


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()
lowercase_	   =     logging.get_logger(__name__)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__=False						):
 __lowerCamelCase		:							Optional[Any]		 =						[]

 # 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"
  __lowerCamelCase		:							List[Any]		 =						[(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__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__=False						):





 for i in range(config.num_hidden_layers						):
  if base_model:
   __lowerCamelCase		:							List[Any]		 =						''
  else:
   __lowerCamelCase		:							List[Any]		 =						'vit.'
  # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
  __lowerCamelCase		:							Any		 =						state_dict.pop(f'blocks.{i}.attn.qkv.weight'						)
  __lowerCamelCase		:							Tuple		 =						state_dict.pop(f'blocks.{i}.attn.qkv.bias'						)
  # next, add query, keys and values (in that order) to the state dict
  __lowerCamelCase		:							Optional[int]		 =						in_proj_weight[
      : config.hidden_size, :
  ]
  __lowerCamelCase		:							Dict		 =						in_proj_bias[: config.hidden_size]
  __lowerCamelCase		:							Optional[Any]		 =						in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __lowerCamelCase		:							List[str]		 =						in_proj_bias[
      config.hidden_size : config.hidden_size * 2
  ]
  __lowerCamelCase		:							List[Any]		 =						in_proj_weight[
      -config.hidden_size :, :
  ]
  __lowerCamelCase		:							Union[str, Any]		 =						in_proj_bias[-config.hidden_size :]
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Tuple		 =						['head.weight', 'head.bias']
 for k in ignore_keys:
  state_dict.pop(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[Any]		 =						dct.pop(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							int		 =						val
def 				UpperCamelCase__					(  ):
 __lowerCamelCase		:							int		 =						'http://images.cocodataset.org/val2017/000000039769.jpg'
 __lowerCamelCase		:							Tuple		 =						Image.open(requests.get(SCREAMING_SNAKE_CASE__      ,	stream=SCREAMING_SNAKE_CASE__						).raw						)
 return im
@torch.no_grad()
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__=False						):
 __lowerCamelCase		:							List[str]		 =						BitConfig(
     global_padding='same'      ,	layer_type='bottleneck'      ,	depths=(3, 4, 9)      ,	out_features=['stage3']      ,	embedding_dynamic_padding=SCREAMING_SNAKE_CASE__      ,	)
 __lowerCamelCase		:							Tuple		 =						ViTHybridConfig(backbone_config=SCREAMING_SNAKE_CASE__      ,	image_size=384      ,	num_labels=1_000						)
 __lowerCamelCase		:							Dict		 =						False

 # load original model from timm
 __lowerCamelCase		:							Any		 =						timm.create_model(SCREAMING_SNAKE_CASE__      ,	pretrained=SCREAMING_SNAKE_CASE__						)
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 __lowerCamelCase		:							Union[str, Any]		 =						timm_model.state_dict()
 if base_model:
  remove_classification_head_(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Any		 =						create_rename_keys(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
 for src, dest in rename_keys:
  rename_key(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
 read_in_q_k_v(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							Any		 =						'huggingface/label-files'
 __lowerCamelCase		:							Tuple		 =						'imagenet-1k-id2label.json'
 __lowerCamelCase		:							List[Any]		 =						json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	repo_type='dataset'						)      ,	'r'						)						)
 __lowerCamelCase		:							Any		 =						{int(SCREAMING_SNAKE_CASE__						): v for k, v in idalabel.items()}
 __lowerCamelCase		:							Dict		 =						idalabel
 __lowerCamelCase		:							Optional[int]		 =						{v: k for k, v in idalabel.items()}

 # load HuggingFace model
 if vit_name[-5:] == "in21k":
  __lowerCamelCase		:							Dict		 =						ViTHybridModel(SCREAMING_SNAKE_CASE__						).eval()
 else:
  __lowerCamelCase		:							Any		 =						ViTHybridForImageClassification(SCREAMING_SNAKE_CASE__						).eval()
 model.load_state_dict(SCREAMING_SNAKE_CASE__						)

 # create image processor
 __lowerCamelCase		:							Tuple		 =						create_transform(**resolve_data_config({}      ,	model=SCREAMING_SNAKE_CASE__						)						)
 __lowerCamelCase		:							str		 =						transform.transforms

 __lowerCamelCase		:							Optional[int]		 =						{
     'bilinear': PILImageResampling.BILINEAR,
     'bicubic': PILImageResampling.BICUBIC,
     'nearest': PILImageResampling.NEAREST,
 }

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

 __lowerCamelCase		:							str		 =						prepare_img()
 __lowerCamelCase		:							int		 =						transform(SCREAMING_SNAKE_CASE__						).unsqueeze(0						)
 __lowerCamelCase		:							str		 =						processor(SCREAMING_SNAKE_CASE__      ,	return_tensors='pt'						).pixel_values

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

 # verify logits
 with torch.no_grad():
  __lowerCamelCase		:							int		 =						model(SCREAMING_SNAKE_CASE__						)
  __lowerCamelCase		:							Optional[int]		 =						outputs.logits

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

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

 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__":
       lowercase_	   =     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.'
       )

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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase_	   =     {'configuration_mbart': ['MBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MBartConfig', 'MBartOnnxConfig']}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     ['MBartTokenizer']

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     ['MBartTokenizerFast']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'MBART_PRETRAINED_MODEL_ARCHIVE_LIST',
           'MBartForCausalLM',
           'MBartForConditionalGeneration',
           'MBartForQuestionAnswering',
           'MBartForSequenceClassification',
           'MBartModel',
           'MBartPreTrainedModel',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'TFMBartForConditionalGeneration',
           'TFMBartModel',
           'TFMBartPreTrainedModel',
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'FlaxMBartForConditionalGeneration',
           'FlaxMBartForQuestionAnswering',
           'FlaxMBartForSequenceClassification',
           'FlaxMBartModel',
           'FlaxMBartPreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart import MBartTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_mbart_fast import MBartTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mbart import (
                  MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MBartForCausalLM,
                  MBartForConditionalGeneration,
                  MBartForQuestionAnswering,
                  MBartForSequenceClassification,
                  MBartModel,
                  MBartPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_mbart import (
                  FlaxMBartForConditionalGeneration,
                  FlaxMBartForQuestionAnswering,
                  FlaxMBartForSequenceClassification,
                  FlaxMBartModel,
                  FlaxMBartPreTrainedModel,
              )

else:
       import sys

       lowercase_	   =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

