





'''simple docstring'''

import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=False   )    ->       Any:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							[]
   for i in range(config.num_hidden_layers   ):
      # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
      rename_keys.append((f"""module.blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")   )
      rename_keys.append((f"""module.blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")   )
      rename_keys.append(
          (f"""module.blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")   )
      rename_keys.append((f"""module.blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")   )
      rename_keys.append((f"""module.blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")   )
      rename_keys.append((f"""module.blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")   )
      rename_keys.append((f"""module.blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")   )
      rename_keys.append((f"""module.blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")   )
      rename_keys.append((f"""module.blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")   )
      rename_keys.append((f"""module.blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")   )

   # projection layer + position embeddings
   rename_keys.extend(
       [
           ("""module.cls_token""", """vit.embeddings.cls_token"""),
           ("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
           ("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
           ("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
       ]   )

   if base_model:
      # layernorm + pooler
      rename_keys.extend(
          [
              ("""module.norm.weight""", """layernorm.weight"""),
              ("""module.norm.bias""", """layernorm.bias"""),
          ]   )

      # if just the base model, we should remove "vit" from all keys that start with "vit"
      __SCREAMING_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"""),
          ]   )

   return rename_keys

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=False   )    ->       str:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase   )    ->       int:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase   )    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							[
       """module.fc.fc1.weight""",
       """module.fc.fc1.bias""",
       """module.fc.bn1.weight""",
       """module.fc.bn1.bias""",
       """module.fc.bn1.running_mean""",
       """module.fc.bn1.running_var""",
       """module.fc.bn1.num_batches_tracked""",
       """module.fc.fc2.weight""",
       """module.fc.fc2.bias""",
       """module.fc.bn2.weight""",
       """module.fc.bn2.bias""",
       """module.fc.bn2.running_mean""",
       """module.fc.bn2.running_var""",
       """module.fc.bn2.num_batches_tracked""",
       """module.fc.fc3.weight""",
       """module.fc.fc3.bias""",
   ]
   for k in ignore_keys:
      state_dict.pop(__UpperCAmelCase				,						__UpperCAmelCase   )

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							dct.pop(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							val

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Any:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							ViTMSNConfig()
   __SCREAMING_SNAKE_CASE   				=							1000

   __SCREAMING_SNAKE_CASE   				=							"""datasets/huggingface/label-files"""
   __SCREAMING_SNAKE_CASE   				=							"""imagenet-1k-id2label.json"""
   __SCREAMING_SNAKE_CASE   				=							json.load(open(hf_hub_download(__UpperCAmelCase				,						__UpperCAmelCase   )				,						"""r"""   )   )
   __SCREAMING_SNAKE_CASE   				=							{int(__UpperCAmelCase   ): v for k, v in idalabel.items()}
   __SCREAMING_SNAKE_CASE   				=							idalabel
   __SCREAMING_SNAKE_CASE   				=							{v: k for k, v in idalabel.items()}

   if "s16" in checkpoint_url:
      __SCREAMING_SNAKE_CASE   				=							384
      __SCREAMING_SNAKE_CASE   				=							1536
      __SCREAMING_SNAKE_CASE   				=							6
   elif "l16" in checkpoint_url:
      __SCREAMING_SNAKE_CASE   				=							1024
      __SCREAMING_SNAKE_CASE   				=							4096
      __SCREAMING_SNAKE_CASE   				=							24
      __SCREAMING_SNAKE_CASE   				=							16
      __SCREAMING_SNAKE_CASE   				=							0.1
   elif "b4" in checkpoint_url:
      __SCREAMING_SNAKE_CASE   				=							4
   elif "l7" in checkpoint_url:
      __SCREAMING_SNAKE_CASE   				=							7
      __SCREAMING_SNAKE_CASE   				=							1024
      __SCREAMING_SNAKE_CASE   				=							4096
      __SCREAMING_SNAKE_CASE   				=							24
      __SCREAMING_SNAKE_CASE   				=							16
      __SCREAMING_SNAKE_CASE   				=							0.1

   __SCREAMING_SNAKE_CASE   				=							ViTMSNModel(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							torch.hub.load_state_dict_from_url(__UpperCAmelCase				,						map_location="""cpu"""   )["""target_encoder"""]

   __SCREAMING_SNAKE_CASE   				=							ViTImageProcessor(size=config.image_size   )

   remove_projection_head(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							create_rename_keys(__UpperCAmelCase				,						base_model=__UpperCAmelCase   )

   for src, dest in rename_keys:
      rename_key(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )
   read_in_q_k_v(__UpperCAmelCase				,						__UpperCAmelCase				,						base_model=__UpperCAmelCase   )

   model.load_state_dict(__UpperCAmelCase   )
   model.eval()

   __SCREAMING_SNAKE_CASE   				=							"""http://images.cocodataset.org/val2017/000000039769.jpg"""

   __SCREAMING_SNAKE_CASE   				=							Image.open(requests.get(__UpperCAmelCase				,						stream=__UpperCAmelCase   ).raw   )
   __SCREAMING_SNAKE_CASE   				=							ViTImageProcessor(
       size=config.image_size				,						image_mean=__UpperCAmelCase				,						image_std=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							image_processor(images=__UpperCAmelCase				,						return_tensors="""pt"""   )

   # forward pass
   torch.manual_seed(2   )
   __SCREAMING_SNAKE_CASE   				=							model(**__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							outputs.last_hidden_state

   # The following Colab Notebook was used to generate these outputs:
   # https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
   if "s16" in checkpoint_url:
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([[-1.0_9_1_5, -1.4_8_7_6, -1.1_8_0_9]]   )
   elif "b16" in checkpoint_url:
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([[1_4.2_8_8_9, -1_8.9_0_4_5, 1_1.7_2_8_1]]   )
   elif "l16" in checkpoint_url:
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([[4_1.5_0_2_8, -2_2.8_6_8_1, 4_5.6_4_7_5]]   )
   elif "b4" in checkpoint_url:
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([[-4.3_8_6_8, 5.2_9_3_2, -0.4_1_3_7]]   )
   else:
      __SCREAMING_SNAKE_CASE   				=							torch.tensor([[-0.1_7_9_2, -0.6_4_6_5, 2.4_2_6_3]]   )

   # verify logits
   assert torch.allclose(last_hidden_state[:, 0, :3]				,						__UpperCAmelCase				,						atol=1e-4   )

   print(f"""Saving model to {pytorch_dump_folder_path}"""   )
   model.save_pretrained(__UpperCAmelCase   )

   print(f"""Saving image processor to {pytorch_dump_folder_path}"""   )
   image_processor.save_pretrained(__UpperCAmelCase   )


if __name__ == "__main__":
    a   	=    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--checkpoint_url",
        default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
        type=str,
        help="URL of the checkpoint you'd like to convert.",
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
    )

    a   	=    parser.parse_args()
    convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase							:       str					 =	{
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase							:       Optional[Any]					 =	[
							    """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """PegasusXForConditionalGeneration""",
							    """PegasusXModel""",
							    """PegasusXPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_pegasus_x import (
														    PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
														    PegasusXForConditionalGeneration,
														    PegasusXModel,
														    PegasusXPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase							:       str					 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



