



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()
_lowerCamelCase   				=			logging.get_logger(__name__)






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Union[str, Any]					,   UpperCamelCase__:							List[Any]=False    ):
		SCREAMING_SNAKE_CASE__			=      []

		# 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__			=      [(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 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[Any]					,   UpperCamelCase__:							Optional[Any]					,   UpperCamelCase__:							Optional[Any]=False    ):
		for i in range(config.num_hidden_layers    ):
				if base_model:
						SCREAMING_SNAKE_CASE__			=      ""
				else:
						SCREAMING_SNAKE_CASE__			=      "vit."
				# 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 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							str    ):
		SCREAMING_SNAKE_CASE__			=      ["head.weight", "head.bias"]
		for k in ignore_keys:
				state_dict.pop(snake_case_					,   snake_case_    )






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Any					,   UpperCamelCase__:							int					,   UpperCamelCase__:							str    ):
		SCREAMING_SNAKE_CASE__			=      dct.pop(snake_case_    )
		SCREAMING_SNAKE_CASE__			=      val






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






@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]					,   UpperCamelCase__:							List[str]					,   UpperCamelCase__:							str=False    ):
		SCREAMING_SNAKE_CASE__			=      BitConfig(
		    global_padding="""same"""					,   layer_type="""bottleneck"""					,   depths=(3, 4, 9)					,   out_features=["""stage3"""]					,   embedding_dynamic_padding=snake_case_					,   )
		SCREAMING_SNAKE_CASE__			=      ViTHybridConfig(backbone_config=snake_case_					,   image_size=384					,   num_labels=1_000    )
		SCREAMING_SNAKE_CASE__			=      False

		# load original model from timm
		SCREAMING_SNAKE_CASE__			=      timm.create_model(snake_case_					,   pretrained=snake_case_    )
		timm_model.eval()

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

		SCREAMING_SNAKE_CASE__			=      "huggingface/label-files"
		SCREAMING_SNAKE_CASE__			=      "imagenet-1k-id2label.json"
		SCREAMING_SNAKE_CASE__			=      json.load(open(hf_hub_download(snake_case_					,   snake_case_					,   repo_type="""dataset"""    )					,   """r"""    )    )
		SCREAMING_SNAKE_CASE__			=      {int(snake_case_    ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE__			=      idalabel
		SCREAMING_SNAKE_CASE__			=      {v: k for k, v in idalabel.items()}

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

		# create image processor
		SCREAMING_SNAKE_CASE__			=      create_transform(**resolve_data_config({}					,   model=snake_case_    )    )
		SCREAMING_SNAKE_CASE__			=      transform.transforms

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

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

		SCREAMING_SNAKE_CASE__			=      prepare_img()
		SCREAMING_SNAKE_CASE__			=      transform(snake_case_    ).unsqueeze(0    )
		SCREAMING_SNAKE_CASE__			=      processor(snake_case_					,   return_tensors="""pt"""    ).pixel_values

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

		# verify logits
		with torch.no_grad():
				SCREAMING_SNAKE_CASE__			=      model(snake_case_    )
				SCREAMING_SNAKE_CASE__			=      outputs.logits

		print("""Predicted class:"""					,   logits.argmax(-1    ).item()    )
		if base_model:
				SCREAMING_SNAKE_CASE__			=      timm_model.forward_features(snake_case_    )
				assert timm_pooled_output.shape == outputs.pooler_output.shape
				assert torch.allclose(snake_case_					,   outputs.pooler_output					,   atol=1e-3    )
		else:
				SCREAMING_SNAKE_CASE__			=      timm_model(snake_case_    )
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(snake_case_					,   outputs.logits					,   atol=1e-3    )
		print("""Looks ok!"""    )

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

		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__":
	_lowerCamelCase   				=			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.'
	)

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






import warnings

from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor


_lowerCamelCase   				=			logging.get_logger(__name__)




class   UpperCamelCase_       (     UpperCamelCase__    ):


							def __init__(							self    :List[Any]							,				*__A    :Tuple							,				**__A    :Dict		)   ->   None:



									"""simple docstring"""



									warnings.warn(
									    """The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
									    """ Please use LayoutLMv2ImageProcessor instead."""							,				__A							,				)
									super().__init__(*__A							,				**__A		)