


import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


__lowerCamelCase   	=  torch.cuda.is_available()
__lowerCamelCase   	=  [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__lowerCamelCase   	=  ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__lowerCamelCase   	=  {
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 42,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1e-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase			):
     a_     :  str				      =     """\n""".join(__UpperCamelCase			)
     Path(__UpperCamelCase			).open("""w"""			).writelines(__UpperCamelCase			)



def 			_a      (    __UpperCamelCase			):
     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(__UpperCamelCase	,	F'''{split}.source'''			)	,	__UpperCamelCase			)
          _dump_articles(os.path.join(__UpperCamelCase	,	F'''{split}.target'''			)	,	__UpperCamelCase			)
     return tmp_dir


class 	a__						(    lowerCAmelCase_     ):

       @classmethod
       def     UpperCAmelCase( cls   :  Any     ):
            return cls

       @require_torch_multi_gpu
       def     UpperCAmelCase( self   :  Any     ):
            a_     :  Any				      =     {
                """no_teacher""": True,
                """freeze_encoder""": True,
                """gpus""": 2,
                """overwrite_output_dir""": True,
                """sortish_sampler""": True,
            }
            self._test_distiller_cli_fork(lowerCamelCase_   ,				check_contents=lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Any   ,				lowerCamelCase_   :  int   ,				lowerCamelCase_   :  str=True     ):
            a_     :  Union[str, Any]				      =     {
                """label_smoothing""": 0.0,
                """early_stopping_patience""": -1,
                """train_batch_size""": 1,
                """eval_batch_size""": 2,
                """max_epochs""": 2,
                """alpha_mlm""": 0.2,
                """alpha_ce""": 0.8,
                """do_predict""": True,
                """model_name_or_path""": """sshleifer/tinier_bart""",
                """teacher""": CHEAP_ARGS["""model_name_or_path"""],
                """val_check_interval""": 0.5,
            }
            default_updates.update(lowerCamelCase_     )
            a_     :  dict				      =     CHEAP_ARGS.copy()
            a_     :  Union[str, Any]				      =     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
            a_     :  Union[str, Any]				      =     self.get_auto_remove_tmp_dir()
            args_d.update(data_dir=lowerCamelCase_   ,				output_dir=lowerCamelCase_   ,				**lowerCamelCase_     )

            def convert(lowerCamelCase_   :  Optional[Any]   ,				lowerCamelCase_   :  Dict     ):
                 if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
                      return ""
                 if v is False or v is None:
                      return ""
                 if v is True:  # or len(str(v))==0:
                      return F'''--{k}'''
                 return F'''--{k}={v}'''

            a_     :  Optional[int]				      =     [x for x in (convert(lowerCamelCase_   ,				lowerCamelCase_     ) for k, v in args_d.items()) if len(lowerCamelCase_     )]
            a_     :  str				      =     [sys.executable, F'''{self.test_file_dir}/distillation.py'''] + cli_args
            execute_subprocess_async(lowerCamelCase_   ,				env=self.get_env()     )

            a_     :  Any				      =     os.listdir(lowerCamelCase_     )
            a_     :  List[Any]				      =     {os.path.basename(lowerCamelCase_     ) for p in contents}
            a_     :  Dict				      =     [p for p in contents if p.endswith("""ckpt"""     )]
            assert len(lowerCamelCase_     ) > 0

            self.assertIn("""test_generations.txt"""   ,				lowerCamelCase_     )
            self.assertIn("""test_results.txt"""   ,				lowerCamelCase_     )

            # get the following from the module, (we don't have access to `model` here)
            a_     :  List[str]				      =     os.path.join(lowerCamelCase_   ,				"""metrics.json"""     )
            a_     :  Union[str, Any]				      =     """rouge2"""

            a_     :  Tuple				      =     load_json(lowerCamelCase_     )
            # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
            print(lowerCamelCase_     )
            a_     :  Any				      =     metrics["""val"""][-1]
            self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]   ,				0.0_1     )
            self.assertIsInstance(last_step_stats[F'''val_avg_{val_metric}''']   ,				lowerCamelCase_     )
            self.assertEqual(len(metrics["""test"""]     )   ,				1     )
            a_     :  str				      =     int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1     )
            self.assertEqual(len(metrics["""val"""]     )   ,				lowerCamelCase_     )




'''simple docstring'''
import numpy as np
from PIL import Image







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				,      lowercase				,      lowercase				:							Dict									=   rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
  return 0.2989 * r + 0.5870 * g + 0.1140 * b







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array			)							->	np.array:
  return (gray > 127) & (gray <= 255)







def 				__UpperCamelCase (					__UpperCAmelCase  :					np.array ,   __UpperCAmelCase  :					np.array			)							->	np.array:
  lowercase				:							str									=   np.zeros_like(__UpperCAmelCase			)
  lowercase				:							Tuple									=   np.zeros(
      (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)			)

  # Copy image to padded image
  lowercase				:							int									=   image

  # Iterate over image & apply kernel
  for x in range(image.shape[1]			):
    for y in range(image.shape[0]			):
      lowercase				:							str									=   (
          kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
      ).sum()
      lowercase				:							Tuple									=   int(summation == 5			)
  return output


# kernel to be applied
_A		:      Union[str, Any]						   =np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
 # read original image
 _A		:      Optional[int]						   =np.array(Image.open(r'''..\image_data\lena.jpg'''))
 # Apply erosion operation to a binary image
 _A		:      Optional[int]						   =erosion(grayabinary(rgbagray(image)), structuring_element)
 # Save the output image
 _A		:      int						   =Image.fromarray(output).convert('''RGB''')
 pil_img.save('''result_erosion.png''')