






'''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": 4_2,
    "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": 1_2,
    "max_target_length": 1_2,
    "val_max_target_length": 1_2,
    "test_max_target_length": 1_2,
    "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   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 __a    :	Union[str, Any]									=						'\n'.join(SCREAMING_SNAKE_CASE__		)
 Path(SCREAMING_SNAKE_CASE__		).open('w'		).writelines(SCREAMING_SNAKE_CASE__		)






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




class 							lowerCAmelCase					(							lowerCamelCase__   ):







   """simple docstring"""







   @classmethod
   def    __magic_name__    ( cls			)    ->   int:
    return cls



   @require_torch_multi_gpu
   def    __magic_name__    ( self			)    ->   Dict:
    __a    :	str									=						{
        'no_teacher': True,
        'freeze_encoder': True,
        'gpus': 2,
        'overwrite_output_dir': True,
        'sortish_sampler': True,
    }
    self._test_distiller_cli_fork(_A   ,   check_contents=_A			)



   def    __magic_name__    ( self   ,   _A   ,   _A=True			)    ->   int:
    __a    :	List[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(_A			)
    __a    :	dict									=						CHEAP_ARGS.copy()
    __a    :	List[str]									=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
    __a    :	Optional[int]									=						self.get_auto_remove_tmp_dir()
    args_d.update(data_dir=_A   ,   output_dir=_A   ,   **_A			)

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

    __a    :	Any									=						os.listdir(_A			)
    __a    :	List[Any]									=						{os.path.basename(_A			) for p in contents}
    __a    :	Union[str, Any]									=						[p for p in contents if p.endswith('ckpt'			)]
    assert len(_A			) > 0

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

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

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












'''simple docstring'''


import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()




class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   @property
   def    __magic_name__    ( self			)    ->   Any:
    torch.manual_seed(0			)
    __a    :	Optional[Any]									=						UNetaDModel(
        block_out_channels=(32, 64)   ,   layers_per_block=2   ,   sample_size=32   ,   in_channels=3   ,   out_channels=3   ,   down_block_types=('DownBlock2D', 'AttnDownBlock2D')   ,   up_block_types=('AttnUpBlock2D', 'UpBlock2D')   ,   )
    return model



   def    __magic_name__    ( self			)    ->   Tuple:
    __a    :	Union[str, Any]									=						'cpu'
    __a    :	Tuple									=						self.dummy_uncond_unet
    __a    :	str									=						DDPMScheduler()

    __a    :	int									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	str									=						torch.Generator(device=_A			).manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			).images

    __a    :	Union[str, Any]									=						torch.Generator(device=_A			).manual_seed(0			)
    __a    :	Optional[int]									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'   ,   return_dict=_A			)[0]

    __a    :	List[str]									=						image[0, -3:, -3:, -1]
    __a    :	List[str]									=						image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	List[Any]									=						np.array(
        [9.9_5_6E-0_1, 5.7_8_5E-0_1, 4.6_7_5E-0_1, 9.9_3_0E-0_1, 0.0, 1.000, 1.1_9_9E-0_3, 2.6_4_8E-0_4, 5.1_0_1E-0_4]			)

    assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2



   def    __magic_name__    ( self			)    ->   Tuple:
    __a    :	Optional[Any]									=						self.dummy_uncond_unet
    __a    :	List[str]									=						DDPMScheduler(prediction_type='sample'			)

    __a    :	List[str]									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	Optional[Any]									=						torch.manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			).images

    __a    :	Union[str, Any]									=						torch.manual_seed(0			)
    __a    :	int									=						ddpm(generator=_A   ,   num_inference_steps=2   ,   output_type='numpy'			)[0]

    __a    :	Dict									=						image[0, -3:, -3:, -1]
    __a    :	int									=						image_eps[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	int									=						1E-2 if torch_device != 'mps' else 3E-2
    assert np.abs(image_slice.flatten() - image_eps_slice.flatten()			).max() < tolerance




@slow
@require_torch_gpu
class 							lowerCAmelCase					(							unittest.TestCase   ):







   """simple docstring"""







   def    __magic_name__    ( self			)    ->   List[str]:
    __a    :	Union[str, Any]									=						'google/ddpm-cifar10-32'

    __a    :	str									=						UNetaDModel.from_pretrained(_A			)
    __a    :	List[str]									=						DDPMScheduler.from_pretrained(_A			)

    __a    :	List[str]									=						DDPMPipeline(unet=_A   ,   scheduler=_A			)
    ddpm.to(_A			)
    ddpm.set_progress_bar_config(disable=_A			)

    __a    :	int									=						torch.manual_seed(0			)
    __a    :	Optional[Any]									=						ddpm(generator=_A   ,   output_type='numpy'			).images

    __a    :	int									=						image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    __a    :	Optional[Any]									=						np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385]			)
    assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2




