


def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=generate_pascal_triangle(SCREAMING_SNAKE_CASE__				)
     for row_idx in range(SCREAMING_SNAKE_CASE__				):
          # Print left spaces
          for _ in range(num_rows - row_idx - 1				):
               print(end=' '				)
          # Print row values
          for col_idx in range(row_idx + 1				):
               if col_idx != row_idx:
                    print(triangle[row_idx][col_idx]			,     end=' '				)
               else:
                    print(triangle[row_idx][col_idx]			,     end=''				)
          print()

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int				):

     if not isinstance(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				):
          raise TypeError('The input value of \'num_rows\' should be \'int\''				)

     if num_rows == 0:
          return []
     elif num_rows < 0:
          raise ValueError(
              'The input value of \'num_rows\' should be greater than or equal to 0'				)

     __UpperCamelCase    							=[]
     for current_row_idx in range(SCREAMING_SNAKE_CASE__				):
          __UpperCamelCase    							=populate_current_row(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
          triangle.append(SCREAMING_SNAKE_CASE__				)
     return triangle

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					list[list[int]]			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=[-1] * (current_row_idx + 1)
     # first and last elements of current row are equal to 1
     __UpperCamelCase							,      __UpperCamelCase    							=1, 1
     for current_col_idx in range(1			,     SCREAMING_SNAKE_CASE__				):
          calculate_current_element(
              SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
     return current_row

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					list[list[int]]			,     SCREAMING_SNAKE_CASE__			:					list[int]			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					int			,     ):
     __UpperCamelCase    							=triangle[current_row_idx - 1][current_col_idx - 1]
     __UpperCamelCase    							=triangle[current_row_idx - 1][current_col_idx]
     __UpperCamelCase    							=above_to_left_elt + above_to_right_elt

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int				):

     if not isinstance(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				):
          raise TypeError('The input value of \'num_rows\' should be \'int\''				)

     if num_rows == 0:
          return []
     elif num_rows < 0:
          raise ValueError(
              'The input value of \'num_rows\' should be greater than or equal to 0'				)

     __UpperCamelCase    							=[[1]]

     for row_index in range(1			,     SCREAMING_SNAKE_CASE__				):
          __UpperCamelCase    							=[0] + result[-1] + [0]
          __UpperCamelCase    							=row_index + 1
          # Calculate the number of distinct elements in a row
          __UpperCamelCase    							=sum(divmod(SCREAMING_SNAKE_CASE__			,     2				)				)
          __UpperCamelCase    							=[
              temp_row[i - 1] + temp_row[i] for i in range(1			,     distinct_elements + 1				)
          ]
          __UpperCamelCase    							=row_first_half[: (row_index + 1) // 2]
          row_second_half.reverse()
          __UpperCamelCase    							=row_first_half + row_second_half
          result.append(SCREAMING_SNAKE_CASE__				)

     return result

def 						_UpperCAmelCase				(			):
     from collections.abc import Callable
     from timeit import timeit

     def benchmark_a_function(SCREAMING_SNAKE_CASE__			:					Callable			,     SCREAMING_SNAKE_CASE__			:					int				) -> None:
          __UpperCamelCase    							=F'{func.__name__}({value})'
          __UpperCamelCase    							=timeit(F'__main__.{call}'			,     setup='import __main__'				)
          # print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
          print(F'{call:38} -- {timing:.4f} seconds'				)

     for value in range(15				):  # (1, 7, 14):
          for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
               benchmark_a_function(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
          print()


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   benchmark()










import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[Any]    =    "huggingface/label-files"
							_lowerCAmelCase     :							int    =    "imagenet-1k-id2label.json"
							_lowerCAmelCase     :							Tuple    =    json.load(open(hf_hub_download(_lowerCamelCase				, _lowerCamelCase				, repo_type="dataset"  )				, "r"  )  )
							_lowerCAmelCase     :							Tuple    =    {int(_lowerCamelCase  ): v for k, v in idalabel.items()}
							_lowerCAmelCase     :							Union[str, Any]    =    {v: k for k, v in idalabel.items()}

							_lowerCAmelCase     :							Tuple    =    "std_conv" if "bit" in model_name else False

