       get_dataset(UpperCAmelCase__      , UpperCAmelCase__  )
					for index in range(UpperCAmelCase__  ):
										SCREAMING_SNAKE_CASE    			=       random.sample(range(len(UpperCAmelCase__  )  )      , 4  )
										SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE    			=       update_image_and_anno(
										    UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , UpperCAmelCase__      , filter_scale=UpperCAmelCase__      , )

										# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
										SCREAMING_SNAKE_CASE    			=       random_chars(3_2  )
										SCREAMING_SNAKE_CASE    			=       path.split(os.sep  )[-1].rsplit("."      , 1  )[0]
										SCREAMING_SNAKE_CASE    			=       F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
										cva.imwrite(F"{file_root}.jpg"      , UpperCAmelCase__      , [cva.IMWRITE_JPEG_QUALITY, 8_5]  )
										print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"  )
										SCREAMING_SNAKE_CASE    			=       []
										for anno in new_annos:
															SCREAMING_SNAKE_CASE    			=       anno[3] - anno[1]
															SCREAMING_SNAKE_CASE    			=       anno[4] - anno[2]
															SCREAMING_SNAKE_CASE    			=       anno[1] + width / 2
															SCREAMING_SNAKE_CASE    			=       anno[2] + height / 2
															SCREAMING_SNAKE_CASE    			=       F"{anno[0]} {x_center} {y_center} {width} {height}"
															annos_list.append(UpperCAmelCase__  )
										with open(F"{file_root}.txt"      , "w"  ) as outfile:
															outfile.write("\n".join(line for line in annos_list  )  )





def 	__lowerCamelCase							(UpperCAmelCase__     :							str      , UpperCAmelCase__     :							str  ):
					SCREAMING_SNAKE_CASE    			=       []
					SCREAMING_SNAKE_CASE    			=       []
					for label_file in glob.glob(os.path.join(UpperCAmelCase__      , "*.txt"  )  ):
										SCREAMING_SNAKE_CASE    			=       label_file.split(os.sep  )[-1].rsplit("."      , 1  )[0]
										with open(UpperCAmelCase__  ) as in_file:
															SCREAMING_SNAKE_CASE    			=       in_file.readlines()
										SCREAMING_SNAKE_CASE    			=       os.path.join(UpperCAmelCase__      , F"{label_name}.jpg"  )

										SCREAMING_SNAKE_CASE    			=       []
										for obj_list in obj_lists:
															SCREAMING_SNAKE_CASE    			=       obj_list.rstrip("\n"  ).split(" "  )
															SCREAMING_SNAKE_CASE    			=       float(obj[1]  ) - float(obj[3]  ) / 2
															SCREAMING_SNAKE_CASE    			=       float(obj[2]  ) - float(obj[4]  ) / 2
															SCREAMING_SNAKE_CASE    			=       float(obj[1]  ) + float(obj[3]  ) / 2
															SCREAMING_SNAKE_CASE    			=       float(obj[2]  ) + float(obj[4]  ) / 2

															boxes.append([int(obj[0]  ), xmin, ymin, xmax, ymax]  )
										if not boxes:
															continue
										img_paths.append(UpperCAmelCase__  )
										labels.append(UpperCAmelCase__  )
					return img_paths, labels





def 	__lowerCamelCase							(UpperCAmelCase__     :							list      , UpperCAmelCase__     :							list      , UpperCAmelCase__     :							list[int]      , UpperCAmelCase__     :							tuple[int, int]      , UpperCAmelCase__     :							tuple[float, float]      , UpperCAmelCase__     :							float = 0.0      , ):
					SCREAMING_SNAKE_CASE    			=       np.zeros([output_size[0], output_size[1], 3]      , dtype=np.uinta  )
					SCREAMING_SNAKE_CASE    			=       scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
					SCREAMING_SNAKE_CASE    			=       scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
					SCREAMING_SNAKE_CASE    			=       int(scale_x * output_size[1]  )
					SCREAMING_SNAKE_CASE    			=       int(scale_y * output_size[0]  )

