



'''simple docstring'''





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


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





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> Any:
      if "xprophetnet" in prophetnet_checkpoint_path:
            snake_case__  :    Any    						=      XLMProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase		)
            snake_case__						,				snake_case__  :    Union[str, Any]    						=      XLMProphetNetForConditionalGeneration.from_pretrained(
                _lowerCAmelCase     ,   output_loading_info=_lowerCAmelCase		)
      else:
            snake_case__  :    str    						=      ProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase		)
            snake_case__						,				snake_case__  :    Tuple    						=      ProphetNetForConditionalGeneration.from_pretrained(
                _lowerCAmelCase     ,   output_loading_info=_lowerCAmelCase		)

      snake_case__  :    List[Any]    						=      ["""key_proj""", """value_proj""", """query_proj"""]

      snake_case__  :    Optional[Any]    						=      {
          """self_attn""": """ngram_self_attn""",
          """cross_attn""": """encoder_attn""",
          """cross_attn_layer_norm""": """encoder_attn_layer_norm""",
          """feed_forward_layer_norm""": """final_layer_norm""",
          """feed_forward""": """""",
          """intermediate""": """fc1""",
          """output""": """fc2""",
          """key_proj""": """k_proj""",
          """query_proj""": """q_proj""",
          """value_proj""": """v_proj""",
          """word_embeddings""": """embed_tokens""",
          """embeddings_layer_norm""": """emb_layer_norm""",
          """relative_pos_embeddings""": """relative_linear""",
          """ngram_embeddings""": """ngram_input_embed""",
          """position_embeddings""": """embed_positions""",
      }

      for key in loading_info["missing_keys"]:
            snake_case__  :    Optional[Any]    						=      key.split("""."""		)

            if attributes[0] == "lm_head":
                  snake_case__  :    List[str]    						=      prophet
                  snake_case__  :    Optional[int]    						=      prophet_old
            else:
                  snake_case__  :    int    						=      prophet.prophetnet
                  snake_case__  :    Tuple    						=      prophet_old.model

            snake_case__  :    Optional[Any]    						=      False
            for attribute in attributes:
                  if attribute in mapping:
                        snake_case__  :    List[str]    						=      mapping[attribute]
                        if not hasattr(_lowerCAmelCase     ,   _lowerCAmelCase		) and len(_lowerCAmelCase		) > 0:
                              snake_case__  :    int    						=      attribute
                  elif hasattr(_lowerCAmelCase     ,   _lowerCAmelCase		):
                        snake_case__  :    List[str]    						=      attribute

                  if attribute == "weight":
                        assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                        snake_case__  :    Dict    						=      old_model.weight
                        logger.info(f"{attribute} is initialized."		)
                        snake_case__  :    str    						=      True
                        break
                  elif attribute == "bias":
                        assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                        snake_case__  :    Optional[int]    						=      old_model.bias
                        logger.info(f"{attribute} is initialized"		)
                        snake_case__  :    str    						=      True
                        break
                  elif attribute in special_keys and hasattr(_lowerCAmelCase     ,   """in_proj_weight"""		):
                        snake_case__  :    Union[str, Any]    						=      old_model.in_proj_weight.shape[0] // 3
                        snake_case__  :    int    						=      getattr(_lowerCAmelCase     ,   _lowerCAmelCase		)
                        param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
                        param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
                        if attribute == "query_proj":
                              snake_case__  :    int    						=      nn.Parameter(old_model.in_proj_weight[:embed_dim, :]		)
                              snake_case__  :    Dict    						=      nn.Parameter(old_model.in_proj_bias[:embed_dim]		)

