


"""simple docstring"""



import argparse
import gc
import json
import os

import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


__snake_case             =   16
__snake_case             =   32







def   __lowerCAmelCase						(						lowercase  :  Dict			)     ->						Tuple:


    """simple docstring"""





    return int(x / 2**20			)







class _lowerCAmelCase    :







   def __enter__(     self						)     ->		Tuple:

       '''simple docstring'''




       gc.collect()
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
       snake_case   :				Any				 =   torch.cuda.memory_allocated()
       return self







   def __exit__(     self						,     *UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       gc.collect()
       torch.cuda.empty_cache()
       snake_case   :				Union[str, Any]				 =   torch.cuda.memory_allocated()
       snake_case   :				List[str]				 =   torch.cuda.max_memory_allocated()
       snake_case   :				List[str]				 =   bamb(self.end - self.begin						)
       snake_case   :				Dict				 =   bamb(self.peak - self.begin						)
       # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")







def   __lowerCAmelCase						(						lowercase  :  Accelerator						,							lowercase  :  int = 16						,							lowercase  :  str = "bert-base-cased"						,							lowercase  :  int = 320						,							lowercase  :  int = 160						,							)     ->						str:


    """simple docstring"""





    snake_case   :				Dict				 =   AutoTokenizer.from_pretrained(lowercase			)
    snake_case   :				Any				 =   load_dataset(
        "glue"						,							"mrpc"						,							split={"train": F'train[:{n_train}]', "validation": F'validation[:{n_val}]'}			)

    def tokenize_function(lowercase  :  Optional[Any]			):
        # max_length=None => use the model max length (it's actually the default)
        snake_case   :				Optional[int]				 =   tokenizer(examples["sentence1"]						,							examples["sentence2"]						,							truncation=lowercase						,							max_length=lowercase			)
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    snake_case   :				List[Any]				 =   datasets.map(
        lowercase						,							batched=lowercase						,							remove_columns=["idx", "sentence1", "sentence2"]						,							load_from_cache_file=lowercase			)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    snake_case   :				Optional[Any]				 =   tokenized_datasets.rename_column("label"						,							"labels"			)

    def collate_fn(lowercase  :  Any			):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(lowercase						,							padding="max_length"						,							max_length=128						,							return_tensors="pt"			)
        return tokenizer.pad(lowercase						,							padding="longest"						,							return_tensors="pt"			)

    # Instantiate dataloaders.
    snake_case   :				List[str]				 =   DataLoader(
        tokenized_datasets["train"]						,							shuffle=lowercase						,							collate_fn=lowercase						,							batch_size=lowercase			)
    snake_case   :				List[Any]				 =   DataLoader(
        tokenized_datasets["validation"]						,							shuffle=lowercase						,							collate_fn=lowercase						,							batch_size=lowercase			)

    return train_dataloader, eval_dataloader







def   __lowerCAmelCase						(						lowercase  :  Union[str, Any]						,							lowercase  :  List[str]			)     ->						Optional[Any]:


    """simple docstring"""





    snake_case   :				Any				 =   Accelerator()

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    snake_case   :				Union[str, Any]				 =   config["lr"]
    snake_case   :				List[Any]				 =   int(config["num_epochs"]			)
    snake_case   :				Union[str, Any]				 =   int(config["seed"]			)
    snake_case   :				List[Any]				 =   int(config["batch_size"]			)
    snake_case   :				Optional[Any]				 =   args.model_name_or_path

    set_seed(lowercase			)
    snake_case ,snake_case   :				Tuple				 =   get_dataloaders(lowercase						,							lowercase						,							lowercase						,							args.n_train						,							args.n_val			)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    snake_case   :				Any				 =   AutoModelForSequenceClassification.from_pretrained(lowercase						,							return_dict=lowercase			)

    # Instantiate optimizer
    snake_case   :				str				 =   (
        AdamW
        if accelerator.state.deepspeed_plugin is None
        or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
        else DummyOptim
    )
    snake_case   :				Any				 =   optimizer_cls(params=model.parameters()						,							lr=lowercase			)

    if accelerator.state.deepspeed_plugin is not None:
        snake_case   :				Dict				 =   accelerator.state.deepspeed_plugin.deepspeed_config[
            "gradient_accumulation_steps"
        ]
    else:
        snake_case   :				List[str]				 =   1
    snake_case   :				str				 =   (len(lowercase			) * num_epochs) // gradient_accumulation_steps

    # Instantiate scheduler
    if (
        accelerator.state.deepspeed_plugin is None
        or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
    ):
        snake_case   :				Any				 =   get_linear_schedule_with_warmup(
            optimizer=lowercase						,							num_warmup_steps=0						,							num_training_steps=lowercase						,							)
    else:
        snake_case   :				List[str]				 =   DummyScheduler(lowercase						,							total_num_steps=lowercase						,							warmup_num_steps=0			)

    # Prepare everything
    # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
    # prepare method.
    snake_case ,snake_case ,snake_case ,snake_case ,snake_case   :				Dict				 =   accelerator.prepare(
        lowercase						,							lowercase						,							lowercase						,							lowercase						,							lowercase			)

