


"""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()
__UpperCAmelCase     					=   logging.get_logger(__name__)





def      lowercase__						(	lowerCamelCase  :	List[str]					,						lowerCamelCase  :	Union[str, Any]=False					)       ->  str:
  lowerCAmelCase__     :     int										=       []
  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"
    lowerCAmelCase__     :     Dict										=       [(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      lowercase__						(	lowerCamelCase  :	Optional[int]					,						lowerCamelCase  :	Dict					,						lowerCamelCase  :	Tuple=False					)       ->  List[str]:





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





def      lowercase__						(	lowerCamelCase  :	Union[str, Any]					,						lowerCamelCase  :	Optional[Any]					,						lowerCamelCase  :	int					)       ->  Optional[int]:
  lowerCAmelCase__     :     Tuple										=       dct.pop(UpperCAmelCase_					)
  lowerCAmelCase__     :     List[str]										=       val





def      lowercase__						(	)       ->  List[str]:
  lowerCAmelCase__     :     str										=       "http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCAmelCase__     :     int										=       Image.open(requests.get(UpperCAmelCase_					,						stream=UpperCAmelCase_					).raw					)
  return im





@torch.no_grad()
def      lowercase__						(	lowerCamelCase  :	Dict					,						lowerCamelCase  :	Union[str, Any]					)       ->  Optional[Any]:
  lowerCAmelCase__     :     Tuple										=       DeiTConfig()
  # all deit models have fine-tuned heads
  lowerCAmelCase__     :     Optional[Any]										=       False
  # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
  lowerCAmelCase__     :     Union[str, Any]										=       1_0_0_0
  lowerCAmelCase__     :     Dict										=       "huggingface/label-files"
  lowerCAmelCase__     :     Optional[Any]										=       "imagenet-1k-id2label.json"
  lowerCAmelCase__     :     Dict										=       json.load(open(hf_hub_download(UpperCAmelCase_					,						UpperCAmelCase_					,						repo_type="dataset"					)					,						"r"					)					)
  lowerCAmelCase__     :     Dict										=       {int(UpperCAmelCase_					): v for k, v in idalabel.items()}
  lowerCAmelCase__     :     int										=       idalabel
  lowerCAmelCase__     :     Any										=       {v: k for k, v in idalabel.items()}
  lowerCAmelCase__     :     Any										=       int(deit_name[-6:-4]					)
  lowerCAmelCase__     :     Tuple										=       int(deit_name[-3:]					)
  # size of the architecture
  if deit_name[9:].startswith("tiny"					):
    lowerCAmelCase__     :     Any										=       1_9_2
    lowerCAmelCase__     :     int										=       7_6_8
    lowerCAmelCase__     :     str										=       1_2
    lowerCAmelCase__     :     Dict										=       3
  elif deit_name[9:].startswith("small"					):
    lowerCAmelCase__     :     Dict										=       3_8_4
    lowerCAmelCase__     :     str										=       1_5_3_6
    lowerCAmelCase__     :     Optional[Any]										=       1_2
    lowerCAmelCase__     :     int										=       6
  if deit_name[9:].startswith("base"					):
    pass
  elif deit_name[4:].startswith("large"					):
    lowerCAmelCase__     :     List[str]										=       1_0_2_4
    lowerCAmelCase__     :     List[str]										=       4_0_9_6
    lowerCAmelCase__     :     Union[str, Any]										=       2_4
    lowerCAmelCase__     :     List[Any]										=       1_6

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

  # load state_dict of original model, remove and rename some keys
  lowerCAmelCase__     :     List[Any]										=       timm_model.state_dict()
  lowerCAmelCase__     :     str										=       create_rename_keys(UpperCAmelCase_					,						UpperCAmelCase_					)
  for src, dest in rename_keys:
    rename_key(UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_					)
  read_in_q_k_v(UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_					)

  # load HuggingFace model
  lowerCAmelCase__     :     Union[str, Any]										=       DeiTForImageClassificationWithTeacher(UpperCAmelCase_					).eval()
  model.load_state_dict(UpperCAmelCase_					)

  # Check outputs on an image, prepared by DeiTImageProcessor
  lowerCAmelCase__     :     str										=       int(
      (2_5_6 / 2_2_4) * config.image_size					)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
  lowerCAmelCase__     :     Dict										=       DeiTImageProcessor(size=UpperCAmelCase_					,						crop_size=config.image_size					)
  lowerCAmelCase__     :     Union[str, Any]										=       image_processor(images=prepare_img()					,						return_tensors="pt"					)
  lowerCAmelCase__     :     int										=       encoding["pixel_values"]
  lowerCAmelCase__     :     Tuple										=       model(UpperCAmelCase_					)

  lowerCAmelCase__     :     List[str]										=       timm_model(UpperCAmelCase_					)
  assert timm_logits.shape == outputs.logits.shape
  assert torch.allclose(UpperCAmelCase_					,						outputs.logits					,						atol=1e-3					)

  Path(UpperCAmelCase_					).mkdir(exist_ok=UpperCAmelCase_					)
  print(F"Saving model {deit_name} 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__":
   __UpperCAmelCase     					=   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."
   )

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





"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase     =							{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase     =							[
			    """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """WavLMForAudioFrameClassification""",
			    """WavLMForCTC""",
			    """WavLMForSequenceClassification""",
			    """WavLMForXVector""",
			    """WavLMModel""",
			    """WavLMPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavlm import (
						    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavLMForAudioFrameClassification,
						    WavLMForCTC,
						    WavLMForSequenceClassification,
						    WavLMForXVector,
						    WavLMModel,
						    WavLMPreTrainedModel,
						)

else:
			import sys

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




