


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					:       str			    =     logging.get_logger(__name__)



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



def UpperCamelCase_				(				__a			,				__a			,				__a      )	->   int:
      a__	:  List[str]     =   dct.pop(__a      )
      a__	:  List[Any]     =   val



def UpperCamelCase_				(				)	->   Optional[Any]:
      a__	:  str     =   "http://images.cocodataset.org/val2017/000000039769.jpg"
      a__	:  Dict     =   Image.open(requests.get(__a			,				stream=__a      ).raw      )
      return im



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

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

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

      # load HuggingFace model
      a__	:  Optional[int]     =   DeiTForImageClassificationWithTeacher(__a      ).eval()
      model.load_state_dict(__a      )

      # Check outputs on an image, prepared by DeiTImageProcessor
      a__	:  int     =   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
      a__	:  Tuple     =   DeiTImageProcessor(size=__a			,				crop_size=config.image_size      )
      a__	:  Optional[int]     =   image_processor(images=prepare_img()			,				return_tensors="pt"      )
      a__	:  Optional[int]     =   encoding["pixel_values"]
      a__	:  Any     =   model(__a      )

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

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


if __name__ == "__main__":
    UpperCamelCase					:       Tuple			    =     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					:       Optional[int]			    =     parser.parse_args()
    convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)









'''simple docstring'''



from timeit import timeit

__UpperCamelCase   :							int         =   {
    """MALAYALAM""": True,
    """String""": False,
    """rotor""": True,
    """level""": True,
    """A""": True,
    """BB""": True,
    """ABC""": False,
    """amanaplanacanalpanama""": True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   __a					  =     0
   __a					  =     len(SCREAMING_SNAKE_CASE__     ) - 1
   while start_i < end_i:
      if s[start_i] == s[end_i]:
         start_i += 1
         end_i -= 1
      else:
         return False
   return True



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   __a					  =     len(SCREAMING_SNAKE_CASE__     ) // 2
   __a					  =     len(SCREAMING_SNAKE_CASE__     )

   # We need to traverse till half of the length of string
   # as we can get access of the i'th last element from
   # i'th index.
   # eg: [0,1,2,3,4,5] => 4th index can be accessed
   # with the help of 1st index (i==n-i-1)
   # where n is length of string
   return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE__     )     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   if len(SCREAMING_SNAKE_CASE__     ) <= 2:
      return True
   if s[0] == s[len(SCREAMING_SNAKE_CASE__     ) - 1]:
      return is_palindrome_recursive(s[1:-1]     )
   else:
      return False



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      bool:

   """simple docstring"""
   return s == s[::-1]



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str     )	->      None:

   """simple docstring"""
   __a					  =     f"""all({name}(key) is value for key, value in test_data.items())"""
   __a					  =     f"""from __main__ import test_data, {name}"""
   __a					  =     500000
   __a					  =     timeit(stmt=SCREAMING_SNAKE_CASE__,       setup=SCREAMING_SNAKE_CASE__,       number=SCREAMING_SNAKE_CASE__     )
   print(f"""{name:<35} finished {number:,} runs in {result:.5f} seconds"""     )


if __name__ == "__main__":
 for key, value in test_data.items():
  assert is_palindrome(key) is is_palindrome_recursive(key)
  assert is_palindrome(key) is is_palindrome_slice(key)
  print(f"""{key:21} {value}""")
 print("""a man a plan a canal panama""")

 # finished 500,000 runs in 0.46793 seconds
 benchmark_function("""is_palindrome_slice""")
 # finished 500,000 runs in 0.85234 seconds
 benchmark_function("""is_palindrome""")
 # finished 500,000 runs in 1.32028 seconds
 benchmark_function("""is_palindrome_recursive""")
 # finished 500,000 runs in 2.08679 seconds
 benchmark_function("""is_palindrome_traversal""")