
"""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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=False )  ->     Tuple:







							"""simple docstring"""


							__snake_case     			=       []
							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'''vit.encoder.layer.{i}.layernorm_before.weight''') )
														rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''') )
														rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
														rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
														rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''') )
														rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''') )
														rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
														rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
														rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''') )
														rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''') )

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ("cls_token", "vit.embeddings.cls_token"),
							        ("patch_embed.proj.weight", "vit.embeddings.patch_embeddings.projection.weight"),
							        ("patch_embed.proj.bias", "vit.embeddings.patch_embeddings.projection.bias"),
							        ("pos_embed", "vit.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 "vit" from all keys that start with "vit"
														__snake_case     			=       [(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   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE=False )  ->     str:







							"""simple docstring"""


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







							"""simple docstring"""


							__snake_case     			=       ["head.weight", "head.bias"]
							for k in ignore_keys:
														state_dict.pop(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Dict:







							"""simple docstring"""


							__snake_case     			=       dct.pop(SCREAMING_SNAKE_CASE )
							__snake_case     			=       val
def   __UpperCamelCase   (					)  ->     str:







							"""simple docstring"""


							__snake_case     			=       "http://images.cocodataset.org/val2017/000000039769.jpg"
							__snake_case     			=       Image.open(requests.get(SCREAMING_SNAKE_CASE							,    stream=SCREAMING_SNAKE_CASE ).raw )
							return im
@torch.no_grad()
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     Dict:







							"""simple docstring"""


							__snake_case     			=       ViTConfig()
							__snake_case     			=       False
							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							if vit_name[-5:] == "in21k":
														__snake_case     			=       True
														__snake_case     			=       int(vit_name[-12:-10] )
														__snake_case     			=       int(vit_name[-9:-6] )
							else:
														__snake_case     			=       10_00
														__snake_case     			=       "huggingface/label-files"
														__snake_case     			=       "imagenet-1k-id2label.json"
														__snake_case     			=       json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    repo_type="dataset" )							,    "r" ) )
														__snake_case     			=       {int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
														__snake_case     			=       idalabel
														__snake_case     			=       {v: k for k, v in idalabel.items()}
														__snake_case     			=       int(vit_name[-6:-4] )
														__snake_case     			=       int(vit_name[-3:] )
							# size of the architecture
							if "deit" in vit_name:
														if vit_name[9:].startswith("tiny" ):
																					__snake_case     			=       1_92
																					__snake_case     			=       7_68
																					__snake_case     			=       12
																					__snake_case     			=       3
														elif vit_name[9:].startswith("small" ):
																					__snake_case     			=       3_84
																					__snake_case     			=       15_36
																					__snake_case     			=       12
																					__snake_case     			=       6
														else:
																					pass
							else:
														if vit_name[4:].startswith("small" ):
																					__snake_case     			=       7_68
																					__snake_case     			=       23_04
																					__snake_case     			=       8
																					__snake_case     			=       8
														elif vit_name[4:].startswith("base" ):
																					pass
														elif vit_name[4:].startswith("large" ):
																					__snake_case     			=       10_24
																					__snake_case     			=       40_96
																					__snake_case     			=       24
																					__snake_case     			=       16
														elif vit_name[4:].startswith("huge" ):
																					__snake_case     			=       12_80
																					__snake_case     			=       51_20
																					__snake_case     			=       32
																					__snake_case     			=       16

    # load original model from timm
							__snake_case     			=       timm.create_model(SCREAMING_SNAKE_CASE							,    pretrained=SCREAMING_SNAKE_CASE )
							timm_model.eval()

							# load state_dict of original model, remove and rename some keys
							__snake_case     			=       timm_model.state_dict()
							if base_model:
														remove_classification_head_(SCREAMING_SNAKE_CASE )
							__snake_case     			=       create_rename_keys(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							for src, dest in rename_keys:
														rename_key(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )
							read_in_q_k_v(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )

