





import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)







def 			_SCREAMING_SNAKE_CASE    (       a ,						a=False ,						a=False ,						a="data2vec."  )				->				Any:
					__A     :						Optional[int]          =    'backbone.' if is_semantic else ''

					__A     :						List[str]          =    []
					for i in range(config.num_hidden_layers  ):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append(
										    (F"""{prefix}blocks.{i}.norm1.weight""", F"""{hf_prefix}encoder.layer.{i}.layernorm_before.weight""")  )
										rename_keys.append((F"""{prefix}blocks.{i}.norm1.bias""", F"""{hf_prefix}encoder.layer.{i}.layernorm_before.bias""")  )
										rename_keys.append(
										    (F"""{prefix}blocks.{i}.attn.proj.weight""", F"""{hf_prefix}encoder.layer.{i}.attention.output.dense.weight""")  )
										rename_keys.append(
										    (F"""{prefix}blocks.{i}.attn.proj.bias""", F"""{hf_prefix}encoder.layer.{i}.attention.output.dense.bias""")  )
										rename_keys.append(
										    (F"""{prefix}blocks.{i}.norm2.weight""", F"""{hf_prefix}encoder.layer.{i}.layernorm_after.weight""")  )
										rename_keys.append((F"""{prefix}blocks.{i}.norm2.bias""", F"""{hf_prefix}encoder.layer.{i}.layernorm_after.bias""")  )
										rename_keys.append(
										    (F"""{prefix}blocks.{i}.mlp.fc1.weight""", F"""{hf_prefix}encoder.layer.{i}.intermediate.dense.weight""")  )
										rename_keys.append(
										    (F"""{prefix}blocks.{i}.mlp.fc1.bias""", F"""{hf_prefix}encoder.layer.{i}.intermediate.dense.bias""")  )
										rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.weight""", F"""{hf_prefix}encoder.layer.{i}.output.dense.weight""")  )
										rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.bias""", F"""{hf_prefix}encoder.layer.{i}.output.dense.bias""")  )

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        (F"""{prefix}cls_token""", F"""{hf_prefix}embeddings.cls_token"""),
					        (F"""{prefix}patch_embed.proj.weight""", F"""{hf_prefix}embeddings.patch_embeddings.projection.weight"""),
					        (F"""{prefix}patch_embed.proj.bias""", F"""{hf_prefix}embeddings.patch_embeddings.projection.bias"""),
					    ]  )

					if has_lm_head:
										# mask token + shared relative position bias + layernorm
										rename_keys.extend(
										    [
										        ('mask_token', F"""{hf_prefix}embeddings.mask_token"""),
										        (
										            'rel_pos_bias.relative_position_bias_table',
										            F"""{hf_prefix}encoder.relative_position_bias.relative_position_bias_table""",
										        ),
										        (
										            'rel_pos_bias.relative_position_index',
										            F"""{hf_prefix}encoder.relative_position_bias.relative_position_index""",
										        ),
										        ('norm.weight', 'layernorm.weight'),
										        ('norm.bias', 'layernorm.bias'),
										    ]  )
					elif is_semantic:
										# semantic segmentation classification heads
										rename_keys.extend(
										    [
										        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
										        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
										        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
										        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
										    ]  )
					else:
										# layernorm + classification head
										rename_keys.extend(
										    [
										        ('fc_norm.weight', F"""{hf_prefix}pooler.layernorm.weight"""),
										        ('fc_norm.bias', F"""{hf_prefix}pooler.layernorm.bias"""),
										        ('head.weight', 'classifier.weight'),
										        ('head.bias', 'classifier.bias'),
										    ]  )

					return rename_keys







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a=False ,						a=False ,						a="data2vec_vision."  )				->				str:
					for i in range(config.num_hidden_layers  ):
										__A     :						Optional[int]          =    'backbone.' if is_semantic else ''
										# queries, keys and values
										__A     :						Tuple          =    state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight"""  )
										__A     :						Tuple          =    state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias"""  )
										__A     :						Union[str, Any]          =    state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias"""  )

