

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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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

def 			lowerCamelCase__			(       _a				,      _a=False):
	SCREAMING_SNAKE_CASE :   Any					  =							[]

	# fmt: off
	# stem:
	rename_keys.append(("cls_token", "vit.embeddings.cls_token"))
	rename_keys.append(("pos_embed", "vit.embeddings.position_embeddings"))

	rename_keys.append(("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"))
	rename_keys.append(("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"))

	# backbone
	rename_keys.append(("patch_embed.backbone.stem.conv.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight"))
	rename_keys.append(("patch_embed.backbone.stem.norm.weight", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight"))
	rename_keys.append(("patch_embed.backbone.stem.norm.bias", "vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias"))

	for stage_idx in range(len(config.backbone_config.depths)):
		for layer_idx in range(config.backbone_config.depths[stage_idx]):
			rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight"))
			rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight"))
			rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias"))
			rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight"))
			rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight"))
			rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias"))
			rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight"))
			rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight"))
			rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias"))

		rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight"))
		rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight"))
		rename_keys.append((f"patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias", f"vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias"))

	# transformer encoder
	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"))

	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"
		SCREAMING_SNAKE_CASE :   List[str]					  =							[(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"),
		    ])
	# fmt: on

	return rename_keys

def 			lowerCamelCase__			(       _a				,      _a				,      _a=False):
	for i in range(config.num_hidden_layers):
		if base_model:
			SCREAMING_SNAKE_CASE :   Any					  =							""
		else:
			SCREAMING_SNAKE_CASE :   Optional[int]					  =							"vit."
		# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							state_dict.pop(f"blocks.{i}.attn.qkv.weight")
		SCREAMING_SNAKE_CASE :   List[Any]					  =							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 :   str					  =							in_proj_weight[
		    : config.hidden_size, :
		]
		SCREAMING_SNAKE_CASE :   Any					  =							in_proj_bias[: config.hidden_size]
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							in_proj_weight[
		    config.hidden_size : config.hidden_size * 2, :
		]
		SCREAMING_SNAKE_CASE :   Dict					  =							in_proj_bias[
		    config.hidden_size : config.hidden_size * 2
		]
		SCREAMING_SNAKE_CASE :   str					  =							in_proj_weight[
		    -config.hidden_size :, :
		]
		SCREAMING_SNAKE_CASE :   Any					  =							in_proj_bias[-config.hidden_size :]

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							["head.weight", "head.bias"]
	for k in ignore_keys:
		state_dict.pop(_a				,      _a)

def 			lowerCamelCase__			(       _a				,      _a				,      _a):
	SCREAMING_SNAKE_CASE :   Any					  =							dct.pop(_a)
	SCREAMING_SNAKE_CASE :   int					  =							val

def 			lowerCamelCase__			(       ):
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							"http://images.cocodataset.org/val2017/000000039769.jpg"
	SCREAMING_SNAKE_CASE :   Tuple					  =							Image.open(requests.get(_a				,      stream=_a).raw)
	return im

@torch.no_grad()
def 			lowerCamelCase__			(       _a				,      _a				,      _a=False):
	SCREAMING_SNAKE_CASE :   Any					  =							BitConfig(
	    global_padding="same"				,      layer_type="bottleneck"				,      depths=(3, 4, 9)				,      out_features=["stage3"]				,      embedding_dynamic_padding=_a				,      )
	SCREAMING_SNAKE_CASE :   Tuple					  =							ViTHybridConfig(backbone_config=_a				,      image_size=384				,      num_labels=1000)
	SCREAMING_SNAKE_CASE :   Any					  =							False

	# load original model from timm
	SCREAMING_SNAKE_CASE :   Any					  =							timm.create_model(_a				,      pretrained=_a)
	timm_model.eval()

	# load state_dict of original model, remove and rename some keys
	SCREAMING_SNAKE_CASE :   Any					  =							timm_model.state_dict()
	if base_model:
		remove_classification_head_(_a)
	SCREAMING_SNAKE_CASE :   Dict					  =							create_rename_keys(_a				,      _a)
	for src, dest in rename_keys:
		rename_key(_a				,      _a				,      _a)
	read_in_q_k_v(_a				,      _a				,      _a)

	SCREAMING_SNAKE_CASE :   List[str]					  =							"huggingface/label-files"
	SCREAMING_SNAKE_CASE :   List[Any]					  =							"imagenet-1k-id2label.json"
	SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							json.load(open(hf_hub_download(_a				,      _a				,      repo_type="dataset")				,      "r"))
	SCREAMING_SNAKE_CASE :   List[Any]					  =							{int(_a): v for k, v in idalabel.items()}
	SCREAMING_SNAKE_CASE :   List[str]					  =							idalabel
	SCREAMING_SNAKE_CASE :   List[Any]					  =							{v: k for k, v in idalabel.items()}

