



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase__		:					str							={'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[int]							=[
			    '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''ViTMAEForPreTraining''',
			    '''ViTMAELayer''',
			    '''ViTMAEModel''',
			    '''ViTMAEPreTrainedModel''',
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					List[str]							=[
			    '''TFViTMAEForPreTraining''',
			    '''TFViTMAEModel''',
			    '''TFViTMAEPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_vit_mae import (
						    VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ViTMAEForPreTraining,
						    ViTMAELayer,
						    ViTMAEModel,
						    ViTMAEPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
			import sys

			lowerCAmelCase__		:					Optional[int]							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)











import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__		:					Optional[int]							=logging.get_logger(__name__)






def      __lowercase       (			a__							,       a__=False       )    ->			Tuple:
							__SCREAMING_SNAKE_CASE									= []
							for i in range(config.num_hidden_layers       ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""deit.encoder.layer.{i}.layernorm_before.weight""")       )
														rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""deit.encoder.layer.{i}.layernorm_before.bias""")       )
														rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""deit.encoder.layer.{i}.attention.output.dense.weight""")       )
														rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""deit.encoder.layer.{i}.attention.output.dense.bias""")       )
														rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""deit.encoder.layer.{i}.layernorm_after.weight""")       )
														rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""deit.encoder.layer.{i}.layernorm_after.bias""")       )
														rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""deit.encoder.layer.{i}.intermediate.dense.weight""")       )
														rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""deit.encoder.layer.{i}.intermediate.dense.bias""")       )
														rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""deit.encoder.layer.{i}.output.dense.weight""")       )
														rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""deit.encoder.layer.{i}.output.dense.bias""")       )

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ('cls_token', 'deit.embeddings.cls_token'),
							        ('dist_token', 'deit.embeddings.distillation_token'),
							        ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
							        ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
							        ('pos_embed', 'deit.embeddings.position_embeddings'),
							    ]       )

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ('norm.weight', 'layernorm.weight'),
														        ('norm.bias', 'layernorm.bias'),
														        ('pre_logits.fc.weight', 'pooler.dense.weight'),
														        ('pre_logits.fc.bias', 'pooler.dense.bias'),
														    ]       )

														# if just the base model, we should remove "deit" from all keys that start with "deit"
														__SCREAMING_SNAKE_CASE									= [(pair[0], pair[1][4:]) if pair[1].startswith('deit'       ) else pair for pair in rename_keys]
							else:
														# layernorm + classification heads
														rename_keys.extend(
														    [
														        ('norm.weight', 'deit.layernorm.weight'),
														        ('norm.bias', 'deit.layernorm.bias'),
														        ('head.weight', 'cls_classifier.weight'),
														        ('head.bias', 'cls_classifier.bias'),
														        ('head_dist.weight', 'distillation_classifier.weight'),
														        ('head_dist.bias', 'distillation_classifier.bias'),
														    ]       )

							return rename_keys






def      __lowercase       (			a__							,       a__							,       a__=False       )    ->			Tuple:
							for i in range(config.num_hidden_layers       ):
														if base_model:
																					__SCREAMING_SNAKE_CASE									= ''
														else:
																					__SCREAMING_SNAKE_CASE									= 'deit.'
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														__SCREAMING_SNAKE_CASE									= state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""       )
														__SCREAMING_SNAKE_CASE									= state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""       )
														# next, add query, keys and values (in that order) to the state dict
														__SCREAMING_SNAKE_CASE									= in_proj_weight[
														    : config.hidden_size, :
														]
														__SCREAMING_SNAKE_CASE									= in_proj_bias[: config.hidden_size]
														__SCREAMING_SNAKE_CASE									= in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														__SCREAMING_SNAKE_CASE									= in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														__SCREAMING_SNAKE_CASE									= in_proj_weight[
														    -config.hidden_size :, :
														]
														__SCREAMING_SNAKE_CASE									= in_proj_bias[-config.hidden_size :]






def      __lowercase       (			a__							,       a__							,       a__       )    ->			str:
							__SCREAMING_SNAKE_CASE									= dct.pop(a__       )
							__SCREAMING_SNAKE_CASE									= val






def      __lowercase       (			)    ->			List[Any]:
							__SCREAMING_SNAKE_CASE									= 'http://images.cocodataset.org/val2017/000000039769.jpg'
							__SCREAMING_SNAKE_CASE									= Image.open(requests.get(a__							,       stream=a__       ).raw       )
							return im






