


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

import cva
import numpy as np

# Parrameters
lowerCAmelCase__    :str       =				(7_2_0, 1_2_8_0)  # Height, Width
lowerCAmelCase__    :Optional[int]       =				(0.4, 0.6)  # if height or width lower than this scale, drop it.
lowerCAmelCase__    :int       =				1 / 1_0_0
lowerCAmelCase__    :Dict       =				''''''
lowerCAmelCase__    :Union[str, Any]       =				''''''
lowerCAmelCase__    :int       =				''''''
lowerCAmelCase__    :Dict       =				2_5_0

def 			lowerCAmelCase__					(     )    ->						None:


							'''simple docstring'''





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

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

def 			lowerCAmelCase__					(     a__:						str     ,    a__:						str			)    ->						tuple[list, list]:


							'''simple docstring'''





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

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

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

def 			lowerCAmelCase__					(     a__:						list     ,    a__:						list     ,    a__:						list[int]     ,    a__:						tuple[int, int]     ,    a__:						tuple[float, float]     ,    a__:						float = 0.0     ,    )    ->						tuple[list, list, str]:


							'''simple docstring'''





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

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


							'''simple docstring'''





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


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




"""simple docstring"""



from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__snake_case       =       logging.get_logger(__name__)

__snake_case       =       {
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			Dict				    =    '''umt5'''
   __UpperCAmelCase					:			str				    =    ['''past_key_values''']







   def __init__(      self		,      UpperCamelCase__=25_0112		,      UpperCamelCase__=512		,      UpperCamelCase__=64		,      UpperCamelCase__=1024		,      UpperCamelCase__=8		,      UpperCamelCase__=None		,      UpperCamelCase__=6		,      UpperCamelCase__=32		,      UpperCamelCase__=128		,      UpperCamelCase__=0.1		,      UpperCamelCase__=1e-6		,      UpperCamelCase__=1.0		,      UpperCamelCase__="gated-gelu"		,      UpperCamelCase__=True		,      UpperCamelCase__=True		,      UpperCamelCase__="T5Tokenizer"		,      UpperCamelCase__=True		,      UpperCamelCase__=0		,      UpperCamelCase__=1		,      UpperCamelCase__=0		,      **UpperCamelCase__		,      )      ->						Dict:

       '''simple docstring'''




       super().__init__(
           is_encoder_decoder=UpperCamelCase__		,      tokenizer_class=UpperCamelCase__		,      tie_word_embeddings=UpperCamelCase__		,      pad_token_id=UpperCamelCase__		,      eos_token_id=UpperCamelCase__		,      decoder_start_token_id=UpperCamelCase__		,      **UpperCamelCase__		,      )
       snake_case       : Optional[int]    =       vocab_size
       snake_case       : Any    =       d_model
       snake_case       : Optional[Any]    =       d_kv
       snake_case       : Any    =       d_ff
       snake_case       : Optional[Any]    =       num_layers
       snake_case       : Optional[Any]    =       (
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       snake_case       : Optional[Any]    =       num_heads
       snake_case       : Optional[Any]    =       relative_attention_num_buckets
       snake_case       : Dict    =       relative_attention_max_distance
       snake_case       : Tuple    =       dropout_rate
       snake_case       : List[Any]    =       layer_norm_epsilon
       snake_case       : Any    =       initializer_factor
       snake_case       : Optional[Any]    =       feed_forward_proj
       snake_case       : Tuple    =       use_cache

       snake_case       : Any    =       self.feed_forward_proj.split("-"		)
       snake_case       : Tuple    =       act_info[-1]
       snake_case       : Optional[int]    =       act_info[0] == "gated"

       if len(UpperCamelCase__		) > 1 and act_info[0] != "gated" or len(UpperCamelCase__		) > 2:
           raise ValueError(
               F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
               "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
               "'gated-gelu' or 'relu'"		)

       if feed_forward_proj == "gated-gelu":
           snake_case       : List[str]    =       "gelu_new"







   @property
   def 					lowerCamelCase						(      self		)      ->						Any:

       '''simple docstring'''




       return self.d_model







   @property
   def 					lowerCamelCase						(      self		)      ->						str:

       '''simple docstring'''




       return self.num_heads







   @property
   def 					lowerCamelCase						(      self		)      ->						Union[str, Any]:

       '''simple docstring'''




       return self.num_layers







class _lowerCAmelCase							( snake_case_      ):







   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
   def 					lowerCamelCase						(      self		)      ->						Mapping[str, Mapping[int, str]]:

       '''simple docstring'''




       snake_case       : Optional[int]    =       {
           "input_ids": {0: "batch", 1: "encoder_sequence"},
           "attention_mask": {0: "batch", 1: "encoder_sequence"},
       }
       if self.use_past:
           snake_case       : Tuple    =       "past_encoder_sequence + sequence"
           snake_case       : int    =       {0: "batch"}
           snake_case       : int    =       {0: "batch", 1: "past_decoder_sequence + sequence"}
       else:
           snake_case       : List[Any]    =       {0: "batch", 1: "decoder_sequence"}
           snake_case       : Any    =       {0: "batch", 1: "decoder_sequence"}

       if self.use_past:
           self.fill_with_past_key_values_(UpperCamelCase__		,      direction="inputs"		)

       return common_inputs







   @property
   # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
   def 					lowerCamelCase						(      self		)      ->						int:

       '''simple docstring'''




       return 13







   @property
   def 					lowerCamelCase						(      self		)      ->						float:

       '''simple docstring'''




       return 5e-4





