
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


_A							=       torch.cuda.is_available()
_A							=       [''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
_A							=       ['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
_A							=       {
    '''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-0_8,
    '''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 					lowerCamelCase__					(    a__	:	Path						,   a__	:	list  )		->						Optional[Any]:
       UpperCamelCase_											=							"""\n""".join(a__  )
       Path(a__  ).open("""w"""  ).writelines(a__  )




def 					lowerCamelCase__					(    a__	:	List[Any]  )		->						Tuple:
       for split in ["train", "val", "test"]:
              _dump_articles(os.path.join(a__						,   f'''{split}.source'''  )						,   a__  )
              _dump_articles(os.path.join(a__						,   f'''{split}.target'''  )						,   a__  )
       return tmp_dir







class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):

 @classmethod
 def lowerCamelCase_					(   cls  ):




        """simple docstring"""


        return cls

 @require_torch_multi_gpu
 def lowerCamelCase_					(   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 lowerCamelCase_					(   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"""]  ,						0.01  )
        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  )





import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					List[Any]    =     (DPMSolverMultistepScheduler,)
 A__    :					List[str]    =     (("""num_inference_steps""", 25),)

 def lowerCamelCase_					(   self  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							{
            """num_train_timesteps""": 1_0_0_0,
            """beta_start""": 0.0_001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
            """solver_order""": 2,
            """prediction_type""": """epsilon""",
            """thresholding""": False,
            """sample_max_value""": 1.0,
            """algorithm_type""": """dpmsolver++""",
            """solver_type""": """midpoint""",
            """lower_order_final""": False,
            """lambda_min_clipped""": -float("""inf"""  ),
            """variance_type""": None,
        }

        config.update(**__UpperCamelCase  )
        return config

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )
        UpperCamelCase_											=							kwargs.pop("""num_inference_steps"""  ,						__UpperCamelCase  )
        UpperCamelCase_											=							self.dummy_sample
        UpperCamelCase_											=							0.1 * sample
        UpperCamelCase_											=							[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.get_scheduler_config(**__UpperCamelCase  )
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )
               scheduler.set_timesteps(__UpperCamelCase  )
               # copy over dummy past residuals
               UpperCamelCase_											=							dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )
                      new_scheduler.set_timesteps(__UpperCamelCase  )
                      # copy over dummy past residuals
                      UpperCamelCase_											=							dummy_past_residuals[: new_scheduler.config.solver_order]

               UpperCamelCase_							,					UpperCamelCase_											=							sample, sample
               for t in range(__UpperCamelCase  ,						time_step + scheduler.config.solver_order + 1  ):
                      UpperCamelCase_											=							scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample
                      UpperCamelCase_											=							new_scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample

                      assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        pass

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=0  ,						**__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )
        UpperCamelCase_											=							kwargs.pop("""num_inference_steps"""  ,						__UpperCamelCase  )
        UpperCamelCase_											=							self.dummy_sample
        UpperCamelCase_											=							0.1 * sample
        UpperCamelCase_											=							[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )
               scheduler.set_timesteps(__UpperCamelCase  )

               # copy over dummy past residuals (must be after setting timesteps)
               UpperCamelCase_											=							dummy_past_residuals[: scheduler.config.solver_order]

               with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(__UpperCamelCase  )
                      UpperCamelCase_											=							scheduler_class.from_pretrained(__UpperCamelCase  )
                      # copy over dummy past residuals
                      new_scheduler.set_timesteps(__UpperCamelCase  )

                      # copy over dummy past residual (must be after setting timesteps)
                      UpperCamelCase_											=							dummy_past_residuals[: new_scheduler.config.solver_order]

               UpperCamelCase_											=							scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							new_scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample

               assert torch.sum(torch.abs(output - new_output  )  ) < 1e-5, "Scheduler outputs are not identical"

 def lowerCamelCase_					(   self  ,						__UpperCamelCase=None  ,						**__UpperCamelCase  ):




        """simple docstring"""


        if scheduler is None:
               UpperCamelCase_											=							self.scheduler_classes[0]
               UpperCamelCase_											=							self.get_scheduler_config(**__UpperCamelCase  )
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

        UpperCamelCase_											=							1_0
        UpperCamelCase_											=							self.dummy_model()
        UpperCamelCase_											=							self.dummy_sample_deter
        scheduler.set_timesteps(__UpperCamelCase  )

        for i, t in enumerate(scheduler.timesteps  ):
               UpperCamelCase_											=							model(__UpperCamelCase  ,						__UpperCamelCase  )
               UpperCamelCase_											=							scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ).prev_sample

        return sample

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							dict(self.forward_default_kwargs  )

        UpperCamelCase_											=							kwargs.pop("""num_inference_steps"""  ,						__UpperCamelCase  )

        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.get_scheduler_config()
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               UpperCamelCase_											=							self.dummy_sample
               UpperCamelCase_											=							0.1 * sample

               if num_inference_steps is not None and hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      scheduler.set_timesteps(__UpperCamelCase  )
               elif num_inference_steps is not None and not hasattr(__UpperCamelCase  ,						"""set_timesteps"""  ):
                      UpperCamelCase_											=							num_inference_steps

