'''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
# and perform 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
#
########################################################################


SCREAMING_SNAKE_CASE_:	Union[str, Any]    =16
SCREAMING_SNAKE_CASE_:	List[str]    =32





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


     '''simple docstring'''




     UpperCAmelCase_			       =  AutoTokenizer.from_pretrained("bert-base-cased"     )
     UpperCAmelCase_			       =  load_dataset("glue"   ,       "mrpc"     )

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

     def collate_fn(snake_case_			:					List[Any]     ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          UpperCAmelCase_			       =  1_28 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               UpperCAmelCase_			       =  16
          elif accelerator.mixed_precision != "no":
               UpperCAmelCase_			       =  8
          else:
               UpperCAmelCase_			       =  None

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

     # Instantiate dataloaders.
     UpperCAmelCase_			       =  DataLoader(
         tokenized_datasets["train"]   ,       shuffle=snake_case_   ,       collate_fn=snake_case_   ,       batch_size=snake_case_     )
     UpperCAmelCase_			       =  DataLoader(
         tokenized_datasets["validation"]   ,       shuffle=snake_case_   ,       collate_fn=snake_case_   ,       batch_size=snake_case_     )

     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

    SCREAMING_SNAKE_CASE_:	Tuple    =mocked_dataloaders  # noqa: F811





def       lowerCAmelCase_							(   snake_case_			:					Union[str, Any]   ,       snake_case_			:					Union[str, Any]     )       -> Optional[int]:


     '''simple docstring'''




     if os.environ.get("TESTING_MOCKED_DATALOADERS"   ,       snake_case_     ) == "1":
          UpperCAmelCase_			       =  2
     # New Code #
     UpperCAmelCase_			       =  int(args.gradient_accumulation_steps     )
     # Initialize accelerator
     UpperCAmelCase_			       =  Accelerator(
         cpu=args.cpu   ,       mixed_precision=args.mixed_precision   ,       gradient_accumulation_steps=snake_case_     )
     if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
          raise NotImplementedError(
              "Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"     )
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     UpperCAmelCase_			       =  config["lr"]
     UpperCAmelCase_			       =  int(config["num_epochs"]     )
     UpperCAmelCase_			       =  int(config["seed"]     )
     UpperCAmelCase_			       =  int(config["batch_size"]     )

     UpperCAmelCase_			       =  evaluate.load("glue"   ,       "mrpc"     )

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

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

     # Instantiate optimizer
     UpperCAmelCase_			       =  AdamW(params=model.parameters()   ,       lr=snake_case_     )

     # Instantiate scheduler
     UpperCAmelCase_			       =  get_linear_schedule_with_warmup(
         optimizer=snake_case_   ,       num_warmup_steps=1_00   ,       num_training_steps=(len(snake_case_     ) * 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.
     UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_       ,					UpperCAmelCase_			       =  accelerator.prepare(
         snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_   ,       snake_case_     )

     # Now we train the model
     for epoch in range(snake_case_     ):
          model.train()
          for step, batch in enumerate(snake_case_     ):
               # 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(snake_case_     ):
                    UpperCAmelCase_			       =  model(**snake_case_     )
                    UpperCAmelCase_			       =  output.loss
                    accelerator.backward(snake_case_     )
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

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

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





def       lowerCAmelCase_							(   )       -> Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_			       =  argparse.ArgumentParser(description="Simple example of training script."     )
     parser.add_argument(
         "--mixed_precision"   ,       type=snake_case_   ,       default=snake_case_   ,       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=snake_case_   ,       default=1   ,       help="The number of minibatches to be ran before gradients are accumulated."   ,       )
     parser.add_argument("--cpu"   ,       action="store_true"   ,       help="If passed, will train on the CPU."     )
     UpperCAmelCase_			       =  parser.parse_args()
     UpperCAmelCase_			       =  {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
     training_function(snake_case_   ,       snake_case_     )


if __name__ == "__main__":
    main()









