


"""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 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__    :							int            =					logging.get_logger(__name__)




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_=False       ):




					'''simple docstring'''


					__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        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=False       ):




					'''simple docstring'''


					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        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


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




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    dct.pop(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    val




def        UpperCAmelCase__	():




					'''simple docstring'''


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




@torch.no_grad()
def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=False       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    BitConfig(
					    global_padding="same"  ,						layer_type="bottleneck"  ,						depths=(3, 4, 9)  ,						out_features=["stage3"]  ,						embedding_dynamic_padding=lowerCAmelCase_  ,						)
					__SCREAMING_SNAKE_CASE						       =    ViTHybridConfig(backbone_config=lowerCAmelCase_  ,						image_size=384  ,						num_labels=1000       )
					__SCREAMING_SNAKE_CASE						       =    False

					# load original model from timm
					__SCREAMING_SNAKE_CASE						       =    timm.create_model(lowerCAmelCase_  ,						pretrained=lowerCAmelCase_       )
					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_(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    create_rename_keys(lowerCAmelCase_  ,						lowerCAmelCase_       )
					for src, dest in rename_keys:
										rename_key(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )
					read_in_q_k_v(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )

					__SCREAMING_SNAKE_CASE						       =    "huggingface/label-files"
					__SCREAMING_SNAKE_CASE						       =    "imagenet-1k-id2label.json"
					__SCREAMING_SNAKE_CASE						       =    json.load(open(hf_hub_download(lowerCAmelCase_  ,						lowerCAmelCase_  ,						repo_type="dataset"       )  ,						"r"       )       )
					__SCREAMING_SNAKE_CASE						       =    {int(lowerCAmelCase_       ): 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(lowerCAmelCase_       ).eval()
					else:
										__SCREAMING_SNAKE_CASE						       =    ViTHybridForImageClassification(lowerCAmelCase_       ).eval()
					model.load_state_dict(lowerCAmelCase_       )

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

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

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

					__SCREAMING_SNAKE_CASE						       =    prepare_img()
					__SCREAMING_SNAKE_CASE						       =    transform(lowerCAmelCase_       ).unsqueeze(0       )
					__SCREAMING_SNAKE_CASE						       =    processor(lowerCAmelCase_  ,						return_tensors="pt"       ).pixel_values

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

					# verify logits
					with torch.no_grad():
										__SCREAMING_SNAKE_CASE						       =    model(lowerCAmelCase_       )
										__SCREAMING_SNAKE_CASE						       =    outputs.logits

					print("Predicted class:"  ,						logits.argmax(-1       ).item()       )
					if base_model:
										__SCREAMING_SNAKE_CASE						       =    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:
										__SCREAMING_SNAKE_CASE						       =    timm_model(lowerCAmelCase_       )
										assert timm_logits.shape == outputs.logits.shape
										assert torch.allclose(lowerCAmelCase_  ,						outputs.logits  ,						atol=1E-3       )
					print("Looks ok!"       )

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

					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__    :							Union[str, Any]            =					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__    :							Any            =					parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import _LazyModule


a__    :							Optional[int]            =					{'''tokenization_byt5''': ['''ByT5Tokenizer''']}


if TYPE_CHECKING:
		from .tokenization_byta import ByTaTokenizer
else:
		import sys

		a__    :							Any            =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
