



'''simple docstring'''



import argparse
import gc
import os
import threading

import evaluate
import psutil
import torch
from datasets import load_dataset
from torch.distributed.fsdp.fully_sharded_data_parallel import FullOptimStateDictConfig, FullStateDictConfig
from torch.utils.data import DataLoader
from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    get_linear_schedule_with_warmup,
    set_seed,
)

from accelerate import Accelerator, DistributedType, FullyShardedDataParallelPlugin


########################################################################
# 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)
#   - FSDP
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# 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
#
########################################################################


_lowercase       :						List[str]						=16
_lowercase       :						Tuple						=32


def 							__UpperCAmelCase				(				UpperCamelCase__				:Optional[Any]			)		->						Any:
				return int(x / 2**20			)






class 			_SCREAMING_SNAKE_CASE				:



			def __enter__(     self		:						Dict			)      ->      Union[str, Any]:
							"""simple docstring"""





							gc.collect()
							torch.cuda.empty_cache()
							torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
							snake_case__      :   Optional[int]      =    torch.cuda.memory_allocated()
							snake_case__      :   Optional[int]      =    psutil.Process()

							snake_case__      :   Union[str, Any]      =    self.cpu_mem_used()
							snake_case__      :   Any      =    True
							snake_case__      :   Optional[int]      =    threading.Thread(target=self.peak_monitor_func			)
							snake_case__      :   List[str]      =    True
							peak_monitor_thread.start()
							return self



			def 				lowerCAmelCase (     self		:						Optional[int]			)      ->      Tuple:
							"""simple docstring"""





							return self.process.memory_info().rss



			def 				lowerCAmelCase (     self		:						Union[str, Any]			)      ->      List[str]:
							"""simple docstring"""





							snake_case__      :   List[Any]      =    -1

							while True:
											snake_case__      :   Optional[Any]      =    max(self.cpu_mem_used()    ,			self.cpu_peak			)

											# can't sleep or will not catch the peak right (this comment is here on purpose)
											# time.sleep(0.001) # 1msec

											if not self.peak_monitoring:
															break







			def __exit__(     self		:						List[Any]    ,			*__UpperCamelCase		:						Tuple			)      ->      Optional[Any]:
							"""simple docstring"""





							snake_case__      :   Union[str, Any]      =    False

							gc.collect()
							torch.cuda.empty_cache()
							snake_case__      :   Optional[int]      =    torch.cuda.memory_allocated()
							snake_case__      :   Optional[Any]      =    torch.cuda.max_memory_allocated()
							snake_case__      :   int      =    bamb(self.end - self.begin			)
							snake_case__      :   Tuple      =    bamb(self.peak - self.begin			)

							snake_case__      :   Dict      =    self.cpu_mem_used()
							snake_case__      :   Dict      =    bamb(self.cpu_end - self.cpu_begin			)
							snake_case__      :   Any      =    bamb(self.cpu_peak - self.cpu_begin			)
							# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")


# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS", None) == "1":
				from accelerate.test_utils.training import mocked_dataloaders

				_lowercase       :						List[str]						=mocked_dataloaders  # noqa: F811


def 							__UpperCAmelCase				(				UpperCamelCase__				:int   ,				UpperCamelCase__				:Tuple			)		->						str:
				# For testing only
				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''   ,				UpperCamelCase__			) == "1":
								snake_case__      :   List[str]      =    2

				# New Code #
				# Pass the advanced FSDP settings not part of the accelerate config by creating fsdp_plugin
				snake_case__      :   Optional[int]      =    FullyShardedDataParallelPlugin(
				    state_dict_config=FullStateDictConfig(offload_to_cpu=UpperCamelCase__   ,				ranka_only=UpperCamelCase__			)   ,				optim_state_dict_config=FullOptimStateDictConfig(offload_to_cpu=UpperCamelCase__   ,				ranka_only=UpperCamelCase__			)   ,				)