                        elif attribute == "key_proj":
                              snake_case__  :    Union[str, Any]    						=      nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]		)
                              snake_case__  :    List[str]    						=      nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]		)
                        elif attribute == "value_proj":
                              snake_case__  :    Dict    						=      nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]		)
                              snake_case__  :    Optional[Any]    						=      nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]		)
                        snake_case__  :    Optional[Any]    						=      True
                        break
                  elif attribute == "position_embeddings":
                        assert (
                            model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
                        ), "Hidden size has to match"
                        assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
                        snake_case__  :    Any    						=      nn.Parameter(old_model.embed_positions.weight[:512, :]		)
                        snake_case__  :    List[str]    						=      True
                        break

                  if attribute.isdigit():
                        snake_case__  :    Optional[int]    						=      model[int(_lowerCAmelCase		)]
                        snake_case__  :    Dict    						=      old_model[int(_lowerCAmelCase		)]
                  else:
                        snake_case__  :    Optional[int]    						=      getattr(_lowerCAmelCase     ,   _lowerCAmelCase		)

                        if old_attribute == "":
                              snake_case__  :    List[Any]    						=      old_model
                        else:
                              if not hasattr(_lowerCAmelCase     ,   _lowerCAmelCase		):
                                    raise ValueError(f"{old_model} does not have {old_attribute}"		)
                              snake_case__  :    str    						=      getattr(_lowerCAmelCase     ,   _lowerCAmelCase		)

            if not is_key_init:
                  raise ValueError(f"{key} was not correctly initialized!"		)

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


if __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--prophetnet_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       __a          =		parser.parse_args()
       convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





'''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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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





def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase=False		)		-> str:
      snake_case__  :    Union[str, Any]    						=      []
      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"deit.encoder.layer.{i}.layernorm_before.weight")		)
            rename_keys.append((f"blocks.{i}.norm1.bias", f"deit.encoder.layer.{i}.layernorm_before.bias")		)
            rename_keys.append((f"blocks.{i}.attn.proj.weight", f"deit.encoder.layer.{i}.attention.output.dense.weight")		)
            rename_keys.append((f"blocks.{i}.attn.proj.bias", f"deit.encoder.layer.{i}.attention.output.dense.bias")		)
            rename_keys.append((f"blocks.{i}.norm2.weight", f"deit.encoder.layer.{i}.layernorm_after.weight")		)
            rename_keys.append((f"blocks.{i}.norm2.bias", f"deit.encoder.layer.{i}.layernorm_after.bias")		)
            rename_keys.append((f"blocks.{i}.mlp.fc1.weight", f"deit.encoder.layer.{i}.intermediate.dense.weight")		)
            rename_keys.append((f"blocks.{i}.mlp.fc1.bias", f"deit.encoder.layer.{i}.intermediate.dense.bias")		)
            rename_keys.append((f"blocks.{i}.mlp.fc2.weight", f"deit.encoder.layer.{i}.output.dense.weight")		)
            rename_keys.append((f"blocks.{i}.mlp.fc2.bias", f"deit.encoder.layer.{i}.output.dense.bias")		)

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

      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 "deit" from all keys that start with "deit"
            snake_case__  :    List[Any]    						=      [(pair[0], pair[1][4:]) if pair[1].startswith("""deit"""		) else pair for pair in rename_keys]
      else:
            # layernorm + classification heads
            rename_keys.extend(
                [
                    ("""norm.weight""", """deit.layernorm.weight"""),
                    ("""norm.bias""", """deit.layernorm.bias"""),
                    ("""head.weight""", """cls_classifier.weight"""),
                    ("""head.bias""", """cls_classifier.bias"""),
                    ("""head_dist.weight""", """distillation_classifier.weight"""),
                    ("""head_dist.bias""", """distillation_classifier.bias"""),
                ]		)

