



"""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()
UpperCAmelCase  :						List[Any]	=       logging.get_logger(__name__)


def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase       ,     __lowerCAmelCase=False							)       ->  Any:







  '''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 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase=False							)       ->  str:







  '''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 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase							)       ->  Union[str, Any]:







  '''simple docstring'''


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


def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase							)       ->  Any:







  '''simple docstring'''


  lowercase_	    =       dct.pop(__snake_case							)
  lowercase_	    =       val


def 	_SCREAMING_SNAKE_CASE  ()       ->  Tuple:







  '''simple docstring'''


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


@torch.no_grad()
def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase=False							)       ->  str:







  '''simple docstring'''


  lowercase_	    =       BitConfig(
      global_padding="""same"""       ,     layer_type="""bottleneck"""       ,     depths=(3, 4, 9)       ,     out_features=["""stage3"""]       ,     embedding_dynamic_padding=__snake_case       ,     )
  lowercase_	    =       ViTHybridConfig(backbone_config=__snake_case       ,     image_size=3_84       ,     num_labels=10_00							)
  lowercase_	    =       False

  # load original model from timm
  lowercase_	    =       timm.create_model(__snake_case       ,     pretrained=__snake_case							)
  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_(__snake_case							)
  lowercase_	    =       create_rename_keys(__snake_case       ,     __snake_case							)
  for src, dest in rename_keys:
    rename_key(__snake_case       ,     __snake_case       ,     __snake_case							)
  read_in_q_k_v(__snake_case       ,     __snake_case       ,     __snake_case							)

  lowercase_	    =       """huggingface/label-files"""
  lowercase_	    =       """imagenet-1k-id2label.json"""
  lowercase_	    =       json.load(open(hf_hub_download(__snake_case       ,     __snake_case       ,     repo_type="""dataset"""							)       ,     """r"""							)							)
  lowercase_	    =       {int(__snake_case							): 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(__snake_case							).eval()
  else:
    lowercase_	    =       ViTHybridForImageClassification(__snake_case							).eval()
  model.load_state_dict(__snake_case							)

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

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

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

  lowercase_	    =       prepare_img()
  lowercase_	    =       transform(__snake_case							).unsqueeze(0							)
  lowercase_	    =       processor(__snake_case       ,     return_tensors="""pt"""							).pixel_values

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

  # verify logits
  with torch.no_grad():
    lowercase_	    =       model(__snake_case							)
    lowercase_	    =       outputs.logits

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

  if pytorch_dump_folder_path is not None:
    Path(__snake_case							).mkdir(exist_ok=__snake_case							)
    print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}'''							)
    model.save_pretrained(__snake_case							)
    print(F'''Saving processor to {pytorch_dump_folder_path}'''							)
    processor.save_pretrained(__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__":
       UpperCAmelCase  :						Union[str, Any]	=       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  :						Optional[Any]	=       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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_a 						=      {
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a 						=      ["LxmertTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a 						=      [
			    "LxmertEncoder",
			    "LxmertForPreTraining",
			    "LxmertForQuestionAnswering",
			    "LxmertModel",
			    "LxmertPreTrainedModel",
			    "LxmertVisualFeatureEncoder",
			    "LxmertXLayer",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a 						=      [
			    "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFLxmertForPreTraining",
			    "TFLxmertMainLayer",
			    "TFLxmertModel",
			    "TFLxmertPreTrainedModel",
			    "TFLxmertVisualFeatureEncoder",
			]


if TYPE_CHECKING:
			from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
			from .tokenization_lxmert import LxmertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_lxmert_fast import LxmertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_lxmert import (
						    LxmertEncoder,
						    LxmertForPreTraining,
						    LxmertForQuestionAnswering,
						    LxmertModel,
						    LxmertPreTrainedModel,
						    LxmertVisualFeatureEncoder,
						    LxmertXLayer,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_lxmert import (
						    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFLxmertForPreTraining,
						    TFLxmertMainLayer,
						    TFLxmertModel,
						    TFLxmertPreTrainedModel,
						    TFLxmertVisualFeatureEncoder,
						)

else:
			import sys

			_a 						=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



