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


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







    '''simple docstring'''
    __snake_case       :				Tuple 			=    """\n""".join(__SCREAMING_SNAKE_CASE   )
    Path(__SCREAMING_SNAKE_CASE   ).open("""w"""   ).writelines(__SCREAMING_SNAKE_CASE   )







def 					__lowerCAmelCase				(		__SCREAMING_SNAKE_CASE       :					Union[str, Any]   ):







    '''simple docstring'''
    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      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):

       @classmethod
       def 			snake_case__    (							cls					:     str			):
           return cls

       @require_torch_multi_gpu
       def 			snake_case__    (							self					:     List[str]			):
           __snake_case       :				Optional[Any] 			=    {
               """no_teacher""": True,
               """freeze_encoder""": True,
               """gpus""": 2,
               """overwrite_output_dir""": True,
               """sortish_sampler""": True,
           }
           self._test_distiller_cli_fork(_lowerCAmelCase			,			check_contents=_lowerCAmelCase			)





       def 			snake_case__    (							self					:     Optional[Any]			,			_lowerCAmelCase					:     int			,			_lowerCAmelCase					:     Optional[int]=True			):
           __snake_case       :				Dict 			=    {
               """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(_lowerCAmelCase			)
           __snake_case       :				dict 			=    CHEAP_ARGS.copy()
           __snake_case       :				List[Any] 			=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
           __snake_case       :				Any 			=    self.get_auto_remove_tmp_dir()
           args_d.update(data_dir=_lowerCAmelCase			,			output_dir=_lowerCAmelCase			,			**_lowerCAmelCase			)

           def convert(_lowerCAmelCase					:     Optional[int]			,			_lowerCAmelCase					:     Union[str, 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}'''

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

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

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

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

           __snake_case       :				Union[str, Any] 			=    load_json(_lowerCAmelCase			)
           # {'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(_lowerCAmelCase			)
           __snake_case       :				List[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}''']			,			_lowerCAmelCase			)
           self.assertEqual(len(metrics["""test"""]			)			,			1			)
           __snake_case       :				Tuple 			=    int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1			)
           self.assertEqual(len(metrics["""val"""]			)			,			_lowerCAmelCase			)




import tempfile
import unittest

from diffusers import (
    DDIMScheduler,
    DDPMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    PNDMScheduler,
    logging,
)
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.utils.testing_utils import CaptureLogger







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	str          =	"config.json"

       @register_to_config
       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Tuple=2			,			_lowerCAmelCase					:     List[Any]=5			,			_lowerCAmelCase					:     int=(2, 5)			,			_lowerCAmelCase					:     Dict="for diffusion"			,			_lowerCAmelCase					:     List[str]=[1, 3]			,			):
           pass







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	Optional[int]          =	"config.json"

       @register_to_config
       def __init__(							self					:     int			,			_lowerCAmelCase					:     Dict=2			,			_lowerCAmelCase					:     Union[str, Any]=5			,			_lowerCAmelCase					:     Any=(2, 5)			,			_lowerCAmelCase					:     Optional[int]="for diffusion"			,			_lowerCAmelCase					:     int=[1, 3]			,			):
           pass







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	str          =	"config.json"

       @register_to_config
       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Optional[int]=2			,			_lowerCAmelCase					:     Optional[int]=5			,			_lowerCAmelCase					:     int=(2, 5)			,			_lowerCAmelCase					:     Tuple="for diffusion"			,			_lowerCAmelCase					:     Any=[1, 3]			,			_lowerCAmelCase					:     str=[1, 3]			,			):
           pass







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	Union[str, Any]          =	"config.json"

