
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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_A   =     logging.get_logger(__name__)






def    lowerCamelCase__				(				__lowerCAmelCase  :  Tuple    ,							__lowerCAmelCase  :  Dict=False				):




			"""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"""vit.encoder.layer.{i}.layernorm_before.weight""")				)
						rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")				)
						rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")				)
						rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")				)
						rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")				)
						rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")				)
						rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")				)
						rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")				)
						rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")				)
						rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")				)

			# projection layer + position embeddings
			rename_keys.extend(
			    [
			        ("cls_token", "vit.embeddings.cls_token"),
			        ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
			        ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
			        ("pos_embed", "vit.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 "vit" from all keys that start with "vit"
						lowerCAmelCase_						=	[(pair[0], pair[1][4:]) if pair[1].startswith("vit"				) else pair for pair in rename_keys]
			else:
						# layernorm + classification head
						rename_keys.extend(
						    [
						        ("norm.weight", "vit.layernorm.weight"),
						        ("norm.bias", "vit.layernorm.bias"),
						        ("head.weight", "classifier.weight"),
						        ("head.bias", "classifier.bias"),
						    ]				)

			return rename_keys






def    lowerCamelCase__				(				__lowerCAmelCase  :  Any    ,							__lowerCAmelCase  :  Tuple    ,							__lowerCAmelCase  :  Tuple=False				):




			"""simple docstring"""


			for i in range(config.num_hidden_layers				):
						if base_model:
									lowerCAmelCase_						=	""
						else:
									lowerCAmelCase_						=	"vit."
						# 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    lowerCamelCase__				(				__lowerCAmelCase  :  Tuple				):




			"""simple docstring"""


			lowerCAmelCase_						=	["head.weight", "head.bias"]
			for k in ignore_keys:
						state_dict.pop(__lowerCAmelCase    ,							__lowerCAmelCase				)






def    lowerCamelCase__				(				__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  Union[str, Any]    ,							__lowerCAmelCase  :  Any				):




			"""simple docstring"""


			lowerCAmelCase_						=	dct.pop(__lowerCAmelCase				)
			lowerCAmelCase_						=	val






def    lowerCamelCase__				(				):




			"""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    lowerCamelCase__				(				__lowerCAmelCase  :  Dict    ,							__lowerCAmelCase  :  Tuple				):




			"""simple docstring"""


			lowerCAmelCase_						=	ViTConfig()
			lowerCAmelCase_						=	False
			# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
			if vit_name[-5:] == "in21k":
						lowerCAmelCase_						=	True
						lowerCAmelCase_						=	int(vit_name[-12:-10]				)
						lowerCAmelCase_						=	int(vit_name[-9:-6]				)
			else:
						lowerCAmelCase_						=	1000
						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(vit_name[-6:-4]				)
						lowerCAmelCase_						=	int(vit_name[-3:]				)
			# size of the architecture
			if "deit" in vit_name:
						if vit_name[9:].startswith("tiny"				):
									lowerCAmelCase_						=	192
									lowerCAmelCase_						=	768
									lowerCAmelCase_						=	12
									lowerCAmelCase_						=	3
						elif vit_name[9:].startswith("small"				):
									lowerCAmelCase_						=	384
									lowerCAmelCase_						=	1536
									lowerCAmelCase_						=	12
									lowerCAmelCase_						=	6
						else:
									pass
			else:
						if vit_name[4:].startswith("small"				):
									lowerCAmelCase_						=	768
									lowerCAmelCase_						=	2304
									lowerCAmelCase_						=	8
									lowerCAmelCase_						=	8
						elif vit_name[4:].startswith("base"				):
									pass
						elif vit_name[4:].startswith("large"				):
									lowerCAmelCase_						=	1024
									lowerCAmelCase_						=	4096
									lowerCAmelCase_						=	24
									lowerCAmelCase_						=	16
						elif vit_name[4:].startswith("huge"				):
									lowerCAmelCase_						=	1280
									lowerCAmelCase_						=	5120
									lowerCAmelCase_						=	32
									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()
			if base_model:
						remove_classification_head_(__lowerCAmelCase				)
			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
			if vit_name[-5:] == "in21k":
						lowerCAmelCase_						=	ViTModel(__lowerCAmelCase				).eval()
			else:
						lowerCAmelCase_						=	ViTForImageClassification(__lowerCAmelCase				).eval()
			model.load_state_dict(__lowerCAmelCase				)

			# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
			if "deit" in vit_name:
						lowerCAmelCase_						=	DeiTImageProcessor(size=config.image_size				)
			else:
						lowerCAmelCase_						=	ViTImageProcessor(size=config.image_size				)
			lowerCAmelCase_						=	image_processor(images=prepare_img()    ,							return_tensors="pt"				)
			lowerCAmelCase_						=	encoding["pixel_values"]
			lowerCAmelCase_						=	model(__lowerCAmelCase				)

			if base_model:
						lowerCAmelCase_						=	timm_model.forward_features(__lowerCAmelCase				)
						assert timm_pooled_output.shape == outputs.pooler_output.shape
						assert torch.allclose(__lowerCAmelCase    ,							outputs.pooler_output    ,							atol=1e-3				)
			else:
						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 {vit_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(
			    "--vit_name",
			    default="vit_base_patch16_224",
			    type=str,
			    help="Name of the ViT 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_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)





from pathlib import Path

import fire






def    lowerCamelCase__				(				__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  str    ,							__lowerCAmelCase  :  int				):




			"""simple docstring"""


			lowerCAmelCase_						=	Path(__lowerCAmelCase				)
			lowerCAmelCase_						=	Path(__lowerCAmelCase				)
			dest_dir.mkdir(exist_ok=__lowerCAmelCase				)
			for path in src_dir.iterdir():
						lowerCAmelCase_						=	[x.rstrip() for x in list(path.open().readlines()				)][:n]
						lowerCAmelCase_						=	dest_dir.joinpath(path.name				)
						print(__lowerCAmelCase				)
						dest_path.open("w"				).write("\n".join(__lowerCAmelCase				)				)


if __name__ == "__main__":
			fire.Fire(minify)