							# load HuggingFace model
							if vit_name[-5:] == "in21k":
														__snake_case     			=       ViTModel(SCREAMING_SNAKE_CASE ).eval()
							else:
														__snake_case     			=       ViTForImageClassification(SCREAMING_SNAKE_CASE ).eval()
							model.load_state_dict(SCREAMING_SNAKE_CASE )

							# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
							if "deit" in vit_name:
														__snake_case     			=       DeiTImageProcessor(size=config.image_size )
							else:
														__snake_case     			=       ViTImageProcessor(size=config.image_size )
							__snake_case     			=       image_processor(images=prepare_img()							,    return_tensors="pt" )
							__snake_case     			=       encoding["pixel_values"]
							__snake_case     			=       model(SCREAMING_SNAKE_CASE )

							if base_model:
														__snake_case     			=       timm_model.forward_features(SCREAMING_SNAKE_CASE )
														assert timm_pooled_output.shape == outputs.pooler_output.shape
														assert torch.allclose(SCREAMING_SNAKE_CASE							,    outputs.pooler_output							,    atol=1e-3 )
							else:
														__snake_case     			=       timm_model(SCREAMING_SNAKE_CASE )
														assert timm_logits.shape == outputs.logits.shape
														assert torch.allclose(SCREAMING_SNAKE_CASE							,    outputs.logits							,    atol=1e-3 )

							Path(SCREAMING_SNAKE_CASE ).mkdir(exist_ok=SCREAMING_SNAKE_CASE )
							print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}''' )
							model.save_pretrained(SCREAMING_SNAKE_CASE )
							print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
							image_processor.save_pretrained(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--vit_name""",
		    default="""vit_base_patch16_224""",
		    type=str,
		    help="""Name of the ViT 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."""
		)

		_SCREAMING_SNAKE_CASE	       =      parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)


"""simple docstring"""







from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat
def   __UpperCamelCase   (					)  ->     tuple[list[int], int]:







							"""simple docstring"""


							__snake_case     			=       [randint(-10_00							,    10_00 ) for i in range(10 )]
							__snake_case     			=       randint(-50_00							,    50_00 )
							return (arr, r)


_SCREAMING_SNAKE_CASE	       =      make_dataset()
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     tuple[int, ...]:







							"""simple docstring"""


							for triplet in permutations(SCREAMING_SNAKE_CASE							,    3 ):
														if sum(SCREAMING_SNAKE_CASE ) == target:
																					return tuple(sorted(SCREAMING_SNAKE_CASE ) )
							return (0, 0, 0)
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     tuple[int, int, int]:







							"""simple docstring"""


							arr.sort()
							__snake_case     			=       len(SCREAMING_SNAKE_CASE )
							for i in range(n - 1 ):
														__snake_case			,   __snake_case     			=       i + 1, n - 1
														while left < right:
																					if arr[i] + arr[left] + arr[right] == target:
																												return (arr[i], arr[left], arr[right])
																					elif arr[i] + arr[left] + arr[right] < target:
																												left += 1
																					elif arr[i] + arr[left] + arr[right] > target:
																												right -= 1
							return (0, 0, 0)
def   __UpperCamelCase   (					)  ->     tuple[float, float]:







							"""simple docstring"""


							__snake_case     			=       "\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n"
							__snake_case     			=       "\ntriplet_sum1(*dataset)\n"
							__snake_case     			=       "\ntriplet_sum2(*dataset)\n"
							__snake_case     			=       repeat(setup=SCREAMING_SNAKE_CASE							,    stmt=SCREAMING_SNAKE_CASE							,    repeat=5							,    number=1_00_00 )
							__snake_case     			=       repeat(setup=SCREAMING_SNAKE_CASE							,    stmt=SCREAMING_SNAKE_CASE							,    repeat=5							,    number=1_00_00 )
							return (min(SCREAMING_SNAKE_CASE ), min(SCREAMING_SNAKE_CASE ))


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		_SCREAMING_SNAKE_CASE	       =      solution_times()
		print(F"""The time for naive implementation is {times[0]}.""")
		print(F"""The time for optimized implementation is {times[1]}.""")
