

"""simple docstring"""




import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_	  =			logging.get_logger(__name__)



def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      Optional[Any]:




   """simple docstring"""
   lowerCamelCase__						=			os.path.abspath(_UpperCamelCase   )
   logger.info(f'Converting TensorFlow checkpoint from {tf_path}'   )
   # Load weights from TF model
   lowerCamelCase__						=			tf.train.list_variables(_UpperCamelCase   )
   lowerCamelCase__						=			[]
   lowerCamelCase__						=			[]
   lowerCamelCase__						=			[]
   for full_name, shape in init_vars:
      # logger.info(f"Loading TF weight {name} with shape {shape}")
      lowerCamelCase__						=			full_name.split("/"   )
      if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
         logger.info(f'Skipping non-model layer {full_name}'   )
         continue
      if "optimizer" in full_name:
         logger.info(f'Skipping optimization layer {full_name}'   )
         continue
      if name[0] == "model":
         # ignore initial 'model'
         lowerCamelCase__						=			name[1:]
      # figure out how many levels deep the name is
      lowerCamelCase__						=			0
      for _name in name:
         if _name.startswith("layer_with_weights"   ):
            depth += 1
         else:
            break
      layer_depth.append(_UpperCamelCase   )
      # read data
      lowerCamelCase__						=			tf.train.load_variable(_UpperCamelCase	,    _UpperCamelCase   )
      names.append("/".join(_UpperCamelCase   )   )
      arrays.append(_UpperCamelCase   )
   logger.info(f'Read a total of {len(_UpperCamelCase   ):,} layers'   )

   # Sanity check
   if len(set(_UpperCamelCase   )   ) != 1:
      raise ValueError(f'Found layer names with different depths (layer depth {list(set(_UpperCamelCase   )   )})'   )
   lowerCamelCase__						=			list(set(_UpperCamelCase   )   )[0]
   if layer_depth != 1:
      raise ValueError(
          "The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"
          " heads."   )

   # convert layers
   logger.info("Converting weights..."   )
   for full_name, array in zip(_UpperCamelCase	,    _UpperCamelCase   ):
      lowerCamelCase__						=			full_name.split("/"   )
      lowerCamelCase__						=			model
      lowerCamelCase__						=			[]
      for i, m_name in enumerate(_UpperCamelCase   ):
         if m_name == ".ATTRIBUTES":
            # variable names end with .ATTRIBUTES/VARIABLE_VALUE
            break
         if m_name.startswith("layer_with_weights"   ):
            lowerCamelCase__						=			int(m_name.split("-"   )[-1]   )
            if layer_num <= 2:
               # embedding layers
               # layer_num 0: word_embeddings
               # layer_num 1: position_embeddings
               # layer_num 2: token_type_embeddings
               continue
            elif layer_num == 3:
               # embedding LayerNorm
               trace.extend(["embeddings", "LayerNorm"]   )
               lowerCamelCase__						=			getattr(_UpperCamelCase	,    "embeddings"   )
               lowerCamelCase__						=			getattr(_UpperCamelCase	,    "LayerNorm"   )
            elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
               # encoder layers
               trace.extend(["encoder", "layer", str(layer_num - 4   )]   )
               lowerCamelCase__						=			getattr(_UpperCamelCase	,    "encoder"   )
               lowerCamelCase__						=			getattr(_UpperCamelCase	,    "layer"   )
               lowerCamelCase__						=			pointer[layer_num - 4]
            elif layer_num == config.num_hidden_layers + 4:
               # pooler layer
               trace.extend(["pooler", "dense"]   )
               lowerCamelCase__						=			getattr(_UpperCamelCase	,    "pooler"   )
               lowerCamelCase__						=			getattr(_UpperCamelCase	,    "dense"   )
         elif m_name == "embeddings":
            trace.append("embeddings"   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "embeddings"   )
            if layer_num == 0:
               trace.append("word_embeddings"   )
               lowerCamelCase__						=			getattr(_UpperCamelCase	,    "word_embeddings"   )
            elif layer_num == 1:
               trace.append("position_embeddings"   )
               lowerCamelCase__						=			getattr(_UpperCamelCase	,    "position_embeddings"   )
            elif layer_num == 2:
               trace.append("token_type_embeddings"   )
               lowerCamelCase__						=			getattr(_UpperCamelCase	,    "token_type_embeddings"   )
            else:
               raise ValueError(f'Unknown embedding layer with name {full_name}'   )
            trace.append("weight"   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "weight"   )
         elif m_name == "_attention_layer":
            # self-attention layer
            trace.extend(["attention", "self"]   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "attention"   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "self"   )
         elif m_name == "_attention_layer_norm":
            # output attention norm
            trace.extend(["attention", "output", "LayerNorm"]   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "attention"   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "output"   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "LayerNorm"   )
         elif m_name == "_attention_output_dense":
            # output attention dense
            trace.extend(["attention", "output", "dense"]   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "attention"   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "output"   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "dense"   )
         elif m_name == "_output_dense":
            # output dense
            trace.extend(["output", "dense"]   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "output"   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "dense"   )
         elif m_name == "_output_layer_norm":
            # output dense
            trace.extend(["output", "LayerNorm"]   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "output"   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "LayerNorm"   )
         elif m_name == "_key_dense":
            # attention key
            trace.append("key"   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "key"   )
         elif m_name == "_query_dense":
            # attention query
            trace.append("query"   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "query"   )
         elif m_name == "_value_dense":
            # attention value
            trace.append("value"   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "value"   )
         elif m_name == "_intermediate_dense":
            # attention intermediate dense
            trace.extend(["intermediate", "dense"]   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "intermediate"   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "dense"   )
         elif m_name == "_output_layer_norm":
            # output layer norm
            trace.append("output"   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "output"   )
         # weights & biases
         elif m_name in ["bias", "beta"]:
            trace.append("bias"   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "bias"   )
         elif m_name in ["kernel", "gamma"]:
            trace.append("weight"   )
            lowerCamelCase__						=			getattr(_UpperCamelCase	,    "weight"   )
         else:
            logger.warning(f'Ignored {m_name}'   )
        # for certain layers reshape is necessary
      lowerCamelCase__						=			".".join(_UpperCamelCase   )
      if re.match(R"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"	,    _UpperCamelCase   ) or re.match(
          R"(\S+)\.attention\.output\.dense\.weight"	,    _UpperCamelCase   ):
         lowerCamelCase__						=			array.reshape(pointer.data.shape   )
      if "kernel" in full_name:
         lowerCamelCase__						=			array.transpose()
      if pointer.shape == array.shape:
         lowerCamelCase__						=			torch.from_numpy(_UpperCamelCase   )
      else:
         raise ValueError(
             f'Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'
             f' {array.shape}'   )
      logger.info(f'Successfully set variable {full_name} to PyTorch layer {trace}'   )
   return model



