

'''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()
lowerCAmelCase__   		=      logging.get_logger(__name__)


def 						_A	(       A__	,							A__=False ):






 """simple docstring"""



 __lowercase				    =					[]

 # 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"
  __lowercase				    =					[(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 						_A	(       A__	,							A__	,							A__=False ):






 """simple docstring"""



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


def 						_A	(       A__ ):






 """simple docstring"""



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


def 						_A	(       A__	,							A__	,							A__ ):






 """simple docstring"""



 __lowercase				    =					dct.pop(A__ )
 __lowercase				    =					val


def 						_A	(       ):






 """simple docstring"""



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


@torch.no_grad()
def 						_A	(       A__	,							A__	,							A__=False ):






 """simple docstring"""



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

 # load original model from timm
 __lowercase				    =					timm.create_model(A__	,							pretrained=A__ )
 timm_model.eval()

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

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

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

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

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

 __lowercase				    =					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()	,							)

 __lowercase				    =					prepare_img()
 __lowercase				    =					transform(A__ ).unsqueeze(0 )
 __lowercase				    =					processor(A__	,							return_tensors='''pt''' ).pixel_values

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

 # verify logits
 with torch.no_grad():
  __lowercase				    =					model(A__ )
  __lowercase				    =					outputs.logits

 print('''Predicted class:'''	,							logits.argmax(-1 ).item() )
 if base_model:
  __lowercase				    =					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:
  __lowercase				    =					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__   		=      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)








"""simple docstring"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_camembert import CamembertTokenizer
else:
							_UpperCamelCase					      =   None


_UpperCamelCase					      =   logging.get_logger(__name__)

_UpperCamelCase					      =   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase					      =   {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
    },
}

_UpperCamelCase					      =   {
    'camembert-base': 512,
}

_UpperCamelCase					      =   '▁'
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							List[str]									=		VOCAB_FILES_NAMES
				__snake_case :							Optional[int]									=		PRETRAINED_VOCAB_FILES_MAP
				__snake_case :							Dict									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__snake_case :							List[str]									=		["""input_ids""", """attention_mask"""]
				__snake_case :							List[Any]									=		CamembertTokenizer




				def __init__(  self	:List[Any]    ,     __lowercase	:Optional[int]=None    ,     __lowercase	:str=None    ,     __lowercase	:Optional[Any]="<s>"    ,     __lowercase	:List[str]="</s>"    ,     __lowercase	:Tuple="</s>"    ,     __lowercase	:int="<s>"    ,     __lowercase	:Union[str, Any]="<unk>"    ,     __lowercase	:Optional[int]="<pad>"    ,     __lowercase	:Union[str, Any]="<mask>"    ,     __lowercase	:Tuple=["<s>NOTUSED", "</s>NOTUSED"]    ,     **__lowercase	:List[str]    ,     ):
											# Mask token behave like a normal word, i.e. include the space before it
											__lowerCamelCase   :   Optional[int]       =AddedToken(__lowercase    ,     lstrip=__lowercase    ,     rstrip=__lowercase      ) if isinstance(__lowercase    ,     __lowercase      ) else mask_token

											super().__init__(
											    __lowercase    ,     tokenizer_file=__lowercase    ,     bos_token=__lowercase    ,     eos_token=__lowercase    ,     sep_token=__lowercase    ,     cls_token=__lowercase    ,     unk_token=__lowercase    ,     pad_token=__lowercase    ,     mask_token=__lowercase    ,     additional_special_tokens=__lowercase    ,     **__lowercase    ,     )

											__lowerCamelCase   :   Any       =vocab_file
											__lowerCamelCase   :   Union[str, Any]       =False if not self.vocab_file else True




				def        __lowercase	(  self	:List[str]    ,     __lowercase	:List[int]    ,     __lowercase	:Optional[List[int]] = None      ):

											if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											__lowerCamelCase   :   Tuple       =[self.cls_token_id]
											__lowerCamelCase   :   str       =[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep




				def        __lowercase	(  self	:Tuple    ,     __lowercase	:List[int]    ,     __lowercase	:Optional[List[int]] = None      ):
											__lowerCamelCase   :   Any       =[self.sep_token_id]
											__lowerCamelCase   :   str       =[self.cls_token_id]

											if token_ids_a is None:
																		return len(cls + token_ids_a + sep      ) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:str    ,     __lowercase	:Optional[str] = None      ):
											if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																		    '''tokenizer.'''      )

											if not os.path.isdir(__lowercase      ):
																		logger.error(f'Vocabulary path ({save_directory}) should be a directory'      )
																		return
											__lowerCamelCase   :   List[str]       =os.path.join(
											    __lowercase    ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

											if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowercase      ):
																		copyfile(self.vocab_file    ,     __lowercase      )

											return (out_vocab_file,)




