




import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_					    = logging.get_logger()






@dataclass
class  _lowerCAmelCase     :
      A__		     =		42
      A__		     =		field(default_factory=_lowercase	)
      A__		     =		field(default_factory=_lowercase	)

      def 	__magic_name__(   self      ,				__UpperCAmelCase      ,				__UpperCAmelCase      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Tuple		       =     len(list(m.modules()		)		) == 1 or isinstance(__UpperCAmelCase      ,				nn.Convad		) or isinstance(__UpperCAmelCase      ,				nn.BatchNormad		)
        if has_not_submodules:
          self.traced.append(__UpperCAmelCase		)

      def __call__(   self      ,				__UpperCAmelCase		):
        for m in self.module.modules():
          self.handles.append(m.register_forward_hook(self._forward_hook		)		)
        self.module(__UpperCAmelCase		)
        [x.remove() for x in self.handles]
        return self







      @property
      def 	__magic_name__(   self		):
        # check the len of the state_dict keys to see if we have learnable params
        return list(filter(lambda __UpperCAmelCase		: len(list(x.state_dict().keys()		)		) > 0      ,				self.traced		)		)






@dataclass
class  _lowerCAmelCase     :
      A__		     =		42
      A__		     =		42
      A__		     =		1
      A__		     =		field(default_factory=_lowercase	)
      A__		     =		field(default_factory=_lowercase	)
      A__		     =		True

      def __call__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  List[str]		       =     Tracker(self.dest		)(__UpperCAmelCase		).parametrized
        lowerCAmelCase__				:  List[str]		       =     Tracker(self.src		)(__UpperCAmelCase		).parametrized

        lowerCAmelCase__				:  Optional[int]		       =     list(filter(lambda __UpperCAmelCase		: type(__UpperCAmelCase		) not in self.src_skip      ,				__UpperCAmelCase		)		)
        lowerCAmelCase__				:  Dict		       =     list(filter(lambda __UpperCAmelCase		: type(__UpperCAmelCase		) not in self.dest_skip      ,				__UpperCAmelCase		)		)

        if len(__UpperCAmelCase		) != len(__UpperCAmelCase		) and self.raise_if_mismatch:
          raise Exception(
              f"""Numbers of operations are different. Source module has {len(__UpperCAmelCase		)} operations while"""
              f""" destination module has {len(__UpperCAmelCase		)}."""		)

        for dest_m, src_m in zip(__UpperCAmelCase      ,				__UpperCAmelCase		):
          dest_m.load_state_dict(src_m.state_dict()		)
          if self.verbose == 1:
            print(f"""Transfered from={src_m} to={dest_m}"""		)






class  _lowerCAmelCase     (  nn.Module	):

      def __init__(   self      ,				__UpperCAmelCase		):
        super().__init__()

        lowerCAmelCase__				:  List[Tuple[str, nn.Module]]		       =     []
        # - get the stem
        feature_blocks.append(('''conv1''', model.stem)		)
        # - get all the feature blocks
        for k, v in model.trunk_output.named_children():
          assert k.startswith('''block'''		), f"""Unexpected layer name {k}"""
          lowerCAmelCase__				:  Tuple		       =     len(__UpperCAmelCase		) + 1
          feature_blocks.append((f"""res{block_index}""", v)		)

        lowerCAmelCase__				:  int		       =     nn.ModuleDict(__UpperCAmelCase		)







      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        return get_trunk_forward_outputs(
            __UpperCAmelCase      ,				out_feat_keys=__UpperCAmelCase      ,				feature_blocks=self._feature_blocks      ,				)






class  _lowerCAmelCase     (  _lowercase	):

      def 	__magic_name__(   self      ,				__UpperCAmelCase		):
        lowerCAmelCase__				:  Tuple		       =     x.split('''-'''		)
        return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]		)







      def __getitem__(   self      ,				__UpperCAmelCase		):
        # default to timm!
        if x not in self:
          lowerCAmelCase__				:  Dict		       =     self.convert_name_to_timm(__UpperCAmelCase		)
          lowerCAmelCase__				:  Optional[int]		       =     partial(lambda: (timm.create_model(__UpperCAmelCase      ,				pretrained=__UpperCAmelCase		).eval(), None)		)

        else:
          lowerCAmelCase__				:  Union[str, Any]		       =     super().__getitem__(__UpperCAmelCase		)

        return val






class  _lowerCAmelCase     (  _lowercase	):

      def __getitem__(   self      ,				__UpperCAmelCase		):
        if "seer" in x and "in1k" not in x:
          lowerCAmelCase__				:  Dict		       =     RegNetModel
        else:
          lowerCAmelCase__				:  Dict		       =     RegNetForImageClassification
        return val



