



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





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase=False						)						->					Any:
      snake_case__    :       List[str]       		= []

      # 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__    :       Tuple       		= [(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 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase=False						)						->					List[Any]:
      for i in range(config.num_hidden_layers						):
            if base_model:
                  snake_case__    :       Optional[Any]       		= """"""
            else:
                  snake_case__    :       Dict       		= """vit."""
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            snake_case__    :       Union[str, Any]       		= state_dict.pop(f"blocks.{i}.attn.qkv.weight"						)
            snake_case__    :       List[str]       		= state_dict.pop(f"blocks.{i}.attn.qkv.bias"						)
            # next, add query, keys and values (in that order) to the state dict
            snake_case__    :       Optional[Any]       		= in_proj_weight[
                : config.hidden_size, :
            ]
            snake_case__    :       str       		= in_proj_bias[: config.hidden_size]
            snake_case__    :       List[Any]       		= in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            snake_case__    :       Tuple       		= in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            snake_case__    :       int       		= in_proj_weight[
                -config.hidden_size :, :
            ]
            snake_case__    :       Tuple       		= in_proj_bias[-config.hidden_size :]





def 		__snake_case(							_lowerCAmelCase						)						->					Any:
      snake_case__    :       Any       		= ["""head.weight""", """head.bias"""]
      for k in ignore_keys:
            state_dict.pop(_lowerCAmelCase	,  _lowerCAmelCase						)





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase						)						->					Any:
      snake_case__    :       str       		= dct.pop(_lowerCAmelCase						)
      snake_case__    :       Tuple       		= val





def 		__snake_case(							)						->					List[str]:
      snake_case__    :       Tuple       		= """http://images.cocodataset.org/val2017/000000039769.jpg"""
      snake_case__    :       Optional[Any]       		= Image.open(requests.get(_lowerCAmelCase	,  stream=_lowerCAmelCase						).raw						)
      return im





@torch.no_grad()
def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase=False						)						->					List[str]:
      snake_case__    :       Tuple       		= BitConfig(
          global_padding="""same"""	,  layer_type="""bottleneck"""	,  depths=(3, 4, 9)	,  out_features=["""stage3"""]	,  embedding_dynamic_padding=_lowerCAmelCase	,  )
      snake_case__    :       str       		= ViTHybridConfig(backbone_config=_lowerCAmelCase	,  image_size=384	,  num_labels=1_000						)
      snake_case__    :       str       		= False

      # load original model from timm
      snake_case__    :       Tuple       		= timm.create_model(_lowerCAmelCase	,  pretrained=_lowerCAmelCase						)
      timm_model.eval()

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

      snake_case__    :       List[str]       		= """huggingface/label-files"""
      snake_case__    :       int       		= """imagenet-1k-id2label.json"""
      snake_case__    :       List[Any]       		= json.load(open(hf_hub_download(_lowerCAmelCase	,  _lowerCAmelCase	,  repo_type="""dataset"""						)	,  """r"""						)						)
      snake_case__    :       Union[str, Any]       		= {int(_lowerCAmelCase						): v for k, v in idalabel.items()}
      snake_case__    :       Optional[int]       		= idalabel
      snake_case__    :       str       		= {v: k for k, v in idalabel.items()}

      # load HuggingFace model
      if vit_name[-5:] == "in21k":
            snake_case__    :       Any       		= ViTHybridModel(_lowerCAmelCase						).eval()
      else:
            snake_case__    :       int       		= ViTHybridForImageClassification(_lowerCAmelCase						).eval()
      model.load_state_dict(_lowerCAmelCase						)

      # create image processor
      snake_case__    :       Any       		= create_transform(**resolve_data_config({}	,  model=_lowerCAmelCase						)						)
      snake_case__    :       str       		= transform.transforms

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

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

      snake_case__    :       Any       		= prepare_img()
      snake_case__    :       Optional[Any]       		= transform(_lowerCAmelCase						).unsqueeze(0						)
      snake_case__    :       Tuple       		= processor(_lowerCAmelCase	,  return_tensors="""pt"""						).pixel_values

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

      # verify logits
      with torch.no_grad():
            snake_case__    :       List[Any]       		= model(_lowerCAmelCase						)
            snake_case__    :       Optional[Any]       		= outputs.logits

      print("""Predicted class:"""	,  logits.argmax(-1						).item()						)
      if base_model:
            snake_case__    :       Tuple       		= timm_model.forward_features(_lowerCAmelCase						)
            assert timm_pooled_output.shape == outputs.pooler_output.shape
            assert torch.allclose(_lowerCAmelCase	,  outputs.pooler_output	,  atol=1e-3						)
      else:
            snake_case__    :       List[Any]       		= timm_model(_lowerCAmelCase						)
            assert timm_logits.shape == outputs.logits.shape
            assert torch.allclose(_lowerCAmelCase	,  outputs.logits	,  atol=1e-3						)
      print("""Looks ok!"""						)

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

      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__":
       __a  			=						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."
       )

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





'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a  			=						{
    "configuration_bigbird_pegasus": [
        "BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BigBirdPegasusConfig",
        "BigBirdPegasusOnnxConfig",
    ],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __a  			=						[
           "BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST",
           "BigBirdPegasusForCausalLM",
           "BigBirdPegasusForConditionalGeneration",
           "BigBirdPegasusForQuestionAnswering",
           "BigBirdPegasusForSequenceClassification",
           "BigBirdPegasusModel",
           "BigBirdPegasusPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_bigbird_pegasus import (
           BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
           BigBirdPegasusConfig,
           BigBirdPegasusOnnxConfig,
       )

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bigbird_pegasus import (
                  BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BigBirdPegasusForCausalLM,
                  BigBirdPegasusForConditionalGeneration,
                  BigBirdPegasusForQuestionAnswering,
                  BigBirdPegasusForSequenceClassification,
                  BigBirdPegasusModel,
                  BigBirdPegasusPreTrainedModel,
              )


else:
       import sys

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