




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

import cva
import numpy as np

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




def  SCREAMING_SNAKE_CASE     (	)  ->							None:
    UpperCAmelCase_			,							UpperCAmelCase_										=					get_dataset(__UpperCamelCase	,					__UpperCamelCase				)
    for index in range(__UpperCamelCase				):
        UpperCAmelCase_										=					random.sample(range(len(__UpperCamelCase				)				)	,					4				)
        UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					update_image_and_anno(
            __UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					filter_scale=__UpperCamelCase	,					)

        # 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'	,					__UpperCamelCase	,					[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(__UpperCamelCase				)
        with open(f'{file_root}.txt'	,					'''w'''				) as outfile:
            outfile.write('''\n'''.join(line for line in annos_list				)				)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str	,					__UpperCamelCase      :						str				)  ->							tuple[list, list]:
    UpperCAmelCase_										=					[]
    UpperCAmelCase_										=					[]
    for label_file in glob.glob(os.path.join(__UpperCamelCase	,					'''*.txt'''				)				):
        UpperCAmelCase_										=					label_file.split(os.sep				)[-1].rsplit('''.'''	,					1				)[0]
        with open(__UpperCamelCase				) as in_file:
            UpperCAmelCase_										=					in_file.readlines()
        UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					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(__UpperCamelCase				)
        labels.append(__UpperCamelCase				)
    return img_paths, labels




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						list	,					__UpperCamelCase      :						list	,					__UpperCamelCase      :						list[int]	,					__UpperCamelCase      :						tuple[int, int]	,					__UpperCamelCase      :						tuple[float, float]	,					__UpperCamelCase      :						float = 0.0	,					)  ->							tuple[list, list, str]:
    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(__UpperCamelCase				):
        UpperCAmelCase_										=					all_img_list[index]
        path_list.append(__UpperCamelCase				)
        UpperCAmelCase_										=					all_annos[index]
        UpperCAmelCase_										=					cva.imread(__UpperCamelCase				)
        if i == 0:  # top-left
            UpperCAmelCase_										=					cva.resize(__UpperCamelCase	,					(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(__UpperCamelCase	,					(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(__UpperCamelCase	,					(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(
                __UpperCamelCase	,					(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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int				)  ->							str:
    assert number_char > 1, "The number of character should greater than 1"
    UpperCAmelCase_										=					ascii_lowercase + digits
    return "".join(random.choice(__UpperCamelCase				) for _ in range(__UpperCamelCase				)				)


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











import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						List[str]=5				)  ->							Dict:
    # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
    assert masked_input.count('''<mask>'''				) == 1
    UpperCAmelCase_										=					torch.tensor(tokenizer.encode(__UpperCamelCase	,					add_special_tokens=__UpperCamelCase				)				).unsqueeze(0				)  # Batch size 1
    UpperCAmelCase_										=					model(__UpperCamelCase				)[0]  # The last hidden-state is the first element of the output tuple
    UpperCAmelCase_										=					(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
    UpperCAmelCase_										=					logits[0, masked_index, :]
    UpperCAmelCase_										=					logits.softmax(dim=0				)
    UpperCAmelCase_			,							UpperCAmelCase_										=					prob.topk(k=__UpperCamelCase	,					dim=0				)
    UpperCAmelCase_										=					''' '''.join(
        [tokenizer.convert_ids_to_tokens(indices[i].item()				) for i in range(len(__UpperCamelCase				)				)]				)
    UpperCAmelCase_										=					tokenizer.mask_token
    UpperCAmelCase_										=					[]
    for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''				)				):
        UpperCAmelCase_										=					predicted_token_bpe.replace('''\u2581'''	,					''' '''				)
        if " {0}".format(__UpperCamelCase				) in masked_input:
            topk_filled_outputs.append(
                (
                    masked_input.replace(''' {0}'''.format(__UpperCamelCase				)	,					__UpperCamelCase				),
                    values[index].item(),
                    predicted_token,
                )				)
        else:
            topk_filled_outputs.append(
                (
                    masked_input.replace(__UpperCamelCase	,					__UpperCamelCase				),
                    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))





