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


lowerCAmelCase      :				Optional[Any]     			=torch.cuda.is_available()
lowerCAmelCase      :				Any     			=[" Sam ate lunch today.", "Sams lunch ingredients."]
lowerCAmelCase      :				Optional[Any]     			=["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
lowerCAmelCase      :				int     			={
    "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     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		List[Any]			    =    """\n""".join(__A						)
    Path(__A						).open("""w"""						).writelines(__A						)







def     A__	(       __A						):







    '''simple docstring'''
    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 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      @classmethod
      def 						_SCREAMING_SNAKE_CASE     (       cls  :					Any)					->int:


          """simple docstring"""


          return cls



      @require_torch_multi_gpu
      def 						_SCREAMING_SNAKE_CASE     (       self  :					Any)					->List[Any]:


          """simple docstring"""


          _lowerCamelCase  :		Dict			    =    {
              """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 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[int]     ,	_UpperCamelCase  :					str     ,	_UpperCamelCase  :					str=True)					->int:


          """simple docstring"""


          _lowerCamelCase  :		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(_UpperCamelCase)
          _lowerCamelCase  :		dict			    =    CHEAP_ARGS.copy()
          _lowerCamelCase  :		Any			    =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
          _lowerCamelCase  :		Any			    =    self.get_auto_remove_tmp_dir()
          args_d.update(data_dir=_UpperCamelCase     ,	output_dir=_UpperCamelCase     ,	**_UpperCamelCase)

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

          _lowerCamelCase  :		Optional[int]			    =    os.listdir(_UpperCamelCase)
          _lowerCamelCase  :		str			    =    {os.path.basename(_UpperCamelCase) for p in contents}
          _lowerCamelCase  :		Optional[Any]			    =    [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)
          _lowerCamelCase  :		Dict			    =    os.path.join(_UpperCamelCase     ,	"""metrics.json""")
          _lowerCamelCase  :		List[str]			    =    """rouge2"""

          _lowerCamelCase  :		List[Any]			    =    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)
          _lowerCamelCase  :		int			    =    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)
          _lowerCamelCase  :		Optional[Any]			    =    int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1)
          self.assertEqual(len(metrics["""val"""])     ,	_UpperCamelCase)





import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


lowerCAmelCase      :				List[Any]     			=logging.get_logger(__name__)

lowerCAmelCase      :				Optional[int]     			={
    "google/vit-hybrid-base-bit-384": "https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}




class 						__snake_case						(			__lowerCAmelCase		):





      '''simple docstring'''






      _snake_case     		=							'vit-hybrid'



      def __init__(       self  :					str     ,	_UpperCamelCase  :					int=None     ,	_UpperCamelCase  :					Union[str, Any]=768     ,	_UpperCamelCase  :					List[Any]=12     ,	_UpperCamelCase  :					List[Any]=12     ,	_UpperCamelCase  :					Optional[int]=3072     ,	_UpperCamelCase  :					Optional[int]="gelu"     ,	_UpperCamelCase  :					int=0.0     ,	_UpperCamelCase  :					Tuple=0.0     ,	_UpperCamelCase  :					Dict=0.0_2     ,	_UpperCamelCase  :					Dict=1E-1_2     ,	_UpperCamelCase  :					Union[str, Any]=224     ,	_UpperCamelCase  :					str=1     ,	_UpperCamelCase  :					Optional[int]=3     ,	_UpperCamelCase  :					Dict=[1, 1024, 24, 24]     ,	_UpperCamelCase  :					Optional[Any]=True     ,	**_UpperCamelCase  :					List[Any]     ,	)					->str:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)

          if backbone_config is None:
              logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone.""")
              _lowerCamelCase  :		Tuple			    =    {
                  """global_padding""": """same""",
                  """layer_type""": """bottleneck""",
                  """depths""": [3, 4, 9],
                  """out_features""": ["""stage3"""],
                  """embedding_dynamic_padding""": True,
              }

          if isinstance(_UpperCamelCase     ,	_UpperCamelCase):
              if "model_type" in backbone_config:
                  _lowerCamelCase  :		int			    =    CONFIG_MAPPING[backbone_config["""model_type"""]]
              else:
                  logger.info(
                      """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.""")
                  _lowerCamelCase  :		Optional[int]			    =    BitConfig
              _lowerCamelCase  :		List[Any]			    =    backbone_config_class(**_UpperCamelCase)

          _lowerCamelCase  :		Any			    =    backbone_featmap_shape
          _lowerCamelCase  :		Dict			    =    backbone_config
          _lowerCamelCase  :		Tuple			    =    hidden_size
          _lowerCamelCase  :		Union[str, Any]			    =    num_hidden_layers
          _lowerCamelCase  :		Optional[Any]			    =    num_attention_heads
          _lowerCamelCase  :		Optional[Any]			    =    intermediate_size
          _lowerCamelCase  :		List[Any]			    =    hidden_act
          _lowerCamelCase  :		Dict			    =    hidden_dropout_prob
          _lowerCamelCase  :		Optional[Any]			    =    attention_probs_dropout_prob
          _lowerCamelCase  :		Union[str, Any]			    =    initializer_range
          _lowerCamelCase  :		Any			    =    layer_norm_eps
          _lowerCamelCase  :		Optional[int]			    =    image_size
          _lowerCamelCase  :		Any			    =    patch_size
          _lowerCamelCase  :		Optional[Any]			    =    num_channels
          _lowerCamelCase  :		List[str]			    =    qkv_bias




      def 						_SCREAMING_SNAKE_CASE     (       self  :					Optional[Any])					->Dict[str, any]:


          """simple docstring"""


          _lowerCamelCase  :		Dict			    =    copy.deepcopy(self.__dict__)
          _lowerCamelCase  :		List[Any]			    =    self.backbone_config.to_dict()
          _lowerCamelCase  :		List[str]			    =    self.__class__.model_type
          return output




