


import argparse
import json
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.utils.deepspeed import DummyOptim, DummyScheduler


SCREAMING_SNAKE_CASE       :		Optional[int]         =				16
SCREAMING_SNAKE_CASE       :		int         =				32




def     UpperCamelCase    (      _a       ,		_a = 1_6       ,		_a = "bert-base-cased"		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :Optional[int]     =       AutoTokenizer.from_pretrained(_a		)
     lowercase_       :Any     =       load_dataset('''glue'''       ,		'''mrpc'''		)

     def tokenize_function(_a		):
          # max_length=None => use the model max length (it's actually the default)
          lowercase_       :Optional[int]     =       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
     lowercase_       :str     =       datasets.map(
         _a       ,		batched=_a       ,		remove_columns=['''idx''', '''sentence1''', '''sentence2''']       ,		load_from_cache_file=_a		)

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     lowercase_       :Union[str, Any]     =       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.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(_a       ,		padding='''max_length'''       ,		max_length=1_2_8       ,		return_tensors='''pt'''		)
          return tokenizer.pad(_a       ,		padding='''longest'''       ,		return_tensors='''pt'''		)

     # Instantiate dataloaders.
     lowercase_       :int     =       DataLoader(
         tokenized_datasets['''train''']       ,		shuffle=_a       ,		collate_fn=_a       ,		batch_size=_a		)
     lowercase_       :Optional[Any]     =       DataLoader(
         tokenized_datasets['''validation''']       ,		shuffle=_a       ,		collate_fn=_a       ,		batch_size=_a		)

     return train_dataloader, eval_dataloader




def     UpperCamelCase    (      _a       ,		_a       ,		_a       ,		_a		)					->							Optional[Any]:
     '''simple docstring'''





     model.eval()
     lowercase_       :List[str]     =       0
     for step, batch in enumerate(_a		):
          # We could avoid this line since we set the accelerator with `device_placement=True`.
          batch.to(accelerator.device		)
          with torch.no_grad():
               lowercase_       :Optional[int]     =       model(**_a		)
          lowercase_       :Optional[int]     =       outputs.logits.argmax(dim=-1		)
          # It is slightly faster to call this once, than multiple times
          lowercase_    ,      lowercase_       :Optional[Any]     =       accelerator.gather(
              (predictions, batch['''labels'''])		)  # If we are in a multiprocess environment, the last batch has duplicates
          if accelerator.use_distributed:
               if step == len(_a		) - 1:
                    lowercase_       :Union[str, Any]     =       predictions[: len(eval_dataloader.dataset		) - samples_seen]
                    lowercase_       :str     =       references[: len(eval_dataloader.dataset		) - samples_seen]
               else:
                    samples_seen += references.shape[0]
          metric.add_batch(
              predictions=_a       ,		references=_a       ,		)

     lowercase_       :str     =       metric.compute()
     return eval_metric["accuracy"]




def     UpperCamelCase    (      _a       ,		_a		)					->							Tuple:
     '''simple docstring'''





     lowercase_       :str     =       Accelerator()

     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     lowercase_       :str     =       config['''lr''']
     lowercase_       :int     =       int(config['''num_epochs''']		)
     lowercase_       :Optional[int]     =       int(config['''seed''']		)
     lowercase_       :int     =       int(config['''batch_size''']		)
     lowercase_       :Tuple     =       args.model_name_or_path

     set_seed(_a		)
     lowercase_    ,      lowercase_       :Any     =       get_dataloaders(_a       ,		_a       ,		_a		)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     lowercase_       :Tuple     =       AutoModelForSequenceClassification.from_pretrained(_a       ,		return_dict=_a		)

