






"""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


__SCREAMING_SNAKE_CASE      =      torch.cuda.is_available()
__SCREAMING_SNAKE_CASE      =      [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
__SCREAMING_SNAKE_CASE      =      ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
__SCREAMING_SNAKE_CASE      =      {
    '''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 					UpperCAmelCase			(				a__    ,			a__   ):





						'''simple docstring'''






						lowerCAmelCase      :List[Any]				  = '\n'.join(snake_case__   )
						Path(snake_case__   ).open('w'   ).writelines(snake_case__   )


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






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







class 	__UpperCamelCase    (    UpperCamelCase_	):
					@classmethod
					def 					UpperCAmelCase__   (       cls       :  Union[str, Any]      )  ->					List[str]:
											return cls
					@require_torch_multi_gpu
					def 					UpperCAmelCase__   (       self       :  str      )  ->					str:
											lowerCAmelCase      :Optional[int]				  = {
											    'no_teacher': True,
											    'freeze_encoder': True,
											    'gpus': 2,
											    'overwrite_output_dir': True,
											    'sortish_sampler': True,
											}
											self._test_distiller_cli_fork(__SCREAMING_SNAKE_CASE    ,   check_contents=__SCREAMING_SNAKE_CASE      )
					def 					UpperCAmelCase__   (       self       :  Optional[int]    ,   UpperCAmelCase       :  Optional[Any]    ,   UpperCAmelCase       :  List[str]=True      )  ->					str:
											lowerCAmelCase      :Optional[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(__SCREAMING_SNAKE_CASE      )
											lowerCAmelCase      :Any				  = CHEAP_ARGS.copy()
											lowerCAmelCase      :Tuple				  = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
											lowerCAmelCase      :Tuple				  = self.get_auto_remove_tmp_dir()
											args_d.update(data_dir=__SCREAMING_SNAKE_CASE    ,   output_dir=__SCREAMING_SNAKE_CASE    ,   **__SCREAMING_SNAKE_CASE      )

											def convert(UpperCAmelCase       :  int    ,   UpperCAmelCase       :  Optional[Any]      ):
																	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}"""

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

											lowerCAmelCase      :Optional[int]				  = os.listdir(__SCREAMING_SNAKE_CASE      )
											lowerCAmelCase      :List[Any]				  = {os.path.basename(__SCREAMING_SNAKE_CASE      ) for p in contents}
											lowerCAmelCase      :int				  = [p for p in contents if p.endswith('ckpt'      )]
											assert len(__SCREAMING_SNAKE_CASE      ) > 0

											self.assertIn('test_generations.txt'    ,   __SCREAMING_SNAKE_CASE      )
											self.assertIn('test_results.txt'    ,   __SCREAMING_SNAKE_CASE      )

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

											lowerCAmelCase      :Optional[int]				  = load_json(__SCREAMING_SNAKE_CASE      )
											# {'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(__SCREAMING_SNAKE_CASE      )
											lowerCAmelCase      :Optional[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}"""]    ,   __SCREAMING_SNAKE_CASE      )
											self.assertEqual(len(metrics['test']      )    ,   1      )
											lowerCAmelCase      :Optional[Any]				  = int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1      )
											self.assertEqual(len(metrics['val']      )    ,   __SCREAMING_SNAKE_CASE      )







"""simple docstring"""


import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference




@slow
@require_torch_gpu
class 	__UpperCamelCase    (    unittest.TestCase	):
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]      )  ->					Optional[Any]:
											lowerCAmelCase      :Union[str, Any]				  = 'runwayml/stable-diffusion-v1-5'
											lowerCAmelCase      :Tuple				  = TextToVideoZeroPipeline.from_pretrained(UpperCAmelCase    ,   torch_dtype=torch.floataa      ).to('cuda'      )
											lowerCAmelCase      :Union[str, Any]				  = DDIMScheduler.from_config(pipe.scheduler.config      )
											lowerCAmelCase      :str				  = torch.Generator(device='cuda'      ).manual_seed(0      )

											lowerCAmelCase      :Any				  = 'A bear is playing a guitar on Times Square'
											lowerCAmelCase      :Optional[Any]				  = pipe(prompt=UpperCAmelCase    ,   generator=UpperCAmelCase      ).images

											lowerCAmelCase      :List[str]				  = load_pt(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'      )

											assert_mean_pixel_difference(UpperCAmelCase    ,   UpperCAmelCase      )