				# Initialize accelerator
				if args.with_tracking:
								snake_case__      :   Union[str, Any]      =    Accelerator(
								    cpu=args.cpu   ,				mixed_precision=args.mixed_precision   ,				log_with='''wandb'''   ,				project_dir=args.logging_dir   ,				fsdp_plugin=UpperCamelCase__   ,				)
				else:
								snake_case__      :   List[str]      =    Accelerator(fsdp_plugin=UpperCamelCase__			)
				accelerator.print(accelerator.distributed_type			)

				if hasattr(args.checkpointing_steps   ,				'''isdigit'''			):
								if args.checkpointing_steps == "epoch":
												snake_case__      :   Union[str, Any]      =    args.checkpointing_steps
								elif args.checkpointing_steps.isdigit():
												snake_case__      :   Optional[Any]      =    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__      :   List[Any]      =    None
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				snake_case__      :   str      =    config['''lr''']
				snake_case__      :   int      =    int(config['''num_epochs''']			)
				snake_case__      :   str      =    int(config['''seed''']			)
				snake_case__      :   Optional[int]      =    int(config['''batch_size''']			)

				# We need to initialize the trackers we use, and also store our configuration
				if args.with_tracking:
								snake_case__      :   Any      =    vars(UpperCamelCase__			)
								accelerator.init_trackers('''fsdp_glue_no_trainer'''   ,				UpperCamelCase__			)

				snake_case__      :   Union[str, Any]      =    AutoTokenizer.from_pretrained(args.model_name_or_path			)
				snake_case__      :   Optional[int]      =    load_dataset('''glue'''   ,				'''mrpc'''			)
				snake_case__      :   Optional[int]      =    evaluate.load('''glue'''   ,				'''mrpc'''			)

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

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

				def collate_fn(UpperCamelCase__				:str			):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								snake_case__      :   str      =    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__      :   Union[str, Any]      =    16
								elif accelerator.mixed_precision != "no":
												snake_case__      :   int      =    8
								else:
												snake_case__      :   Optional[int]      =    None

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

				# Instantiate dataloaders.
				snake_case__      :   Optional[Any]      =    DataLoader(
				    tokenized_datasets['''train''']   ,				shuffle=UpperCamelCase__   ,				collate_fn=UpperCamelCase__   ,				batch_size=UpperCamelCase__			)
				snake_case__      :   str      =    DataLoader(
				    tokenized_datasets['''validation''']   ,				shuffle=UpperCamelCase__   ,				collate_fn=UpperCamelCase__   ,				batch_size=UpperCamelCase__			)

				set_seed(UpperCamelCase__			)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				snake_case__      :   List[str]      =    AutoModelForSequenceClassification.from_pretrained(
				    args.model_name_or_path   ,				return_dict=UpperCamelCase__   ,				low_cpu_mem_usage=UpperCamelCase__			)

				# New Code #
				# For FSDP feature, it is highly recommended and efficient to prepare the model before creating optimizer
				snake_case__      :   Any      =    accelerator.prepare(UpperCamelCase__			)
				accelerator.print(UpperCamelCase__			)

				# Instantiate optimizer
				# New Code #
				# For FSDP feature, at present it doesn't support multiple parameter groups,
				# so we need to create a single parameter group for the whole model
				snake_case__      :   Dict      =    torch.optim.AdamW(params=model.parameters()   ,				lr=UpperCamelCase__   ,				weight_decay=2e-4			)

				# Instantiate scheduler
				snake_case__      :   Any      =    get_linear_schedule_with_warmup(
				    optimizer=UpperCamelCase__   ,				num_warmup_steps=10   ,				num_training_steps=(len(UpperCamelCase__			) * num_epochs) // gradient_accumulation_steps   ,				)

				# New Code #
				# For FSDP feature, prepare everything except the model as we have already prepared the model
				# before creating the optimizer
				# 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__      :   List[str]      =    accelerator.prepare(
				    UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__			)

				snake_case__      :   Tuple      =    0

				# Potentially load in the weights and states from a previous save
				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__      :   Optional[Any]      =    os.path.basename(args.resume_from_checkpoint			)
								else:
												# Get the most recent checkpoint
												snake_case__      :   Optional[Any]      =    [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
												dirs.sort(key=os.path.getctime			)
												snake_case__      :   List[str]      =    dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
								# Extract `epoch_{i}` or `step_{i}`
								snake_case__      :   Union[str, Any]      =    os.path.splitext(UpperCamelCase__			)[0]

								if "epoch" in training_difference:
												num_epochs -= int(training_difference.replace('''epoch_'''   ,				''''''			)			)
												snake_case__      :   Any      =    None
								else:
												snake_case__      :   Any      =    int(training_difference.replace('''step_'''   ,				''''''			)			)
												num_epochs -= resume_step // len(UpperCamelCase__			)
												# If resuming by step, we also need to know exactly how far into the DataLoader we went
												snake_case__      :   int      =    (num_epochs * len(UpperCamelCase__			)) - resume_step