										__A     :						Any          =    in_proj_weight[
										    : config.hidden_size, :
										]
										__A     :						Any          =    q_bias
										__A     :						List[Any]          =    in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										__A     :						List[str]          =    in_proj_weight[
										    -config.hidden_size :, :
										]
										__A     :						Tuple          =    v_bias

										# gamma_1 and gamma_2
										# we call them lambda because otherwise they are renamed when using .from_pretrained
										__A     :						List[str]          =    state_dict.pop(F"""{prefix}blocks.{i}.gamma_1"""  )
										__A     :						str          =    state_dict.pop(F"""{prefix}blocks.{i}.gamma_2"""  )

										__A     :						Optional[int]          =    gamma_a
										__A     :						Union[str, Any]          =    gamma_a

										# relative_position bias table + index
										if not has_lm_head:
															# each layer has its own relative position bias
															__A     :						Any          =    state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_bias_table"""  )
															__A     :						Optional[int]          =    state_dict.pop(F"""{prefix}blocks.{i}.attn.relative_position_index"""  )

															__A     :						int          =    table
															__A     :						str          =    index







def 			_SCREAMING_SNAKE_CASE    (       )				->				List[str]:
					__A     :						List[Any]          =    argparse.ArgumentParser(
					    'Convert Data2VecVision to HF for image classification and pretraining' ,						add_help=a  )
					parser.add_argument('--hf_checkpoint_name' ,						type=a  )
					parser.add_argument('--input_size' ,						default=2_24 ,						type=a ,						help='images input size'  )
					parser.add_argument('--beit_checkpoint' ,						default='' ,						help='beit checkpoint'  )

					return parser.parse_args()







def 			_SCREAMING_SNAKE_CASE    (       a ,						a ,						a  )				->				str:
					def load_state_dict(a ,						a ,						a="" ,						a="relative_position_index"  ):
										__A     :						Tuple          =    []
										__A     :						Union[str, Any]          =    []
										__A     :						Any          =    []
										# copy state_dict so _load_from_state_dict can modify it
										__A     :						Union[str, Any]          =    getattr(a ,						'_metadata' ,						a  )
										__A     :						str          =    state_dict.copy()
										if metadata is not None:
															__A     :						Dict          =    metadata

										def load(a ,						a=""  ):
															__A     :						int          =    {} if metadata is None else metadata.get(prefix[:-1] ,						{}  )
															module._load_from_state_dict(
															    a ,						a ,						a ,						a ,						a ,						a ,						a  )
															for name, child in module._modules.items():
																				if child is not None:
																									load(a ,						prefix + name + '.'  )

										load(a ,						prefix=a  )

										__A     :						Any          =    []
										__A     :						Dict          =    []
										for key in missing_keys:
															__A     :						Dict          =    True
															for ignore_key in ignore_missing.split('|'  ):
																				if ignore_key in key:
																									__A     :						Union[str, Any]          =    False
																									break
															if keep_flag:
																				warn_missing_keys.append(a  )
															else:
																				ignore_missing_keys.append(a  )

										__A     :						Optional[Any]          =    warn_missing_keys

										if len(a  ) > 0:
															print(
															    'Weights of {} not initialized from pretrained model: {}'.format(
															        model.__class__.__name__ ,						a  )  )
										if len(a  ) > 0:
															print('Weights from pretrained model not used in {}: {}'.format(model.__class__.__name__ ,						a  )  )
										if len(a  ) > 0:
															print(
															    'Ignored weights of {} not initialized from pretrained model: {}'.format(
															        model.__class__.__name__ ,						a  )  )
										if len(a  ) > 0:
															print('\n'.join(a  )  )

					__A     :						Optional[int]          =    {
					    'pretrained': False,
					    'use_shared_rel_pos_bias': True,
					    'use_abs_pos_emb': False,
					    'init_values': 0.1,
					}

					if is_finetuned:
										model_kwargs.update(
										    {
										        'num_classes': 10_00,
										        'use_mean_pooling': True,
										        'init_scale': 0.001,
										        'use_rel_pos_bias': True,
										    }  )

