
"""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      :							Optional[int]          =					torch.cuda.is_available()
UpperCamelCase      :							str          =					[" Sam ate lunch today.", "Sams lunch ingredients."]
UpperCamelCase      :							Optional[int]          =					["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
UpperCamelCase      :							Any          =					{
    "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 				A		(							snake_case :Path							,   snake_case :list						) ->      int:
 __UpperCamelCase							    =			'\n'.join(snake_case						)
 Path(snake_case						).open('w'						).writelines(snake_case						)






def 				A		(							snake_case :List[Any]						) ->      Any:
 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 __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ):





     @classmethod
     def 		UpperCAmelCase   (      cls ):

      '''simple docstring'''




      return cls





     @require_torch_multi_gpu
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      __UpperCamelCase							    =			{
          '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 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=True ):

      '''simple docstring'''




      __UpperCamelCase							    =			{
          '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							    =			CHEAP_ARGS.copy()
      __UpperCamelCase							    =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
      __UpperCamelCase							    =			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							    =			[x for x in (convert(__UpperCAmelCase      ,	__UpperCAmelCase ) for k, v in args_d.items()) if len(__UpperCAmelCase )]
      __UpperCamelCase							    =			[sys.executable, F'{self.test_file_dir}/distillation.py'] + cli_args
      execute_subprocess_async(__UpperCAmelCase      ,	env=self.get_env() )

      __UpperCamelCase							    =			os.listdir(__UpperCAmelCase )
      __UpperCamelCase							    =			{os.path.basename(__UpperCAmelCase ) for p in contents}
      __UpperCamelCase							    =			[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							    =			os.path.join(__UpperCAmelCase      ,	'metrics.json' )
      __UpperCamelCase							    =			'rouge2'

      __UpperCamelCase							    =			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							    =			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}']      ,	__UpperCAmelCase )
      self.assertEqual(len(metrics['test'] )      ,	1 )
      __UpperCamelCase							    =			int(args_d['max_epochs'] * (1 / args_d['val_check_interval']) / 2 + 1 )
      self.assertEqual(len(metrics['val'] )      ,	__UpperCAmelCase )





"""simple docstring"""

import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ):
     lowercase		  =		IFSuperResolutionPipeline
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowercase		  =		PipelineTesterMixin.required_optional_params - {"latents"}





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      return self._get_superresolution_dummy_components()





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=0 ):

      '''simple docstring'''




      if str(__UpperCAmelCase ).startswith('mps' ):
       __UpperCamelCase							    =			torch.manual_seed(__UpperCAmelCase )
      else:
       __UpperCamelCase							    =			torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )

      __UpperCamelCase							    =			floats_tensor((1, 3, 32, 32)      ,	rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )

      __UpperCamelCase							    =			{
          'prompt': 'A painting of a squirrel eating a burger',
          'image': image,
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }

      return inputs





     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()      ,	reason='XFormers attention is only available with CUDA and `xformers` installed'      ,	)
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_optional_components()





     @unittest.skipIf(torch_device != 'cuda'      ,	reason='float16 requires CUDA' )
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      super().test_save_load_floataa(expected_max_diff=1E-1 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_local()







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2      ,	)



