




'''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 the checkpointing capability,
# 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
#
########################################################################

a__         =     16
a__         =     32




def    snake_case__     (  a       ,      a = 16	)			->       str:





			'''simple docstring'''






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

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

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

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

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

			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

		a__         =     mocked_dataloaders  # noqa: F811




def    snake_case__     (  a       ,      a	)			->       str:





			'''simple docstring'''






			if os.environ.get("""TESTING_MOCKED_DATALOADERS"""       ,      a	) == "1":
						snake_case__          =						2
			# Initialize accelerator
			snake_case__          =						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__          =						config["""lr"""]
			snake_case__          =						int(config["""num_epochs"""]	)
			snake_case__          =						int(config["""seed"""]	)
			snake_case__          =						int(config["""batch_size"""]	)

			# New Code #
			# Parse out whether we are saving every epoch or after a certain number of batches
			if hasattr(args.checkpointing_steps       ,      """isdigit"""	):
						if args.checkpointing_steps == "epoch":
									snake_case__          =						args.checkpointing_steps
						elif args.checkpointing_steps.isdigit():
									snake_case__          =						int(args.checkpointing_steps	)
						else:
									raise ValueError(
									    F"""Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed."""	)
			else:
						snake_case__          =						None

			set_seed(a	)

			snake_case__    ,			snake_case__          =						get_dataloaders(a       ,      a	)
			snake_case__          =						evaluate.load("""glue"""       ,      """mrpc"""	)

			# If the batch size is too big we use gradient accumulation
			snake_case__          =						1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						snake_case__          =						batch_size // MAX_GPU_BATCH_SIZE
						snake_case__          =						MAX_GPU_BATCH_SIZE

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

			# 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__          =						model.to(accelerator.device	)

			# Instantiate optimizer
			snake_case__          =						AdamW(params=model.parameters()       ,      lr=a	)

			# Instantiate scheduler
			snake_case__          =						get_linear_schedule_with_warmup(
			    optimizer=a       ,      num_warmup_steps=100       ,      num_training_steps=(len(a	) * 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__          =						accelerator.prepare(
			    a       ,      a       ,      a       ,      a       ,      a	)

			# New Code #
			# We need to keep track of how many total steps we have iterated over
			snake_case__          =						0
			# We also need to keep track of the stating epoch so files are named properly
			snake_case__          =						0

			# We need to load the checkpoint back in before training here with `load_state`
			# The total number of epochs is adjusted based on where the state is being loaded from,
			# as we assume continuation of the same training script
			if args.resume_from_checkpoint:
						if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
									accelerator.print(F"""Resumed from checkpoint: {args.resume_from_checkpoint}"""	)
									accelerator.load_state(args.resume_from_checkpoint	)
									snake_case__          =						os.path.basename(args.resume_from_checkpoint	)
						else:
									# Get the most recent checkpoint
									snake_case__          =						[f.name for f in os.scandir(os.getcwd()	) if f.is_dir()]
									dirs.sort(key=os.path.getctime	)
									snake_case__          =						dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
						# Extract `epoch_{i}` or `step_{i}`
						snake_case__          =						os.path.splitext(a	)[0]

						if "epoch" in training_difference:
									snake_case__          =						int(training_difference.replace("""epoch_"""       ,      """"""	)	) + 1
									snake_case__          =						None
						else:
									snake_case__          =						int(training_difference.replace("""step_"""       ,      """"""	)	)
									snake_case__          =						resume_step // len(a	)
									resume_step -= starting_epoch * len(a	)

    # Now we train the model
			for epoch in range(a       ,      a	):
						model.train()
						# New Code #
						if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
									# We need to skip steps until we reach the resumed step
									snake_case__          =						accelerator.skip_first_batches(a       ,      a	)
									overall_step += resume_step
						else:
									# After the first iteration though, we need to go back to the original dataloader
									snake_case__          =						train_dataloader
						for step, batch in enumerate(a	):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device	)
									snake_case__          =						model(**a	)
									snake_case__          =						outputs.loss
									snake_case__          =						loss / gradient_accumulation_steps
									accelerator.backward(a	)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()
									# New Code #
									overall_step += 1

									# New Code #
									# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
									# These are saved to folders named `step_{overall_step}`
									# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
									# If mixed precision was used, will also save a "scalar.bin" file
									if isinstance(a       ,      a	):
												snake_case__          =						F"""step_{overall_step}"""
												if overall_step % checkpointing_steps == 0:
															if args.output_dir is not None:
																		snake_case__          =						os.path.join(args.output_dir       ,      a	)
															accelerator.save_state(a	)

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

						snake_case__          =						metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F"""epoch {epoch}:"""       ,      a	)

