




"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


A_						=				r'''
    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and
    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.

    Args:
        title_sep (`str`, *optional*, defaults to  `" / "`):
            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].
        doc_sep (`str`, *optional*, defaults to  `" // "`):
            Separator inserted between the text of the retrieved document and the original input when calling
            [`RagRetriever`].
        n_docs (`int`, *optional*, defaults to 5):
            Number of documents to retrieve.
        max_combined_length (`int`, *optional*, defaults to 300):
            Max length of contextualized input returned by [`~RagRetriever.__call__`].
        retrieval_vector_size (`int`, *optional*, defaults to 768):
            Dimensionality of the document embeddings indexed by [`RagRetriever`].
        retrieval_batch_size (`int`, *optional*, defaults to 8):
            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated
            [`RagRetriever`].
        dataset (`str`, *optional*, defaults to `"wiki_dpr"`):
            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids
            using `datasets.list_datasets()`).
        dataset_split (`str`, *optional*, defaults to `"train"`)
            Which split of the `dataset` to load.
        index_name (`str`, *optional*, defaults to `"compressed"`)
            The index name of the index associated with the `dataset`. One can choose between `"legacy"`, `"exact"` and
            `"compressed"`.
        index_path (`str`, *optional*)
            The path to the serialized faiss index on disk.
        passages_path (`str`, *optional*):
            A path to text passages compatible with the faiss index. Required if using
            [`~models.rag.retrieval_rag.LegacyIndex`]
        use_dummy_dataset (`bool`, *optional*, defaults to `False`)
            Whether to load a "dummy" variant of the dataset specified by `dataset`.
        label_smoothing (`float`, *optional*, defaults to 0.0):
            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing
            in the loss calculation. If set to 0, no label smoothing is performed.
        do_marginalize (`bool`, *optional*, defaults to `False`):
            If `True`, the logits are marginalized over all documents by making use of
            `torch.nn.functional.log_softmax`.
        reduce_loss (`bool`, *optional*, defaults to `False`):
            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.
        do_deduplication (`bool`, *optional*, defaults to `True`):
            Whether or not to deduplicate the generations from different context documents for a given input. Has to be
            set to `False` if used while training with distributed backend.
        exclude_bos_score (`bool`, *optional*, defaults to `False`):
            Whether or not to disregard the BOS token when computing the loss.
        output_retrieved(`bool`, *optional*, defaults to `False`):
            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and
            `context_attention_mask` are returned. See returned tensors for more detail.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        forced_eos_token_id (`int`, *optional*):
            The id of the token to force as the last generated token when `max_length` is reached. Usually set to
            `eos_token_id`.
'''






@add_start_docstrings(__a )
class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					"rag"
 lowercase__ 					=					True


 def __init__(				self:		Union[str, Any],      a_:		int=None,      a_:		Tuple=True,      a_:		Optional[int]=None,      a_:		List[str]=None,      a_:		int=None,      a_:		Optional[Any]=None,      a_:		List[str]=None,      a_:		Optional[Any]=" / ",      a_:		Tuple=" // ",      a_:		List[Any]=5,      a_:		Dict=300,      a_:		Tuple=768,      a_:		Optional[Any]=8,      a_:		int="wiki_dpr",      a_:		Any="train",      a_:		Optional[int]="compressed",      a_:		Optional[int]=None,      a_:		List[Any]=None,      a_:		Optional[Any]=False,      a_:		str=False,      a_:		Dict=0.0,      a_:		Union[str, Any]=True,      a_:		Union[str, Any]=False,      a_:		str=False,      a_:		List[str]=False,      a_:		Union[str, Any]=True,      a_:		Any=None,      **a_:		List[Any],      ):




