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()
SCREAMING_SNAKE_CASE		:     Union[str, Any]         =	logging.get_logger(__name__)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Optional[int]		, _SCREAMING_SNAKE_CASE     :				int=False			):
						UpperCamelCase_		:							Tuple  =		[]

						# 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"
												UpperCamelCase_		:							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_			(   _SCREAMING_SNAKE_CASE     :				Optional[Any]		, _SCREAMING_SNAKE_CASE     :				Optional[int]		, _SCREAMING_SNAKE_CASE     :				Dict=False			):


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






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				List[str]			):
						UpperCamelCase_		:							str  =		["""head.weight""", """head.bias"""]
						for k in ignore_keys:
												state_dict.pop(a__		, a__			)






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				Tuple		, _SCREAMING_SNAKE_CASE     :				List[str]		, _SCREAMING_SNAKE_CASE     :				Optional[Any]			):
						UpperCamelCase_		:							List[str]  =		dct.pop(a__			)
						UpperCamelCase_		:							Optional[Any]  =		val






def     lowerCAmelCase_			(   ):
						UpperCamelCase_		:							List[Any]  =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						UpperCamelCase_		:							List[Any]  =		Image.open(requests.get(a__		, stream=a__			).raw			)
						return im






@torch.no_grad()
def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				int		, _SCREAMING_SNAKE_CASE     :				int		, _SCREAMING_SNAKE_CASE     :				Union[str, Any]=False			):
						UpperCamelCase_		:							Dict  =		BitConfig(
						    global_padding="""same"""		, layer_type="""bottleneck"""		, depths=(3, 4, 9)		, out_features=["""stage3"""]		, embedding_dynamic_padding=a__		, )
						UpperCamelCase_		:							Optional[int]  =		ViTHybridConfig(backbone_config=a__		, image_size=384		, num_labels=1000			)
						UpperCamelCase_		:							List[str]  =		False

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

						# load state_dict of original model, remove and rename some keys
						UpperCamelCase_		:							List[str]  =		timm_model.state_dict()
						if base_model:
												remove_classification_head_(a__			)
						UpperCamelCase_		:							str  =		create_rename_keys(a__		, a__			)
						for src, dest in rename_keys:
												rename_key(a__		, a__		, a__			)
						read_in_q_k_v(a__		, a__		, a__			)

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

						# load HuggingFace model
						if vit_name[-5:] == "in21k":
												UpperCamelCase_		:							str  =		ViTHybridModel(a__			).eval()
						else:
												UpperCamelCase_		:							Union[str, Any]  =		ViTHybridForImageClassification(a__			).eval()
						model.load_state_dict(a__			)

						# create image processor
						UpperCamelCase_		:							Optional[Any]  =		create_transform(**resolve_data_config({}		, model=a__			)			)
						UpperCamelCase_		:							Union[str, Any]  =		transform.transforms

						UpperCamelCase_		:							Tuple  =		{
						    """bilinear""": PILImageResampling.BILINEAR,
						    """bicubic""": PILImageResampling.BICUBIC,
						    """nearest""": PILImageResampling.NEAREST,
						}

						UpperCamelCase_		:							Optional[Any]  =		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()		, )

						UpperCamelCase_		:							Tuple  =		prepare_img()
						UpperCamelCase_		:							int  =		transform(a__			).unsqueeze(0			)
						UpperCamelCase_		:							Optional[int]  =		processor(a__		, return_tensors="""pt"""			).pixel_values

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

						# verify logits
						with torch.no_grad():
												UpperCamelCase_		:							int  =		model(a__			)
												UpperCamelCase_		:							Union[str, Any]  =		outputs.logits

						print("""Predicted class:"""		, logits.argmax(-1			).item()			)
						if base_model:
												UpperCamelCase_		:							int  =		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:
												UpperCamelCase_		:							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__":
	SCREAMING_SNAKE_CASE		:     Dict         =	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."
	)

	SCREAMING_SNAKE_CASE		:     Optional[int]         =	parser.parse_args()
	convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase					:			Tuple       =	logging.get_logger(__name__)

UpperCAmelCase					:			Any       =	{'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'}







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "ctrl"
 lowerCAmelCase__       = ["past_key_values"]
 lowerCAmelCase__       = {
     "max_position_embeddings": "n_positions",
     "hidden_size": "n_embd",
     "num_attention_heads": "n_head",
     "num_hidden_layers": "n_layer",
 }
 def __init__(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Tuple=246_534   ,				__SCREAMING_SNAKE_CASE   :			str=256   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=1_280   ,				__SCREAMING_SNAKE_CASE   :			Dict=8_192   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=48   ,				__SCREAMING_SNAKE_CASE   :			Dict=16   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			str=0.1   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=1E-6   ,				__SCREAMING_SNAKE_CASE   :			Tuple=0.02   ,				__SCREAMING_SNAKE_CASE   :			Tuple=True   ,				**__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				)    ->					Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = n_positions
      __SCREAMING_SNAKE_CASE      = n_embd
      __SCREAMING_SNAKE_CASE      = n_layer
      __SCREAMING_SNAKE_CASE      = n_head
      __SCREAMING_SNAKE_CASE      = dff
      __SCREAMING_SNAKE_CASE      = resid_pdrop
      __SCREAMING_SNAKE_CASE      = embd_pdrop
      __SCREAMING_SNAKE_CASE      = layer_norm_epsilon
      __SCREAMING_SNAKE_CASE      = initializer_range

      __SCREAMING_SNAKE_CASE      = use_cache

      super().__init__(**__SCREAMING_SNAKE_CASE     )






