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


			'''simple docstring'''





			lowerCAmelCase_       :List[str]     	=    """\n""".join(lowercase__						)
			Path(lowercase__						).open("""w"""						).writelines(lowercase__						)






def 		_snake_case			(      lowercase__						:    List[Any]						)    ->							Any:


			'''simple docstring'''





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




class      _SCREAMING_SNAKE_CASE   (     __SCREAMING_SNAKE_CASE				):






			@classmethod
			def 					__lowerCAmelCase					(   cls			)     ->	Any:
						return cls






			@require_torch_multi_gpu
			def 					__lowerCAmelCase					(   self			)     ->	Dict:
						lowerCAmelCase_       :List[str]     	=    {
						    """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			,  __A			,  __A=True			)     ->	Any:
						lowerCAmelCase_       :Union[str, 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(__UpperCAmelCase			)
						lowerCAmelCase_       :List[Any]     	=    CHEAP_ARGS.copy()
						lowerCAmelCase_       :Union[str, Any]     	=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
						lowerCAmelCase_       :Optional[Any]     	=    self.get_auto_remove_tmp_dir()
						args_d.update(data_dir=__UpperCAmelCase			,  output_dir=__UpperCAmelCase			,  **__UpperCAmelCase			)

						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}"""

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

						lowerCAmelCase_       :Tuple     	=    os.listdir(__UpperCAmelCase			)
						lowerCAmelCase_       :Any     	=    {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_       :Tuple     	=    os.path.join(__UpperCAmelCase			,  """metrics.json"""			)
						lowerCAmelCase_       :Optional[Any]     	=    """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_       :str     	=    int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1			)
						self.assertEqual(len(metrics["""val"""]			)			,  __UpperCAmelCase			)








"""simple docstring"""

import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()






def 				A		(							snake_case :Dict							,   snake_case :int							,   snake_case :List[str]							,   snake_case :int							,   snake_case :List[str]						) ->      List[str]:
 # Initialise PyTorch model
 __UpperCamelCase							    =			BertConfig.from_pretrained(
     'bert-large-cased'							,   vocab_size=snake_case							,   max_position_embeddings=5_1_2							,   is_decoder=snake_case							,   add_cross_attention=snake_case							,   )
 __UpperCamelCase							    =			bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 __UpperCamelCase							    =			BertGenerationConfig(**snake_case						)
 if is_encoder:
  __UpperCamelCase							    =			BertGenerationEncoder(snake_case						)
 else:
  __UpperCamelCase							    =			BertGenerationDecoder(snake_case						)
 print(f'Building PyTorch model from configuration: {config}'						)

 # Load weights from tf checkpoint
 load_tf_weights_in_bert_generation(
     snake_case							,   snake_case							,   model_class='bert'							,   is_encoder_named_decoder=snake_case							,   is_encoder=snake_case							,   )

 # Save pytorch-model
 print(f'Save PyTorch model and config to {pytorch_dump_path}'						)
 model.save_pretrained(snake_case						)


if __name__ == "__main__":
      UpperCamelCase      :							List[str]          =					argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--is_encoder_named_decoder",
          action="store_true",
          help="If decoder has to be renamed to encoder in PyTorch model.",
      )
      parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
      parser.add_argument("--vocab_size", default=5_0_3_5_8, type=int, help="Vocab size of model")
      UpperCamelCase      :							Dict          =					parser.parse_args()
      convert_tf_checkpoint_to_pytorch(
          args.tf_hub_path,
          args.pytorch_dump_path,
          args.is_encoder_named_decoder,
          args.vocab_size,
          is_encoder=args.is_encoder,
      )