               # copy over dummy past residuals (must be done after set_timesteps)
               UpperCamelCase_											=							[residual + 0.2, residual + 0.15, residual + 0.10]
               UpperCamelCase_											=							dummy_past_residuals[: scheduler.config.solver_order]

               UpperCamelCase_											=							scheduler.timesteps[5]
               UpperCamelCase_											=							scheduler.timesteps[6]

               UpperCamelCase_											=							scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample
               UpperCamelCase_											=							scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						**__UpperCamelCase  ).prev_sample

               self.assertEqual(output_a.shape  ,						sample.shape  )
               self.assertEqual(output_a.shape  ,						output_a.shape  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
               self.check_over_configs(num_train_timesteps=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.check_over_configs(thresholding=__UpperCamelCase  )
        for order in [1, 2, 3]:
               for solver_type in ["midpoint", "heun"]:
                      for threshold in [0.5, 1.0, 2.0]:
                             for prediction_type in ["epsilon", "sample"]:
                                    self.check_over_configs(
                                        thresholding=__UpperCamelCase  ,						prediction_type=__UpperCamelCase  ,						sample_max_value=__UpperCamelCase  ,						algorithm_type="""dpmsolver++"""  ,						solver_order=__UpperCamelCase  ,						solver_type=__UpperCamelCase  ,						)

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for prediction_type in ["epsilon", "v_prediction"]:
               self.check_over_configs(prediction_type=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
               for solver_type in ["midpoint", "heun"]:
                      for order in [1, 2, 3]:
                             for prediction_type in ["epsilon", "sample"]:
                                    if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                           if order == 3:
                                                  continue
                                    else:
                                           self.check_over_configs(
                                               solver_order=__UpperCamelCase  ,						solver_type=__UpperCamelCase  ,						prediction_type=__UpperCamelCase  ,						algorithm_type=__UpperCamelCase  ,						)
                                    UpperCamelCase_											=							self.full_loop(
                                        solver_order=__UpperCamelCase  ,						solver_type=__UpperCamelCase  ,						prediction_type=__UpperCamelCase  ,						algorithm_type=__UpperCamelCase  ,						)
                                    assert not torch.isnan(__UpperCamelCase  ).any(), "Samples have nan numbers"

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.check_over_configs(lower_order_final=__UpperCamelCase  )
        self.check_over_configs(lower_order_final=__UpperCamelCase  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.check_over_configs(lambda_min_clipped=-float("""inf"""  )  )
        self.check_over_configs(lambda_min_clipped=-5.1  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        self.check_over_configs(variance_type=__UpperCamelCase  )
        self.check_over_configs(variance_type="""learned_range"""  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
               self.check_over_forward(num_inference_steps=__UpperCamelCase  ,						time_step=0  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.full_loop()
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.3_301  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.full_loop(thresholding=__UpperCamelCase  ,						dynamic_thresholding_ratio=0.87  ,						sample_max_value=0.5  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 1.1_364  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.full_loop(prediction_type="""v_prediction"""  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.2_251  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.full_loop(prediction_type="""v_prediction"""  ,						use_karras_sigmas=__UpperCamelCase  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.2_096  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							DPMSolverMultistepScheduler(**self.get_scheduler_config()  )
        UpperCamelCase_											=							self.full_loop(scheduler=__UpperCamelCase  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.3_301  ) < 1e-3

        UpperCamelCase_											=							DPMSolverSinglestepScheduler.from_config(scheduler.config  )
        UpperCamelCase_											=							UniPCMultistepScheduler.from_config(scheduler.config  )
        UpperCamelCase_											=							DEISMultistepScheduler.from_config(scheduler.config  )
        UpperCamelCase_											=							DPMSolverMultistepScheduler.from_config(scheduler.config  )

        UpperCamelCase_											=							self.full_loop(scheduler=__UpperCamelCase  )
        UpperCamelCase_											=							torch.mean(torch.abs(__UpperCamelCase  )  )

        assert abs(result_mean.item() - 0.3_301  ) < 1e-3

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.scheduler_classes[0]
        UpperCamelCase_											=							self.get_scheduler_config(thresholding=__UpperCamelCase  ,						dynamic_thresholding_ratio=0  )
        UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

        UpperCamelCase_											=							1_0
        UpperCamelCase_											=							self.dummy_model()
        UpperCamelCase_											=							self.dummy_sample_deter.half()
        scheduler.set_timesteps(__UpperCamelCase  )

        for i, t in enumerate(scheduler.timesteps  ):
               UpperCamelCase_											=							model(__UpperCamelCase  ,						__UpperCamelCase  )
               UpperCamelCase_											=							scheduler.step(__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ).prev_sample

        assert sample.dtype == torch.floataa

 def lowerCamelCase_					(   self  ,						**__UpperCamelCase  ):




        """simple docstring"""


        for scheduler_class in self.scheduler_classes:
               UpperCamelCase_											=							self.get_scheduler_config(**__UpperCamelCase  )
               UpperCamelCase_											=							scheduler_class(**__UpperCamelCase  )

               scheduler.set_timesteps(scheduler.config.num_train_timesteps  )
               assert len(scheduler.timesteps.unique()  ) == scheduler.num_inference_steps