     # Instantiate optimizer
     lowercase_       :List[Any]     =       (
         AdamW
         if accelerator.state.deepspeed_plugin is None
         or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
         else DummyOptim
     )
     lowercase_       :Union[str, Any]     =       optimizer_cls(params=model.parameters()       ,		lr=_a		)

     if accelerator.state.deepspeed_plugin is not None:
          lowercase_       :Optional[int]     =       accelerator.state.deepspeed_plugin.deepspeed_config[
              '''gradient_accumulation_steps'''
          ]
     else:
          lowercase_       :Any     =       1
     lowercase_       :Union[str, Any]     =       (len(_a		) * num_epochs) // gradient_accumulation_steps

     # Instantiate scheduler
     if (
         accelerator.state.deepspeed_plugin is None
         or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
     ):
          lowercase_       :Tuple     =       get_linear_schedule_with_warmup(
              optimizer=_a       ,		num_warmup_steps=0       ,		num_training_steps=_a       ,		)
     else:
          lowercase_       :List[Any]     =       DummyScheduler(_a       ,		total_num_steps=_a       ,		warmup_num_steps=0		)

     # 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_       :Tuple     =       accelerator.prepare(
         _a       ,		_a       ,		_a       ,		_a       ,		_a		)

     # We need to keep track of how many total steps we have iterated over
     lowercase_       :Optional[int]     =       0
     # We also need to keep track of the stating epoch so files are named properly
     lowercase_       :List[Any]     =       0
     lowercase_       :List[str]     =       evaluate.load('''glue'''       ,		'''mrpc'''		)
     lowercase_       :List[Any]     =       num_epochs

     if args.partial_train_epoch is not None:
          lowercase_       :str     =       args.partial_train_epoch

     if args.resume_from_checkpoint:
          accelerator.load_state(args.resume_from_checkpoint		)
          lowercase_       :List[Any]     =       args.resume_from_checkpoint.split('''epoch_'''		)[1]
          lowercase_       :Optional[Any]     =       ''''''
          for char in epoch_string:
               if char.isdigit():
                    state_epoch_num += char
               else:
                    break
          lowercase_       :Optional[int]     =       int(_a		) + 1
          lowercase_       :Any     =       evaluation_loop(_a       ,		_a       ,		_a       ,		_a		)
          accelerator.print('''resumed checkpoint performance:'''       ,		_a		)
          accelerator.print('''resumed checkpoint\'s scheduler\'s lr:'''       ,		lr_scheduler.get_lr()[0]		)
          accelerator.print('''resumed optimizers\'s lr:'''       ,		optimizer.param_groups[0]['''lr''']		)
          with open(os.path.join(args.output_dir       ,		f"state_{starting_epoch-1}.json"		)       ,		'''r'''		) as f:
               lowercase_       :str     =       json.load(_a		)
               assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
               assert (
                   resumed_state["lr"] == lr_scheduler.get_lr()[0]
               ), "Scheduler learning rate mismatch, loading from checkpoint failed"
               assert (
                   resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
               ), "Optimizer learning rate mismatch, loading from checkpoint failed"
               assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
               return

    # Now we train the model
     lowercase_       :int     =       {}
     for epoch in range(_a       ,		_a		):
          model.train()
          for step, batch in enumerate(_a		):
               lowercase_       :List[Any]     =       model(**_a		)
               lowercase_       :Dict     =       outputs.loss
               lowercase_       :Optional[Any]     =       loss / gradient_accumulation_steps
               accelerator.backward(_a		)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1
          lowercase_       :List[str]     =       f"epoch_{epoch}"
          lowercase_       :str     =       os.path.join(args.output_dir       ,		_a		)
          accelerator.save_state(_a		)
          lowercase_       :List[str]     =       evaluation_loop(_a       ,		_a       ,		_a       ,		_a		)
          lowercase_       :Any     =       accuracy
          lowercase_       :List[str]     =       lr_scheduler.get_lr()[0]
          lowercase_       :int     =       optimizer.param_groups[0]['''lr''']
          lowercase_       :int     =       epoch
          lowercase_       :Dict     =       overall_step
          accelerator.print(f"epoch {epoch}:"       ,		_a		)