def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      Optional[Any]:




   """simple docstring"""
   logger.info(f'Loading model based on config from {config_path}...'   )
   lowerCamelCase__						=			BertConfig.from_json_file(_UpperCamelCase   )
   lowerCamelCase__						=			BertModel(_UpperCamelCase   )

   # Load weights from checkpoint
   logger.info(f'Loading weights from checkpoint {tf_checkpoint_path}...'   )
   load_tfa_weights_in_bert(_UpperCamelCase	,    _UpperCamelCase	,    _UpperCamelCase   )

   # Save pytorch-model
   logger.info(f'Saving PyTorch model to {pytorch_dump_path}...'   )
   torch.save(model.state_dict()	,    _UpperCamelCase   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_	  =			argparse.ArgumentParser()
     parser.add_argument(
         '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow 2.x checkpoint path.'''
     )
     parser.add_argument(
         '''--bert_config_file''',
         type=str,
         required=True,
         help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
     )
     parser.add_argument(
         '''--pytorch_dump_path''',
         type=str,
         required=True,
         help='''Path to the output PyTorch model (must include filename).''',
     )
     SCREAMING_SNAKE_CASE_	  =			parser.parse_args()
     convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







"""simple docstring"""




import argparse
import gc
import json
import os

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_     :			int	  =			16
SCREAMING_SNAKE_CASE_     :			int	  =			32



def 	UpperCAmelCase__   (				A__   )      ->      Optional[int]:




   """simple docstring"""
   return int(x / 2**20   )


class 		_A       :





      def __enter__(  self		)   ->					Dict:
         gc.collect()
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
         lowerCamelCase__						=			torch.cuda.memory_allocated()
         return self





      def __exit__(  self    ,						*SCREAMING_SNAKE_CASE__		)   ->					Dict:
         gc.collect()
         torch.cuda.empty_cache()
         lowerCamelCase__						=			torch.cuda.memory_allocated()
         lowerCamelCase__						=			torch.cuda.max_memory_allocated()
         lowerCamelCase__						=			bamb(self.end - self.begin		)
         lowerCamelCase__						=			bamb(self.peak - self.begin		)
         # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")






def 	UpperCAmelCase__   (				A__	,    A__ = 16	,    A__ = "bert-base-cased"	,    A__ = 320	,    A__ = 160	,    )      ->      Dict:




