



'''simple docstring'''
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


_UpperCamelCase											=			torch.cuda.is_available()
_UpperCamelCase											=			[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
_UpperCamelCase											=			['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
_UpperCamelCase											=			{
    '''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   lowercase_	(   lowerCAmelCase__	:  Path			,							lowerCAmelCase__	:  list ):

			"""simple docstring"""





			__UpperCAmelCase   :      int										=     """\n""".join(lowerCAmelCase__ )
			Path(lowerCAmelCase__ ).open("""w""" ).writelines(lowerCAmelCase__ )


def   lowercase_	(   lowerCAmelCase__	:  Optional[Any] ):

			"""simple docstring"""





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






class _A  (    __SCREAMING_SNAKE_CASE    ):





		@classmethod
		def 							__A       (     cls				)       ->     Dict:

					'''simple docstring'''
					return cls





		@require_torch_multi_gpu
		def 							__A       (     self				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      List[str]										=     {
					    """no_teacher""": True,
					    """freeze_encoder""": True,
					    """gpus""": 2,
					    """overwrite_output_dir""": True,
					    """sortish_sampler""": True,
					}
					self._test_distiller_cli_fork(__UpperCAmelCase ,       check_contents=__UpperCAmelCase				)





		def 							__A       (     self ,       __UpperCAmelCase ,       __UpperCAmelCase=True				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      int										=     {
					    """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(__UpperCAmelCase				)
					__UpperCAmelCase   :      dict										=     CHEAP_ARGS.copy()
					__UpperCAmelCase   :      Optional[int]										=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
					__UpperCAmelCase   :      Any										=     self.get_auto_remove_tmp_dir()
					args_d.update(data_dir=__UpperCAmelCase ,       output_dir=__UpperCAmelCase ,       **__UpperCAmelCase				)

					def convert(__UpperCAmelCase ,       __UpperCAmelCase				):
								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}'

					__UpperCAmelCase   :      Any										=     [x for x in (convert(__UpperCAmelCase ,       __UpperCAmelCase				) for k, v in args_d.items()) if len(__UpperCAmelCase				)]
					__UpperCAmelCase   :      Any										=     [sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
					execute_subprocess_async(__UpperCAmelCase ,       env=self.get_env()				)

					__UpperCAmelCase   :      List[str]										=     os.listdir(__UpperCAmelCase				)
					__UpperCAmelCase   :      List[str]										=     {os.path.basename(__UpperCAmelCase				) for p in contents}
					__UpperCAmelCase   :      int										=     [p for p in contents if p.endswith("""ckpt"""				)]
					assert len(__UpperCAmelCase				) > 0

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

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

					__UpperCAmelCase   :      int										=     load_json(__UpperCAmelCase				)
					# {'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(__UpperCAmelCase				)
					__UpperCAmelCase   :      Union[str, Any]										=     metrics["""val"""][-1]
					self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""] ,       0.01				)
					self.assertIsInstance(last_step_stats[f'val_avg_{val_metric}'] ,       __UpperCAmelCase				)
					self.assertEqual(len(metrics["""test"""]				) ,       1				)
					__UpperCAmelCase   :      Dict										=     int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1				)
					self.assertEqual(len(metrics["""val"""]				) ,       __UpperCAmelCase				)









'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey



class _A  :





		def __init__(     self ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase   :      Optional[int]										=     0
					# max greyscale value for #FFFFFF
					__UpperCAmelCase   :      List[Any]										=     int(self.get_greyscale(255 ,       255 ,       255				)				)

					if not self.min_threshold < threshold < self.max_threshold:
								__UpperCAmelCase   :      str										=     f'Factor value should be from 0 to {self.max_threshold}'
								raise ValueError(__UpperCAmelCase				)

					__UpperCAmelCase   :      Tuple										=     input_img
					__UpperCAmelCase   :      Optional[Any]										=     threshold
					__UpperCAmelCase				,  __UpperCAmelCase   :      List[Any]										=     self.input_img.shape[1], self.input_img.shape[0]

					# error table size (+4 columns and +1 row) greater than input image because of
					# lack of if statements
					__UpperCAmelCase   :      Optional[Any]										=     [
					    [0 for _ in range(self.height + 4				)] for __ in range(self.width + 1				)
					]
					__UpperCAmelCase   :      Any										=     np.ones((self.width, self.height, 3) ,       np.uinta				) * 255





		@classmethod
		def 							__A       (     cls ,       __UpperCAmelCase ,       __UpperCAmelCase ,       __UpperCAmelCase				)       ->     float:

					'''simple docstring'''
					return 0.114 * blue + 0.587 * green + 0.2126 * red





		def 							__A       (     self				)       ->     None:

					'''simple docstring'''
					for y in range(self.height				):
								for x in range(self.width				):
											__UpperCAmelCase   :      Optional[Any]										=     int(self.get_greyscale(*self.input_img[y][x]				)				)
											if self.threshold > greyscale + self.error_table[y][x]:
														__UpperCAmelCase   :      List[Any]										=     (0, 0, 0)
														__UpperCAmelCase   :      Union[str, Any]										=     greyscale + self.error_table[x][y]
											else:
														__UpperCAmelCase   :      Optional[Any]										=     (255, 255, 255)
														__UpperCAmelCase   :      Dict										=     greyscale + self.error_table[x][y] - 255
											self.error_table[y][x + 1] += int(8 / 32 * current_error				)
											self.error_table[y][x + 2] += int(4 / 32 * current_error				)
											self.error_table[y + 1][x] += int(8 / 32 * current_error				)
											self.error_table[y + 1][x + 1] += int(4 / 32 * current_error				)
											self.error_table[y + 1][x + 2] += int(2 / 32 * current_error				)
											self.error_table[y + 1][x - 1] += int(4 / 32 * current_error				)
											self.error_table[y + 1][x - 2] += int(2 / 32 * current_error				)


if __name__ == "__main__":
					# create Burke's instances with original images in greyscale
					_UpperCamelCase											=			[
					    Burkes(imread('''image_data/lena.jpg''', 1), threshold)
					    for threshold in (1, 126, 130, 140)
					]

					for burkes in burkes_instances:
										burkes.process()

					for burkes in burkes_instances:
										imshow(
										    F'Original image with dithering threshold: {burkes.threshold}',
										    burkes.output_img,
										)

					waitKey(0)
					destroyAllWindows()