   '''simple docstring'''



   super().__init__(
       bos_token_id=a_,      pad_token_id=a_,      eos_token_id=a_,      decoder_start_token_id=a_,      forced_eos_token_id=a_,      is_encoder_decoder=a_,      prefix=a_,      vocab_size=a_,      **a_,      )
   assert (
       "question_encoder" in kwargs and "generator" in kwargs
   ), "Config has to be initialized with question_encoder and generator config"
   _snake_case							:		Union[str, Any]								=  kwargs.pop("""question_encoder"""		)
   _snake_case							:		List[str]								=  question_encoder_config.pop("""model_type"""		)
   _snake_case							:		Union[str, Any]								=  kwargs.pop("""generator"""		)
   _snake_case							:		Any								=  decoder_config.pop("""model_type"""		)

   from ..auto.configuration_auto import AutoConfig

   _snake_case							:		Union[str, Any]								=  AutoConfig.for_model(a_,      **a_		)
   _snake_case							:		Optional[Any]								=  AutoConfig.for_model(a_,      **a_		)

   _snake_case							:		Any								=  reduce_loss
   _snake_case							:		Optional[int]								=  label_smoothing
   _snake_case							:		Dict								=  exclude_bos_score
   _snake_case							:		int								=  do_marginalize

   _snake_case							:		Optional[Any]								=  title_sep
   _snake_case							:		Any								=  doc_sep
   _snake_case							:		List[str]								=  n_docs
   _snake_case							:		Tuple								=  max_combined_length

   _snake_case							:		Optional[Any]								=  dataset
   _snake_case							:		Union[str, Any]								=  dataset_split
   _snake_case							:		Tuple								=  index_name

   _snake_case							:		Any								=  retrieval_vector_size
   _snake_case							:		Union[str, Any]								=  retrieval_batch_size
   _snake_case							:		str								=  passages_path
   _snake_case							:		Tuple								=  index_path
   _snake_case							:		List[Any]								=  use_dummy_dataset

   _snake_case							:		Optional[Any]								=  output_retrieved

   _snake_case							:		Tuple								=  do_deduplication

   _snake_case							:		Union[str, Any]								=  use_cache

   if self.forced_eos_token_id is None:
     _snake_case							:		Dict								=  getattr(self.generator,      """forced_eos_token_id""",      a_		)


 @classmethod
 def  UpperCamelCase_   (				cls:		Any,      a_:		PretrainedConfig,      a_:		PretrainedConfig,      **a_:		Optional[Any]		):




   '''simple docstring'''



   return cls(question_encoder=question_encoder_config.to_dict(),      generator=generator_config.to_dict(),      **a_		)


 def  UpperCamelCase_   (				self:		Tuple		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  copy.deepcopy(self.__dict__		)
   _snake_case							:		List[str]								=  self.question_encoder.to_dict()
   _snake_case							:		Tuple								=  self.generator.to_dict()
   _snake_case							:		Dict								=  self.__class__.model_type
   return output








"""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 					UpperCAmelCase__       (snake_case__							:					str		,   snake_case__							:					List[str]=False    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  []
  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"
    _snake_case							:		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"""),
        ]    )

  return rename_keys


def 					UpperCAmelCase__       (snake_case__							:					Dict		,   snake_case__							:					Dict		,   snake_case__							:					List[str]=False    ):
  """simple docstring"""

  for i in range(config.num_hidden_layers    ):
    if base_model:
      _snake_case							:		List[Any]								=  """"""
    else:
      _snake_case							:		List[Any]								=  """vit."""
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _snake_case							:		Optional[Any]								=  state_dict.pop(F"module.blocks.{i}.attn.qkv.weight"    )
    _snake_case							:		Optional[Any]								=  state_dict.pop(F"module.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							:		Union[str, Any]								=  in_proj_bias[: config.hidden_size]
    _snake_case							:		Union[str, Any]								=  in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _snake_case							:		Optional[Any]								=  in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _snake_case							:		Union[str, Any]								=  in_proj_weight[
        -config.hidden_size :, :
    ]
    _snake_case							:		List[str]								=  in_proj_bias[-config.hidden_size :]


def 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		Tuple								=  ["""head.weight""", """head.bias"""]
  for k in ignore_keys:
    state_dict.pop(snake_case__		,   snake_case__    )


def 					UpperCAmelCase__       (snake_case__							:					int    ):
  """simple docstring"""

