


"""simple docstring"""



import argparse
import os

import evaluate
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


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__snake_case             =   16
__snake_case             =   32







def   __lowerCAmelCase						(						lowercase  :  Accelerator						,							lowercase  :  int = 16			)     ->						Union[str, Any]:


				"""simple docstring"""





				snake_case   :				Union[str, Any]				 =   AutoTokenizer.from_pretrained("bert-base-cased"			)
				snake_case   :				Optional[Any]				 =   load_dataset("glue"						,							"mrpc"			)

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

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								snake_case   :				int				 =   datasets.map(
								    UpperCAmelCase_						,							batched=UpperCAmelCase_						,							remove_columns=["idx", "sentence1", "sentence2"]						,							)

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

				def collate_fn(lowercase  :  Optional[Any]			):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								snake_case   :				Dict				 =   128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												snake_case   :				Optional[Any]				 =   16
								elif accelerator.mixed_precision != "no":
												snake_case   :				Optional[int]				 =   8
								else:
												snake_case   :				List[Any]				 =   None

								return tokenizer.pad(
								    UpperCAmelCase_						,							padding="longest"						,							max_length=UpperCAmelCase_						,							pad_to_multiple_of=UpperCAmelCase_						,							return_tensors="pt"						,							)

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

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		__snake_case             =   mocked_dataloaders  # noqa: F811







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


				"""simple docstring"""





				if os.environ.get("TESTING_MOCKED_DATALOADERS"						,							UpperCAmelCase_			) == "1":
								snake_case   :				str				 =   2
				# Initialize accelerator
				snake_case   :				Any				 =   Accelerator(cpu=args.cpu						,							mixed_precision=args.mixed_precision			)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				snake_case   :				str				 =   config['lr']
				snake_case   :				Tuple				 =   int(config["num_epochs"]			)
				snake_case   :				Any				 =   int(config["seed"]			)
				snake_case   :				Union[str, Any]				 =   int(config["batch_size"]			)

				snake_case   :				int				 =   evaluate.load("glue"						,							"mrpc"			)

				# If the batch size is too big we use gradient accumulation
				snake_case   :				Optional[int]				 =   1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								snake_case   :				Any				 =   batch_size // MAX_GPU_BATCH_SIZE
								snake_case   :				Dict				 =   MAX_GPU_BATCH_SIZE

				set_seed(UpperCAmelCase_			)
				snake_case   :				List[Any]				 =   get_dataloaders(UpperCAmelCase_						,							UpperCAmelCase_			)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				snake_case   :				List[Any]				 =   AutoModelForSequenceClassification.from_pretrained("bert-base-cased"						,							return_dict=UpperCAmelCase_			)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				snake_case   :				str				 =   model.to(accelerator.device			)

				# Instantiate optimizer
				snake_case   :				Optional[int]				 =   AdamW(params=model.parameters()						,							lr=UpperCAmelCase_			)

				# Instantiate scheduler
				snake_case   :				str				 =   get_linear_schedule_with_warmup(
				    optimizer=UpperCAmelCase_						,							num_warmup_steps=100						,							num_training_steps=(len(UpperCAmelCase_			) * num_epochs) // gradient_accumulation_steps						,							)

				# 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   :				str				 =   accelerator.prepare(
				    UpperCAmelCase_						,							UpperCAmelCase_						,							UpperCAmelCase_						,							UpperCAmelCase_						,							UpperCAmelCase_			)

				# Now we train the model
				for epoch in range(UpperCAmelCase_			):
								model.train()
								for step, batch in enumerate(UpperCAmelCase_			):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device			)
												snake_case   :				Union[str, Any]				 =   model(**UpperCAmelCase_			)
												snake_case   :				Optional[Any]				 =   outputs.loss
												snake_case   :				Union[str, Any]				 =   loss / gradient_accumulation_steps
												accelerator.backward(UpperCAmelCase_			)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								snake_case   :				Optional[Any]				 =   0
								for step, batch in enumerate(UpperCAmelCase_			):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device			)
												with torch.no_grad():
																snake_case   :				Union[str, Any]				 =   model(**UpperCAmelCase_			)
												snake_case   :				List[Any]				 =   outputs.logits.argmax(dim=-1			)
												snake_case   :				int				 =   accelerator.gather((predictions, batch["labels"])			)
												# New Code #
												# First we check if it's a distributed system
												if accelerator.use_distributed:
																# Then see if we're on the last batch of our eval dataloader
																if step == len(UpperCAmelCase_			) - 1:
																				# Last batch needs to be truncated on distributed systems as it contains additional samples
																				snake_case   :				Tuple				 =   predictions[: len(eval_dataloader.dataset			) - samples_seen]
																				snake_case   :				Optional[Any]				 =   references[: len(eval_dataloader.dataset			) - samples_seen]
																else:
																				# Otherwise we add the number of samples seen
																				samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
												metric.add_batch(
												    predictions=UpperCAmelCase_						,							references=UpperCAmelCase_						,							)

								snake_case   :				Any				 =   metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F'epoch {epoch}:'						,							UpperCAmelCase_			)







def   __lowerCAmelCase						(						)     ->						Tuple:


				"""simple docstring"""





				snake_case   :				Union[str, Any]				 =   argparse.ArgumentParser(description="Simple example of training script."			)
				parser.add_argument(
				    "--mixed_precision"						,							type=UpperCAmelCase_						,							default=UpperCAmelCase_						,							choices=["no", "fp16", "bf16", "fp8"]						,							help="Whether to use mixed precision. Choose"
				    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
				    "and an Nvidia Ampere GPU."						,							)
				parser.add_argument("--cpu"						,							action="store_true"						,							help="If passed, will train on the CPU."			)
				snake_case   :				Dict				 =   parser.parse_args()
				snake_case   :				Optional[Any]				 =   {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
				training_function(UpperCAmelCase_						,							UpperCAmelCase_			)


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,
  )