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )     ->	str:
  for from_key, to_key in keys:
    lowerCAmelCase__				:  int		       =     from_state_dict[from_key].clone()
    print(F"""Copied key={from_key} to={to_key}"""      )
  return to_state_dict



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase = True	,      )     ->	Optional[int]:
  print(F"""Converting {name}..."""      )
  with torch.no_grad():
    lowerCAmelCase__       ,			lowerCAmelCase__				:  List[str]		       =     from_model_func()
    lowerCAmelCase__				:  str		       =     our_model_func(UpperCamelCase      ).eval()
    lowerCAmelCase__				:  Optional[int]		       =     ModuleTransfer(src=UpperCamelCase	,      dest=UpperCamelCase	,      raise_if_mismatch=UpperCamelCase      )
    lowerCAmelCase__				:  List[str]		       =     torch.randn((1, 3, 224, 224)      )
    module_transfer(UpperCamelCase      )

  if from_state_dict is not None:
    lowerCAmelCase__				:  Dict		       =     []
    # for seer - in1k finetuned we have to manually copy the head
    if "seer" in name and "in1k" in name:
      lowerCAmelCase__				:  List[str]		       =     [('''0.clf.0.weight''', '''classifier.1.weight'''), ('''0.clf.0.bias''', '''classifier.1.bias''')]
    lowerCAmelCase__				:  Dict		       =     manually_copy_vissl_head(UpperCamelCase	,      our_model.state_dict()	,      UpperCamelCase      )
    our_model.load_state_dict(UpperCamelCase      )

  lowerCAmelCase__				:  Optional[Any]		       =     our_model(UpperCamelCase	,      output_hidden_states=UpperCamelCase      )
  lowerCAmelCase__				:  Dict		       =     (
      our_outputs.logits if isinstance(UpperCamelCase	,      UpperCamelCase      ) else our_outputs.last_hidden_state
  )

  lowerCAmelCase__				:  List[str]		       =     from_model(UpperCamelCase      )
  lowerCAmelCase__				:  Optional[Any]		       =     from_output[-1] if type(UpperCamelCase      ) is list else from_output

  # now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
  if "seer" in name and "in1k" in name:
    lowerCAmelCase__				:  List[str]		       =     our_outputs.hidden_states[-1]

  assert torch.allclose(UpperCamelCase	,      UpperCamelCase      ), "The model logits don't match the original one."

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / name	,      commit_message='''Add model'''	,      use_temp_dir=UpperCamelCase	,      )

    lowerCAmelCase__				:  str		       =     224 if '''seer''' not in name else 384
    # we can use the convnext one
    lowerCAmelCase__				:  List[str]		       =     AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''	,      size=UpperCamelCase      )
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / name	,      commit_message='''Add image processor'''	,      use_temp_dir=UpperCamelCase	,      )

    print(F"""Pushed {name}"""      )



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase = None	,      UpperCamelCase = True      )     ->	Any:
  lowerCAmelCase__				:  Tuple		       =     '''imagenet-1k-id2label.json'''
  lowerCAmelCase__				:  List[Any]		       =     1000
  lowerCAmelCase__				:  List[Any]		       =     (1, num_labels)

  lowerCAmelCase__				:  Tuple		       =     '''huggingface/label-files'''
  lowerCAmelCase__				:  Optional[Any]		       =     num_labels
  lowerCAmelCase__				:  List[Any]		       =     json.load(open(cached_download(hf_hub_url(UpperCamelCase	,      UpperCamelCase	,      repo_type='''dataset'''      )      )	,      '''r'''      )      )
  lowerCAmelCase__				:  Union[str, Any]		       =     {int(UpperCamelCase      ): v for k, v in idalabel.items()}

  lowerCAmelCase__				:  Optional[int]		       =     idalabel
  lowerCAmelCase__				:  List[str]		       =     {v: k for k, v in idalabel.items()}

  lowerCAmelCase__				:  Optional[int]		       =     partial(UpperCamelCase	,      num_labels=UpperCamelCase	,      idalabel=UpperCamelCase	,      labelaid=UpperCamelCase      )