    # We need to keep track of how many total steps we have iterated over
    snake_case   :				Optional[int]				 =   0
    # We also need to keep track of the stating epoch so files are named properly
    snake_case   :				Optional[Any]				 =   0

    # Now we train the model
    snake_case   :				int				 =   {}
    for epoch in range(lowercase						,							lowercase			):
        with TorchTracemalloc() as tracemalloc:
            model.train()
            for step, batch in enumerate(lowercase			):
                snake_case   :				Optional[Any]				 =   model(**lowercase			)
                snake_case   :				List[Any]				 =   outputs.loss
                snake_case   :				Any				 =   loss / gradient_accumulation_steps
                accelerator.backward(lowercase			)
                if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

                overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
        accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin			)			)			)
        accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used			)			)
        accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked			)			)
        accelerator.print(
            "Total Peak Memory consumed during the train (max): {}".format(
                tracemalloc.peaked + bamb(tracemalloc.begin			)			)			)
        snake_case   :				Union[str, Any]				 =   tracemalloc.peaked + bamb(tracemalloc.begin			)
        if args.peak_memory_upper_bound is not None:
            assert (
                train_total_peak_memory[F'epoch-{epoch}'] <= args.peak_memory_upper_bound
            ), "Peak memory usage exceeded the upper bound"

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        with open(os.path.join(args.output_dir						,							"peak_memory_utilization.json"			)						,							"w"			) as f:
            json.dump(lowercase						,							lowercase			)







def   __lowerCAmelCase						(						)     ->						str:


    """simple docstring"""





    snake_case   :				str				 =   argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."			)
    parser.add_argument(
        "--model_name_or_path"						,							type=lowercase						,							default="bert-base-cased"						,							help="Path to pretrained model or model identifier from huggingface.co/models."						,							required=lowercase						,							)
    parser.add_argument(
        "--output_dir"						,							type=lowercase						,							default="."						,							help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."						,							)
    parser.add_argument(
        "--peak_memory_upper_bound"						,							type=lowercase						,							default=lowercase						,							help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."						,							)
    parser.add_argument(
        "--n_train"						,							type=lowercase						,							default=320						,							help="Number of training examples to use."						,							)
    parser.add_argument(
        "--n_val"						,							type=lowercase						,							default=160						,							help="Number of validation examples to use."						,							)
    parser.add_argument(
        "--num_epochs"						,							type=lowercase						,							default=1						,							help="Number of train epochs."						,							)
    snake_case   :				int				 =   parser.parse_args()
    snake_case   :				str				 =   {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
    training_function(lowercase						,							lowercase			)


if __name__ == "__main__":
  main()









"""simple docstring"""



import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
__snake_case             =   {
    """/attention/""": """/0/SelfAttention/""",
    """/self_attention/""": """/0/SelfAttention/""",
    """/encoder_decoder_attention/""": """/1/EncDecAttention/""",
    """value""": """v""",
    """query""": """q""",
    """key""": """k""",
    """out""": """o""",
    """pre_self_attention_layer_norm""": """0/layer_norm""",
    """pre_cross_attention_layer_norm""": """1/layer_norm""",
    """pre_attention_layer_norm""": """0/layer_norm""",  # previously 1, but seems wrong
    """token_embedder""": """shared""",
    """encoder_norm""": """final_layer_norm""",
    """decoder_norm""": """final_layer_norm""",
    """relpos_bias/rel_embedding""": """block/0/layer/0/SelfAttention/relative_attention_bias/weight""",
    """router/router_weights/w/""": """router/classifier/""",
    """roer/roer_weights/w/""": """router/classifier/""",
    """logits_dense""": """lm_head""",
}







def   __lowerCAmelCase						(						lowercase  :  Optional[int]			)     ->						List[str]:


    """simple docstring"""





    snake_case   :				Optional[Any]				 =   list(s_dict.keys()			)
    for key in keys:
        snake_case   :				Any				 =   R".*/layers_(\d+)"
        snake_case   :				Tuple				 =   key
        if re.match(lowercase						,							lowercase			):
            snake_case   :				List[str]				 =   re.sub(R"layers_(\d+)"						,							R"block/\1/layer"						,							lowercase			)

        snake_case   :				Union[str, Any]				 =   R"(encoder|decoder)\/"

        if re.match(lowercase						,							lowercase			):
            snake_case   :				Any				 =   re.match(lowercase						,							lowercase			).groups()
            if groups[0] == "encoder":
                snake_case   :				Union[str, Any]				 =   re.sub(R"/mlp/"						,							R"/1/mlp/"						,							lowercase			)
                snake_case   :				int				 =   re.sub(R"/pre_mlp_layer_norm/"						,							R"/1/layer_norm/"						,							lowercase			)

            elif groups[0] == "decoder":
                snake_case   :				str				 =   re.sub(R"/mlp/"						,							R"/2/mlp/"						,							lowercase			)
                snake_case   :				List[str]				 =   re.sub(R"/pre_mlp_layer_norm/"						,							R"/2/layer_norm/"						,							lowercase			)