@torch.no_grad()
def      __lowercase       (			a__							,       a__       )    ->			Dict:
							__SCREAMING_SNAKE_CASE									= DeiTConfig()
							# all deit models have fine-tuned heads
							__SCREAMING_SNAKE_CASE									= False
							# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
							__SCREAMING_SNAKE_CASE									= 10_00
							__SCREAMING_SNAKE_CASE									= 'huggingface/label-files'
							__SCREAMING_SNAKE_CASE									= 'imagenet-1k-id2label.json'
							__SCREAMING_SNAKE_CASE									= json.load(open(hf_hub_download(a__							,       a__							,       repo_type='dataset'       )							,       'r'       )       )
							__SCREAMING_SNAKE_CASE									= {int(a__       ): v for k, v in idalabel.items()}
							__SCREAMING_SNAKE_CASE									= idalabel
							__SCREAMING_SNAKE_CASE									= {v: k for k, v in idalabel.items()}
							__SCREAMING_SNAKE_CASE									= int(deit_name[-6:-4]       )
							__SCREAMING_SNAKE_CASE									= int(deit_name[-3:]       )
							# size of the architecture
							if deit_name[9:].startswith('tiny'       ):
														__SCREAMING_SNAKE_CASE									= 1_92
														__SCREAMING_SNAKE_CASE									= 7_68
														__SCREAMING_SNAKE_CASE									= 12
														__SCREAMING_SNAKE_CASE									= 3
							elif deit_name[9:].startswith('small'       ):
														__SCREAMING_SNAKE_CASE									= 3_84
														__SCREAMING_SNAKE_CASE									= 15_36
														__SCREAMING_SNAKE_CASE									= 12
														__SCREAMING_SNAKE_CASE									= 6
							if deit_name[9:].startswith('base'       ):
														pass
							elif deit_name[4:].startswith('large'       ):
														__SCREAMING_SNAKE_CASE									= 10_24
														__SCREAMING_SNAKE_CASE									= 40_96
														__SCREAMING_SNAKE_CASE									= 24
														__SCREAMING_SNAKE_CASE									= 16

							# load original model from timm
							__SCREAMING_SNAKE_CASE									= timm.create_model(a__							,       pretrained=a__       )
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							__SCREAMING_SNAKE_CASE									= timm_model.state_dict()
							__SCREAMING_SNAKE_CASE									= create_rename_keys(a__							,       a__       )
							for src, dest in rename_keys:
														rename_key(a__							,       a__							,       a__       )
							read_in_q_k_v(a__							,       a__							,       a__       )

							# load HuggingFace model
							__SCREAMING_SNAKE_CASE									= DeiTForImageClassificationWithTeacher(a__       ).eval()
							model.load_state_dict(a__       )

							# Check outputs on an image, prepared by DeiTImageProcessor
							__SCREAMING_SNAKE_CASE									= int(
							    (2_56 / 2_24) * config.image_size       )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
							__SCREAMING_SNAKE_CASE									= DeiTImageProcessor(size=a__							,       crop_size=config.image_size       )
							__SCREAMING_SNAKE_CASE									= image_processor(images=prepare_img()							,       return_tensors='pt'       )
							__SCREAMING_SNAKE_CASE									= encoding['pixel_values']
							__SCREAMING_SNAKE_CASE									= model(a__       )

							__SCREAMING_SNAKE_CASE									= timm_model(a__       )
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(a__							,       outputs.logits							,       atol=1E-3       )

							Path(a__       ).mkdir(exist_ok=a__       )
							print(f"""Saving model {deit_name} to {pytorch_dump_folder_path}"""       )
							model.save_pretrained(a__       )
							print(f"""Saving image processor to {pytorch_dump_folder_path}"""       )
							image_processor.save_pretrained(a__       )


if __name__ == "__main__":
			lowerCAmelCase__		:					Union[str, Any]							=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--deit_name''',
			    default='''vit_deit_base_distilled_patch16_224''',
			    type=str,
			    help='''Name of the DeiT timm model you\'d like to convert.''',
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
			)

			lowerCAmelCase__		:					str							=parser.parse_args()
			convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)