      return rename_keys





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





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





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





@torch.no_grad()
def 						__snake_case(				_lowerCAmelCase     ,   _lowerCAmelCase		)		-> str:
      snake_case__  :    Optional[int]    						=      DeiTConfig()
      # all deit models have fine-tuned heads
      snake_case__  :    Union[str, Any]    						=      False
      # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
      snake_case__  :    int    						=      1_000
      snake_case__  :    Any    						=      """huggingface/label-files"""
      snake_case__  :    Optional[Any]    						=      """imagenet-1k-id2label.json"""
      snake_case__  :    Tuple    						=      json.load(open(hf_hub_download(_lowerCAmelCase     ,   _lowerCAmelCase     ,   repo_type="""dataset"""		)     ,   """r"""		)		)
      snake_case__  :    List[Any]    						=      {int(_lowerCAmelCase		): v for k, v in idalabel.items()}
      snake_case__  :    List[Any]    						=      idalabel
      snake_case__  :    List[str]    						=      {v: k for k, v in idalabel.items()}
      snake_case__  :    Tuple    						=      int(deit_name[-6:-4]		)
      snake_case__  :    Optional[Any]    						=      int(deit_name[-3:]		)
      # size of the architecture
      if deit_name[9:].startswith("""tiny"""		):
            snake_case__  :    Tuple    						=      192
            snake_case__  :    Union[str, Any]    						=      768
            snake_case__  :    Tuple    						=      12
            snake_case__  :    Union[str, Any]    						=      3
      elif deit_name[9:].startswith("""small"""		):
            snake_case__  :    str    						=      384
            snake_case__  :    Any    						=      1_536
            snake_case__  :    str    						=      12
            snake_case__  :    int    						=      6
      if deit_name[9:].startswith("""base"""		):
            pass
      elif deit_name[4:].startswith("""large"""		):
            snake_case__  :    Union[str, Any]    						=      1_024
            snake_case__  :    Any    						=      4_096
            snake_case__  :    List[Any]    						=      24
            snake_case__  :    Tuple    						=      16

      # load original model from timm
      snake_case__  :    List[Any]    						=      timm.create_model(_lowerCAmelCase     ,   pretrained=_lowerCAmelCase		)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      snake_case__  :    Optional[Any]    						=      timm_model.state_dict()
      snake_case__  :    Optional[int]    						=      create_rename_keys(_lowerCAmelCase     ,   _lowerCAmelCase		)
      for src, dest in rename_keys:
            rename_key(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)
      read_in_q_k_v(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase		)

      # load HuggingFace model
      snake_case__  :    Optional[Any]    						=      DeiTForImageClassificationWithTeacher(_lowerCAmelCase		).eval()
      model.load_state_dict(_lowerCAmelCase		)

      # Check outputs on an image, prepared by DeiTImageProcessor
      snake_case__  :    List[Any]    						=      int(
          (256 / 224) * config.image_size		)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
      snake_case__  :    Optional[Any]    						=      DeiTImageProcessor(size=_lowerCAmelCase     ,   crop_size=config.image_size		)
      snake_case__  :    str    						=      image_processor(images=prepare_img()     ,   return_tensors="""pt"""		)
      snake_case__  :    Optional[Any]    						=      encoding["""pixel_values"""]
      snake_case__  :    Tuple    						=      model(_lowerCAmelCase		)

      snake_case__  :    Optional[int]    						=      timm_model(_lowerCAmelCase		)
      assert timm_logits.shape == outputs.logits.shape
      assert torch.allclose(_lowerCAmelCase     ,   outputs.logits     ,   atol=1e-3		)

      Path(_lowerCAmelCase		).mkdir(exist_ok=_lowerCAmelCase		)
      print(f"Saving model {deit_name} to {pytorch_dump_folder_path}"		)
      model.save_pretrained(_lowerCAmelCase		)
      print(f"Saving image processor to {pytorch_dump_folder_path}"		)
      image_processor.save_pretrained(_lowerCAmelCase		)


if __name__ == "__main__":
       __a          =		argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--deit_name",
           default="vit_deit_base_distilled_patch16_224",
           type=str,
           help="Name of the DeiT 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."
       )

       __a          =		parser.parse_args()
       convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)