  _snake_case							:		List[str]								=  [
      """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(snake_case__		,   snake_case__    )


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					Tuple		,   snake_case__							:					int    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  dct.pop(snake_case__    )
  _snake_case							:		Union[str, Any]								=  val


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		str								=  ViTMSNConfig()
  _snake_case							:		Any								=  10_00

  _snake_case							:		Tuple								=  """datasets/huggingface/label-files"""
  _snake_case							:		Dict								=  """imagenet-1k-id2label.json"""
  _snake_case							:		int								=  json.load(open(hf_hub_download(snake_case__		,   snake_case__    )		,   """r"""    )    )
  _snake_case							:		Any								=  {int(snake_case__    ): v for k, v in idalabel.items()}
  _snake_case							:		List[Any]								=  idalabel
  _snake_case							:		str								=  {v: k for k, v in idalabel.items()}

  if "s16" in checkpoint_url:
    _snake_case							:		Tuple								=  3_84
    _snake_case							:		Dict								=  15_36
    _snake_case							:		Tuple								=  6
  elif "l16" in checkpoint_url:
    _snake_case							:		Any								=  10_24
    _snake_case							:		int								=  40_96
    _snake_case							:		str								=  24
    _snake_case							:		Optional[int]								=  16
    _snake_case							:		List[Any]								=  0.1
  elif "b4" in checkpoint_url:
    _snake_case							:		Tuple								=  4
  elif "l7" in checkpoint_url:
    _snake_case							:		int								=  7
    _snake_case							:		Dict								=  10_24
    _snake_case							:		Optional[Any]								=  40_96
    _snake_case							:		Any								=  24
    _snake_case							:		Union[str, Any]								=  16
    _snake_case							:		Optional[int]								=  0.1

  _snake_case							:		int								=  ViTMSNModel(snake_case__    )

  _snake_case							:		Optional[int]								=  torch.hub.load_state_dict_from_url(snake_case__		,   map_location="""cpu"""    )["""target_encoder"""]

  _snake_case							:		List[str]								=  ViTImageProcessor(size=config.image_size    )

  remove_projection_head(snake_case__    )
  _snake_case							:		List[str]								=  create_rename_keys(snake_case__		,   base_model=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__		,   base_model=snake_case__    )

  model.load_state_dict(snake_case__    )
  model.eval()

  _snake_case							:		Union[str, Any]								=  """http://images.cocodataset.org/val2017/000000039769.jpg"""

  _snake_case							:		Tuple								=  Image.open(requests.get(snake_case__		,   stream=snake_case__    ).raw    )
  _snake_case							:		str								=  ViTImageProcessor(
      size=config.image_size		,   image_mean=snake_case__		,   image_std=snake_case__    )
  _snake_case							:		Any								=  image_processor(images=snake_case__		,   return_tensors="""pt"""    )

  # forward pass
  torch.manual_seed(2    )
  _snake_case							:		int								=  model(**snake_case__    )
  _snake_case							:		List[Any]								=  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:
    _snake_case							:		Optional[Any]								=  torch.tensor([[-1.09_15, -1.48_76, -1.18_09]]    )
  elif "b16" in checkpoint_url:
    _snake_case							:		str								=  torch.tensor([[14.28_89, -18.90_45, 11.72_81]]    )
  elif "l16" in checkpoint_url:
    _snake_case							:		Optional[int]								=  torch.tensor([[41.50_28, -22.86_81, 45.64_75]]    )
  elif "b4" in checkpoint_url:
    _snake_case							:		List[Any]								=  torch.tensor([[-4.38_68, 5.29_32, -0.41_37]]    )
  else:
    _snake_case							:		Optional[int]								=  torch.tensor([[-0.17_92, -0.64_65, 2.42_63]]    )

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

  print(F"Saving model to {pytorch_dump_folder_path}"    )
  model.save_pretrained(snake_case__    )

  print(F"Saving image processor to {pytorch_dump_folder_path}"    )
  image_processor.save_pretrained(snake_case__    )


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)