'''simple docstring'''

import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Dict:
		return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_="attention"    )     -> int:
		_a	: int								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]    )
		_a	: Dict								=				k_tmp.reshape(k_tmp.shape[0]		,     k_tmp.shape[1] * k_tmp.shape[2]    )
		_a	: Any								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]    )
		_a	: int								=				o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]		,     o_tmp.shape[2]    )
		_a	: List[str]								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]    )
		_a	: Dict								=				q_tmp.reshape(q_tmp.shape[0]		,     q_tmp.shape[1] * q_tmp.shape[2]    )
		_a	: List[str]								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]    )
		_a	: int								=				v_tmp.reshape(v_tmp.shape[0]		,     v_tmp.shape[1] * v_tmp.shape[2]    )
		return k, o, q, v



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_=False    )     -> Optional[Any]:
		if split_mlp_wi:
				_a	: Dict								=				params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
				_a	: Dict								=				params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
				_a	: Optional[int]								=				(wi_a, wi_a)
		else:
				_a	: Tuple								=				params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

		_a	: Tuple								=				params[f"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
		return wi, wo



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Any:
		return params[f"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]



def 	__lowerCamelCase	( lowerCAmelCase_		,     *, lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_ = False    )     -> Any:
		_a	: Dict								=				traverse_util.flatten_dict(variables['target']    )
		_a	: Tuple								=				{'/'.join(lowerCAmelCase_    ): v for k, v in old.items()}

		# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
		_a	: int								=				'encoder/encoder/mlp/wi_0/kernel' in old
		print('Split MLP:'		,     lowerCAmelCase_    )

		_a	: str								=				collections.OrderedDict()

		# Shared embeddings.
		_a	: List[str]								=				old['token_embedder/embedding']

		# Encoder.
		for i in range(lowerCAmelCase_    ):
				# Block i, layer 0 (Self Attention).
				_a	: Any								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     'pre_attention_layer_norm'    )
				_a			,   _a			,   _a			,   _a	: List[str]								=				tax_attention_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     'attention'    )
				_a	: str								=				layer_norm
				_a	: Union[str, Any]								=				k.T
				_a	: Dict								=				o.T
				_a	: int								=				q.T
				_a	: List[str]								=				v.T

				# Block i, layer 1 (MLP).
				_a	: Optional[int]								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     'pre_mlp_layer_norm'    )
				_a			,   _a	: Any								=				tax_mlp_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     lowerCAmelCase_    )
				_a	: Optional[Any]								=				layer_norm
				if split_mlp_wi:
						_a	: Tuple								=				wi[0].T
						_a	: List[str]								=				wi[1].T
				else:
						_a	: Union[str, Any]								=				wi.T
				_a	: Any								=				wo.T
				if scalable_attention:
						# convert the rel_embedding of each layer
						_a	: Any								=				tax_relpos_bias_lookup(
						    lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'    ).T

		_a	: Optional[Any]								=				old['encoder/encoder_norm/scale']

		if not scalable_attention:
				_a	: Union[str, Any]								=				tax_relpos_bias_lookup(
				    lowerCAmelCase_		,     0		,     'encoder'    ).T
				_a	: Any								=				tax_relpos_bias_lookup(
				    lowerCAmelCase_		,     0		,     'decoder'    ).T

		if not is_encoder_only:
				# Decoder.
				for i in range(lowerCAmelCase_    ):
						# Block i, layer 0 (Self Attention).
						_a	: Dict								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'pre_self_attention_layer_norm'    )
						_a			,   _a			,   _a			,   _a	: Union[str, Any]								=				tax_attention_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'self_attention'    )
						_a	: str								=				layer_norm
						_a	: List[Any]								=				k.T
						_a	: Union[str, Any]								=				o.T
						_a	: int								=				q.T
						_a	: List[Any]								=				v.T