       @register_to_config
       def __init__(							self					:     Any			,			_lowerCAmelCase					:     Dict=2			,			_lowerCAmelCase					:     Optional[int]=5			,			_lowerCAmelCase					:     Optional[Any]=(2, 5)			,			_lowerCAmelCase					:     int="for diffusion"			,			_lowerCAmelCase					:     Dict=[1, 5]			,			_lowerCAmelCase					:     Optional[Any]=[5, 4]			,			):
           pass







class      SCREAMING_SNAKE_CASE__					(  unittest.TestCase    ):

       def 			snake_case__    (							self					:     List[str]			):
           with self.assertRaises(_lowerCAmelCase			):
               ConfigMixin.load_config("""dummy_path"""			)

       def 			snake_case__    (							self					:     List[Any]			):
           __snake_case       :				List[Any] 			=    SampleObject()
           __snake_case       :				Optional[Any] 			=    obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # init ignore private arguments
           __snake_case       :				Optional[Any] 			=    SampleObject(_name_or_path="""lalala"""			)
           __snake_case       :				List[Any] 			=    obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # can override default
           __snake_case       :				int 			=    SampleObject(c=6			)
           __snake_case       :				Union[str, Any] 			=    obj.config
           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == 6
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           # can use positional arguments.
           __snake_case       :				List[str] 			=    SampleObject(1			,			c=6			)
           __snake_case       :				str 			=    obj.config
           assert config["a"] == 1
           assert config["b"] == 5
           assert config["c"] == 6
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

       def 			snake_case__    (							self					:     Union[str, Any]			):
           __snake_case       :				Union[str, Any] 			=    SampleObject()
           __snake_case       :				Optional[Any] 			=    obj.config

           assert config["a"] == 2
           assert config["b"] == 5
           assert config["c"] == (2, 5)
           assert config["d"] == "for diffusion"
           assert config["e"] == [1, 3]

           with tempfile.TemporaryDirectory() as tmpdirname:
               obj.save_config(_lowerCAmelCase			)
               __snake_case       :				List[Any] 			=    SampleObject.from_config(SampleObject.load_config(_lowerCAmelCase			)			)
               __snake_case       :				Union[str, Any] 			=    new_obj.config

           # unfreeze configs
           __snake_case       :				Optional[Any] 			=    dict(_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    dict(_lowerCAmelCase			)

           assert config.pop("""c"""			) == (2, 5)  # instantiated as tuple
           assert new_config.pop("""c"""			) == [2, 5]  # saved & loaded as list because of json
           config.pop("""_use_default_values"""			)
           assert config == new_config

       def 			snake_case__    (							self					:     Tuple			):
           __snake_case       :				Optional[Any] 			=    logging.get_logger("""diffusers.configuration_utils"""			)
           # 30 for warning
           logger.setLevel(30			)

           with CaptureLogger(_lowerCAmelCase			) as cap_logger:
               __snake_case       :				List[Any] 			=    DDIMScheduler.from_pretrained(
                   """hf-internal-testing/tiny-stable-diffusion-torch"""			,			subfolder="""scheduler"""			)

           assert ddim.__class__ == DDIMScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 			snake_case__    (							self					:     Optional[int]			):
           __snake_case       :				int 			=    logging.get_logger("""diffusers.configuration_utils"""			)
           # 30 for warning
           logger.setLevel(30			)

           with CaptureLogger(_lowerCAmelCase			) as cap_logger:
               __snake_case       :				Optional[int] 			=    EulerDiscreteScheduler.from_pretrained(
                   """hf-internal-testing/tiny-stable-diffusion-torch"""			,			subfolder="""scheduler"""			)

           assert euler.__class__ == EulerDiscreteScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 			snake_case__    (							self					:     Tuple			):
           __snake_case       :				int 			=    logging.get_logger("""diffusers.configuration_utils"""			)
           # 30 for warning
           logger.setLevel(30			)

           with CaptureLogger(_lowerCAmelCase			) as cap_logger:
               __snake_case       :				Any 			=    EulerAncestralDiscreteScheduler.from_pretrained(
                   """hf-internal-testing/tiny-stable-diffusion-torch"""			,			subfolder="""scheduler"""			)