    # Now we train the model
				for epoch in range(UpperCamelCase__			):
								# New Code #
								# context manager to track the peak memory usage during the training epoch
								with TorchTracemalloc() as tracemalloc:
												model.train()
												if args.with_tracking:
																snake_case__      :   List[str]      =    0
												for step, batch in enumerate(UpperCamelCase__			):
																# We need to skip steps until we reach the resumed step
																if args.resume_from_checkpoint and epoch == 0:
																				if resume_step is not None and step < resume_step:
																								pass
                # We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device			)
																snake_case__      :   List[Any]      =    model(**UpperCamelCase__			)
																snake_case__      :   Dict      =    outputs.loss
																snake_case__      :   str      =    loss / gradient_accumulation_steps
																# We keep track of the loss at each epoch
																if args.with_tracking:
																				total_loss += loss.detach().float()
																accelerator.backward(UpperCamelCase__			)
																if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()
																				# accelerator.print(lr_scheduler.get_lr())

																overall_step += 1

																if isinstance(UpperCamelCase__   ,				UpperCamelCase__			):
																				snake_case__      :   Dict      =    F'''step_{overall_step}'''
																				if overall_step % checkpointing_steps == 0:
																								if args.output_dir is not None:
																												snake_case__      :   List[str]      =    os.path.join(args.output_dir   ,				UpperCamelCase__			)
																								accelerator.save_state(UpperCamelCase__			)
        # New Code #
        # 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			)			)			)
								# Logging the peak memory usage of the GPU to the tracker
								if args.with_tracking:
												accelerator.log(
												    {
												        '''train_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin			),
												    }   ,				step=UpperCamelCase__   ,				)

								# New Code #
								# context manager to track the peak memory usage during the evaluation
								with TorchTracemalloc() as tracemalloc:
												model.eval()
												for step, batch in enumerate(UpperCamelCase__			):
																# We could avoid this line since we set the accelerator with `device_placement=True`.
																batch.to(accelerator.device			)
																with torch.no_grad():
																				snake_case__      :   List[Any]      =    model(**UpperCamelCase__			)
																snake_case__      :   Dict      =    outputs.logits.argmax(dim=-1			)
																snake_case__  ,      snake_case__      :   Dict      =    accelerator.gather_for_metrics((predictions, batch['''labels'''])			)
																metric.add_batch(
																    predictions=UpperCamelCase__   ,				references=UpperCamelCase__   ,				)

												snake_case__      :   List[Any]      =    metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''   ,				UpperCamelCase__			)
												if args.with_tracking:
																accelerator.log(
																    {
																        '''accuracy''': eval_metric['''accuracy'''],
																        '''f1''': eval_metric['''f1'''],
																        '''train_loss''': total_loss.item() / len(UpperCamelCase__			),
																    }   ,				step=UpperCamelCase__   ,				)

												if checkpointing_steps == "epoch":
																snake_case__      :   str      =    F'''epoch_{epoch}'''
																if args.output_dir is not None:
																				snake_case__      :   str      =    os.path.join(args.output_dir   ,				UpperCamelCase__			)
																accelerator.save_state(UpperCamelCase__			)
        # New Code #
        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
								accelerator.print('''Memory before entering the eval : {}'''.format(bamb(tracemalloc.begin			)			)			)
								accelerator.print('''Memory consumed at the end of the eval (end-begin): {}'''.format(tracemalloc.used			)			)
								accelerator.print('''Peak Memory consumed during the eval (max-begin): {}'''.format(tracemalloc.peaked			)			)
								accelerator.print(
								    '''Total Peak Memory consumed during the eval (max): {}'''.format(tracemalloc.peaked + bamb(tracemalloc.begin			)			)			)
								# Logging the peak memory usage of the GPU to the tracker
								if args.with_tracking:
												accelerator.log(
												    {
												        '''eval_total_peak_memory''': tracemalloc.peaked + bamb(tracemalloc.begin			),
												    }   ,				step=UpperCamelCase__   ,				)

				if args.with_tracking:
								accelerator.end_training()