          accelerator.wait_for_everyone()
          if accelerator.is_main_process:
               with open(os.path.join(args.output_dir       ,		f"state_{epoch}.json"		)       ,		'''w'''		) as f:
                    json.dump(_a       ,		_a		)




def     UpperCamelCase    (      )					->							int:
     '''simple docstring'''





     lowercase_       :str     =       argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.'''		)
     parser.add_argument(
         '''--model_name_or_path'''       ,		type=_a       ,		default='''bert-base-cased'''       ,		help='''Path to pretrained model or model identifier from huggingface.co/models.'''       ,		required=_a       ,		)
     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.'''       ,		)
     parser.add_argument(
         '''--partial_train_epoch'''       ,		type=_a       ,		default=_a       ,		help='''If passed, the training will stop after this number of epochs.'''       ,		)
     parser.add_argument(
         '''--num_epochs'''       ,		type=_a       ,		default=2       ,		help='''Number of train epochs.'''       ,		)
     lowercase_       :Dict     =       parser.parse_args()
     lowercase_       :Optional[int]     =       {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 4_2, '''batch_size''': 1_6}

     training_function(_a       ,		_a		)


if __name__ == "__main__":
   main()





import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()




def     UpperCamelCase    (      _a       ,		_a       ,		_a=None		)					->							Dict:
     '''simple docstring'''





     assert torch_layer.weight.shape == weight.shape, f"{torch_layer} layer.weight does not match"
     lowercase_       :str     =       nn.Parameter(_a		)
     if bias is not None:
          assert torch_layer.bias.shape == bias.shape, f"{torch_layer} layer.bias does not match"
          lowercase_       :List[str]     =       nn.Parameter(_a		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							List[str]:
     '''simple docstring'''





     lowercase_       :Optional[int]     =       np.asarray(weights[0]		)
     lowercase_       :str     =       np.asarray(weights[1]		)
     lowercase_       :Union[str, Any]     =       np.asarray(weights[2]		)

     set_param(
         torch_layer.self_attention.query_key       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.self_attention.value       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.output.dense       ,		torch.tensor(_a		).view(-1       ,		_a		).contiguous().transpose(0       ,		1		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[int]:
     '''simple docstring'''





     lowercase_       :Union[str, Any]     =       np.asarray(weights[0]		)
     lowercase_       :Tuple     =       np.asarray(weights[1]		)
     lowercase_       :List[Any]     =       np.asarray(weights[2]		)
     lowercase_       :Optional[int]     =       np.asarray(weights[3]		)

     set_param(
         torch_layer.self_attention.query       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.self_attention.key       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.self_attention.value       ,		torch.tensor(_a		).transpose(1       ,		2		).contiguous().view(-1       ,		_a		)       ,		)
     set_param(
         torch_layer.output.dense       ,		torch.tensor(_a		).view(-1       ,		_a		).contiguous().transpose(0       ,		1		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :List[str]     =       weights[0][0][0]
     lowercase_       :Optional[Any]     =       np.asarray(layer_norm_a[0]		)
     lowercase_       :Any     =       np.asarray(layer_norm_a[1]		)
     set_param(
         torch_block.attention.layer_norm       ,		torch.tensor(_a		)       ,		torch.tensor(_a		)       ,		)

     # lsh weights + output
     lowercase_       :int     =       weights[0][1]
     if len(_a		) < 4:
          set_layer_weights_in_torch_lsh(_a       ,		torch_block.attention       ,		_a		)
     else:
          set_layer_weights_in_torch_local(_a       ,		torch_block.attention       ,		_a		)

     # intermediate weighs
     lowercase_       :Optional[int]     =       weights[2][0][1][2]

     # Chunked Feed Forward
     if len(_a		) == 4:
          lowercase_       :Tuple     =       intermediate_weights[2]

     # layernorm 2
     lowercase_       :int     =       np.asarray(intermediate_weights[0][0]		)
     lowercase_       :Any     =       np.asarray(intermediate_weights[0][1]		)
     set_param(
         torch_block.feed_forward.layer_norm       ,		torch.tensor(_a		)       ,		torch.tensor(_a		)       ,		)

