


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()
snake_case_						:			Any													=							logging.get_logger(__name__)







def 						lowerCamelCase(   a__	,a__=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 						lowerCamelCase(   a__	,a__	,a__=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 						lowerCamelCase(   a__):
    _SCREAMING_SNAKE_CASE			  =['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(a__	,a__)







def 						lowerCamelCase(   a__	,a__	,a__):
    _SCREAMING_SNAKE_CASE			  =dct.pop(a__)
    _SCREAMING_SNAKE_CASE			  =val







def 						lowerCamelCase(   ):
    _SCREAMING_SNAKE_CASE			  ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
    _SCREAMING_SNAKE_CASE			  =Image.open(requests.get(a__	,stream=a__).raw)
    return im







@torch.no_grad()
def 						lowerCamelCase(   a__	,a__	,a__=False):
    _SCREAMING_SNAKE_CASE			  =BitConfig(
        global_padding='''same'''	,layer_type='''bottleneck'''	,depths=(3, 4, 9)	,out_features=['''stage3''']	,embedding_dynamic_padding=a__	,)
    _SCREAMING_SNAKE_CASE			  =ViTHybridConfig(backbone_config=a__	,image_size=384	,num_labels=1000)
    _SCREAMING_SNAKE_CASE			  =False

    # load original model from timm
    _SCREAMING_SNAKE_CASE			  =timm.create_model(a__	,pretrained=a__)
    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_(a__)
    _SCREAMING_SNAKE_CASE			  =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			  ='''huggingface/label-files'''
    _SCREAMING_SNAKE_CASE			  ='''imagenet-1k-id2label.json'''
    _SCREAMING_SNAKE_CASE			  =json.load(open(hf_hub_download(a__	,a__	,repo_type='''dataset''')	,'''r'''))
    _SCREAMING_SNAKE_CASE			  ={int(a__): 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(a__).eval()
    else:
        _SCREAMING_SNAKE_CASE			  =ViTHybridForImageClassification(a__).eval()
    model.load_state_dict(a__)

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

    _SCREAMING_SNAKE_CASE			  ={
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

    _SCREAMING_SNAKE_CASE			  =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			  =prepare_img()
    _SCREAMING_SNAKE_CASE			  =transform(a__).unsqueeze(0)
    _SCREAMING_SNAKE_CASE			  =processor(a__	,return_tensors='''pt''').pixel_values

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

    # verify logits
    with torch.no_grad():
        _SCREAMING_SNAKE_CASE			  =model(a__)
        _SCREAMING_SNAKE_CASE			  =outputs.logits

    print('''Predicted class:'''	,logits.argmax(-1).item())
    if base_model:
        _SCREAMING_SNAKE_CASE			  =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			  =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__":
       snake_case_						:			str													=							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.'''
       )

       snake_case_						:			Any													=							parser.parse_args()
       convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)



import string
from math import logaa







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =document.translate(
				    str.maketrans(''''''	,''''''	,string.punctuation)).replace('''\n'''	,'''''')
				_SCREAMING_SNAKE_CASE			  =document_without_punctuation.split(''' ''')  # word tokenization
				return len([word for word in tokenize_document if word.lower() == term.lower()])







def 						lowerCamelCase(   a__	,a__):
				_SCREAMING_SNAKE_CASE			  =corpus.lower().translate(
				    str.maketrans(''''''	,''''''	,string.punctuation))  # strip all punctuation and replace it with ''
				_SCREAMING_SNAKE_CASE			  =corpus_without_punctuation.split('''\n''')
				_SCREAMING_SNAKE_CASE			  =term.lower()
				return (len([doc for doc in docs if term in doc]), len(a__))







def 						lowerCamelCase(   a__	,a__	,a__=False):
				if smoothing:
								if n == 0:
												raise ValueError('''log10(0) is undefined.''')
								return round(1 + logaa(n / (1 + df))	,3)

				if df == 0:
								raise ZeroDivisionError('''df must be > 0''')
				elif n == 0:
								raise ValueError('''log10(0) is undefined.''')
				return round(logaa(n / df)	,3)







def 						lowerCamelCase(   a__	,a__):
				return round(tf * idf	,3)