




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

import cva
import numpy as np

# Parrameters
__A        =    (7_20, 12_80)  # Height, Width
__A        =    (0.4, 0.6)  # if height or width lower than this scale, drop it.
__A        =    1 / 1_00
__A        =    ""
__A        =    ""
__A        =    ""
__A        =    2_50







def 			lowerCamelCase_		(       )     ->       None:





				"""simple docstring"""


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

								# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
								__lowerCamelCase		       =    random_chars(32      )
								__lowerCamelCase		       =    path.split(os.sep      )[-1].rsplit('.' ,     1      )[0]
								__lowerCamelCase		       =    F"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
								cva.imwrite(F"""{file_root}.jpg""" ,     UpperCamelCase__ ,     [cva.IMWRITE_JPEG_QUALITY, 85]      )
								print(F"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""      )
								__lowerCamelCase		       =    []
								for anno in new_annos:
												__lowerCamelCase		       =    anno[3] - anno[1]
												__lowerCamelCase		       =    anno[4] - anno[2]
												__lowerCamelCase		       =    anno[1] + width / 2
												__lowerCamelCase		       =    anno[2] + height / 2
												__lowerCamelCase		       =    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      )     ->       tuple[list, list]:





				"""simple docstring"""


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

								__lowerCamelCase		       =    []
								for obj_list in obj_lists:
												__lowerCamelCase		       =    obj_list.rstrip('\n'      ).split(' '      )
												__lowerCamelCase		       =    float(obj[1]      ) - float(obj[3]      ) / 2
												__lowerCamelCase		       =    float(obj[2]      ) - float(obj[4]      ) / 2
												__lowerCamelCase		       =    float(obj[1]      ) + float(obj[3]      ) / 2
												__lowerCamelCase		       =    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 ,     )     ->       tuple[list, list, str]:





				"""simple docstring"""


				__lowerCamelCase		       =    np.zeros([output_size[0], output_size[1], 3] ,     dtype=np.uinta      )
				__lowerCamelCase		       =    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
				__lowerCamelCase		       =    scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
				__lowerCamelCase		       =    int(scale_x * output_size[1]      )
				__lowerCamelCase		       =    int(scale_y * output_size[0]      )

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





				"""simple docstring"""


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


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



'''simple docstring'''




import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

_lowercase						: Tuple						=						{
    "BAAI/AltCLIP": "https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json",
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}





class     __magic_name__						(    _UpperCAmelCase):
			UpperCamelCase__				=       '''altclip_text_model'''







			def __init__(	self				:  Union[str, Any]				,			lowercase_				:  str=250002				,			lowercase_				:  Union[str, Any]=1024				,			lowercase_				:  Any=24				,			lowercase_				:  Union[str, Any]=16				,			lowercase_				:  Any=4096				,			lowercase_				:  Union[str, Any]="gelu"				,			lowercase_				:  Union[str, Any]=0.1				,			lowercase_				:  Union[str, Any]=0.1				,			lowercase_				:  int=514				,			lowercase_				:  Union[str, Any]=1				,			lowercase_				:  List[str]=0.02				,			lowercase_				:  Optional[int]=0.02				,			lowercase_				:  str=1E-05				,			lowercase_				:  List[str]=1				,			lowercase_				:  List[Any]=0				,			lowercase_				:  Dict=2				,			lowercase_				:  Union[str, Any]="absolute"				,			lowercase_				:  Any=True				,			lowercase_				:  Union[str, Any]=768				,			**lowercase_				:  Any				,			):
										super().__init__(pad_token_id=lowercase_				,			bos_token_id=lowercase_				,			eos_token_id=lowercase_				,			**lowercase_    )

										lowercase_					:			Union[str, Any]										=       vocab_size
										lowercase_					:			str										=       hidden_size
										lowercase_					:			Optional[Any]										=       num_hidden_layers
										lowercase_					:			int										=       num_attention_heads
										lowercase_					:			str										=       hidden_act
										lowercase_					:			List[str]										=       intermediate_size
										lowercase_					:			Optional[int]										=       hidden_dropout_prob
										lowercase_					:			str										=       attention_probs_dropout_prob
										lowercase_					:			str										=       max_position_embeddings
										lowercase_					:			List[str]										=       type_vocab_size
										lowercase_					:			Union[str, Any]										=       initializer_range
										lowercase_					:			List[Any]										=       initializer_factor
										lowercase_					:			str										=       layer_norm_eps
										lowercase_					:			Tuple										=       position_embedding_type
										lowercase_					:			List[Any]										=       use_cache
										lowercase_					:			Tuple										=       project_dim





class     __magic_name__						(    _UpperCAmelCase):
			UpperCamelCase__				=       '''altclip_vision_model'''







			def __init__(	self				:  Dict				,			lowercase_				:  Any=768				,			lowercase_				:  Dict=3072				,			lowercase_				:  Optional[Any]=512				,			lowercase_				:  Dict=12				,			lowercase_				:  Optional[int]=12				,			lowercase_				:  Optional[Any]=3				,			lowercase_				:  str=224				,			lowercase_				:  List[Any]=32				,			lowercase_				:  Union[str, Any]="quick_gelu"				,			lowercase_				:  Dict=1E-5				,			lowercase_				:  Optional[int]=0.0				,			lowercase_				:  Optional[Any]=0.02				,			lowercase_				:  Optional[Any]=1.0				,			**lowercase_				:  Dict				,			):
										super().__init__(**lowercase_    )

										lowercase_					:			Tuple										=       hidden_size
										lowercase_					:			Optional[Any]										=       intermediate_size
										lowercase_					:			Optional[Any]										=       projection_dim
										lowercase_					:			Tuple										=       num_hidden_layers
										lowercase_					:			List[Any]										=       num_attention_heads
										lowercase_					:			Any										=       num_channels
										lowercase_					:			Any										=       patch_size
										lowercase_					:			Dict										=       image_size
										lowercase_					:			Optional[Any]										=       initializer_range
										lowercase_					:			str										=       initializer_factor
										lowercase_					:			Any										=       attention_dropout
										lowercase_					:			Optional[int]										=       layer_norm_eps
										lowercase_					:			int										=       hidden_act







