
'''simple docstring'''
import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
  import torch

  from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
  import pyanvml.pyanvml as nvml


a_           =    logging.get_logger(__name__)






class  UpperCAmelCase_					(						snake_case__       ):
 UpperCAmelCase_		     =    42
 UpperCAmelCase_		     =    42
 UpperCAmelCase_		     =    "PyTorch"





 @property
 def 							snake_case__		(     self):
     return torch.__version__





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_):
     snake_case_			:     str        =    self._prepare_inference_func(lowercase_			,							lowercase_			,							lowercase_)
     return self._measure_speed(_inference)





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_):
     snake_case_			:     Union[str, Any]        =    self._prepare_inference_func(lowercase_			,							lowercase_			,							lowercase_)
     return self._measure_memory(_inference)





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_):
     snake_case_			:     List[Any]        =    self._prepare_train_func(lowercase_			,							lowercase_			,							lowercase_)
     return self._measure_speed(_train)





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_):
     snake_case_			:     int        =    self._prepare_train_func(lowercase_			,							lowercase_			,							lowercase_)
     return self._measure_memory(_train)





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_):
     snake_case_			:     Any        =    self.config_dict[model_name]

     if self.args.torchscript:
         snake_case_			:     Tuple        =    True

     snake_case_			:     Tuple        =    (
         hasattr(lowercase_			,							"architectures")
         and isinstance(config.architectures			,							lowercase_)
         and len(config.architectures) > 0
     )
     if not self.args.only_pretrain_model and has_model_class_in_config:
         try:
             snake_case_			:     Union[str, Any]        =    config.architectures[0]
             snake_case_			:     Optional[Any]        =    __import__("transformers"			,							fromlist=[model_class])
             snake_case_			:     int        =    getattr(lowercase_			,							lowercase_)
             snake_case_			:     Tuple        =    model_cls(lowercase_)
         except ImportError:
             raise ImportError(
                 F'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
                 " set `--only_pretrain_model` or `args.only_pretrain_model=True`.")
     else:
         snake_case_			:     Optional[int]        =    MODEL_MAPPING[config.__class__](lowercase_)

     model.eval()
     model.to(self.args.device)

     # encoder-decoder has vocab size saved differently
     snake_case_			:     Optional[int]        =    config.vocab_size if hasattr(lowercase_			,							"vocab_size") else config.encoder.vocab_size
     snake_case_			:     Dict        =    torch.randint(lowercase_			,							(batch_size, sequence_length)			,							dtype=torch.long			,							device=self.args.device)

     if self.args.fpaa:
         logger.info("Running training in Mixed Precision...")
         if not self.args.is_gpu:
             raise ValueError("Mixed precision is possible only for GPU.")
         # amp seems to have memory leaks so that memory usage
         # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
         model.half()

     if self.args.torchscript:
         with torch.no_grad():
             snake_case_			:     str        =    torch.jit.trace(lowercase_			,							lowercase_)
     else:
         snake_case_			:     int        =    model

     def encoder_decoder_forward():
         with torch.no_grad():
             snake_case_			:     List[Any]        =    inference_model(lowercase_			,							decoder_input_ids=lowercase_)
         return outputs

     def encoder_forward():
         with torch.no_grad():
             snake_case_			:     Dict        =    inference_model(lowercase_)
         return outputs

     snake_case_			:     Optional[int]        =    encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
     return _forward





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_):
     snake_case_			:     List[str]        =    self.config_dict[model_name]

     snake_case_			:     Dict        =    (
         hasattr(lowercase_			,							"architectures")
         and isinstance(config.architectures			,							lowercase_)
         and len(config.architectures) > 0
     )
     if not self.args.only_pretrain_model and has_model_class_in_config:
         try:
             snake_case_			:     List[Any]        =    config.architectures[0]
             snake_case_			:     List[Any]        =    __import__("transformers"			,							fromlist=[model_class])
             snake_case_			:     Union[str, Any]        =    getattr(lowercase_			,							lowercase_)
             snake_case_			:     Tuple        =    model_cls(lowercase_)
         except ImportError:
             raise ImportError(
                 F'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
                 " set `--only_pretrain_model` or `args.only_pretrain_model=True`.")
     else:
         snake_case_			:     Tuple        =    MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowercase_)

     if self.args.torchscript:
         raise NotImplementedError("Training for torchscript is currently not implemented")
     else:
         snake_case_			:     Tuple        =    model

     model.train()
     model.to(self.args.device)