					SCREAMING_SNAKE_CASE    			=       []
					SCREAMING_SNAKE_CASE    			=       []
					for i, index in enumerate(UpperCAmelCase__  ):
										SCREAMING_SNAKE_CASE    			=       all_img_list[index]
										path_list.append(UpperCAmelCase__  )
										SCREAMING_SNAKE_CASE    			=       all_annos[index]
										SCREAMING_SNAKE_CASE    			=       cva.imread(UpperCAmelCase__  )
										if i == 0:  # top-left
															SCREAMING_SNAKE_CASE    			=       cva.resize(UpperCAmelCase__      , (divid_point_x, divid_point_y)  )
															SCREAMING_SNAKE_CASE    			=       img
															for bbox in img_annos:
																				SCREAMING_SNAKE_CASE    			=       bbox[1] * scale_x
																				SCREAMING_SNAKE_CASE    			=       bbox[2] * scale_y
																				SCREAMING_SNAKE_CASE    			=       bbox[3] * scale_x
																				SCREAMING_SNAKE_CASE    			=       bbox[4] * scale_y
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]  )
										elif i == 1:  # top-right
															SCREAMING_SNAKE_CASE    			=       cva.resize(UpperCAmelCase__      , (output_size[1] - divid_point_x, divid_point_y)  )
															SCREAMING_SNAKE_CASE    			=       img
															for bbox in img_annos:
																				SCREAMING_SNAKE_CASE    			=       scale_x + bbox[1] * (1 - scale_x)
																				SCREAMING_SNAKE_CASE    			=       bbox[2] * scale_y
																				SCREAMING_SNAKE_CASE    			=       scale_x + bbox[3] * (1 - scale_x)
																				SCREAMING_SNAKE_CASE    			=       bbox[4] * scale_y
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]  )
										elif i == 2:  # bottom-left
															SCREAMING_SNAKE_CASE    			=       cva.resize(UpperCAmelCase__      , (divid_point_x, output_size[0] - divid_point_y)  )
															SCREAMING_SNAKE_CASE    			=       img
															for bbox in img_annos:
																				SCREAMING_SNAKE_CASE    			=       bbox[1] * scale_x
																				SCREAMING_SNAKE_CASE    			=       scale_y + bbox[2] * (1 - scale_y)
																				SCREAMING_SNAKE_CASE    			=       bbox[3] * scale_x
																				SCREAMING_SNAKE_CASE    			=       scale_y + bbox[4] * (1 - scale_y)
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]  )
										else:  # bottom-right
															SCREAMING_SNAKE_CASE    			=       cva.resize(
															    UpperCAmelCase__      , (output_size[1] - divid_point_x, output_size[0] - divid_point_y)  )
															SCREAMING_SNAKE_CASE    			=       img
															for bbox in img_annos:
																				SCREAMING_SNAKE_CASE    			=       scale_x + bbox[1] * (1 - scale_x)
																				SCREAMING_SNAKE_CASE    			=       scale_y + bbox[2] * (1 - scale_y)
																				SCREAMING_SNAKE_CASE    			=       scale_x + bbox[3] * (1 - scale_x)
																				SCREAMING_SNAKE_CASE    			=       scale_y + bbox[4] * (1 - scale_y)
																				new_anno.append([bbox[0], xmin, ymin, xmax, ymax]  )

    # Remove bounding box small than scale of filter
					if filter_scale > 0:
										SCREAMING_SNAKE_CASE    			=       [
										    anno
										    for anno in new_anno
										    if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
										]

					return output_img, new_anno, path_list[0]





def 	__lowerCamelCase							(UpperCAmelCase__     :							int  ):
					assert number_char > 1, "The number of character should greater than 1"
					SCREAMING_SNAKE_CASE    			=       ascii_lowercase + digits
					return "".join(random.choice(UpperCAmelCase__  ) for _ in range(UpperCAmelCase__  )  )


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





from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


_lowerCamelCase					:   Optional[Any]							=		logging.get_logger(__name__)


class 						lowercase    (     a     ):




							def __init__(						self   :					str	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					int	,							_UpperCamelCase   :					float	,							**_UpperCamelCase   :					str      )					->				Optional[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       feature_size
												SCREAMING_SNAKE_CASE    			=       sampling_rate
												SCREAMING_SNAKE_CASE    			=       padding_value

