






'''simple docstring'''





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()
a_							=    logging.get_logger(__name__)


def        _a  (     UpperCamelCase_					:				List[Any]    ,   UpperCamelCase_					:				Union[str, Any]=False   ) ->   Optional[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						[]
						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"
												lowerCAmelCase__       =						[(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        _a  (     UpperCamelCase_					:				List[str]    ,   UpperCamelCase_					:				Optional[int]    ,   UpperCamelCase_					:				str=False   ) ->   Dict:
						"""simple docstring"""







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


def        _a  (     UpperCamelCase_					:				Union[str, Any]    ,   UpperCamelCase_					:				str    ,   UpperCamelCase_					:				Optional[int]   ) ->   Dict:
						"""simple docstring"""







						lowerCAmelCase__       =						dct.pop(_lowerCAmelCase   )
						lowerCAmelCase__       =						val


def        _a  (     ) ->   int:
						"""simple docstring"""







						lowerCAmelCase__       =						"http://images.cocodataset.org/val2017/000000039769.jpg"
						lowerCAmelCase__       =						Image.open(requests.get(_lowerCAmelCase    ,   stream=_lowerCAmelCase   ).raw   )
						return im


@torch.no_grad()
def        _a  (     UpperCamelCase_					:				str    ,   UpperCamelCase_					:				Union[str, Any]   ) ->   List[Any]:
						"""simple docstring"""







						lowerCAmelCase__       =						DeiTConfig()
						# all deit models have fine-tuned heads
						lowerCAmelCase__       =						False
						# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
						lowerCAmelCase__       =						1_000
						lowerCAmelCase__       =						"huggingface/label-files"
						lowerCAmelCase__       =						"imagenet-1k-id2label.json"
						lowerCAmelCase__       =						json.load(open(hf_hub_download(_lowerCAmelCase    ,   _lowerCAmelCase    ,   repo_type="dataset"   )    ,   "r"   )   )
						lowerCAmelCase__       =						{int(_lowerCAmelCase   ): v for k, v in idalabel.items()}
						lowerCAmelCase__       =						idalabel
						lowerCAmelCase__       =						{v: k for k, v in idalabel.items()}
						lowerCAmelCase__       =						int(deit_name[-6:-4]   )
						lowerCAmelCase__       =						int(deit_name[-3:]   )
						# size of the architecture
						if deit_name[9:].startswith("tiny"   ):
												lowerCAmelCase__       =						192
												lowerCAmelCase__       =						768
												lowerCAmelCase__       =						12
												lowerCAmelCase__       =						3
						elif deit_name[9:].startswith("small"   ):
												lowerCAmelCase__       =						384
												lowerCAmelCase__       =						1_536
												lowerCAmelCase__       =						12
												lowerCAmelCase__       =						6
						if deit_name[9:].startswith("base"   ):
												pass
						elif deit_name[4:].startswith("large"   ):
												lowerCAmelCase__       =						1_024
												lowerCAmelCase__       =						4_096
												lowerCAmelCase__       =						24
												lowerCAmelCase__       =						16

						# load original model from timm
						lowerCAmelCase__       =						timm.create_model(_lowerCAmelCase    ,   pretrained=_lowerCAmelCase   )
						timm_model.eval()

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

						# load HuggingFace model
						lowerCAmelCase__       =						DeiTForImageClassificationWithTeacher(_lowerCAmelCase   ).eval()
						model.load_state_dict(_lowerCAmelCase   )

						# Check outputs on an image, prepared by DeiTImageProcessor
						lowerCAmelCase__       =						int(
						    (256 / 224) * config.image_size   )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
						lowerCAmelCase__       =						DeiTImageProcessor(size=_lowerCAmelCase    ,   crop_size=config.image_size   )
						lowerCAmelCase__       =						image_processor(images=prepare_img()    ,   return_tensors="pt"   )
						lowerCAmelCase__       =						encoding["pixel_values"]
						lowerCAmelCase__       =						model(_lowerCAmelCase   )

						lowerCAmelCase__       =						timm_model(_lowerCAmelCase   )
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(_lowerCAmelCase    ,   outputs.logits    ,   atol=1e-3   )

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


if __name__ == "__main__":
	a_							=    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.'''
	)

	a_							=    parser.parse_args()
	convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_							=    {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''SEWForCTC''',
	    '''SEWForSequenceClassification''',
	    '''SEWModel''',
	    '''SEWPreTrainedModel''',
	]

if TYPE_CHECKING:
	from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_sew import (
		    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SEWForCTC,
		    SEWForSequenceClassification,
		    SEWModel,
		    SEWPreTrainedModel,
		)


else:
	import sys

	a_							=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