        # 2. Convert other classic mappings
        for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
            if old_key in new_key:
                snake_case   :				int				 =   new_key.replace(lowercase						,							lowercase			)

        print(F'{key} -> {new_key}'			)
        snake_case   :				Optional[Any]				 =   s_dict.pop(lowercase			)

    if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
        snake_case   :				int				 =   s_dict[
            "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
        ].T
    if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
        snake_case   :				Optional[Any]				 =   s_dict[
            "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"
        ].T

    # 3. Take extra care of the EXPERTS layer
    for key in list(s_dict.keys()			):
        if "expert" in key:
            snake_case   :				Tuple				 =   s_dict[key].shape[0]
            snake_case   :				int				 =   s_dict[key]
            for idx in range(lowercase			):
                snake_case   :				List[str]				 =   expert_weihts[idx]
                print(F'{key} -> {key.replace("expert/"						,							"nested fstring"			)}'			)

            s_dict.pop(lowercase			)

    return s_dict


__snake_case             =   {
    """NUM_ENCODER_LAYERS""": """num_layers""",
    """NUM_DECODER_LAYERS""": """num_decoder_layers""",
    """NUM_HEADS""": """num_heads""",
    """HEAD_DIM""": """d_kv""",
    """EMBED_DIM""": """d_model""",
    """MLP_DIM""": """d_ff""",
    """NUM_SELECTED_EXPERTS""": """num_selected_experts""",
    """NUM_ENCODER_SPARSE_LAYERS""": """num_sparse_encoder_layers""",
    """NUM_DECODER_SPARSE_LAYERS""": """num_sparse_decoder_layers""",
    """dense.MlpBlock.activations""": """feed_forward_proj""",
}







def   __lowerCAmelCase						(						lowercase  :  Dict						,							lowercase  :  Optional[Any]			)     ->						int:


    """simple docstring"""





    import regex as re

    with open(lowercase						,							"r"			) as f:
        snake_case   :				List[str]				 =   f.read()

    snake_case   :				Tuple				 =   re.findall(R"(.*) = ([0-9.]*)"						,							lowercase			)
    snake_case   :				Any				 =   {}
    for param, value in regex_match:
        if param in GIN_TO_CONFIG_MAPPING and value != "":
            snake_case   :				Tuple				 =   float(lowercase			) if "." in value else int(lowercase			)

    snake_case   :				List[str]				 =   re.findall(R"(.*activations) = \(\'(.*)\',\)"						,							lowercase			)[0]
    snake_case   :				List[Any]				 =   str(activation[1]			)

    snake_case   :				Optional[Any]				 =   num_experts
    snake_case   :				List[Any]				 =   SwitchTransformersConfig(**lowercase			)
    return config







def   __lowerCAmelCase						(						lowercase  :  Tuple						,							lowercase  :  Tuple						,							lowercase  :  Union[str, Any]=None						,							lowercase  :  Any="./"						,							lowercase  :  int=8			)     ->						Dict:


    """simple docstring"""





    print(F'Loading flax weights from : {flax_checkpoint_path}'			)
    snake_case   :				Union[str, Any]				 =   checkpoints.load_tax_checkpoint(lowercase			)

    if gin_file is not None:
        snake_case   :				List[str]				 =   convert_gin_to_config(lowercase						,							lowercase			)
    else:
        snake_case   :				str				 =   SwitchTransformersConfig.from_pretrained(lowercase			)

    snake_case   :				Any				 =   SwitchTransformersForConditionalGeneration(lowercase			)

    snake_case   :				Optional[Any]				 =   flax_params["target"]
    snake_case   :				Optional[int]				 =   flatten_dict(lowercase						,							sep="/"			)
    snake_case   :				Optional[Any]				 =   rename_keys(lowercase			)
    snake_case   :				List[str]				 =   unflatten_dict(lowercase						,							sep="/"			)

    # Load the flax params in the PT model
    load_flax_weights_in_pytorch_model(lowercase						,							lowercase			)

    print(F'Save PyTorch model to {pytorch_dump_path}'			)
    pt_model.save_pretrained(lowercase			)


if __name__ == "__main__":
  __snake_case             =   argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--switch_t5x_checkpoint_path""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"""
          """ model architecture. If not provided, a `gin_file` has to be provided."""
      ),
  )
  parser.add_argument(
      """--gin_file""",
      default=None,
      type=str,
      required=False,
      help="""Path to the gin config file. If not provided, a `config_file` has to be passed   """,
  )
  parser.add_argument(
      """--config_name""", default=None, type=str, required=False, help="""Config name of SwitchTransformers model."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output pytorch model."""
  )
  parser.add_argument("""--num_experts""", default=8, type=int, required=False, help="""Number of experts""")
  __snake_case             =   parser.parse_args()
  convert_flax_checkpoint_to_pytorch(
      args.switch_tax_checkpoint_path,
      args.config_name,
      args.gin_file,
      args.pytorch_dump_folder_path,
      args.num_experts,
  )