  lowerCAmelCase__				:  Any		       =     {
      '''regnet-x-002''': ImageNetPreTrainedConfig(
          depths=[1, 1, 4, 7]	,      hidden_sizes=[24, 56, 152, 368]	,      groups_width=8	,      layer_type='''x'''      ),
      '''regnet-x-004''': ImageNetPreTrainedConfig(
          depths=[1, 2, 7, 12]	,      hidden_sizes=[32, 64, 160, 384]	,      groups_width=16	,      layer_type='''x'''      ),
      '''regnet-x-006''': ImageNetPreTrainedConfig(
          depths=[1, 3, 5, 7]	,      hidden_sizes=[48, 96, 240, 528]	,      groups_width=24	,      layer_type='''x'''      ),
      '''regnet-x-008''': ImageNetPreTrainedConfig(
          depths=[1, 3, 7, 5]	,      hidden_sizes=[64, 128, 288, 672]	,      groups_width=16	,      layer_type='''x'''      ),
      '''regnet-x-016''': ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 2]	,      hidden_sizes=[72, 168, 408, 912]	,      groups_width=24	,      layer_type='''x'''      ),
      '''regnet-x-032''': ImageNetPreTrainedConfig(
          depths=[2, 6, 15, 2]	,      hidden_sizes=[96, 192, 432, 1008]	,      groups_width=48	,      layer_type='''x'''      ),
      '''regnet-x-040''': ImageNetPreTrainedConfig(
          depths=[2, 5, 14, 2]	,      hidden_sizes=[80, 240, 560, 1360]	,      groups_width=40	,      layer_type='''x'''      ),
      '''regnet-x-064''': ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 1]	,      hidden_sizes=[168, 392, 784, 1624]	,      groups_width=56	,      layer_type='''x'''      ),
      '''regnet-x-080''': ImageNetPreTrainedConfig(
          depths=[2, 5, 15, 1]	,      hidden_sizes=[80, 240, 720, 1920]	,      groups_width=120	,      layer_type='''x'''      ),
      '''regnet-x-120''': ImageNetPreTrainedConfig(
          depths=[2, 5, 11, 1]	,      hidden_sizes=[224, 448, 896, 2240]	,      groups_width=112	,      layer_type='''x'''      ),
      '''regnet-x-160''': ImageNetPreTrainedConfig(
          depths=[2, 6, 13, 1]	,      hidden_sizes=[256, 512, 896, 2048]	,      groups_width=128	,      layer_type='''x'''      ),
      '''regnet-x-320''': ImageNetPreTrainedConfig(
          depths=[2, 7, 13, 1]	,      hidden_sizes=[336, 672, 1344, 2520]	,      groups_width=168	,      layer_type='''x'''      ),
      # y variant
      '''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]	,      hidden_sizes=[24, 56, 152, 368]	,      groups_width=8      ),
      '''regnet-y-004''': ImageNetPreTrainedConfig(
          depths=[1, 3, 6, 6]	,      hidden_sizes=[48, 104, 208, 440]	,      groups_width=8      ),
      '''regnet-y-006''': ImageNetPreTrainedConfig(
          depths=[1, 3, 7, 4]	,      hidden_sizes=[48, 112, 256, 608]	,      groups_width=16      ),
      '''regnet-y-008''': ImageNetPreTrainedConfig(
          depths=[1, 3, 8, 2]	,      hidden_sizes=[64, 128, 320, 768]	,      groups_width=16      ),
      '''regnet-y-016''': ImageNetPreTrainedConfig(
          depths=[2, 6, 17, 2]	,      hidden_sizes=[48, 120, 336, 888]	,      groups_width=24      ),
      '''regnet-y-032''': ImageNetPreTrainedConfig(
          depths=[2, 5, 13, 1]	,      hidden_sizes=[72, 216, 576, 1512]	,      groups_width=24      ),
      '''regnet-y-040''': ImageNetPreTrainedConfig(
          depths=[2, 6, 12, 2]	,      hidden_sizes=[128, 192, 512, 1088]	,      groups_width=64      ),
      '''regnet-y-064''': ImageNetPreTrainedConfig(
          depths=[2, 7, 14, 2]	,      hidden_sizes=[144, 288, 576, 1296]	,      groups_width=72      ),
      '''regnet-y-080''': ImageNetPreTrainedConfig(
          depths=[2, 4, 10, 1]	,      hidden_sizes=[168, 448, 896, 2016]	,      groups_width=56      ),
      '''regnet-y-120''': ImageNetPreTrainedConfig(
          depths=[2, 5, 11, 1]	,      hidden_sizes=[224, 448, 896, 2240]	,      groups_width=112      ),
      '''regnet-y-160''': ImageNetPreTrainedConfig(
          depths=[2, 4, 11, 1]	,      hidden_sizes=[224, 448, 1232, 3024]	,      groups_width=112      ),
      '''regnet-y-320''': ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1]	,      hidden_sizes=[232, 696, 1392, 3712]	,      groups_width=232      ),
      # models created by SEER -> https://arxiv.org/abs/2202.08360
      '''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 12, 1]	,      hidden_sizes=[232, 696, 1392, 3712]	,      groups_width=232      ),
      '''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1]	,      hidden_sizes=[328, 984, 1968, 4920]	,      groups_width=328      ),
      '''regnet-y-1280-seer''': RegNetConfig(
          depths=[2, 7, 17, 1]	,      hidden_sizes=[528, 1056, 2904, 7392]	,      groups_width=264      ),
      '''regnet-y-2560-seer''': RegNetConfig(
          depths=[3, 7, 16, 1]	,      hidden_sizes=[640, 1696, 2544, 5088]	,      groups_width=640      ),
      '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]	,      hidden_sizes=[2020, 4040, 11110, 28280]	,      groups_width=1010      ),
      # finetuned on imagenet
      '''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1]	,      hidden_sizes=[232, 696, 1392, 3712]	,      groups_width=232      ),
      '''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 5, 12, 1]	,      hidden_sizes=[328, 984, 1968, 4920]	,      groups_width=328      ),
      '''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]	,      hidden_sizes=[528, 1056, 2904, 7392]	,      groups_width=264      ),
      '''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[3, 7, 16, 1]	,      hidden_sizes=[640, 1696, 2544, 5088]	,      groups_width=640      ),
      '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
          depths=[2, 7, 17, 1]	,      hidden_sizes=[2020, 4040, 11110, 28280]	,      groups_width=1010      ),
  }