	# load HuggingFace model
	if vit_name[-5:] == "in21k":
		SCREAMING_SNAKE_CASE :   Dict					  =							ViTHybridModel(_a).eval()
	else:
		SCREAMING_SNAKE_CASE :   Dict					  =							ViTHybridForImageClassification(_a).eval()
	model.load_state_dict(_a)

	# create image processor
	SCREAMING_SNAKE_CASE :   int					  =							create_transform(**resolve_data_config({}				,      model=_a))
	SCREAMING_SNAKE_CASE :   Tuple					  =							transform.transforms

	SCREAMING_SNAKE_CASE :   Optional[int]					  =							{
	    "bilinear": PILImageResampling.BILINEAR,
	    "bicubic": PILImageResampling.BICUBIC,
	    "nearest": PILImageResampling.NEAREST,
	}

	SCREAMING_SNAKE_CASE :   List[str]					  =							ViTHybridImageProcessor(
	    do_resize=_a				,      size={"shortest_edge": timm_transforms[0].size}				,      resample=pillow_resamplings[timm_transforms[0].interpolation.value]				,      do_center_crop=_a				,      crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}				,      do_normalize=_a				,      image_mean=timm_transforms[-1].mean.tolist()				,      image_std=timm_transforms[-1].std.tolist()				,      )

	SCREAMING_SNAKE_CASE :   List[Any]					  =							prepare_img()
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							transform(_a).unsqueeze(0)
	SCREAMING_SNAKE_CASE :   str					  =							processor(_a				,      return_tensors="pt").pixel_values

	# verify pixel values
	assert torch.allclose(_a				,      _a)

	# verify logits
	with torch.no_grad():
		SCREAMING_SNAKE_CASE :   int					  =							model(_a)
		SCREAMING_SNAKE_CASE :   int					  =							outputs.logits

	print("Predicted class:"				,      logits.argmax(-1).item())
	if base_model:
		SCREAMING_SNAKE_CASE :   List[Any]					  =							timm_model.forward_features(_a)
		assert timm_pooled_output.shape == outputs.pooler_output.shape
		assert torch.allclose(_a				,      outputs.pooler_output				,      atol=1E-3)
	else:
		SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							timm_model(_a)
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(_a				,      outputs.logits				,      atol=1E-3)
	print("Looks ok!")

	if pytorch_dump_folder_path is not None:
		Path(_a).mkdir(exist_ok=_a)
		print(f"Saving model {vit_name} to {pytorch_dump_folder_path}")
		model.save_pretrained(_a)
		print(f"Saving processor to {pytorch_dump_folder_path}")
		processor.save_pretrained(_a)

	if push_to_hub:
		print(f"Pushing model and processor to the hub {vit_name}")
		model.push_to_hub(f"ybelkada/{vit_name}")
		processor.push_to_hub(f"ybelkada/{vit_name}")


if __name__ == "__main__":
			a_      =     argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--vit_name',
			    default='vit_base_r50_s16_384',
			    type=str,
			    help='Name of the hybrid 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.'
			)
			parser.add_argument(
			    '--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
			)

			a_      =     parser.parse_args()
			convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)


a_      =     frozenset(
    [
        'prompt',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

a_      =     frozenset(['prompt', 'negative_prompt'])

a_      =     frozenset([])

a_      =     frozenset(['image'])

a_      =     frozenset(
    [
        'image',
        'height',
        'width',
        'guidance_scale',
    ]
)

a_      =     frozenset(['image'])

a_      =     frozenset(
    [
        'prompt',
        'image',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
    ]
)

a_      =     frozenset(['prompt', 'image', 'negative_prompt'])

a_      =     frozenset(
    [
        # Text guided image variation with an image mask
        'prompt',
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
    ]
)

a_      =     frozenset(['prompt', 'image', 'mask_image', 'negative_prompt'])

a_      =     frozenset(
    [
        # image variation with an image mask
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
    ]
)

a_      =     frozenset(['image', 'mask_image'])

a_      =     frozenset(
    [
        'example_image',
        'image',
        'mask_image',
        'height',
        'width',
        'guidance_scale',
    ]
)

a_      =     frozenset(['example_image', 'image', 'mask_image'])

a_      =     frozenset(['class_labels'])

a_      =     frozenset(['class_labels'])

a_      =     frozenset(['batch_size'])

a_      =     frozenset([])

a_      =     frozenset(['batch_size'])

a_      =     frozenset([])

a_      =     frozenset(
    [
        'prompt',
        'audio_length_in_s',
        'guidance_scale',
        'negative_prompt',
        'prompt_embeds',
        'negative_prompt_embeds',
        'cross_attention_kwargs',
    ]
)

a_      =     frozenset(['prompt', 'negative_prompt'])
a_      =     frozenset(['input_tokens'])

a_      =     frozenset(['input_tokens'])