						# Block i, layer 1 (Cross Attention).
						_a	: List[Any]								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'pre_cross_attention_layer_norm'    )
						_a			,   _a			,   _a			,   _a	: Optional[int]								=				tax_attention_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'encoder_decoder_attention'    )
						_a	: str								=				layer_norm
						_a	: Union[str, Any]								=				k.T
						_a	: Union[str, Any]								=				o.T
						_a	: Any								=				q.T
						_a	: str								=				v.T

						# Block i, layer 2 (MLP).
						_a	: str								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'pre_mlp_layer_norm'    )
						_a			,   _a	: int								=				tax_mlp_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     lowerCAmelCase_    )
						_a	: List[str]								=				layer_norm
						if split_mlp_wi:
								_a	: List[str]								=				wi[0].T
								_a	: Union[str, Any]								=				wi[1].T
						else:
								_a	: Optional[Any]								=				wi.T
						_a	: List[str]								=				wo.T

						if scalable_attention:
								# convert the rel_embedding of each layer
								_a	: int								=				tax_relpos_bias_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'    ).T

				_a	: List[Any]								=				old['decoder/decoder_norm/scale']

				# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
				if "decoder/logits_dense/kernel" in old:
						_a	: List[Any]								=				old['decoder/logits_dense/kernel'].T

		return new



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_    )     -> str:
		_a	: List[str]								=				collections.OrderedDict([(k, torch.from_numpy(v.copy()    )) for (k, v) in converted_params.items()]    )

		# Add what is missing.
		if "encoder.embed_tokens.weight" not in state_dict:
				_a	: Tuple								=				state_dict['shared.weight']

		if not is_encoder_only:
				if "decoder.embed_tokens.weight" not in state_dict:
						_a	: Union[str, Any]								=				state_dict['shared.weight']

				if "lm_head.weight" not in state_dict:  # For old 1.0 models.
						print('Using shared word embeddings as lm_head.'    )
						_a	: Optional[Any]								=				state_dict['shared.weight']

		return state_dict



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> int:
		_a	: Any								=				checkpoints.load_tax_checkpoint(lowerCAmelCase_    )
		_a	: str								=				convert_tax_to_pytorch(
		    lowerCAmelCase_		,     num_layers=config.num_layers		,     is_encoder_only=lowerCAmelCase_		,     scalable_attention=lowerCAmelCase_    )
		_a	: Dict								=				make_state_dict(lowerCAmelCase_		,     lowerCAmelCase_    )
		model.load_state_dict(lowerCAmelCase_		,     strict=lowerCAmelCase_    )



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_ = False		,     lowerCAmelCase_ = False		,     )     -> str:
		_a	: int								=				MTaConfig.from_json_file(lowerCAmelCase_    )
		print(f"""Building PyTorch model from configuration: {config}"""    )
		# Non-v1.1 checkpoints could also use T5Model, but this works for all.
		# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
		if is_encoder_only:
				_a	: Any								=				UMTaEncoderModel(lowerCAmelCase_    )
		else:
				_a	: Optional[int]								=				UMTaForConditionalGeneration(lowerCAmelCase_    )

		# Load weights from tf checkpoint
		load_tax_weights_in_ta(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )

		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""    )
		model.save_pretrained(lowerCAmelCase_    )

		# Verify that we can load the checkpoint.
		model.from_pretrained(lowerCAmelCase_    )
		print('Done'    )


if __name__ == "__main__":
							__lowerCAmelCase		     =  argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
							# Required parameters
							parser.add_argument(
							    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
							)
							parser.add_argument(
							    '''--config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
							)
							parser.add_argument(
							    '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
							)
							parser.add_argument(
							    '''--scalable_attention''',
							    action='''store_true''',
							    help='''Whether the model uses scaled attention (umt5 model)''',
							    default=False,
							)
							__lowerCAmelCase		     =  parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path,
							    args.config_file,
							    args.pytorch_dump_path,
							    args.is_encoder_only,
							    args.scalable_attention,
							)