					__A     :						str          =    create_model(
					    'beit_large_patch16_224' if is_large else 'beit_base_patch16_224' ,						**a ,						)
					__A     :						Tuple          =    model.patch_embed.patch_size
					__A     :						List[str]          =    (args.input_size // patch_size[0], args.input_size // patch_size[1])
					__A     :						str          =    torch.load(args.beit_checkpoint ,						map_location='cpu'  )

					print(F"""Load ckpt from {args.beit_checkpoint}"""  )
					__A     :						Optional[Any]          =    None
					for model_key in ("model", "module"):
										if model_key in checkpoint:
															__A     :						Optional[Any]          =    checkpoint[model_key]
															print(F"""Load state_dict by model_key = {model_key}"""  )
															break

					__A     :						Tuple          =    list(checkpoint_model.keys()  )
					for key in all_keys:
										if "relative_position_index" in key:
															checkpoint_model.pop(a  )

										if "relative_position_bias_table" in key:
															__A     :						str          =    checkpoint_model[key]
															__A   ,    __A     :						Union[str, Any]          =    rel_pos_bias.size()
															__A   ,    __A     :						Optional[Any]          =    model.state_dict()[key].size()
															__A     :						Dict          =    model.patch_embed.patch_shape
															if dst_patch_shape[0] != dst_patch_shape[1]:
																				raise NotImplementedError()

					load_state_dict(a ,						a ,						prefix=''  )

					return model







def 			_SCREAMING_SNAKE_CASE    (       )				->				List[str]:
					__A     :						Optional[int]          =    get_args()

					__A     :						Tuple          =    'ft1k' in args.hf_checkpoint_name
					__A     :						str          =    'large' in args.hf_checkpoint_name

					if is_finetuned:
										# To convert Beit's data2vec_vision to HF you need to copy
										# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
										# into this folder.
										import modeling_finetune  # noqa: F401
					else:
										# To convert Beit's data2vec_vision to HF you need to copy
										# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
										# into this folder
										# IMPORTANT: Note that for now we've only converted the down-stream
										# model and not the full pretrained model. This means for the integration
										# test you need to add a `return x` after the following line:
										# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
										# to make the integration test pass.
										import modeling_cyclical  # noqa: F401

					# 1. Create model config
					__A     :						str          =    DataaVecVisionConfig()
					if is_finetuned:
										__A     :						Tuple          =    True
										__A     :						Tuple          =    False
										__A     :						Optional[Any]          =    True
										__A     :						Dict          =    10_00

										__A     :						Union[str, Any]          =    'huggingface/label-files'
										__A     :						int          =    'imagenet-1k-id2label.json'
										__A     :						Dict          =    json.load(open(hf_hub_download(a ,						a ,						repo_type='dataset'  ) ,						'r'  )  )
										__A     :						Dict          =    {int(a  ): v for k, v in idalabel.items()}
										__A     :						Tuple          =    idalabel
										__A     :						List[Any]          =    {v: k for k, v in idalabel.items()}
					else:
										__A     :						List[Any]          =    False
										__A     :						Any          =    True
										__A     :						List[str]          =    False

					if is_large:
										__A     :						Dict          =    10_24
										__A     :						List[str]          =    40_96
										__A     :						Union[str, Any]          =    24
										__A     :						Dict          =    16

					# 2. Load Beit model
					__A     :						Optional[int]          =    load_beit_model(a ,						a ,						a  )
					orig_model.eval()

					# 3. Forward Beit model
					__A     :						Dict          =    BeitImageProcessor(size=config.image_size ,						do_center_crop=a  )
					__A     :						List[str]          =    Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png'  )
					__A     :						Dict          =    image_processor(images=a ,						return_tensors='pt'  )
					__A     :						List[Any]          =    encoding['pixel_values']

					__A     :						Optional[int]          =    (pixel_values,) if is_finetuned else (pixel_values, None)
					with torch.no_grad():
										__A     :						List[str]          =    orig_model(*a  )

					# 4. Load HF Data2VecVision model
					if is_finetuned:
										__A     :						int          =    DataaVecVisionForImageClassification(a  )
										hf_model.eval()
										__A     :						Any          =    False
										__A     :						Optional[Any]          =    'data2vec_vision.'
					else:
										__A     :						Dict          =    DataaVecVisionModel(a  )
										hf_model.eval()
										__A     :						Optional[Any]          =    True
										__A     :						str          =    ''

