




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	:       List[str]     =	logging.get_logger(__name__)




def     __magic_name__       (				A	:      Any,						A	:      int=False     ):






			'''simple docstring'''







			a					=    []

			# 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"
						a					=    [(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     __magic_name__       (				A	:      Dict,						A	:      Tuple,						A	:      Dict=False     ):






			'''simple docstring'''







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




def     __magic_name__       (				A	:      Tuple     ):






			'''simple docstring'''







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




def     __magic_name__       (				A	:      List[Any],						A	:      List[str],						A	:      str     ):






			'''simple docstring'''







			a					=    dct.pop(A     )
			a					=    val




def     __magic_name__       (				):






			'''simple docstring'''







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




@torch.no_grad()
def     __magic_name__       (				A	:      Optional[Any],						A	:      Union[str, Any],						A	:      Any=False     ):






			'''simple docstring'''







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

			# load original model from timm
			a					=    timm.create_model(A,						pretrained=A     )
			timm_model.eval()

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

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

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

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

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

			a					=    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(),						)

			a					=    prepare_img()
			a					=    transform(A     ).unsqueeze(0     )
			a					=    processor(A,						return_tensors="pt"     ).pixel_values

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

			# verify logits
			with torch.no_grad():
						a					=    model(A     )
						a					=    outputs.logits

			print("Predicted class:",						logits.argmax(-1     ).item()     )
			if base_model:
						a					=    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:
						a					=    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__":
		__lowerCAmelCase	:       Tuple     =	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	:       Tuple     =	parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)












import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def     __magic_name__       (				A	:      List[str]     ):






			'''simple docstring'''







			a					=    {}
			a					=    tokenizer(example["content"],						truncation=A     )["input_ids"]
			a					=    len(example["content"]     ) / len(output["input_ids"]     )
			return output


__lowerCAmelCase	:       Dict     =	HfArgumentParser(PretokenizationArguments)
__lowerCAmelCase	:       str     =	parser.parse_args()
if args.num_workers is None:
		__lowerCAmelCase	:       List[Any]     =	multiprocessing.cpu_count()
__lowerCAmelCase	:       str     =	AutoTokenizer.from_pretrained(args.tokenizer_dir)

__lowerCAmelCase	:       List[Any]     =	time.time()
__lowerCAmelCase	:       str     =	load_dataset(args.dataset_name, split='train')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       int     =	time.time()
__lowerCAmelCase	:       Optional[int]     =	ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        'repo_name',
        'path',
        'copies',
        'size',
        'content',
        'license',
        'hash',
        'line_mean',
        'line_max',
        'alpha_frac',
        'autogenerated',
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

__lowerCAmelCase	:       Tuple     =	time.time()
ds.push_to_hub(args.tokenized_data_repo)






print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')