  lowerCAmelCase__				:  Tuple		       =     NameToOurModelFuncMap()
  lowerCAmelCase__				:  List[str]		       =     NameToFromModelFuncMap()
  # add seer weights logic

  def load_using_classy_vision(UpperCamelCase	,      UpperCamelCase      ) -> Tuple[nn.Module, Dict]:
    lowerCAmelCase__				:  int		       =     torch.hub.load_state_dict_from_url(UpperCamelCase	,      model_dir=str(UpperCamelCase      )	,      map_location='''cpu'''      )
    lowerCAmelCase__				:  Tuple		       =     model_func()
    # check if we have a head, if yes add it
    lowerCAmelCase__				:  Dict		       =     files['''classy_state_dict''']['''base_model''']['''model''']
    lowerCAmelCase__				:  str		       =     model_state_dict['''trunk''']
    model.load_state_dict(UpperCamelCase      )
    return model.eval(), model_state_dict["heads"]

  # pretrained
  lowerCAmelCase__				:  Optional[Any]		       =     partial(
      UpperCamelCase	,      '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''	,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )	,      )

  lowerCAmelCase__				:  Tuple		       =     partial(
      UpperCamelCase	,      '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'''	,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )	,      )

  lowerCAmelCase__				:  List[str]		       =     partial(
      UpperCamelCase	,      '''https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'''	,      lambda: FakeRegNetVisslWrapper(RegNetYaaagf()      )	,      )

  lowerCAmelCase__				:  Optional[int]		       =     partial(
      UpperCamelCase	,      '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''	,      lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=27	,      group_width=1010	,      w_a=1744	,      w_a=620.83	,      w_m=2.52      )      )      )	,      )