					__A     :						Optional[int]          =    create_rename_keys(a ,						hf_prefix=a ,						has_lm_head=a  )
					__A     :						Optional[int]          =    orig_model.state_dict()
					for src, dest in rename_keys:
										__A     :						Optional[Any]          =    state_dict.pop(a  )
										__A     :						Optional[Any]          =    val

					read_in_q_k_v(a ,						a ,						hf_prefix=a ,						has_lm_head=a  )
					__A   ,    __A     :						List[Any]          =    hf_model.load_state_dict(a ,						strict=a  )
					print('HF missing' ,						a  )
					print('HF unexpected_keys' ,						a  )

					# 5. Forward HF Data2VecVision model
					with torch.no_grad():
										__A     :						Optional[int]          =    hf_model(a  )

					__A     :						Optional[Any]          =    hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

					# 6. Compare
					__A     :						Union[str, Any]          =    torch.max(torch.abs(hf_output - orig_model_output  )  ).item()

					print(F"""max_absolute_diff = {max_absolute_diff}"""  )
					__A     :						List[str]          =    torch.allclose(a ,						a ,						atol=1e-3  )
					print('Do both models output the same tensors?' ,						'🔥' if success else '💩'  )
					if not success:
										raise Exception('Something went wRoNg'  )

					# 7. Save
					print(F"""Saving to {args.hf_checkpoint_name}"""  )
					hf_model.save_pretrained(args.hf_checkpoint_name  )
					image_processor.save_pretrained(args.hf_checkpoint_name  )


if __name__ == "__main__":
				main()
				# Run the following to convert checkpoints
				#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
				#          --beit_checkpoint ./pretrained_base.pt \
				#          --hf_checkpoint_name "./data2vec-vision-base"
				#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
				#          --beit_checkpoint ./finetuned_base.pt \
				#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
				#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
				#          --beit_checkpoint ./pretrained_large.pt \
				#          --hf_checkpoint_name "./data2vec-vision-large"
				#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
				#          --beit_checkpoint ./finetuned_large.pt \
				#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"











import argparse
import os

# New Code #
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
from accelerate.utils import find_executable_batch_size


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to combine both the gradient accumulation
# and automatic batch size finder utilities of Accelerate to perfrom
# automatic gradient accumulation
#
# 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)
#
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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						:       List[Any]        =					32







def 			_SCREAMING_SNAKE_CASE    (       a ,						a = 16  )				->				List[Any]:
					__A     :						List[str]          =    AutoTokenizer.from_pretrained('bert-base-cased'  )
					__A     :						int          =    load_dataset('glue' ,						'mrpc'  )

					def tokenize_function(a  ):
										# max_length=None => use the model max length (it's actually the default)
										__A     :						str          =    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
					# starting with the main process first:
					with accelerator.main_process_first():
										__A     :						Optional[int]          =    datasets.map(
										    a ,						batched=a ,						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
					__A     :						Tuple          =    tokenized_datasets.rename_column('label' ,						'labels'  )

					def collate_fn(a  ):
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															__A     :						Union[str, Any]          =    16
										elif accelerator.mixed_precision != "no":
															__A     :						Any          =    8
										else:
															__A     :						Union[str, Any]          =    None

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

					# Instantiate dataloaders.
					__A     :						Any          =    DataLoader(
					    tokenized_datasets['train'] ,						shuffle=a ,						collate_fn=a ,						batch_size=a  )
					__A     :						List[Any]          =    DataLoader(
					    tokenized_datasets['validation'] ,						shuffle=a ,						collate_fn=a ,						batch_size=a  )

					return train_dataloader, eval_dataloader


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

				UpperCAmelCase						:       Tuple        =					mocked_dataloaders  # noqa: F811







def 			_SCREAMING_SNAKE_CASE    (       a ,						a  )				->				Optional[Any]:
					# For testing only
					if os.environ.get('TESTING_MOCKED_DATALOADERS' ,						a  ) == "1":
										__A     :						Dict          =    2
					# Initialize accelerator
					__A     :						int          =    Accelerator(cpu=args.cpu ,						mixed_precision=args.mixed_precision  )
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					__A     :						Dict          =    config['lr']
					__A     :						Optional[Any]          =    int(config['num_epochs']  )
					__A     :						int          =    int(config['seed']  )
					__A     :						int          =    int(config['batch_size']  )