           assert euler.__class__ == EulerAncestralDiscreteScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 			snake_case__    (							self					:     List[Any]			):
           __snake_case       :				str 			=    logging.get_logger("""diffusers.configuration_utils"""			)
           # 30 for warning
           logger.setLevel(30			)

           with CaptureLogger(_lowerCAmelCase			) as cap_logger:
               __snake_case       :				Optional[int] 			=    PNDMScheduler.from_pretrained(
                   """hf-internal-testing/tiny-stable-diffusion-torch"""			,			subfolder="""scheduler"""			)

           assert pndm.__class__ == PNDMScheduler
           # no warning should be thrown
           assert cap_logger.out == ""

       def 			snake_case__    (							self					:     Tuple			):
           __snake_case       :				int 			=    logging.get_logger("""diffusers.configuration_utils"""			)
           # 30 for warning
           logger.setLevel(30			)

           with CaptureLogger(_lowerCAmelCase			) as cap_logger:
               __snake_case       :				List[str] 			=    DDPMScheduler.from_pretrained(
                   """hf-internal-testing/tiny-stable-diffusion-torch"""			,			subfolder="""scheduler"""			,			prediction_type="""sample"""			,			beta_end=8			,			)

           with CaptureLogger(_lowerCAmelCase			) as cap_logger_a:
               __snake_case       :				Optional[Any] 			=    DDPMScheduler.from_pretrained("""google/ddpm-celebahq-256"""			,			beta_start=88			)

           assert ddpm.__class__ == DDPMScheduler
           assert ddpm.config.prediction_type == "sample"
           assert ddpm.config.beta_end == 8
           assert ddpm_a.config.beta_start == 88

           # no warning should be thrown
           assert cap_logger.out == ""
           assert cap_logger_a.out == ""

       def 			snake_case__    (							self					:     List[Any]			):
           __snake_case       :				List[Any] 			=    logging.get_logger("""diffusers.configuration_utils"""			)
           # 30 for warning
           logger.setLevel(30			)

           with CaptureLogger(_lowerCAmelCase			) as cap_logger:
               __snake_case       :				str 			=    DPMSolverMultistepScheduler.from_pretrained(
                   """hf-internal-testing/tiny-stable-diffusion-torch"""			,			subfolder="""scheduler"""			)

           assert dpm.__class__ == DPMSolverMultistepScheduler
           # no warning should be thrown
           assert cap_logger.out == ""





       def 			snake_case__    (							self					:     List[Any]			):
           # let's first save a config that should be in the form
           #    a=2,
           #    b=5,
           #    c=(2, 5),
           #    d="for diffusion",
           #    e=[1, 3],

           __snake_case       :				Optional[Any] 			=    SampleObject()

           __snake_case       :				Union[str, Any] 			=    {k: v for k, v in config.config.items() if not k.startswith("""_"""			)}

           # make sure that default config has all keys in `_use_default_values`
           assert set(config_dict.keys()			) == set(config.config._use_default_values			)

           with tempfile.TemporaryDirectory() as tmpdirname:
               config.save_config(_lowerCAmelCase			)

               # now loading it with SampleObject2 should put f into `_use_default_values`
               __snake_case       :				Union[str, Any] 			=    SampleObjecta.from_config(_lowerCAmelCase			)

               assert "f" in config._use_default_values
               assert config.f == [1, 3]

           # now loading the config, should **NOT** use [1, 3] for `f`, but the default [1, 4] value
           # **BECAUSE** it is part of `config._use_default_values`
           __snake_case       :				Optional[int] 			=    SampleObjecta.from_config(config.config			)
           assert new_config.f == [5, 4]

           config.config._use_default_values.pop()
           __snake_case       :				Optional[Any] 			=    SampleObjecta.from_config(config.config			)
           assert new_config_a.f == [1, 3]

           # Nevertheless "e" should still be correctly loaded to [1, 3] from SampleObject2 instead of defaulting to [1, 5]
           assert new_config_a.e == [1, 3]