							# note that when using BiT as backbone for ViT-hybrid checkpoints,
							# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
							# config.conv_layer = "std_conv_same"
							_lowerCAmelCase     :							Optional[int]    =    BitConfig(
							    conv_layer=_lowerCamelCase				, num_labels=1_000				, idalabel=_lowerCamelCase				, labelaid=_lowerCamelCase				, )

							return config






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							if "stem.conv" in name:
														_lowerCAmelCase     :							List[str]    =    name.replace("stem.conv"				, "bit.embedder.convolution"  )
							if "blocks" in name:
														_lowerCAmelCase     :							Any    =    name.replace("blocks"				, "layers"  )
							if "head.fc" in name:
														_lowerCAmelCase     :							Optional[Any]    =    name.replace("head.fc"				, "classifier.1"  )
							if name.startswith("norm"  ):
														_lowerCAmelCase     :							Any    =    "bit." + name
							if "bit" not in name and "classifier" not in name:
														_lowerCAmelCase     :							Dict    =    "bit.encoder." + name

							return name






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    "http://images.cocodataset.org/val2017/000000039769.jpg"
							_lowerCAmelCase     :							Optional[int]    =    Image.open(requests.get(_lowerCamelCase				, stream=_lowerCamelCase  ).raw  )
							return im






@torch.no_grad()
def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=False  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    get_config(_lowerCamelCase  )

							# load original model from timm
							_lowerCAmelCase     :							int    =    create_model(_lowerCamelCase				, pretrained=_lowerCamelCase  )
							timm_model.eval()

							# load state_dict of original model
							_lowerCAmelCase     :							Any    =    timm_model.state_dict()
							for key in state_dict.copy().keys():
														_lowerCAmelCase     :							Dict    =    state_dict.pop(_lowerCamelCase  )
														_lowerCAmelCase     :							Tuple    =    val.squeeze() if "head" in key else val

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

							# create image processor
							_lowerCAmelCase     :							Dict    =    create_transform(**resolve_data_config({}				, model=_lowerCamelCase  )  )
							_lowerCAmelCase     :							Optional[int]    =    transform.transforms

							_lowerCAmelCase     :							Tuple    =    {
							    "bilinear": PILImageResampling.BILINEAR,
							    "bicubic": PILImageResampling.BICUBIC,
							    "nearest": PILImageResampling.NEAREST,
							}

							_lowerCAmelCase     :							Tuple    =    BitImageProcessor(
							    do_resize=_lowerCamelCase				, size={"shortest_edge": timm_transforms[0].size}				, resample=pillow_resamplings[timm_transforms[0].interpolation.value]				, do_center_crop=_lowerCamelCase				, crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}				, do_normalize=_lowerCamelCase				, image_mean=timm_transforms[-1].mean.tolist()				, image_std=timm_transforms[-1].std.tolist()				, )

							_lowerCAmelCase     :							Optional[int]    =    prepare_img()
							_lowerCAmelCase     :							Any    =    transform(_lowerCamelCase  ).unsqueeze(0  )
							_lowerCAmelCase     :							Optional[int]    =    processor(_lowerCamelCase				, return_tensors="pt"  ).pixel_values

							# verify pixel values
							assert torch.allclose(_lowerCamelCase				, _lowerCamelCase  )

							# verify logits
							with torch.no_grad():
														_lowerCAmelCase     :							Tuple    =    model(_lowerCamelCase  )
														_lowerCAmelCase     :							str    =    outputs.logits

							print("Logits:"				, logits[0, :3]  )
							print("Predicted class:"				, model.config.idalabel[logits.argmax(-1  ).item()]  )
							_lowerCAmelCase     :							Union[str, Any]    =    timm_model(_lowerCamelCase  )
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(_lowerCamelCase				, outputs.logits				, atol=1e-3  )
							print("Looks ok!"  )

							if pytorch_dump_folder_path is not None:
														Path(_lowerCamelCase  ).mkdir(exist_ok=_lowerCamelCase  )
														print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}"  )
														model.save_pretrained(_lowerCamelCase  )
														processor.save_pretrained(_lowerCamelCase  )

							if push_to_hub:
														print(F"Pushing model {model_name} and processor to the hub"  )
														model.push_to_hub(F"ybelkada/{model_name}"  )
														processor.push_to_hub(F"ybelkada/{model_name}"  )


if __name__ == "__main__":
						_snake_case		=  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model_name",
						    default="resnetv2_50x1_bitm",
						    type=str,
						    help="Name of the BiT 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."
						)
						parser.add_argument(
						    "--push_to_hub",
						    action="store_true",
						    help="Whether to push the model to the hub.",
						)

						_snake_case		=  parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