     # encoder-decoder has vocab size saved differently
     snake_case_			:     str        =    config.vocab_size if hasattr(lowercase_			,							"vocab_size") else config.encoder.vocab_size
     snake_case_			:     Tuple        =    torch.randint(lowercase_			,							(batch_size, sequence_length)			,							dtype=torch.long			,							device=self.args.device)

     if self.args.fpaa:
         logger.info("Running training in Mixed Precision...")
         if not self.args.is_gpu:
             raise ValueError("Mixed precision is possible only for GPU.")

         # amp seems to have memory leaks so that memory usage
         # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
         model.half()

     def compute_loss_and_backprob_encoder():
         snake_case_			:     Optional[Any]        =    train_model(lowercase_			,							labels=lowercase_)[0]
         loss.backward()
         return loss

     def compute_loss_and_backprob_encoder_decoder():
         snake_case_			:     List[Any]        =    train_model(lowercase_			,							decoder_input_ids=lowercase_			,							labels=lowercase_)[0]
         loss.backward()
         return loss

     snake_case_			:     Any        =    (
         compute_loss_and_backprob_encoder_decoder
         if config.is_encoder_decoder
         else compute_loss_and_backprob_encoder
     )
     return _train





 def 							snake_case__		(     self			,							lowercase_):
     try:
         if self.args.is_tpu or self.args.torchscript:
             # run additional 10 times to stabilize compilation for tpu and torchscript
             logger.info("Do inference on TPU or torchscript. Running model 5 times to stabilize compilation")
             timeit.repeat(
                 lowercase_			,							repeat=1			,							number=5			,							)

         # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
         snake_case_			:     Dict        =    timeit.repeat(
             lowercase_			,							repeat=self.args.repeat			,							number=10			,							)

         if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
             import torch_xla.debug.metrics as met

             self.print_fn(met.metrics_report())

         return min(lowercase_) / 10.0
     except RuntimeError as e:
         self.print_fn(F'Doesn\'t fit on GPU. {e}')
         return "N/A"





 def 							snake_case__		(     self			,							lowercase_):
     try:
         if self.args.trace_memory_line_by_line:
             snake_case_			:     str        =    start_memory_tracing("transformers")

         if self.args.is_tpu:
             # tpu
             raise NotImplementedError(
                 "Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"
                 " `--no-memory` or `args.memory=False`")
         elif self.args.is_gpu:
             if not is_pyanvml_available():
                 logger.warning(
                     "py3nvml not installed, we won't log GPU memory usage. "
                     "Install py3nvml (pip install py3nvml) to log information about GPU.")
                 snake_case_			:     str        =    "N/A"
             else:
                 logger.info(
                     "Measuring total GPU usage on GPU device. Make sure to not have additional processes running"
                     " on the same GPU.")
                 # init nvml
                 nvml.nvmlInit()
                 func()
                 snake_case_			:     List[str]        =    nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx)
                 snake_case_			:     Union[str, Any]        =    nvml.nvmlDeviceGetMemoryInfo(lowercase_)
                 snake_case_			:     List[Any]        =    meminfo.used
                 snake_case_			:     Optional[int]        =    Memory(lowercase_)
                 # shutdown nvml
                 nvml.nvmlShutdown()
         else:
             # cpu
             snake_case_			:     int        =    measure_peak_memory_cpu(lowercase_)
             snake_case_			:     Tuple        =    Memory(lowercase_) if isinstance(lowercase_			,							lowercase_) else memory_bytes

         if self.args.trace_memory_line_by_line:
             snake_case_			:     List[str]        =    stop_memory_tracing(lowercase_)
         else:
             snake_case_			:     Optional[int]        =    None

         return memory, summary
     except RuntimeError as e:
         self.print_fn(F'Doesn\'t fit on GPU. {e}')
         return "N/A", None






'''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)