def 							__UpperCAmelCase				(				)		->						List[Any]:
				snake_case__      :   Any      =    argparse.ArgumentParser(description='''Simple example of training script.'''			)
				parser.add_argument(
				    '''--mixed_precision'''   ,				type=UpperCamelCase__   ,				default=UpperCamelCase__   ,				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=UpperCamelCase__   ,				default=UpperCamelCase__   ,				help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''   ,				)
				parser.add_argument(
				    '''--resume_from_checkpoint'''   ,				type=UpperCamelCase__   ,				default=UpperCamelCase__   ,				help='''If the training should continue from a checkpoint folder.'''   ,				)
				parser.add_argument(
				    '''--with_tracking'''   ,				action='''store_true'''   ,				help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''   ,				)
				parser.add_argument(
				    '''--output_dir'''   ,				type=UpperCamelCase__   ,				default='''.'''   ,				help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''   ,				)
				parser.add_argument(
				    '''--logging_dir'''   ,				type=UpperCamelCase__   ,				default='''logs'''   ,				help='''Location on where to store experiment tracking logs`'''   ,				)
				parser.add_argument(
				    '''--model_name_or_path'''   ,				type=UpperCamelCase__   ,				help='''Path to pretrained model or model identifier from huggingface.co/models.'''   ,				required=UpperCamelCase__   ,				)
				snake_case__      :   int      =    parser.parse_args()
				snake_case__      :   List[Any]      =    {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(UpperCamelCase__   ,				UpperCamelCase__			)


if __name__ == "__main__":
				main()









'''simple docstring'''



import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("0.9.0"):
				raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
_lowercase       :						Tuple						=logging.get_logger(__name__)

_lowercase       :						Optional[int]						="Hello world! cécé herlolip"


def 							__UpperCAmelCase				(				UpperCamelCase__				:str   ,				UpperCamelCase__				:str   ,				UpperCamelCase__				:bool			)		->						int:
				snake_case__  ,      snake_case__      :   int      =    os.path.split(UpperCamelCase__			)
				snake_case__      :   List[Any]      =    DataaVecTextModel.from_pretrained(
				    UpperCamelCase__   ,				checkpoint_file=UpperCamelCase__			)
				dataavec.eval()  # disable dropout
				snake_case__      :   Dict      =    dataavec.models[0]
				snake_case__      :   int      =    dataavec_model.encoder.sentence_encoder
				snake_case__      :   List[Any]      =    DataaVecTextConfig(
				    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings   ,				hidden_size=dataavec_model.args.encoder_embed_dim   ,				num_hidden_layers=dataavec_model.args.encoder_layers   ,				num_attention_heads=dataavec_model.args.encoder_attention_heads   ,				intermediate_size=dataavec_model.args.encoder_ffn_embed_dim   ,				max_position_embeddings=514   ,				type_vocab_size=1   ,				layer_norm_eps=1e-5   ,				)
				if classification_head:
								snake_case__      :   int      =    dataavec.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
				print('''Our BERT config:'''   ,				UpperCamelCase__			)

				snake_case__      :   Optional[int]      =    DataaVecTextForSequenceClassification(UpperCamelCase__			) if classification_head else DataaVecTextForMaskedLM(UpperCamelCase__			)
				model.eval()

				# Now let's copy all the weights.
				# Embeddings
				snake_case__      :   Optional[Any]      =    dataavec_sent_encoder.embed_tokens.weight
				snake_case__      :   Union[str, Any]      =    dataavec_sent_encoder.embed_positions.weight
				snake_case__      :   Any      =    torch.zeros_like(
				    model.dataavec_text.embeddings.token_type_embeddings.weight			)  # just zero them out b/c data2vec doesn't use them.
				snake_case__      :   Tuple      =    dataavec_sent_encoder.layernorm_embedding.weight
				snake_case__      :   Dict      =    dataavec_sent_encoder.layernorm_embedding.bias

				for i in range(config.num_hidden_layers			):
								# Encoder: start of layer
								snake_case__      :   BertLayer      =    model.dataavec_text.encoder.layer[i]
								snake_case__      :   TransformerSentenceEncoderLayer      =    dataavec_sent_encoder.layers[i]

