
import sys
from typing import Dict

from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.utils import logging


__snake_case					:							Dict           =							logging.get_logger(__name__)


if is_torch_available():
						import torch
						from torch import nn
						from torch.utils.data import Dataset

						from transformers import Trainer


						class 	UpperCamelCase__       (  snake_case__):

												'''simple docstring'''



												def __init__(  self  , A = 1_01	)  ->List[Any]:
															UpperCAmelCase__		:Optional[int] 		=    length


												def __len__(  self	)  ->int:
															return self.length


												def __getitem__(  self  , A	)  ->Optional[int]:
															return i


						class 	UpperCamelCase__       :

												'''simple docstring'''



												def __call__(  self  , A	)  ->List[str]:
															return {"input_ids": torch.tensor(lowercase_	), "labels": torch.tensor(lowercase_	)}


						class 	UpperCamelCase__       (  nn.Module):

												'''simple docstring'''



												def __init__(  self	)  ->str:
															super().__init__()
															# Add some (unused) params otherwise DDP will complain.
															UpperCAmelCase__		:str 		=    nn.Linear(1_20  , 80	)


												def    A__       (  self  , A  , A=None	)  ->Optional[Any]:
															if labels is not None:
																		return torch.tensor(0.0  , device=input_ids.device	), input_ids
															else:
																		return input_ids




def 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Optional[Any] 		=    HfArgumentParser((TrainingArguments,)							)
			sys.argv += ["--output_dir", "./examples"]
			UpperCAmelCase__		:Dict 		=    parser.parse_args_into_dataclasses()[0]

			logger.warning(
			    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, """
			    f"""tpu_num_cores: {training_args.tpu_num_cores}"""     ,						)

			# Essentially, what we want to verify in the distributed case is
			# that we get all samples back, in the right order.
			# (this is crucial for prediction for instance)
			for dataset_length in [1001, 256, 15]:
						UpperCAmelCase__		:List[Any] 		=    DummyDataset(__SCREAMING_SNAKE_CASE							)

						def compute_metrics(SCREAMING_SNAKE_CASE							) -> Dict:
									UpperCAmelCase__		:str 		=    list(range(len(__SCREAMING_SNAKE_CASE							)							)							)
									UpperCAmelCase__		:Dict 		=    p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
									return {"success": success}

						UpperCAmelCase__		:Dict 		=    Trainer(
						    model=DummyModel()     ,						args=__SCREAMING_SNAKE_CASE     ,						data_collator=DummyDataCollator()     ,						eval_dataset=__SCREAMING_SNAKE_CASE     ,						compute_metrics=__SCREAMING_SNAKE_CASE     ,						)
						UpperCAmelCase__		:Optional[Any] 		=    trainer.evaluate()
						logger.info(__SCREAMING_SNAKE_CASE							)
						if metrics["eval_success"] is not True:
									logger.error(__SCREAMING_SNAKE_CASE							)
									exit(1							)

						UpperCAmelCase__		:Union[str, Any] 		=    trainer.predict(__SCREAMING_SNAKE_CASE							)
						logger.info(p.metrics							)
						if p.metrics["test_success"] is not True:
									logger.error(p.metrics							)
									exit(1							)

						UpperCAmelCase__		:Optional[int] 		=    2

						UpperCAmelCase__		:Dict 		=    trainer.evaluate()
						logger.info(__SCREAMING_SNAKE_CASE							)
						if metrics["eval_success"] is not True:
									logger.error(__SCREAMING_SNAKE_CASE							)
									exit(1							)

						UpperCAmelCase__		:Any 		=    trainer.predict(__SCREAMING_SNAKE_CASE							)
						logger.info(p.metrics							)
						if p.metrics["test_success"] is not True:
									logger.error(p.metrics							)
									exit(1							)

						UpperCAmelCase__		:str 		=    None

			logger.info('🔥 All distributed tests successful'							)




def 		A						(  SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			main()


if __name__ == "__main__":
						main()







'''simple docstring'''
import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()

def     UpperCamelCase_  (						__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							):
    """simple docstring"""






    snake_case_			:     Optional[Any]        =    json.load(open(__SCREAMING_SNAKE_CASE,		"r"							)							)
    snake_case_			:     Optional[int]        =    GPTNeoConfig(
        hidden_size=config_json["n_embd"],		num_layers=config_json["n_layer"],		num_heads=config_json["n_head"],		attention_types=config_json["attention_types"],		max_position_embeddings=config_json["n_positions"],		resid_dropout=config_json["res_dropout"],		embed_dropout=config_json["embed_dropout"],		attention_dropout=config_json["attn_dropout"],		)
    print(f'Building PyTorch model from configuration: {config}'							)
    snake_case_			:     Optional[Any]        =    GPTNeoForCausalLM(__SCREAMING_SNAKE_CASE							)

    # Load weights from tf checkpoint
    load_tf_weights_in_gpt_neo(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							)

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


if __name__ == "__main__":
  a_           =    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  )
  parser.add_argument(
      "--config_file",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained mesh-tf model. \n"
          "This specifies the model architecture."
      ),
  )
  parser.add_argument(
      "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  a_           =    parser.parse_args()
  convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)




