



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()
_lowercase		:				Union[str, Any]      		=logging.get_logger(__name__)

def      lowerCAmelCase_					(  _lowercase    :       Tuple						,       _lowercase    :       Any						,       _lowercase    :       int)							->	List[Any]:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  os.path.abspath(_a)
			logger.info(F'''Converting TensorFlow checkpoint from {tf_path}''')
			# Load weights from TF model
			a__       :						int							=  tf.train.list_variables(_a)
			a__       :						Union[str, Any]							=  []
			a__       :						List[str]							=  []
			a__       :						Union[str, Any]							=  []
			for full_name, shape in init_vars:
						# logger.info(f"Loading TF weight {name} with shape {shape}")
						a__       :						Dict							=  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'
									a__       :						Tuple							=  name[1:]
						# figure out how many levels deep the name is
						a__       :						Dict							=  0
						for _name in name:
									if _name.startswith("""layer_with_weights"""):
												depth += 1
									else:
												break
						layer_depth.append(_a)
						# read data
						a__       :						Optional[Any]							=  tf.train.load_variable(_a						,       _a)
						names.append("""/""".join(_a))
						arrays.append(_a)
			logger.info(F'''Read a total of {len(_a):,} layers''')

			# Sanity check
			if len(set(_a)) != 1:
						raise ValueError(F'''Found layer names with different depths (layer depth {list(set(_a))})''')
			a__       :						Optional[int]							=  list(set(_a))[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(_a						,       _a):
						a__       :						Any							=  full_name.split("""/""")
						a__       :						Optional[Any]							=  model
						a__       :						Union[str, Any]							=  []
						for i, m_name in enumerate(_a):
									if m_name == ".ATTRIBUTES":
												# variable names end with .ATTRIBUTES/VARIABLE_VALUE
												break
									if m_name.startswith("""layer_with_weights"""):
												a__       :						List[Any]							=  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"""])
															a__       :						Union[str, Any]							=  getattr(_a						,       """embeddings""")
															a__       :						Optional[Any]							=  getattr(_a						,       """LayerNorm""")
												elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
															# encoder layers
															trace.extend(["""encoder""", """layer""", str(layer_num - 4)])
															a__       :						Optional[int]							=  getattr(_a						,       """encoder""")
															a__       :						List[Any]							=  getattr(_a						,       """layer""")
															a__       :						List[Any]							=  pointer[layer_num - 4]
												elif layer_num == config.num_hidden_layers + 4:
															# pooler layer
															trace.extend(["""pooler""", """dense"""])
															a__       :						List[Any]							=  getattr(_a						,       """pooler""")
															a__       :						int							=  getattr(_a						,       """dense""")
									elif m_name == "embeddings":
												trace.append("""embeddings""")
												a__       :						Optional[int]							=  getattr(_a						,       """embeddings""")
												if layer_num == 0:
															trace.append("""word_embeddings""")
															a__       :						Optional[Any]							=  getattr(_a						,       """word_embeddings""")
												elif layer_num == 1:
															trace.append("""position_embeddings""")
															a__       :						Union[str, Any]							=  getattr(_a						,       """position_embeddings""")
												elif layer_num == 2:
															trace.append("""token_type_embeddings""")
															a__       :						Dict							=  getattr(_a						,       """token_type_embeddings""")
												else:
															raise ValueError(F'''Unknown embedding layer with name {full_name}''')
												trace.append("""weight""")
												a__       :						str							=  getattr(_a						,       """weight""")
									elif m_name == "_attention_layer":
												# self-attention layer
												trace.extend(["""attention""", """self"""])
												a__       :						Tuple							=  getattr(_a						,       """attention""")
												a__       :						List[Any]							=  getattr(_a						,       """self""")
									elif m_name == "_attention_layer_norm":
												# output attention norm
												trace.extend(["""attention""", """output""", """LayerNorm"""])
												a__       :						str							=  getattr(_a						,       """attention""")
												a__       :						Any							=  getattr(_a						,       """output""")
												a__       :						str							=  getattr(_a						,       """LayerNorm""")
									elif m_name == "_attention_output_dense":
												# output attention dense
												trace.extend(["""attention""", """output""", """dense"""])
												a__       :						Any							=  getattr(_a						,       """attention""")
												a__       :						Tuple							=  getattr(_a						,       """output""")
												a__       :						Optional[int]							=  getattr(_a						,       """dense""")
									elif m_name == "_output_dense":
												# output dense
												trace.extend(["""output""", """dense"""])
												a__       :						Union[str, Any]							=  getattr(_a						,       """output""")
												a__       :						str							=  getattr(_a						,       """dense""")
									elif m_name == "_output_layer_norm":
												# output dense
												trace.extend(["""output""", """LayerNorm"""])
												a__       :						Dict							=  getattr(_a						,       """output""")
												a__       :						Optional[int]							=  getattr(_a						,       """LayerNorm""")
									elif m_name == "_key_dense":
												# attention key
												trace.append("""key""")
												a__       :						Tuple							=  getattr(_a						,       """key""")
									elif m_name == "_query_dense":
												# attention query
												trace.append("""query""")
												a__       :						Optional[int]							=  getattr(_a						,       """query""")
									elif m_name == "_value_dense":
												# attention value
												trace.append("""value""")
												a__       :						List[Any]							=  getattr(_a						,       """value""")
									elif m_name == "_intermediate_dense":
												# attention intermediate dense
												trace.extend(["""intermediate""", """dense"""])
												a__       :						Tuple							=  getattr(_a						,       """intermediate""")
												a__       :						Union[str, Any]							=  getattr(_a						,       """dense""")
									elif m_name == "_output_layer_norm":
												# output layer norm
												trace.append("""output""")
												a__       :						Any							=  getattr(_a						,       """output""")
									# weights & biases
									elif m_name in ["bias", "beta"]:
												trace.append("""bias""")
												a__       :						Any							=  getattr(_a						,       """bias""")
									elif m_name in ["kernel", "gamma"]:
												trace.append("""weight""")
												a__       :						List[Any]							=  getattr(_a						,       """weight""")
									else:
												logger.warning(F'''Ignored {m_name}''')
        # for certain layers reshape is necessary
						a__       :						Optional[int]							=  """.""".join(_a)
						if re.match(R"""(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"""						,       _a) or re.match(
						    R"""(\S+)\.attention\.output\.dense\.weight"""						,       _a):
									a__       :						Union[str, Any]							=  array.reshape(pointer.data.shape)
						if "kernel" in full_name:
									a__       :						List[Any]							=  array.transpose()
						if pointer.shape == array.shape:
									a__       :						int							=  torch.from_numpy(_a)
						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      lowerCAmelCase_					(  _lowercase    :       Any						,       _lowercase    :       int						,       _lowercase    :       str)							->	str:







			"""simple docstring"""
			logger.info(F'''Loading model based on config from {config_path}...''')
			a__       :						Optional[Any]							=  BertConfig.from_json_file(_a)
			a__       :						str							=  BertModel(_a)

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

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


if __name__ == "__main__":
		_lowercase		:				Any      		=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).",
		)
		_lowercase		:				List[str]      		=parser.parse_args()
		convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)









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


_lowercase		:				Union[str, Any]      		=16
_lowercase		:				Dict      		=32

def      lowerCAmelCase_					(  _lowercase    :       Accelerator						,       _lowercase    :       int = 16						,       _lowercase    :       str = "bert-base-cased")							->	Union[str, Any]:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  AutoTokenizer.from_pretrained(_lowercase)
			a__       :						Any							=  load_dataset("""glue"""						,       """mrpc""")

			def tokenize_function(_lowercase    :       str):
						# max_length=None => use the model max length (it's actually the default)
						a__       :						List[str]							=  tokenizer(examples["""sentence1"""]						,       examples["""sentence2"""]						,       truncation=_lowercase						,       max_length=_lowercase)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			a__       :						Any							=  datasets.map(
			    _lowercase						,       batched=_lowercase						,       remove_columns=["""idx""", """sentence1""", """sentence2"""]						,       load_from_cache_file=_lowercase)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			a__       :						Tuple							=  tokenized_datasets.rename_column("""label"""						,       """labels""")

			def collate_fn(_lowercase    :       Optional[Any]):
						# 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(_lowercase						,       padding="""max_length"""						,       max_length=128						,       return_tensors="""pt""")
						return tokenizer.pad(_lowercase						,       padding="""longest"""						,       return_tensors="""pt""")