     # intermediate dense
     lowercase_       :Any     =       np.asarray(intermediate_weights[1][0]		)
     lowercase_       :int     =       np.asarray(intermediate_weights[1][1]		)
     set_param(
         torch_block.feed_forward.dense.dense       ,		torch.tensor(_a		).transpose(0       ,		1		).contiguous()       ,		torch.tensor(_a		)       ,		)

     # intermediate out
     lowercase_       :Optional[int]     =       np.asarray(intermediate_weights[4][0]		)
     lowercase_       :Any     =       np.asarray(intermediate_weights[4][1]		)
     set_param(
         torch_block.feed_forward.output.dense       ,		torch.tensor(_a		).transpose(0       ,		1		).contiguous()       ,		torch.tensor(_a		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							Optional[Any]:
     '''simple docstring'''





     lowercase_       :Any     =       torch_model.reformer

     # word embeds
     lowercase_       :int     =       np.asarray(weights[1]		)
     set_param(
         torch_model_reformer.embeddings.word_embeddings       ,		torch.tensor(_a		)       ,		)

     if isinstance(weights[3]       ,		_a		):
          lowercase_       :Union[str, Any]     =       torch_model_reformer.embeddings.position_embeddings
          for emb_idx in range(len(position_embeddings.weights		)		):
               lowercase_       :Tuple     =       np.asarray(weights[3][emb_idx][0]		)
               assert (
                   position_embeddings.weights[emb_idx].shape == emb_weights.shape
               ), f"{position_embeddings[emb_idx]} emb does not match"
               lowercase_       :Optional[int]     =       nn.Parameter(torch.tensor(_a		)		)

     lowercase_       :Any     =       weights[5]
     assert len(torch_model_reformer.encoder.layers		) * 4 == len(
         _a		), "HF and trax model do not have the same number of layers"
     for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers		):
          lowercase_       :int     =       trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
          set_block_weights_in_torch(_a       ,		_a       ,		_a		)

     # output layer norm
     lowercase_       :Optional[Any]     =       np.asarray(weights[7][0]		)
     lowercase_       :int     =       np.asarray(weights[7][1]		)
     set_param(
         torch_model_reformer.encoder.layer_norm       ,		torch.tensor(_a		)       ,		torch.tensor(_a		)       ,		)

     # output embeddings
     lowercase_       :Optional[int]     =       np.asarray(weights[9][0]		)
     lowercase_       :Union[str, Any]     =       np.asarray(weights[9][1]		)
     set_param(
         torch_model.lm_head.decoder       ,		torch.tensor(_a		).transpose(0       ,		1		).contiguous()       ,		torch.tensor(_a		)       ,		)




def     UpperCamelCase    (      _a       ,		_a       ,		_a		)					->							str:
     '''simple docstring'''





     lowercase_       :List[str]     =       ReformerConfig.from_json_file(_a		)
     print(f"Building PyTorch model from configuration: {config}"		)
     lowercase_       :List[str]     =       ReformerModelWithLMHead(_a		)

     with open(_a       ,		'''rb'''		) as f:
          lowercase_       :Dict     =       pickle.load(_a		)['''weights''']

     set_model_weights_in_torch(_a       ,		_a       ,		config.hidden_size		)

     # Save pytorch-model
     print(f"Save PyTorch model to {pytorch_dump_path}"		)
     torch.save(model.state_dict()       ,		_a		)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE       :		Optional[int]         =				argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--trax_model_pkl_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
   )
   parser.add_argument(
       "--config_file",
       default=None,
       type=str,
       required=True,
       help=(
           "The config json file corresponding to the pre-trained Reformer model. \n"
           "This specifies the model architecture."
       ),
   )
   parser.add_argument(
       "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   SCREAMING_SNAKE_CASE       :		Dict         =				parser.parse_args()
   convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)

