


'''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_						:       int      			=				logging.get_logger(__name__)





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





def __snake_case      (   UpperCAmelCase_		:		Dict      ,       UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Optional[int]	):
 lowerCamelCase_  				=						dct.pop(UpperCAmelCase_	)
 lowerCamelCase_  				=						val





def __snake_case      (   ):
 lowerCamelCase_  				=						"http://images.cocodataset.org/val2017/000000039769.jpg"
 lowerCamelCase_  				=						Image.open(requests.get(UpperCAmelCase_      ,       stream=UpperCAmelCase_	).raw	)
 return im





@torch.no_grad()
def __snake_case      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		List[str]	):
 lowerCamelCase_  				=						DeiTConfig()
 # all deit models have fine-tuned heads
 lowerCamelCase_  				=						False
 # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
 lowerCamelCase_  				=						1000
 lowerCamelCase_  				=						"huggingface/label-files"
 lowerCamelCase_  				=						"imagenet-1k-id2label.json"
 lowerCamelCase_  				=						json.load(open(hf_hub_download(UpperCAmelCase_      ,       UpperCAmelCase_      ,       repo_type="dataset"	)      ,       "r"	)	)
 lowerCamelCase_  				=						{int(UpperCAmelCase_	): v for k, v in idalabel.items()}
 lowerCamelCase_  				=						idalabel
 lowerCamelCase_  				=						{v: k for k, v in idalabel.items()}
 lowerCamelCase_  				=						int(deit_name[-6:-4]	)
 lowerCamelCase_  				=						int(deit_name[-3:]	)
 # size of the architecture
 if deit_name[9:].startswith("tiny"	):
  lowerCamelCase_  				=						192
  lowerCamelCase_  				=						768
  lowerCamelCase_  				=						12
  lowerCamelCase_  				=						3
 elif deit_name[9:].startswith("small"	):
  lowerCamelCase_  				=						384
  lowerCamelCase_  				=						1536
  lowerCamelCase_  				=						12
  lowerCamelCase_  				=						6
 if deit_name[9:].startswith("base"	):
  pass
 elif deit_name[4:].startswith("large"	):
  lowerCamelCase_  				=						1024
  lowerCamelCase_  				=						4096
  lowerCamelCase_  				=						24
  lowerCamelCase_  				=						16

 # load original model from timm
 lowerCamelCase_  				=						timm.create_model(UpperCAmelCase_      ,       pretrained=UpperCAmelCase_	)
 timm_model.eval()

 # load state_dict of original model, remove and rename some keys
 lowerCamelCase_  				=						timm_model.state_dict()
 lowerCamelCase_  				=						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_  				=						DeiTForImageClassificationWithTeacher(UpperCAmelCase_	).eval()
 model.load_state_dict(UpperCAmelCase_	)

 # Check outputs on an image, prepared by DeiTImageProcessor
 lowerCamelCase_  				=						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
 lowerCamelCase_  				=						DeiTImageProcessor(size=UpperCAmelCase_      ,       crop_size=config.image_size	)
 lowerCamelCase_  				=						image_processor(images=prepare_img()      ,       return_tensors="pt"	)
 lowerCamelCase_  				=						encoding["pixel_values"]
 lowerCamelCase_  				=						model(UpperCAmelCase_	)

 lowerCamelCase_  				=						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__":
     a_						:       Union[str, Any]      			=				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_						:       List[str]      			=				parser.parse_args()
     convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)







'''simple docstring'''

from collections.abc import Sequence





def __snake_case      (   UpperCAmelCase_		:		Sequence[int] | None = None	):
 if nums is None or not nums:
  raise ValueError("Input sequence should not be empty"	)

 lowerCamelCase_  				=						nums[0]
 for i in range(1      ,       len(UpperCAmelCase_	)	):
  lowerCamelCase_  				=						nums[i]
  lowerCamelCase_  				=						max(UpperCAmelCase_      ,       ans + num      ,       UpperCAmelCase_	)

 return ans


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     # Try on a sample input from the user
     a_						:       Any      			=				int(input("""Enter number of elements : """).strip())
     a_						:       Union[str, Any]      			=				list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
     print(max_subsequence_sum(array))



