

import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration


def 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__ = None  ,       A__ = None  ,       A__ = None  ,       ):






 """simple docstring"""



 if config_name_or_path is None:
  __lowercase													=				'''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''

 if generator_tokenizer_name_or_path is None:
  __lowercase													=				generator_name_or_path

 if question_encoder_tokenizer_name_or_path is None:
  __lowercase													=				question_encoder_name_or_path

 __lowercase													=				RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration

 # Save model.
 __lowercase													=				RagConfig.from_pretrained(A__   )
 __lowercase													=				AutoConfig.from_pretrained(A__   )
 __lowercase													=				AutoConfig.from_pretrained(A__   )

 __lowercase													=				gen_config
 __lowercase													=				question_encoder_config

 __lowercase													=				model_class.from_pretrained_question_encoder_generator(
     A__  ,       A__  ,       config=A__   )
 rag_model.save_pretrained(A__   )

 # Sanity check.
 model_class.from_pretrained(A__   )

 # Save tokenizers.
 __lowercase													=				AutoTokenizer.from_pretrained(A__   )
 gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/'''   )
 __lowercase													=				AutoTokenizer.from_pretrained(A__   )
 question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/'''   )


if __name__ == "__main__":
 lowerCAmelCase__      		=			argparse.ArgumentParser()
 parser.add_argument(
     '''--model_type''',
     choices=['''rag_sequence''', '''rag_token'''],
     required=True,
     type=str,
     help='''RAG model type: rag_sequence, rag_token''',
 )
 parser.add_argument('''--dest''', type=str, required=True, help='''Path to the output checkpoint directory.''')
 parser.add_argument('''--generator_name_or_path''', type=str, required=True, help='''Generator model identifier''')
 parser.add_argument(
     '''--question_encoder_name_or_path''', type=str, required=True, help='''Question encoder model identifier'''
 )

 parser.add_argument(
     '''--generator_tokenizer_name_or_path''',
     type=str,
     help='''Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``''',
 )
 parser.add_argument(
     '''--question_encoder_tokenizer_name_or_path''',
     type=str,
     help='''Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``''',
 )
 parser.add_argument(
     '''--config_name_or_path''',
     type=str,
     help=(
         '''Identifier of the model config to use, if not provided, resolves to a base config for a given'''
         ''' ``model_type``'''
     ),
 )

 lowerCAmelCase__      		=			parser.parse_args()

 lowerCAmelCase__      		=			Path(args.dest)
 dest_dir.mkdir(exist_ok=True)

 consolidate(
     args.model_type,
     args.generator_name_or_path,
     args.question_encoder_name_or_path,
     dest_dir,
     args.config_name_or_path,
     args.generator_tokenizer_name_or_path,
     args.question_encoder_tokenizer_name_or_path,
 )




'''simple docstring'''






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

import cva
import numpy as np

# Parrameters
lowerCAmelCase__      		=			(720, 1280)  # 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 	_A  (					):






 """simple docstring"""



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

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






 """simple docstring"""



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

  __lowercase													=				[]
  for obj_list in obj_lists:
   __lowercase													=				obj_list.rstrip('''\n'''   ).split(''' '''   )
   __lowercase													=				float(obj[1]   ) - float(obj[3]   ) / 2
   __lowercase													=				float(obj[2]   ) - float(obj[4]   ) / 2
   __lowercase													=				float(obj[1]   ) + float(obj[3]   ) / 2
   __lowercase													=				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 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__ = 0.0  ,       ):






 """simple docstring"""



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

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






 """simple docstring"""



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


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