			@classmethod
			def 	SCREAMING_SNAKE_CASE_     (	cls				:  Optional[int]				,			lowercase_				:  Union[str, os.PathLike]				,			**lowercase_				:  Any    ):
										cls._set_token_in_kwargs(lowercase_    )

										lowercase_						,						lowercase_					:			str										=       cls.get_config_dict(lowercase_				,			**lowercase_    )

										# get the vision config dict if we are loading from AltCLIPConfig
										if config_dict.get("""model_type"""    ) == "altclip":
																	lowercase_					:			List[str]										=       config_dict["""vision_config"""]

										if "model_type" in config_dict and hasattr(cls				,			"""model_type"""    ) and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
																	    f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''    )

										return cls.from_dict(lowercase_				,			**lowercase_    )





class     __magic_name__						(    _UpperCAmelCase):
			UpperCamelCase__				=       '''altclip'''
			UpperCamelCase__				=       True







			def __init__(	self				:  Optional[int]				,			lowercase_				:  Dict=None				,			lowercase_				:  List[Any]=None				,			lowercase_				:  Tuple=768				,			lowercase_				:  List[str]=2.65_92				,			**lowercase_				:  List[Any]    ):
										# If `_config_dict` exist, we use them for the backward compatibility.
										# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
										# of confusion!).
										lowercase_					:			Dict										=       kwargs.pop("""text_config_dict"""				,			lowercase_    )
										lowercase_					:			str										=       kwargs.pop("""vision_config_dict"""				,			lowercase_    )

										super().__init__(**lowercase_    )

										# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
										# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
										# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
										if text_config_dict is not None:
																	if text_config is None:
																								lowercase_					:			Dict										=       {}

																	# This is the complete result when using `text_config_dict`.
																	lowercase_					:			List[str]										=       AltCLIPTextConfig(**lowercase_    ).to_dict()

																	# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
																	for key, value in _text_config_dict.items():
																								if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
																															# If specified in `text_config_dict`
																															if key in text_config_dict:
																																						lowercase_					:			Optional[Any]										=       (
																																						    f'''`{key}` is found in both `text_config_dict` and `text_config` but with different values. '''
																																						    f'''The value `text_config_dict["{key}"]` will be used instead.'''
																																						)
																															# If inferred from default argument values (just to be super careful)
																															else:
																																						lowercase_					:			Tuple										=       (
																																						    f'''`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '''
																																						    f'''value `text_config["{key}"]` will be overriden.'''
																																						)
																															logger.warning(lowercase_    )

            # Update all values in `text_config` with the ones in `_text_config_dict`.
																	text_config.update(_text_config_dict    )

										if vision_config_dict is not None:
																	if vision_config is None:
																								lowercase_					:			int										=       {}

																	# This is the complete result when using `vision_config_dict`.
																	lowercase_					:			List[str]										=       AltCLIPVisionConfig(**lowercase_    ).to_dict()
																	# convert keys to string instead of integer
																	if "id2label" in _vision_config_dict:
																								lowercase_					:			List[str]										=       {
																								    str(lowercase_    ): value for key, value in _vision_config_dict["""id2label"""].items()
																								}

																	# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
																	for key, value in _vision_config_dict.items():
																								if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
																															# If specified in `vision_config_dict`
																															if key in vision_config_dict:
																																						lowercase_					:			Any										=       (
																																						    f'''`{key}` is found in both `vision_config_dict` and `vision_config` but with different '''
																																						    f'''values. The value `vision_config_dict["{key}"]` will be used instead.'''
																																						)
																															# If inferred from default argument values (just to be super careful)
																															else:
																																						lowercase_					:			List[str]										=       (
																																						    f'''`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '''
																																						    f'''The value `vision_config["{key}"]` will be overriden.'''
																																						)
																															logger.warning(lowercase_    )

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
																	vision_config.update(_vision_config_dict    )

										if text_config is None:
																	lowercase_					:			int										=       {}
																	logger.info("""`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values."""    )

										if vision_config is None:
																	lowercase_					:			Optional[int]										=       {}
																	logger.info("""`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values."""    )

										lowercase_					:			Optional[int]										=       AltCLIPTextConfig(**lowercase_    )
										lowercase_					:			Any										=       AltCLIPVisionConfig(**lowercase_    )

										lowercase_					:			List[Any]										=       projection_dim
										lowercase_					:			Optional[Any]										=       logit_scale_init_value
										lowercase_					:			int										=       1.0







			@classmethod
			def 	SCREAMING_SNAKE_CASE_     (	cls				:  Dict				,			lowercase_				:  AltCLIPTextConfig				,			lowercase_				:  AltCLIPVisionConfig				,			**lowercase_				:  Optional[int]    ):
										return cls(text_config=text_config.to_dict()				,			vision_config=vision_config.to_dict()				,			**lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										lowercase_					:			Union[str, Any]										=       copy.deepcopy(self.__dict__    )
										lowercase_					:			Optional[int]										=       self.text_config.to_dict()
										lowercase_					:			Any										=       self.vision_config.to_dict()
										lowercase_					:			List[str]										=       self.__class__.model_type
										return output