												SCREAMING_SNAKE_CASE    			=       kwargs.pop("padding_side"	,							"right"      )
												SCREAMING_SNAKE_CASE    			=       kwargs.pop("return_attention_mask"	,							_UpperCamelCase      )

												super().__init__(**_UpperCamelCase      )




							def 	__snake_case(						self   :					List[Any]	,							_UpperCamelCase   :					Union[
							        BatchFeature,
							        List[BatchFeature],
							        Dict[str, BatchFeature],
							        Dict[str, List[BatchFeature]],
							        List[Dict[str, BatchFeature]],
							    ]	,							_UpperCamelCase   :					Union[bool, str, PaddingStrategy] = True	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					bool = False	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							_UpperCamelCase   :					Optional[Union[str, TensorType]] = None	,							)					->				BatchFeature:



												'''simple docstring'''

												if isinstance(_UpperCamelCase	,							(list, tuple)      ) and isinstance(processed_features[0]	,							(dict, BatchFeature)      ):
																	SCREAMING_SNAKE_CASE    			=       {
																	    key: [example[key] for example in processed_features] for key in processed_features[0].keys()
																	}

												# The model's main input name, usually `input_values`, has be passed for padding
												if self.model_input_names[0] not in processed_features:
																	raise ValueError(
																	    "You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
																	    F" to this method that includes {self.model_input_names[0]}, but you provided"
																	    F" {list(processed_features.keys()      )}"      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]
												SCREAMING_SNAKE_CASE    			=       (
												    return_attention_mask if return_attention_mask is not None else self.return_attention_mask
												)

												if len(_UpperCamelCase      ) == 0:
																	if return_attention_mask:
																						SCREAMING_SNAKE_CASE    			=       []
																	return processed_features

												# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
												# and rebuild them afterwards if no return_tensors is specified
												# Note that we lose the specific device the tensor may be on for PyTorch

												SCREAMING_SNAKE_CASE    			=       required_input[0]
												if isinstance(_UpperCamelCase	,							(list, tuple)      ):
																	# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
																	SCREAMING_SNAKE_CASE    			=       0
																	while len(required_input[index]      ) == 0:
																						index += 1
																	if index < len(_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       required_input[index][0]

												if return_tensors is None:
																	if is_tf_tensor(_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       "tf"
																	elif is_torch_tensor(_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       "pt"
																	elif isinstance(_UpperCamelCase	,							(int, float, list, tuple, np.ndarray)      ):
																						SCREAMING_SNAKE_CASE    			=       "np"
																	else:
																						raise ValueError(
																						    F"type of {first_element} unknown: {type(_UpperCamelCase      )}. "
																						    "Should be one of a python, numpy, pytorch or tensorflow object."      )

												for key, value in processed_features.items():
																	if isinstance(value[0]	,							(int, float)      ):
																						SCREAMING_SNAKE_CASE    			=       to_numpy(_UpperCamelCase      )
																	else:
																						SCREAMING_SNAKE_CASE    			=       [to_numpy(_UpperCamelCase      ) for v in value]

        # Convert padding_strategy in PaddingStrategy
												SCREAMING_SNAKE_CASE    			=       self._get_padding_strategies(padding=_UpperCamelCase	,							max_length=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      )
												if not all(len(_UpperCamelCase      ) == batch_size for v in processed_features.values()      ):
																	raise ValueError("Some items in the output dictionary have a different batch size than others."      )

												SCREAMING_SNAKE_CASE    			=       []
												for i in range(_UpperCamelCase      ):
																	SCREAMING_SNAKE_CASE    			=       {k: v[i] for k, v in processed_features.items()}
																	# truncation
																	SCREAMING_SNAKE_CASE    			=       self._truncate(
																	    _UpperCamelCase	,							max_length=_UpperCamelCase	,							pad_to_multiple_of=_UpperCamelCase	,							truncation=_UpperCamelCase	,							)
																	truncated_inputs.append(_UpperCamelCase      )

												if padding_strategy == PaddingStrategy.LONGEST:
																	# make sure that `max_length` cannot be longer than the longest truncated length
																	SCREAMING_SNAKE_CASE    			=       max(len(input_slice[self.model_input_names[0]]      ) for input_slice in truncated_inputs      )
																	SCREAMING_SNAKE_CASE    			=       PaddingStrategy.MAX_LENGTH