   """simple docstring"""
   lowerCamelCase__						=			AutoTokenizer.from_pretrained(A__   )
   lowerCamelCase__						=			load_dataset(
       "glue"	,    "mrpc"	,    split={"train": f'train[:{n_train}]', "validation": f'validation[:{n_val}]'}   )

   def tokenize_function(A__   ):
      # max_length=None => use the model max length (it's actually the default)
      lowerCamelCase__						=			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
   lowerCamelCase__						=			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
   lowerCamelCase__						=			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=128	,    return_tensors="pt"   )
      return tokenizer.pad(A__	,    padding="longest"	,    return_tensors="pt"   )

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

   return train_dataloader, eval_dataloader



def 	UpperCAmelCase__   (				A__	,    A__   )      ->      Optional[int]:




   """simple docstring"""
   # Initialize accelerator
   lowerCamelCase__						=			Accelerator()

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   lowerCamelCase__						=			config["lr"]
   lowerCamelCase__						=			int(config["num_epochs"]   )
   lowerCamelCase__						=			int(config["seed"]   )
   lowerCamelCase__						=			int(config["batch_size"]   )
   lowerCamelCase__						=			args.model_name_or_path

   set_seed(A__   )
   lowerCamelCase__					,       lowerCamelCase__						=			get_dataloaders(A__	,    A__	,    A__	,    args.n_train	,    args.n_val   )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   lowerCamelCase__						=			AutoModelForSequenceClassification.from_pretrained(A__	,    return_dict=A__   )

   # Instantiate optimizer
   lowerCamelCase__						=			(
       AdamW
       if accelerator.state.deepspeed_plugin is None
       or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
       else DummyOptim
   )
   lowerCamelCase__						=			optimizer_cls(params=model.parameters()	,    lr=A__   )

   if accelerator.state.deepspeed_plugin is not None:
      lowerCamelCase__						=			accelerator.state.deepspeed_plugin.deepspeed_config[
          "gradient_accumulation_steps"
      ]
   else:
      lowerCamelCase__						=			1
   lowerCamelCase__						=			(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
   ):
      lowerCamelCase__						=			get_linear_schedule_with_warmup(
          optimizer=A__	,    num_warmup_steps=0	,    num_training_steps=A__	,    )
   else:
      lowerCamelCase__						=			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.
   lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			accelerator.prepare(
       A__	,    A__	,    A__	,    A__	,    A__   )

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

   # Now we train the model
   lowerCamelCase__						=			{}
   for epoch in range(A__	,    A__   ):
      with TorchTracemalloc() as tracemalloc:
         model.train()
         for step, batch in enumerate(A__   ):
            lowerCamelCase__						=			model(**A__   )
            lowerCamelCase__						=			outputs.loss
            lowerCamelCase__						=			loss / gradient_accumulation_steps
            accelerator.backward(A__   )
            if step % gradient_accumulation_steps == 0:
               optimizer.step()
               lr_scheduler.step()
               optimizer.zero_grad()

            overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
      accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin   )   )   )
      accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used   )   )
      accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked   )   )
      accelerator.print(
          "Total Peak Memory consumed during the train (max): {}".format(
              tracemalloc.peaked + bamb(tracemalloc.begin   )   )   )
      lowerCamelCase__						=			tracemalloc.peaked + bamb(tracemalloc.begin   )
      if args.peak_memory_upper_bound is not None:
         assert (
             train_total_peak_memory[f'epoch-{epoch}'] <= args.peak_memory_upper_bound
         ), "Peak memory usage exceeded the upper bound"

   accelerator.wait_for_everyone()
   if accelerator.is_main_process:
      with open(os.path.join(args.output_dir	,    "peak_memory_utilization.json"   )	,    "w"   ) as f:
         json.dump(A__	,    A__   )



def 	UpperCAmelCase__   (				)      ->      Any:




   """simple docstring"""
   lowerCamelCase__						=			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(
       "--peak_memory_upper_bound"	,    type=A__	,    default=A__	,    help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."	,    )
   parser.add_argument(
       "--n_train"	,    type=A__	,    default=320	,    help="Number of training examples to use."	,    )
   parser.add_argument(
       "--n_val"	,    type=A__	,    default=160	,    help="Number of validation examples to use."	,    )
   parser.add_argument(
       "--num_epochs"	,    type=A__	,    default=1	,    help="Number of train epochs."	,    )
   lowerCamelCase__						=			parser.parse_args()
   lowerCamelCase__						=			{"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
   training_function(A__	,    A__   )


if __name__ == "__main__":
     main()




