



'''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
lowerCamelCase	 =    {
    """/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   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



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

														__lowercase       	=r'(encoder|decoder)\/'

														if re.match(_lowerCAmelCase  ,   _lowerCAmelCase					):
																					__lowercase       	=re.match(_lowerCAmelCase  ,   _lowerCAmelCase					).groups()
																					if groups[0] == "encoder":
																												__lowercase       	=re.sub(r'/mlp/'  ,   r'/1/mlp/'  ,   _lowerCAmelCase					)
																												__lowercase       	=re.sub(r'/pre_mlp_layer_norm/'  ,   r'/1/layer_norm/'  ,   _lowerCAmelCase					)

																					elif groups[0] == "decoder":
																												__lowercase       	=re.sub(r'/mlp/'  ,   r'/2/mlp/'  ,   _lowerCAmelCase					)
																												__lowercase       	=re.sub(r'/pre_mlp_layer_norm/'  ,   r'/2/layer_norm/'  ,   _lowerCAmelCase					)

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

														print(f"""{key} -> {new_key}"""					)
														__lowercase       	=s_dict.pop(_lowerCAmelCase					)

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

																					s_dict.pop(_lowerCAmelCase					)

							return s_dict


lowerCamelCase	 =    {
    """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   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							import regex as re

							with open(_lowerCAmelCase  ,   'r'					) as f:
														__lowercase       	=f.read()

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

							__lowercase       	=re.findall(r'(.*activations) = \(\'(.*)\',\)'  ,   _lowerCAmelCase					)[0]
							__lowercase       	=str(activation[1]					)

							__lowercase       	=num_experts
							__lowercase       	=SwitchTransformersConfig(**_lowerCAmelCase					)
							return config



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase=None  ,   _lowerCAmelCase="./"  ,   _lowerCAmelCase=8					):



							"""simple docstring"""



							print(f"""Loading flax weights from : {flax_checkpoint_path}"""					)
							__lowercase       	=checkpoints.load_tax_checkpoint(_lowerCAmelCase					)

							if gin_file is not None:
														__lowercase       	=convert_gin_to_config(_lowerCAmelCase  ,   _lowerCAmelCase					)
							else:
														__lowercase       	=SwitchTransformersConfig.from_pretrained(_lowerCAmelCase					)

							__lowercase       	=SwitchTransformersForConditionalGeneration(_lowerCAmelCase					)

							__lowercase       	=flax_params['target']
							__lowercase       	=flatten_dict(_lowerCAmelCase  ,   sep='/'					)
							__lowercase       	=rename_keys(_lowerCAmelCase					)
							__lowercase       	=unflatten_dict(_lowerCAmelCase  ,   sep='/'					)

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

							print(f"""Save PyTorch model to {pytorch_dump_path}"""					)
							pt_model.save_pretrained(_lowerCAmelCase					)


if __name__ == "__main__":
		lowerCamelCase	 =    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""")
		lowerCamelCase	 =    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,
		)








'''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
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


lowerCamelCase	 =       16
lowerCamelCase	 =       32



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase = 16					):



							"""simple docstring"""



							__lowercase       	=AutoTokenizer.from_pretrained('bert-base-cased'					)
							__lowercase       	=load_dataset('glue'  ,   'mrpc'					)

							def tokenize_function(_lowerCAmelCase					):
														# max_length=None => use the model max length (it's actually the default)
														__lowercase       	=tokenizer(examples['sentence1']  ,   examples['sentence2']  ,   truncation=_lowerCAmelCase  ,   max_length=_lowerCAmelCase					)
														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():
														__lowercase       	=datasets.map(
														    _lowerCAmelCase  ,   batched=_lowerCAmelCase  ,   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
							__lowercase       	=tokenized_datasets.rename_column('label'  ,   'labels'					)

							def collate_fn(_lowerCAmelCase					):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														__lowercase       	=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":
																					__lowercase       	=16
														elif accelerator.mixed_precision != "no":
																					__lowercase       	=8
														else:
																					__lowercase       	=None

														return tokenizer.pad(
														    _lowerCAmelCase  ,   padding='longest'  ,   max_length=_lowerCAmelCase  ,   pad_to_multiple_of=_lowerCAmelCase  ,   return_tensors='pt'  ,   )

							# Instantiate dataloaders.
							__lowercase       	=DataLoader(
							    tokenized_datasets['train']  ,   shuffle=_lowerCAmelCase  ,   collate_fn=_lowerCAmelCase  ,   batch_size=_lowerCAmelCase					)
							__lowercase       	=DataLoader(
							    tokenized_datasets['validation']  ,   shuffle=_lowerCAmelCase  ,   collate_fn=_lowerCAmelCase  ,   batch_size=_lowerCAmelCase					)

