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

import cva
import numpy as np

# Parrameters
_lowerCAmelCase       =		(720, 1_280)  # Height, Width
_lowerCAmelCase       =		(0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowerCAmelCase       =		1 / 100
_lowerCAmelCase       =		""
_lowerCAmelCase       =		""
_lowerCAmelCase       =		""
_lowerCAmelCase       =		250
def 					_snake_case	(  ):
					_UpperCamelCase     ,    _UpperCamelCase			     =     get_dataset(__snake_case  ,     __snake_case						)
					for index in range(__snake_case						):
										_UpperCamelCase			     =     random.sample(range(len(__snake_case						)						)  ,     4						)
										_UpperCamelCase     ,    _UpperCamelCase     ,    _UpperCamelCase			     =     update_image_and_anno(
										    __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     filter_scale=__snake_case  ,     )

										# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
										_UpperCamelCase			     =     random_chars(32						)
										_UpperCamelCase			     =     path.split(os.sep						)[-1].rsplit('''.'''  ,     1						)[0]
										_UpperCamelCase			     =     f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
										cva.imwrite(f"""{file_root}.jpg"""  ,     __snake_case  ,     [cva.IMWRITE_JPEG_QUALITY, 85]						)
										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(__snake_case						)
										with open(f"""{file_root}.txt"""  ,     '''w'''						) as outfile:
															outfile.write('''\n'''.join(line for line in annos_list						)						)
def 					_snake_case	(  __snake_case  ,     __snake_case						):
					_UpperCamelCase			     =     []
					_UpperCamelCase			     =     []
					for label_file in glob.glob(os.path.join(__snake_case  ,     '''*.txt'''						)						):
										_UpperCamelCase			     =     label_file.split(os.sep						)[-1].rsplit('''.'''  ,     1						)[0]
										with open(__snake_case						) as in_file:
															_UpperCamelCase			     =     in_file.readlines()
										_UpperCamelCase			     =     os.path.join(__snake_case  ,     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(__snake_case						)
										labels.append(__snake_case						)
					return img_paths, labels
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case = 0.0  ,     ):
					_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(__snake_case						):
										_UpperCamelCase			     =     all_img_list[index]
										path_list.append(__snake_case						)
										_UpperCamelCase			     =     all_annos[index]
										_UpperCamelCase			     =     cva.imread(__snake_case						)
										if i == 0:  # top-left
															_UpperCamelCase			     =     cva.resize(__snake_case  ,     (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(__snake_case  ,     (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(__snake_case  ,     (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(
															    __snake_case  ,     (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 					_snake_case	(  __snake_case						):
					assert number_char > 1, "The number of character should greater than 1"
					_UpperCamelCase			     =     ascii_lowercase + digits
					return "".join(random.choice(__snake_case						) for _ in range(__snake_case						)						)


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







"""simple docstring"""


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_=5      )       ->		List[Any]:




      """simple docstring"""

      assert masked_input.count("<mask>"      ) == 1
      _UpperCamelCase   :						str    =							torch.tensor(tokenizer.encode(lowercase_		,add_special_tokens=lowercase_      )      ).unsqueeze(0      )  # Batch size 1
      _UpperCamelCase   :						Union[str, Any]    =							model(lowercase_      )[0]  # The last hidden-state is the first element of the output tuple
      _UpperCamelCase   :						List[Any]    =							(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
      _UpperCamelCase   :						Tuple    =							logits[0, masked_index, :]
      _UpperCamelCase   :						List[str]    =							logits.softmax(dim=0      )
      _UpperCamelCase,	_UpperCamelCase   :						str    =							prob.topk(k=lowercase_		,dim=0      )
      _UpperCamelCase   :						str    =							" ".join(
          [tokenizer.convert_ids_to_tokens(indices[i].item()      ) for i in range(len(lowercase_      )      )]      )
      _UpperCamelCase   :						Any    =							tokenizer.mask_token
      _UpperCamelCase   :						List[Any]    =							[]
      for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "      )      ):
            _UpperCamelCase   :						Optional[int]    =							predicted_token_bpe.replace("\u2581"		," "      )
            if " {0}".format(lowercase_      ) in masked_input:
                  topk_filled_outputs.append(
                      (
                          masked_input.replace(" {0}".format(lowercase_      )		,lowercase_      ),
                          values[index].item(),
                          predicted_token,
                      )      )
            else:
                  topk_filled_outputs.append(
                      (
                          masked_input.replace(lowercase_		,lowercase_      ),
                          values[index].item(),
                          predicted_token,
                      )      )
      return topk_filled_outputs


lowerCamelCase__						= CamembertTokenizer.from_pretrained("camembert-base")
lowerCamelCase__						= CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

lowerCamelCase__						= "Le camembert est <mask> :)"

print(fill_mask(masked_input, model, tokenizer, topk=3))