					__A     :						Tuple          =    evaluate.load('glue' ,						'mrpc'  )

					# New Code #
					# We use the `find_executable_batch_size` decorator, passing in the desired observed batch size
					# to train on. If a CUDA OOM error occurs, it will retry this loop cutting the batch size in
					# half each time. From this, we can calculate the number of gradient accumulation steps needed
					# and modify the Accelerator object as a result
					@find_executable_batch_size(starting_batch_size=int(a  )  )
					def inner_training_loop(a  ):
										# Since we need to modify the outside accelerator object, we need to bring it
										# to the local scope
										nonlocal accelerator

										# We can calculate the number of gradient accumulation steps based on the current
										# batch size vs the starting batch size
										__A     :						Optional[Any]          =    observed_batch_size // batch_size

										# And then set it in the Accelerator directly:
										__A     :						List[str]          =    num_gradient_accumulation_steps

										# Next we need to free all of the stored model references in the Accelerator each time
										accelerator.free_memory()

										# And set the seed so our results are reproducable each reset
										set_seed(a  )

										# Instantiate the model (we build the model here so that the seed also control new weights initialization)
										__A     :						List[Any]          =    AutoModelForSequenceClassification.from_pretrained('bert-base-cased' ,						return_dict=a  )

										# 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).
										__A     :						Dict          =    model.to(accelerator.device  )

										# Instantiate optimizer
										__A     :						Optional[Any]          =    AdamW(params=model.parameters() ,						lr=a  )
										__A   ,    __A     :						Dict          =    get_dataloaders(a ,						a  )

										# Instantiate scheduler
										__A     :						List[Any]          =    get_linear_schedule_with_warmup(
										    optimizer=a ,						num_warmup_steps=1_00 ,						num_training_steps=(len(a  ) * num_epochs) ,						)

										# 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     :						Tuple          =    accelerator.prepare(
										    a ,						a ,						a ,						a ,						a  )

										# Now we train the model
										for epoch in range(a  ):
															model.train()
															for step, batch in enumerate(a  ):
																				# And perform gradient accumulation
																				with accelerator.accumulate(a  ):
																									# We could avoid this line since we set the accelerator with `device_placement=True`.
																									batch.to(accelerator.device  )
																									__A     :						List[str]          =    model(**a  )
																									__A     :						List[str]          =    outputs.loss
																									accelerator.backward(a  )
																									optimizer.step()
																									lr_scheduler.step()
																									optimizer.zero_grad()

															model.eval()
															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():
																									__A     :						List[Any]          =    model(**a  )
																				__A     :						List[Any]          =    outputs.logits.argmax(dim=-1  )
																				__A   ,    __A     :						Tuple          =    accelerator.gather_for_metrics((predictions, batch['labels'])  )
																				metric.add_batch(
																				    predictions=a ,						references=a ,						)

															__A     :						Optional[int]          =    metric.compute()
															# Use accelerator.print to print only on the main process.
															accelerator.print(F"""epoch {epoch}:""" ,						a  )

    # New Code #
    # And call it at the end with no arguments
    # Note: You could also refactor this outside of your training loop function
					inner_training_loop()







def 			_SCREAMING_SNAKE_CASE    (       )				->				Union[str, Any]:
					__A     :						List[Any]          =    argparse.ArgumentParser(description='Simple example of training script.'  )
					parser.add_argument(
					    '--mixed_precision' ,						type=a ,						default=a ,						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.'  )
					__A     :						int          =    parser.parse_args()
					# New Code #
					# We modify the starting batch size to be an observed batch size of 256, to guarentee an initial CUDA OOM
					__A     :						Optional[int]          =    {'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 2_56}
					training_function(a ,						a  )


if __name__ == "__main__":
				main()