  # IN1K finetuned
  lowerCAmelCase__				:  Union[str, Any]		       =     partial(
      UpperCamelCase	,      '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''	,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )	,      )

  lowerCAmelCase__				:  Tuple		       =     partial(
      UpperCamelCase	,      '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''	,      lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )	,      )

  lowerCAmelCase__				:  List[str]		       =     partial(
      UpperCamelCase	,      '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'''	,      lambda: FakeRegNetVisslWrapper(RegNetYaaagf()      )	,      )

  lowerCAmelCase__				:  List[Any]		       =     partial(
      UpperCamelCase	,      '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''	,      lambda: FakeRegNetVisslWrapper(
          RegNet(RegNetParams(depth=27	,      group_width=1010	,      w_a=1744	,      w_a=620.83	,      w_m=2.52      )      )      )	,      )

  if model_name:
    convert_weight_and_push(
        UpperCamelCase	,      names_to_from_model_map[model_name]	,      names_to_ours_model_map[model_name]	,      names_to_config[model_name]	,      UpperCamelCase	,      UpperCamelCase	,      )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(
          UpperCamelCase	,      names_to_from_model_map[model_name]	,      names_to_ours_model_map[model_name]	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      )
  return config, expected_shape


if __name__ == "__main__":
     lowerCAmelCase_					    = argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--model_name""",
         default=None,
         type=str,
         help=(
             """The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
             """ currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default=None,
         type=Path,
         required=True,
         help="""Path to the output PyTorch model directory.""",
     )
     parser.add_argument(
         """--push_to_hub""",
         default=True,
         type=bool,
         required=False,
         help="""If True, push model and image processor to the hub.""",
     )

     lowerCAmelCase_					    = parser.parse_args()

     lowerCAmelCase_					    = args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







def      __lowerCAmelCase			(					UpperCamelCase      )     ->	None:
  lowerCAmelCase__				:  Dict		       =     generate_pascal_triangle(UpperCamelCase      )
  for row_idx in range(UpperCamelCase      ):
    # 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      __lowerCAmelCase			(					UpperCamelCase      )     ->	list[list[int]]:

  if not isinstance(UpperCamelCase	,      UpperCamelCase      ):
    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'''      )

  lowerCAmelCase__				:  list[list[int]]		       =     []
  for current_row_idx in range(UpperCamelCase      ):
    lowerCAmelCase__				:  List[str]		       =     populate_current_row(UpperCamelCase	,      UpperCamelCase      )
    triangle.append(UpperCamelCase      )
  return triangle



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase      )     ->	list[int]:
  lowerCAmelCase__				:  Optional[Any]		       =     [-1] * (current_row_idx + 1)
  # first and last elements of current row are equal to 1
  lowerCAmelCase__       ,			lowerCAmelCase__				:  int		       =     1, 1
  for current_col_idx in range(1	,      UpperCamelCase      ):
    calculate_current_element(
        UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase      )
  return current_row



def      __lowerCAmelCase			(					UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase	,      )     ->	None:
  lowerCAmelCase__				:  Optional[int]		       =     triangle[current_row_idx - 1][current_col_idx - 1]
  lowerCAmelCase__				:  List[str]		       =     triangle[current_row_idx - 1][current_col_idx]
  lowerCAmelCase__				:  List[Any]		       =     above_to_left_elt + above_to_right_elt



def      __lowerCAmelCase			(					UpperCamelCase      )     ->	list[list[int]]:

  if not isinstance(UpperCamelCase	,      UpperCamelCase      ):
    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'''      )

  lowerCAmelCase__				:  list[list[int]]		       =     [[1]]

  for row_index in range(1	,      UpperCamelCase      ):
    lowerCAmelCase__				:  Optional[int]		       =     [0] + result[-1] + [0]
    lowerCAmelCase__				:  List[str]		       =     row_index + 1
    # Calculate the number of distinct elements in a row
    lowerCAmelCase__				:  Tuple		       =     sum(divmod(UpperCamelCase	,      2      )      )
    lowerCAmelCase__				:  Tuple		       =     [
        temp_row[i - 1] + temp_row[i] for i in range(1	,      distinct_elements + 1      )
    ]
    lowerCAmelCase__				:  int		       =     row_first_half[: (row_index + 1) // 2]
    row_second_half.reverse()
    lowerCAmelCase__				:  Dict		       =     row_first_half + row_second_half
    result.append(UpperCamelCase      )

  return result



def      __lowerCAmelCase			(					)     ->	None:
  from collections.abc import Callable
  from timeit import timeit

  def benchmark_a_function(UpperCamelCase	,      UpperCamelCase      ) -> None:
    lowerCAmelCase__				:  Tuple		       =     F"""{func.__name__}({value})"""
    lowerCAmelCase__				:  Dict		       =     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(UpperCamelCase	,      UpperCamelCase      )
    print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()