								# self attention
								snake_case__      :   BertSelfAttention      =    layer.attention.self
								assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
								)
								assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
								)
								assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
								    (config.hidden_size, config.hidden_size)			), (
								    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
								    F''' {torch.Size((config.hidden_size, config.hidden_size)			)}'''
								)

								snake_case__      :   List[Any]      =    dataavec_layer.self_attn.q_proj.weight
								snake_case__      :   List[str]      =    dataavec_layer.self_attn.q_proj.bias
								snake_case__      :   str      =    dataavec_layer.self_attn.k_proj.weight
								snake_case__      :   List[Any]      =    dataavec_layer.self_attn.k_proj.bias
								snake_case__      :   Optional[Any]      =    dataavec_layer.self_attn.v_proj.weight
								snake_case__      :   int      =    dataavec_layer.self_attn.v_proj.bias

								# self-attention output
								snake_case__      :   BertSelfOutput      =    layer.attention.output
								assert (
								    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
								), F'''Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}'''
								snake_case__      :   Optional[int]      =    dataavec_layer.self_attn.out_proj.weight
								snake_case__      :   str      =    dataavec_layer.self_attn.out_proj.bias
								snake_case__      :   int      =    dataavec_layer.self_attn_layer_norm.weight
								snake_case__      :   List[Any]      =    dataavec_layer.self_attn_layer_norm.bias

								# intermediate
								snake_case__      :   BertIntermediate      =    layer.intermediate
								assert (
								    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								snake_case__      :   List[str]      =    dataavec_layer.fca.weight
								snake_case__      :   str      =    dataavec_layer.fca.bias

								# output
								snake_case__      :   BertOutput      =    layer.output
								assert (
								    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
								), F'''Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}'''
								snake_case__      :   List[str]      =    dataavec_layer.fca.weight
								snake_case__      :   List[Any]      =    dataavec_layer.fca.bias
								snake_case__      :   str      =    dataavec_layer.final_layer_norm.weight
								snake_case__      :   List[Any]      =    dataavec_layer.final_layer_norm.bias
								# end of layer

				if classification_head:
								snake_case__      :   Any      =    dataavec.model.classification_heads['''mnli'''].dense.weight
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].dense.bias
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].out_proj.weight
								snake_case__      :   List[Any]      =    dataavec.model.classification_heads['''mnli'''].out_proj.bias
				else:
								# LM Head
								snake_case__      :   str      =    dataavec_model.encoder.lm_head.dense.weight
								snake_case__      :   str      =    dataavec_model.encoder.lm_head.dense.bias
								snake_case__      :   Union[str, Any]      =    dataavec_model.encoder.lm_head.layer_norm.weight
								snake_case__      :   Tuple      =    dataavec_model.encoder.lm_head.layer_norm.bias
								snake_case__      :   Tuple      =    dataavec_model.encoder.lm_head.weight
								snake_case__      :   List[str]      =    dataavec_model.encoder.lm_head.bias

				# Let's check that we get the same results.
				snake_case__      :   torch.Tensor      =    dataavec.encode(UpperCamelCase__			).unsqueeze(0			)  # batch of size 1

				snake_case__      :   str      =    model(UpperCamelCase__			)[0]
				if classification_head:
								snake_case__      :   List[str]      =    dataavec.model.classification_heads['''mnli'''](dataavec.extract_features(UpperCamelCase__			)			)
				else:
								snake_case__      :   Tuple      =    dataavec_model(UpperCamelCase__			)[0]
				print(our_output.shape   ,				their_output.shape			)
				snake_case__      :   Dict      =    torch.max(torch.abs(our_output - their_output			)			).item()
				print(F'''max_absolute_diff = {max_absolute_diff}'''			)  # ~ 1e-7
				snake_case__      :   Tuple      =    torch.allclose(UpperCamelCase__   ,				UpperCamelCase__   ,				atol=1e-3			)
				print('''Do both models output the same tensors?'''   ,				'''🔥''' if success else '''💩'''			)
				if not success:
								raise Exception('''Something went wRoNg'''			)

				pathlib.Path(UpperCamelCase__			).mkdir(parents=UpperCamelCase__   ,				exist_ok=UpperCamelCase__			)
				print(F'''Saving model to {pytorch_dump_folder_path}'''			)
				model.save_pretrained(UpperCamelCase__			)


if __name__ == "__main__":
				_lowercase       :						Any						=argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				parser.add_argument(
				    "--classification_head", action="store_true", help="Whether to convert a final classification head."
				)
				_lowercase       :						Tuple						=parser.parse_args()
				convert_dataavec_checkpoint_to_pytorch(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
				)