			# Instantiate dataloaders.
			a__       :						int							=  DataLoader(
			    tokenized_datasets["""train"""]						,       shuffle=_lowercase						,       collate_fn=_lowercase						,       batch_size=_lowercase)
			a__       :						Union[str, Any]							=  DataLoader(
			    tokenized_datasets["""validation"""]						,       shuffle=_lowercase						,       collate_fn=_lowercase						,       batch_size=_lowercase)

			return train_dataloader, eval_dataloader

def      lowerCAmelCase_					(  _lowercase    :       Any						,       _lowercase    :       Dict)							->	int:







			"""simple docstring"""
			# Initialize accelerator
			a__       :						Optional[int]							=  Accelerator()

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

			set_seed(_lowercase)
			a__	,						a__       :						int							=  get_dataloaders(_lowercase						,       _lowercase						,       _lowercase)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			a__       :						Optional[Any]							=  AutoModelForSequenceClassification.from_pretrained(_lowercase						,       return_dict=_lowercase)

			# Instantiate optimizer
			a__       :						int							=  (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			a__       :						Optional[Any]							=  optimizer_cls(params=model.parameters()						,       lr=_lowercase)

			if accelerator.state.deepspeed_plugin is not None:
						a__       :						Dict							=  accelerator.state.deepspeed_plugin.deepspeed_config[
						    """gradient_accumulation_steps"""
						]
			else:
						a__       :						List[str]							=  1
			a__       :						List[Any]							=  (len(_lowercase) * num_epochs) // gradient_accumulation_steps

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

			# We need to keep track of how many total steps we have iterated over
			a__       :						Optional[int]							=  0
			# We also need to keep track of the stating epoch so files are named properly
			a__       :						Optional[int]							=  0

			# Now we train the model
			a__       :						Tuple							=  evaluate.load("""glue"""						,       """mrpc""")
			a__       :						List[Any]							=  0
			a__       :						Tuple							=  {}
			for epoch in range(_lowercase						,       _lowercase):
						model.train()
						for step, batch in enumerate(_lowercase):
									a__       :						Union[str, Any]							=  model(**_lowercase)
									a__       :						Tuple							=  outputs.loss
									a__       :						Any							=  loss / gradient_accumulation_steps
									accelerator.backward(_lowercase)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1

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

						a__       :						Optional[Any]							=  metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'''epoch {epoch}:'''						,       _lowercase)
						a__       :						Any							=  eval_metric["""accuracy"""]

						if best_performance < eval_metric["accuracy"]:
									a__       :						List[str]							=  eval_metric["""accuracy"""]

			if args.performance_lower_bound is not None:
						assert (
						    args.performance_lower_bound <= best_performance
						), F'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

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

def      lowerCAmelCase_					(  )							->	Tuple:







			"""simple docstring"""
			a__       :						Optional[Any]							=  argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""")
			parser.add_argument(
			    """--model_name_or_path"""						,       type=_lowercase						,       default="""bert-base-cased"""						,       help="""Path to pretrained model or model identifier from huggingface.co/models."""						,       required=_lowercase						,       )
			parser.add_argument(
			    """--output_dir"""						,       type=_lowercase						,       default="""."""						,       help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""						,       )
			parser.add_argument(
			    """--performance_lower_bound"""						,       type=_lowercase						,       default=_lowercase						,       help="""Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."""						,       )
			parser.add_argument(
			    """--num_epochs"""						,       type=_lowercase						,       default=3						,       help="""Number of train epochs."""						,       )
			a__       :						Any							=  parser.parse_args()
			a__       :						Dict							=  {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
			training_function(_lowercase						,       _lowercase)


if __name__ == "__main__":
		main()




