





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()
UpperCAmelCase_ 							=       logging.get_logger(__name__)





def   lowerCamelCase__       (       UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    str=False  )     ->	Optional[int]:



					'''simple docstring'''




					_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"
										_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__       (       UpperCamelCase__     :    str    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    int=False  )     ->	Any:



					'''simple docstring'''




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





def   lowerCamelCase__       (       UpperCamelCase__     :    Any  )     ->	Dict:



					'''simple docstring'''




					_snake_case													=			['head.weight', 'head.bias']
					for k in ignore_keys:
										state_dict.pop(UpperCamelCase__    ,      UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    Dict    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    Optional[int]  )     ->	List[str]:



					'''simple docstring'''




					_snake_case													=			dct.pop(UpperCamelCase__  )
					_snake_case													=			val





def   lowerCamelCase__       (       )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			'http://images.cocodataset.org/val2017/000000039769.jpg'
					_snake_case													=			Image.open(requests.get(UpperCamelCase__    ,      stream=UpperCamelCase__  ).raw  )
					return im





@torch.no_grad()
def   lowerCamelCase__       (       UpperCamelCase__     :    Dict    ,      UpperCamelCase__     :    List[str]    ,      UpperCamelCase__     :    List[Any]=False  )     ->	Union[str, Any]:



					'''simple docstring'''




					_snake_case													=			BitConfig(
					    global_padding='same'    ,      layer_type='bottleneck'    ,      depths=(3, 4, 9)    ,      out_features=['stage3']    ,      embedding_dynamic_padding=UpperCamelCase__    ,      )
					_snake_case													=			ViTHybridConfig(backbone_config=UpperCamelCase__    ,      image_size=384    ,      num_labels=1_000  )
					_snake_case													=			False

					# load original model from timm
					_snake_case													=			timm.create_model(UpperCamelCase__    ,      pretrained=UpperCamelCase__  )
					timm_model.eval()

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

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

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

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

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

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

					_snake_case													=			prepare_img()
					_snake_case													=			transform(UpperCamelCase__  ).unsqueeze(0  )
					_snake_case													=			processor(UpperCamelCase__    ,      return_tensors='pt'  ).pixel_values

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

					# verify logits
					with torch.no_grad():
										_snake_case													=			model(UpperCamelCase__  )
										_snake_case													=			outputs.logits

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

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

					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__":
					UpperCAmelCase_ 							=       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."""
					)

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










def   lowerCamelCase__       (       UpperCamelCase__     :    Tuple    ,      UpperCamelCase__     :    Optional[int]  )     ->	Tuple:



					'''simple docstring'''




					_snake_case													=			[0 for i in range(r + 1  )]
					# nc0 = 1
					_snake_case													=			1
					for i in range(1    ,      n + 1  ):
										# to compute current row from previous row.
										_snake_case													=			min(UpperCamelCase__    ,      UpperCamelCase__  )
										while j > 0:
															c[j] += c[j - 1]
															j -= 1
					return c[r]




print(binomial_coefficient(n=10, r=5))