						# New Code #
						# We save the model, optimizer, lr_scheduler, and seed states by calling `save_state`
						# These are saved to folders named `epoch_{epoch}`
						# Will contain files: "pytorch_model.bin", "optimizer.bin", "scheduler.bin", and "random_states.pkl"
						# If mixed precision was used, will also save a "scalar.bin" file
						if checkpointing_steps == "epoch":
									snake_case__          =						F"""epoch_{epoch}"""
									if args.output_dir is not None:
												snake_case__          =						os.path.join(args.output_dir       ,      a	)
									accelerator.save_state(a	)




def    snake_case__     (  )			->       Optional[Any]:





			'''simple docstring'''






			snake_case__          =						argparse.ArgumentParser(description="""Simple example of training script."""	)
			parser.add_argument(
			    """--mixed_precision"""       ,      type=a       ,      default=a       ,      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."""	)
			parser.add_argument(
			    """--checkpointing_steps"""       ,      type=a       ,      default=a       ,      help="""Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."""       ,      )
			parser.add_argument(
			    """--output_dir"""       ,      type=a       ,      default="""."""       ,      help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""       ,      )
			parser.add_argument(
			    """--resume_from_checkpoint"""       ,      type=a       ,      default=a       ,      help="""If the training should continue from a checkpoint folder."""       ,      )
			snake_case__          =						parser.parse_args()
			snake_case__          =						{"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
			training_function(a       ,      a	)


if __name__ == "__main__":
		main()
"""simple docstring"""


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				TaConfig.from_pretrained(A   )
     lowercase__								=				FlaxTaForConditionalGeneration(config=A   )
     lowercase__								=				checkpoints.load_tax_checkpoint(A   )

     lowercase__								=				"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

     # Encoder
     for layer_index in range(config.num_layers   ):
          lowercase__								=				f'''layers_{str(A   )}'''

          # Self-Attention
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

          if split_mlp_wi:
               lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
               lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
          else:
               lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

          lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          lowercase__								=				tax_attention_key
          lowercase__								=				tax_attention_out
          lowercase__								=				tax_attention_query
          lowercase__								=				tax_attention_value

          lowercase__								=				tax_attention_layer_norm

          if split_mlp_wi:
               lowercase__								=				tax_mlp_wi_a
               lowercase__								=				tax_mlp_wi_a
          else:
               lowercase__								=				tax_mlp_wi

          lowercase__								=				tax_mlp_wo
          lowercase__								=				tax_mlp_layer_norm

     # Only for layer 0:
     lowercase__								=				tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
     lowercase__								=				tax_encoder_rel_embedding

     # Assigning
     lowercase__								=				tax_model["target"]["encoder"]["encoder_norm"]["scale"]
     lowercase__								=				tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers   ):
          lowercase__								=				f'''layers_{str(A   )}'''

          # Self-Attention
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
              "scale"
          ]

          # Encoder-Decoder-Attention
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
              "kernel"
          ]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
              "kernel"
          ]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
              "kernel"
          ]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
              "kernel"
          ]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

          # MLP
          if split_mlp_wi:
               lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
               lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
          else:
               lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

          lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          lowercase__								=				tax_attention_key
          lowercase__								=				tax_attention_out
          lowercase__								=				tax_attention_query
          lowercase__								=				tax_attention_value

          lowercase__								=				tax_pre_attention_layer_norm

          lowercase__								=				tax_enc_dec_attention_key
          lowercase__								=				tax_enc_dec_attention_out
          lowercase__								=				tax_enc_dec_attention_query
          lowercase__								=				tax_enc_dec_attention_value

          lowercase__								=				tax_cross_layer_norm

          if split_mlp_wi:
               lowercase__								=				tax_mlp_wi_a
               lowercase__								=				tax_mlp_wi_a
          else:
               lowercase__								=				tax_mlp_wi

          lowercase__								=				tax_mlp_wo

          lowercase__								=				txa_mlp_layer_norm

     # Decoder Normalization
     lowercase__								=				tax_model["target"]["decoder"]["decoder_norm"]["scale"]
     lowercase__								=				txa_decoder_norm

     # Only for layer 0:
     lowercase__								=				tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
     lowercase__								=				tax_decoder_rel_embedding

     # Token Embeddings
     lowercase__								=				tax_model["target"]["token_embedder"]["embedding"]
     lowercase__								=				txa_token_embeddings

     # LM Head (only in v1.1 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          lowercase__								=				tax_model["target"]["decoder"]["logits_dense"]["kernel"]

     flax_model.save_pretrained(A   )
     print("T5X Model was sucessfully converted!"   )


if __name__ == "__main__":
 lowerCAmelCase_:       List[Any]			     =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
 )
 parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
 parser.add_argument(
     "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
 )
 lowerCAmelCase_:       Dict			     =       parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