												SCREAMING_SNAKE_CASE    			=       {}
												for i in range(_UpperCamelCase      ):
																	# padding
																	SCREAMING_SNAKE_CASE    			=       self._pad(
																	    truncated_inputs[i]	,							max_length=_UpperCamelCase	,							padding_strategy=_UpperCamelCase	,							pad_to_multiple_of=_UpperCamelCase	,							return_attention_mask=_UpperCamelCase	,							)

																	for key, value in outputs.items():
																						if key not in batch_outputs:
																											SCREAMING_SNAKE_CASE    			=       []
																						if value.dtype is np.dtype(np.floataa      ):
																											SCREAMING_SNAKE_CASE    			=       value.astype(np.floataa      )
																						batch_outputs[key].append(_UpperCamelCase      )

												return BatchFeature(_UpperCamelCase	,							tensor_type=_UpperCamelCase      )




							def 	__snake_case(						self   :					Union[str, Any]	,							_UpperCamelCase   :					Union[Dict[str, np.ndarray], BatchFeature]	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					PaddingStrategy = PaddingStrategy.DO_NOT_PAD	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							)					->				dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												if padding_strategy == PaddingStrategy.LONGEST:
																	SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      )

												if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	SCREAMING_SNAKE_CASE    			=       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

												SCREAMING_SNAKE_CASE    			=       padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_UpperCamelCase      ) < max_length

												if return_attention_mask and "attention_mask" not in processed_features:
																	SCREAMING_SNAKE_CASE    			=       np.ones(len(_UpperCamelCase      )	,							dtype=np.intaa      )

												if needs_to_be_padded:
																	SCREAMING_SNAKE_CASE    			=       max_length - len(_UpperCamelCase      )
																	if self.padding_side == "right":
																						if return_attention_mask:
																											SCREAMING_SNAKE_CASE    			=       np.pad(
																											    processed_features["attention_mask"]	,							(0, difference)      )
																						SCREAMING_SNAKE_CASE    			=       ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
																						SCREAMING_SNAKE_CASE    			=       np.pad(
																						    _UpperCamelCase	,							_UpperCamelCase	,							"constant"	,							constant_values=self.padding_value      )
																	elif self.padding_side == "left":
																						if return_attention_mask:
																											SCREAMING_SNAKE_CASE    			=       np.pad(
																											    processed_features["attention_mask"]	,							(difference, 0)      )
																						SCREAMING_SNAKE_CASE    			=       ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
																						SCREAMING_SNAKE_CASE    			=       np.pad(
																						    _UpperCamelCase	,							_UpperCamelCase	,							"constant"	,							constant_values=self.padding_value      )
																	else:
																						raise ValueError("Invalid padding strategy:" + str(self.padding_side      )      )

												return processed_features




							def 	__snake_case(						self   :					Dict	,							_UpperCamelCase   :					Union[Dict[str, np.ndarray], BatchFeature]	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[int] = None	,							_UpperCamelCase   :					Optional[bool] = None	,							)					->				Optional[int]:



												'''simple docstring'''

												if not truncation:
																	return processed_features
												elif truncation and max_length is None:
																	raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined."      )

												SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]]

												# find `max_length` that fits `pad_to_multiple_of`
												if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
																	SCREAMING_SNAKE_CASE    			=       ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

												SCREAMING_SNAKE_CASE    			=       len(_UpperCamelCase      ) > max_length

												if needs_to_be_truncated:
																	SCREAMING_SNAKE_CASE    			=       processed_features[self.model_input_names[0]][:max_length]
																	if "attention_mask" in processed_features:
																						SCREAMING_SNAKE_CASE    			=       processed_features["attention_mask"][:max_length]

												return processed_features





							def 	__snake_case(						self   :					Optional[Any]	,							_UpperCamelCase   :					int=False	,							_UpperCamelCase   :					Tuple=None      )					->				Tuple:



												'''simple docstring'''

												if padding is not False:
																	if padding is True:
																						SCREAMING_SNAKE_CASE    			=       PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
																	elif not isinstance(_UpperCamelCase	,							_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       PaddingStrategy(_UpperCamelCase      )
																	elif isinstance(_UpperCamelCase	,							_UpperCamelCase      ):
																						SCREAMING_SNAKE_CASE    			=       padding
												else:
																	SCREAMING_SNAKE_CASE    			=       PaddingStrategy.DO_NOT_PAD

												# Set max length if needed
												if max_length is None:
																	if padding_strategy == PaddingStrategy.MAX_LENGTH:
																						raise ValueError(
																						    F"When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"      )

        # Test if we have a padding value
												if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
																	raise ValueError(
																	    "Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
																	    " as `padding_value`. For example: `feature_extractor.padding_value = 0.0`."      )

												return padding_strategy




