





import argparse
from typing import List

import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset

# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
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 perform Cross Validation,
# 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
#
########################################################################


_UpperCamelCase				=       16
_UpperCamelCase				=       32







def 					lowerCAmelCase__(		lowercase				:			Tuple      ,   lowercase				:			int      ,   lowercase				:			Optional[Any]      ,   lowercase				:			Optional[int]      ,   lowercase				:			Tuple = 16 )       ->			Tuple:
  __snake_case      : List[Any]						=				AutoTokenizer.from_pretrained("bert-base-cased" )
  __snake_case      : List[str]						=				DatasetDict(
      {
          "train": dataset["train"].select(UpperCamelCase__ ),
          "validation": dataset["train"].select(UpperCamelCase__ ),
          "test": dataset["validation"],
      } )

  def tokenize_function(lowercase				:			List[str] ):
    # max_length=None => use the model max length (it's actually the default)
    __snake_case      : Dict						=				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      : List[str]						=				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      : List[Any]						=				tokenized_datasets.rename_column("label"      ,   "labels" )

  def collate_fn(lowercase				:			List[Any] ):
    # On TPU it's best to pad everything to the same length or training will be very slow.
    __snake_case      : List[Any]						=				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      : List[Any]						=				16
    elif accelerator.mixed_precision != "no":
      __snake_case      : str						=				8
    else:
      __snake_case      : Dict						=				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      : List[str]						=				DataLoader(
      tokenized_datasets["validation"]      ,   shuffle=UpperCamelCase__      ,   collate_fn=UpperCamelCase__      ,   batch_size=UpperCamelCase__ )

  __snake_case      : Tuple						=				DataLoader(
      tokenized_datasets["test"]      ,   shuffle=UpperCamelCase__      ,   collate_fn=UpperCamelCase__      ,   batch_size=UpperCamelCase__ )

  return train_dataloader, eval_dataloader, test_dataloader







def 					lowerCAmelCase__(		lowercase				:			Optional[Any]      ,   lowercase				:			Dict )       ->			Optional[Any]:
  __snake_case      : Dict						=				[]
  # Download the dataset
  __snake_case      : Optional[int]						=				load_dataset("glue"      ,   "mrpc" )
  # Create our splits
  __snake_case      : Union[str, Any]						=				StratifiedKFold(n_splits=int(args.num_folds ) )
  # Initialize accelerator
  __snake_case      : Dict						=				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      : Optional[int]						=				config["lr"]
  __snake_case      : Union[str, Any]						=				int(config["num_epochs"] )
  __snake_case      : Union[str, Any]						=				int(config["seed"] )
  __snake_case      : List[str]						=				int(config["batch_size"] )

  __snake_case      : List[str]						=				evaluate.load("glue"      ,   "mrpc" )

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

  set_seed(UpperCamelCase__ )

  # New Code #
  # Create our folds:
  __snake_case      : List[str]						=				kfold.split(np.zeros(datasets["train"].num_rows )      ,   datasets["train"]["label"] )
  __snake_case      : Union[str, Any]						=				[]
  # Iterate over them
  for i, (train_idxs, valid_idxs) in enumerate(UpperCamelCase__ ):
    __snake_case			,      __snake_case			,      __snake_case      : Dict						=				get_fold_dataloaders(
        UpperCamelCase__      ,   UpperCamelCase__      ,   UpperCamelCase__      ,   UpperCamelCase__      ,   )
    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    __snake_case      : Optional[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      : Union[str, Any]						=				model.to(accelerator.device )

    # Instantiate optimizer
    __snake_case      : int						=				AdamW(params=model.parameters()      ,   lr=UpperCamelCase__ )

    # Instantiate scheduler
    __snake_case      : Union[str, Any]						=				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			,      __snake_case			,      __snake_case			,      __snake_case			,      __snake_case      : Tuple						=				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      : Optional[int]						=				model(**UpperCamelCase__ )
        __snake_case      : List[Any]						=				outputs.loss
        __snake_case      : Tuple						=				loss / gradient_accumulation_steps
        accelerator.backward(UpperCamelCase__ )
        if step % gradient_accumulation_steps == 0:
          optimizer.step()
          lr_scheduler.step()
          optimizer.zero_grad()

      model.eval()
      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      : List[str]						=				model(**UpperCamelCase__ )
        __snake_case      : Union[str, Any]						=				outputs.logits.argmax(dim=-1 )
        __snake_case			,      __snake_case      : List[str]						=				accelerator.gather_for_metrics((predictions, batch["labels"]) )
        metric.add_batch(
            predictions=UpperCamelCase__      ,   references=UpperCamelCase__      ,   )

      __snake_case      : str						=				metric.compute()
      # Use accelerator.print to print only on the main process.
      accelerator.print(f"""epoch {epoch}:"""      ,   UpperCamelCase__ )

