
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()
_UpperCAmelCase :			Optional[Any]              =	logging.get_logger(__name__)


def 				A	( lowercase   ,	lowercase   ,	lowercase			)       ->  str:
		'''simple docstring'''

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

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

		logger.info(f'''Loading model based on config from {config_path}...'''			)
		UpperCamelCase          =					BertConfig.from_json_file(lowercase			)
		UpperCamelCase          =					BertModel(lowercase			)

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

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


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


import argparse

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
#
# 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
#
########################################################################


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


def 				A	( lowercase   ,	lowercase = 16			)       ->  str:
		'''simple docstring'''

		UpperCamelCase          =					AutoTokenizer.from_pretrained('bert-base-cased'			)
		UpperCamelCase          =					load_dataset('glue'   ,	'mrpc'			)

		def tokenize_function(lowercase			):
				# max_length=None => use the model max length (it's actually the default)
				UpperCamelCase          =					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
		# starting with the main process first:
		with accelerator.main_process_first():
				UpperCamelCase          =					datasets.map(
				    lowercase   ,	batched=lowercase   ,	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(lowercase			):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				UpperCamelCase          =					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":
						UpperCamelCase          =					16
				elif accelerator.mixed_precision != "no":
						UpperCamelCase          =					8
				else:
						UpperCamelCase          =					None

				return tokenizer.pad(
				    lowercase   ,	padding='longest'   ,	max_length=lowercase   ,	pad_to_multiple_of=lowercase   ,	return_tensors='pt'   ,	)

		# Instantiate dataloaders.
		UpperCamelCase          =					DataLoader(
		    tokenized_datasets['train']   ,	shuffle=lowercase   ,	collate_fn=lowercase   ,	batch_size=lowercase   ,	drop_last=lowercase			)
		UpperCamelCase          =					DataLoader(
		    tokenized_datasets['validation']   ,	shuffle=lowercase   ,	collate_fn=lowercase   ,	batch_size=lowercase   ,	drop_last=(accelerator.mixed_precision == 'fp8')   ,	)

		return train_dataloader, eval_dataloader


def 				A	( lowercase   ,	lowercase			)       ->  Optional[Any]:
		'''simple docstring'''

		UpperCamelCase          =					Accelerator(cpu=args.cpu   ,	mixed_precision=args.mixed_precision			)
		# 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'			)

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

		set_seed(lowercase			)
		UpperCamelCase     ,  UpperCamelCase          =					get_dataloaders(lowercase   ,	lowercase			)
		# 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=lowercase			)

		# 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=lowercase			)

		# Instantiate scheduler
		UpperCamelCase          =					get_linear_schedule_with_warmup(
		    optimizer=lowercase   ,	num_warmup_steps=100   ,	num_training_steps=(len(lowercase			) * 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.

		UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase     ,  UpperCamelCase          =					accelerator.prepare(
		    lowercase   ,	lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)

		# Now we train the model
		for epoch in range(lowercase			):
				model.train()
				for step, batch in enumerate(lowercase			):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device			)
						UpperCamelCase          =					model(**lowercase			)
						UpperCamelCase          =					outputs.loss
						UpperCamelCase          =					loss / gradient_accumulation_steps
						accelerator.backward(lowercase			)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

				model.eval()
				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():
								UpperCamelCase          =					model(**lowercase			)
						UpperCamelCase          =					outputs.logits.argmax(dim=-1			)
						UpperCamelCase     ,  UpperCamelCase          =					accelerator.gather_for_metrics((predictions, batch['labels'])			)
						metric.add_batch(
						    predictions=lowercase   ,	references=lowercase   ,	)

				UpperCamelCase          =					metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}:'''   ,	lowercase			)


def 				A	( )       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					argparse.ArgumentParser(description='Simple example of training script.'			)
		parser.add_argument(
		    '--mixed_precision'   ,	type=lowercase   ,	default=lowercase   ,	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.'			)
		UpperCamelCase          =					parser.parse_args()
		UpperCamelCase          =					{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
		training_function(lowercase   ,	lowercase			)


if __name__ == "__main__":
					main()
