




import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
_lowerCAmelCase						:   Any		=   logging.getLogger()

_lowerCAmelCase						:   Optional[int]		=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 			_UpperCamelCase      (				lowerCAmelCase		):
      def 	UpperCAmelCase_						(			self   :List[Any] ,      lowerCamelCase   :Optional[int]      )     ->			Any:
       os.makedirs(lowerCamelCase ,      exist_ok=lowerCamelCase      )
       UpperCAmelCase__							 =						{"source": "What is love ?", "target": "life"}
       UpperCAmelCase__							 =						{"train": 12, "val": 2, "test": 2}
       for split in ["train", "test", "val"]:
        for field in ["source", "target"]:
         UpperCAmelCase__							 =						"\n".join([contents[field]] * n_lines[split]      )
         with open(os.path.join(lowerCamelCase ,      f'''{split}.{field}'''      ) ,      "w"      ) as f:
          f.write(lowerCamelCase      )
      def 	UpperCAmelCase_						(			self   :List[str] ,      lowerCamelCase   :int ,      lowerCamelCase   :str = "pytorch"      )     ->			Dict:
       UpperCAmelCase__							 =						self.get_auto_remove_tmp_dir()
       UpperCAmelCase__							 =						os.path.join(lowerCamelCase ,      "output"      )
       UpperCAmelCase__							 =						os.path.join(lowerCamelCase ,      "data"      )
       self._create_dummy_data(data_dir=lowerCamelCase      )

       UpperCAmelCase__							 =						f'''
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            '''.split()

       if gpus > 0:
        testargs.append(f'''--gpus={gpus}'''      )
        if is_apex_available():
         testargs.append("--fp16"      )
       else:
        testargs.append("--gpus=0"      )
        testargs.append("--distributed_backend=ddp_cpu"      )
        testargs.append("--num_processes=2"      )

       UpperCAmelCase__							 =						[sys.executable, str(Path(finetune_rag.__file__      ).resolve()      )] + testargs
       execute_subprocess_async(lowerCamelCase ,      env=self.get_env()      )

       UpperCAmelCase__							 =						os.path.join(lowerCamelCase ,      "metrics.json"      )
       with open(lowerCamelCase      ) as f:
        UpperCAmelCase__							 =						json.load(lowerCamelCase      )
       return result
      @require_torch_gpu
      def 	UpperCAmelCase_						(			self   :Any      )     ->			int:
       UpperCAmelCase__							 =						self._run_finetune(gpus=1      )
       self.assertGreaterEqual(result["test"][0]["test_avg_em"] ,      0.2      )
      @require_torch_multi_gpu
      def 	UpperCAmelCase_						(			self   :Any      )     ->			List[str]:
       UpperCAmelCase__							 =						self._run_finetune(gpus=2      )
       self.assertGreaterEqual(result["test"][0]["test_avg_em"] ,      0.2      )
      @require_torch_gpu
      @require_ray
      def 	UpperCAmelCase_						(			self   :Dict      )     ->			Dict:
       UpperCAmelCase__							 =						self._run_finetune(gpus=1 ,      distributed_retriever="ray"      )
       self.assertGreaterEqual(result["test"][0]["test_avg_em"] ,      0.2      )







      @require_torch_multi_gpu
      @require_ray
      def 	UpperCAmelCase_						(			self   :Union[str, Any]      )     ->			Any:
       UpperCAmelCase__							 =						self._run_finetune(gpus=1 ,      distributed_retriever="ray"      )
       self.assertGreaterEqual(result["test"][0]["test_avg_em"] ,      0.2      )











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

import cva
import numpy as np

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



def 						lowerCAmelCase		(		):




 """simple docstring"""







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

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



def 						lowerCAmelCase		(		_lowerCAmelCase							:   str						,							_lowerCAmelCase							:   str	):




 """simple docstring"""







 UpperCAmelCase__							 =						[]
 UpperCAmelCase__							 =						[]
 for label_file in glob.glob(os.path.join(_lowerCAmelCase						,							"*.txt"	)	):
  UpperCAmelCase__							 =						label_file.split(os.sep	)[-1].rsplit("."						,							1	)[0]
  with open(_lowerCAmelCase	) as in_file:
   UpperCAmelCase__							 =						in_file.readlines()
  UpperCAmelCase__							 =						os.path.join(_lowerCAmelCase						,							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(_lowerCAmelCase	)
  labels.append(_lowerCAmelCase	)
 return img_paths, labels



def 						lowerCAmelCase		(		_lowerCAmelCase							:   list						,							_lowerCAmelCase							:   list						,							_lowerCAmelCase							:   list[int]						,							_lowerCAmelCase							:   tuple[int, int]						,							_lowerCAmelCase							:   tuple[float, float]						,							_lowerCAmelCase							:   float = 0.0						,							):




 """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(_lowerCAmelCase	):
  UpperCAmelCase__							 =						all_img_list[index]
  path_list.append(_lowerCAmelCase	)
  UpperCAmelCase__							 =						all_annos[index]
  UpperCAmelCase__							 =						cva.imread(_lowerCAmelCase	)
  if i == 0:  # top-left
   UpperCAmelCase__							 =						cva.resize(_lowerCAmelCase						,							(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(_lowerCAmelCase						,							(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(_lowerCAmelCase						,							(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(
       _lowerCAmelCase						,							(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		(		_lowerCAmelCase							:   int	):




 """simple docstring"""







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


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