							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

		lowerCamelCase	 =       mocked_dataloaders  # noqa: F811



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



							if os.environ.get('TESTING_MOCKED_DATALOADERS'  ,   _lowerCAmelCase					) == "1":
														__lowercase       	=2
							# New Code #
							__lowercase       	=int(args.gradient_accumulation_steps					)
							__lowercase       	=int(args.local_sgd_steps					)
							# Initialize accelerator
							__lowercase       	=Accelerator(
							    cpu=args.cpu  ,   mixed_precision=args.mixed_precision  ,   gradient_accumulation_steps=_lowerCAmelCase					)
							if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
														raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'					)
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							__lowercase       	=config['lr']
							__lowercase       	=int(config['num_epochs']					)
							__lowercase       	=int(config['seed']					)
							__lowercase       	=int(config['batch_size']					)

							__lowercase       	=evaluate.load('glue'  ,   'mrpc'					)

							set_seed(_lowerCAmelCase					)
							__lowercase	,				__lowercase       	=get_dataloaders(_lowerCAmelCase  ,   _lowerCAmelCase					)
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							__lowercase       	=AutoModelForSequenceClassification.from_pretrained('bert-base-cased'  ,   return_dict=_lowerCAmelCase					)

							# 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).
							__lowercase       	=model.to(accelerator.device					)

							# Instantiate optimizer
							__lowercase       	=AdamW(params=model.parameters()  ,   lr=_lowerCAmelCase					)

							# Instantiate scheduler
							__lowercase       	=get_linear_schedule_with_warmup(
							    optimizer=_lowerCAmelCase  ,   num_warmup_steps=100  ,   num_training_steps=(len(_lowerCAmelCase					) * num_epochs)  ,   )

							# 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.
							__lowercase	,				__lowercase	,				__lowercase	,				__lowercase	,				__lowercase       	=accelerator.prepare(
							    _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					)

							# Now we train the model
							for epoch in range(_lowerCAmelCase					):
														model.train()
														with LocalSGD(
														    accelerator=_lowerCAmelCase  ,   model=_lowerCAmelCase  ,   local_sgd_steps=_lowerCAmelCase  ,   enabled=local_sgd_steps is not None					) as local_sgd:
																					for step, batch in enumerate(_lowerCAmelCase					):
																												# We could avoid this line since we set the accelerator with `device_placement=True`.
																												batch.to(accelerator.device					)
																												# New code #
																												# We use the new `accumulate` context manager to perform gradient accumulation
																												# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
																												with accelerator.accumulate(_lowerCAmelCase					):
																																			__lowercase       	=model(**_lowerCAmelCase					)
																																			__lowercase       	=output.loss
																																			accelerator.backward(_lowerCAmelCase					)
																																			optimizer.step()
																																			lr_scheduler.step()
																																			optimizer.zero_grad()
																																			# LocalSGD-specific line
																																			local_sgd.step()

														model.eval()
														for step, batch in enumerate(_lowerCAmelCase					):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device					)
																					with torch.no_grad():
																												__lowercase       	=model(**_lowerCAmelCase					)
																					__lowercase       	=outputs.logits.argmax(dim=-1					)
																					__lowercase	,				__lowercase       	=accelerator.gather_for_metrics((predictions, batch['labels'])					)
																					metric.add_batch(
																					    predictions=_lowerCAmelCase  ,   references=_lowerCAmelCase  ,   )

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



def   _A       (   ):



							"""simple docstring"""



							__lowercase       	=argparse.ArgumentParser(description='Simple example of training script.'					)
							parser.add_argument(
							    '--mixed_precision'  ,   type=_lowerCAmelCase  ,   default=_lowerCAmelCase  ,   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.'  ,   )
							# New Code #
							parser.add_argument(
							    '--gradient_accumulation_steps'  ,   type=_lowerCAmelCase  ,   default=1  ,   help='The number of minibatches to be ran before gradients are accumulated.'  ,   )
							parser.add_argument(
							    '--local_sgd_steps'  ,   type=_lowerCAmelCase  ,   default=8  ,   help='Number of local SGD steps or None to disable local SGD'					)
							parser.add_argument('--cpu'  ,   action='store_true'  ,   help='If passed, will train on the CPU.'					)
							__lowercase       	=parser.parse_args()
							__lowercase       	={'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
							training_function(_lowerCAmelCase  ,   _lowerCAmelCase					)


if __name__ == "__main__":
		main()