    # New Code #
    # We also run predictions on the test set at the very end
    __snake_case      : Dict						=				[]
    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      : Optional[int]						=				model(**UpperCamelCase__ )
      __snake_case      : Optional[Any]						=				outputs.logits
      __snake_case			,      __snake_case      : Dict						=				accelerator.gather_for_metrics((predictions, batch["labels"]) )
      fold_predictions.append(predictions.cpu() )
      if i == 0:
        # We need all of the test predictions
        test_references.append(references.cpu() )
        # Use accelerator.print to print only on the main process.
    test_predictions.append(torch.cat(UpperCamelCase__      ,   dim=0 ) )
    # We now need to release all our memory and get rid of the current model, optimizer, etc
    accelerator.free_memory()
  # New Code #
  # Finally we check the accuracy of our folded results:
  __snake_case      : List[Any]						=				torch.cat(UpperCamelCase__      ,   dim=0 )
  __snake_case      : Union[str, Any]						=				torch.stack(UpperCamelCase__      ,   dim=0 ).sum(dim=0 ).div(int(args.num_folds ) ).argmax(dim=-1 )
  __snake_case      : Union[str, Any]						=				metric.compute(predictions=UpperCamelCase__      ,   references=UpperCamelCase__ )
  accelerator.print("Average test metrics from all folds:"      ,   UpperCamelCase__ )







def 					lowerCAmelCase__(		)       ->			str:
  __snake_case      : List[str]						=				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." )
  # New Code #
  parser.add_argument("--num_folds"      ,   type=UpperCamelCase__      ,   default=3      ,   help="The number of splits to perform across the dataset" )
  __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()








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
__A				:    int       =   {
    "/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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__ )  ->				List[str]:







			'''simple docstring'''

			UpperCAmelCase   		=				list(s_dict.keys() )
			for key in keys:
						UpperCAmelCase   		=				R'''.*/layers_(\d+)'''
						UpperCAmelCase   		=				key
						if re.match(UpperCamelCase__			,			UpperCamelCase__ ):
									UpperCAmelCase   		=				re.sub(R'''layers_(\d+)'''			,			R'''block/\1/layer'''			,			UpperCamelCase__ )

						UpperCAmelCase   		=				R'''(encoder|decoder)\/'''

						if re.match(UpperCamelCase__			,			UpperCamelCase__ ):
									UpperCAmelCase   		=				re.match(UpperCamelCase__			,			UpperCamelCase__ ).groups()
									if groups[0] == "encoder":
												UpperCAmelCase   		=				re.sub(R'''/mlp/'''			,			R'''/1/mlp/'''			,			UpperCamelCase__ )
												UpperCAmelCase   		=				re.sub(R'''/pre_mlp_layer_norm/'''			,			R'''/1/layer_norm/'''			,			UpperCamelCase__ )

									elif groups[0] == "decoder":
												UpperCAmelCase   		=				re.sub(R'''/mlp/'''			,			R'''/2/mlp/'''			,			UpperCamelCase__ )
												UpperCAmelCase   		=				re.sub(R'''/pre_mlp_layer_norm/'''			,			R'''/2/layer_norm/'''			,			UpperCamelCase__ )

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

						print(F"""{key} -> {new_key}""" )
						UpperCAmelCase   		=				s_dict.pop(UpperCamelCase__ )

			if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
						UpperCAmelCase   		=				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:
						UpperCAmelCase   		=				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:
									UpperCAmelCase   		=				s_dict[key].shape[0]
									UpperCAmelCase   		=				s_dict[key]
									for idx in range(UpperCamelCase__ ):
												UpperCAmelCase   		=				expert_weihts[idx]
												print(F"""{key} -> {key.replace("expert/"			,			"nested fstring" )}""" )

									s_dict.pop(UpperCamelCase__ )

			return s_dict


__A				:    Optional[int]       =   {
    "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 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__ )  ->				List[str]:







			'''simple docstring'''

			import regex as re

			with open(UpperCamelCase__			,			'''r''' ) as f:
						UpperCAmelCase   		=				f.read()

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

			UpperCAmelCase   		=				re.findall(R'''(.*activations) = \(\'(.*)\',\)'''			,			UpperCamelCase__ )[0]
			UpperCAmelCase   		=				str(activation[1] )

			UpperCAmelCase   		=				num_experts
			UpperCAmelCase   		=				SwitchTransformersConfig(**UpperCamelCase__ )
			return config





def 						__SCREAMING_SNAKE_CASE  (				UpperCamelCase__			,			UpperCamelCase__			,			UpperCamelCase__=None			,			UpperCamelCase__="./"			,			UpperCamelCase__=8 )  ->				List[Any]:







			'''simple docstring'''

			print(F"""Loading flax weights from : {flax_checkpoint_path}""" )
			UpperCAmelCase   		=				checkpoints.load_tax_checkpoint(UpperCamelCase__ )

			if gin_file is not None:
						UpperCAmelCase   		=				convert_gin_to_config(UpperCamelCase__			,			UpperCamelCase__ )
			else:
						UpperCAmelCase   		=				SwitchTransformersConfig.from_pretrained(UpperCamelCase__ )

			UpperCAmelCase   		=				SwitchTransformersForConditionalGeneration(UpperCamelCase__ )

			UpperCAmelCase   		=				flax_params['''target''']
			UpperCAmelCase   		=				flatten_dict(UpperCamelCase__			,			sep='''/''' )
			UpperCAmelCase   		=				rename_keys(UpperCamelCase__ )
			UpperCAmelCase   		=				unflatten_dict(UpperCamelCase__			,			sep='''/''' )

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

			print(F"""Save PyTorch model to {pytorch_dump_path}""" )
			pt_model.save_pretrained(UpperCamelCase__ )


if __name__ == "__main__":
				__A				:    Tuple       =   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")
				__A				:    Tuple       =   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,
				)



