code: 
  
  
  import warnings
  
  import numpy as np
  import pandas as pd
  from matplotlib import pyplot as plt
  from sklearn.metrics import pairwise_distances
  
  warnings.filterwarnings("ignore")
  
  __lowerCamelCase       :       Optional[Any]				   =			'K-MEANS-CLUST/ '
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_=None	)			->							Optional[int]:
  				if seed is not None:  # useful for obtaining consistent results
  								np.random.seed(lowercase__	)
  				A__     :							Optional[int]   				=data.shape[0]  # number of data points
  
  				# Pick K indices from range [0, N).
  				A__     :							Tuple   				=np.random.randint(0,	lowercase__,	lowercase__	)
  
  				# Keep centroids as dense format, as many entries will be nonzero due to averaging.
  				# As long as at least one document in a cluster contains a word,
  				# it will carry a nonzero weight in the TF-IDF vector of the centroid.
  				A__     :							int   				=data[rand_indices, :]
  
  				return centroids
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							Dict:
  				return pairwise_distances(lowercase__,	lowercase__,	metric='''euclidean'''	)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							List[Any]:
  				# Compute distances between each data point and the set of centroids:
  				# Fill in the blank (RHS only)
  				A__     :							str   				=centroid_pairwise_dist(lowercase__,	lowercase__	)
  
  				# Compute cluster assignments for each data point:
  				# Fill in the blank (RHS only)
  				A__     :							Optional[Any]   				=np.argmin(lowercase__,	axis=1	)
  
  				return cluster_assignment
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_	)			->							Optional[int]:
  				A__     :							List[str]   				=[]
  				for i in range(lowercase__	):
  								# Select all data points that belong to cluster i. Fill in the blank (RHS only)
  								A__     :							Tuple   				=data[cluster_assignment == i]
  								# Compute the mean of the data points. Fill in the blank (RHS only)
  								A__     :							Any   				=member_data_points.mean(axis=0	)
  								new_centroids.append(lowercase__	)
  				A__     :							Any   				=np.array(lowercase__	)
  
  				return new_centroids
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_,	snake_case_	)			->							Dict:
  				A__     :							List[Any]   				=0.0
  				for i in range(lowercase__	):
  								# Select all data points that belong to cluster i. Fill in the blank (RHS only)
  								A__     :							Optional[Any]   				=data[cluster_assignment == i, :]
  
  								if member_data_points.shape[0] > 0:  # check if i-th cluster is non-empty
  												# Compute distances from centroid to data points (RHS only)
  												A__     :							Dict   				=pairwise_distances(
  												    lowercase__,	[centroids[i]],	metric='''euclidean'''	)
  												A__     :							int   				=distances**2
  												heterogeneity += np.sum(lowercase__	)
  
  				return heterogeneity
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							List[str]:
  				plt.figure(figsize=(7, 4)	)
  				plt.plot(lowercase__,	linewidth=4	)
  				plt.xlabel('''# Iterations'''	)
  				plt.ylabel('''Heterogeneity'''	)
  				plt.title(f'Heterogeneity of clustering over time, K={k:d}'	)
  				plt.rcParams.update({'''font.size''': 1_6}	)
  				plt.show()
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_,	snake_case_=5_0_0,	snake_case_=None,	snake_case_=False	)			->							Optional[Any]:
  				A__     :							Tuple   				=initial_centroids[:]
  				A__     :							List[str]   				=None
  
  				for itr in range(lowercase__	):
  								if verbose:
  												print(lowercase__,	end=''''''	)
  
  								# 1. Make cluster assignments using nearest centroids
  								A__     :							Dict   				=assign_clusters(lowercase__,	lowercase__	)
  
  								# 2. Compute a new centroid for each of the k clusters, averaging all data
  								#    points assigned to that cluster.
  								A__     :							str   				=revise_centroids(lowercase__,	lowercase__,	lowercase__	)
  
  								# Check for convergence: if none of the assignments changed, stop
  								if (
  								    prev_cluster_assignment is not None
  								    and (prev_cluster_assignment == cluster_assignment).all()
  								):
  												break
  
  								# Print number of new assignments
  								if prev_cluster_assignment is not None:
  												A__     :							str   				=np.sum(prev_cluster_assignment != cluster_assignment	)
  												if verbose:
  																print(
  																    f'    {num_changed:5d} elements changed their cluster assignment.'	)
  
          # Record heterogeneity convergence metric
  								if record_heterogeneity is not None:
  												# YOUR CODE HERE
  												A__     :							List[Any]   				=compute_heterogeneity(lowercase__,	lowercase__,	lowercase__,	lowercase__	)
  												record_heterogeneity.append(lowercase__	)
  
  								A__     :							Dict   				=cluster_assignment[:]
  
  				return centroids, cluster_assignment
  
  
  # Mock test below
  if False:  # change to true to run this test case.
  						from sklearn import datasets as ds
  
  						__lowerCamelCase       :       str				   =			ds.load_iris()
  						__lowerCamelCase       :       Optional[Any]				   =			3
  						__lowerCamelCase       :       Tuple				   =			[]
  						__lowerCamelCase       :       Any				   =			get_initial_centroids(dataset["data"], k, seed=0)
  						__lowerCamelCase       :       Any				   =			kmeans(
  						    dataset["data"],
  						    k,
  						    initial_centroids,
  						    maxiter=400,
  						    record_heterogeneity=heterogeneity,
  						    verbose=True,
  						)
  						plot_heterogeneity(heterogeneity, k)
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_,	snake_case_=None	)			->							List[Any]:
  				# Fill missing values with given rules
  				if fill_missing_report:
  								A__     :							Optional[int]   				=df.fillna(value=lowercase__	)
  				A__     :							List[str]   				=1
  				A__     :							List[str]   				=df.select_dtypes(np.number	).columns
  				A__     :							str   				=(
  				    df.groupby(['''Cluster''']	)[  # construct report dataframe
  				        numeric_cols
  				    ]  # group by cluster number
  				    .agg(
  				        [
  				            ('''sum''', np.sum),
  				            ('''mean_with_zeros''', lambda snake_case_	: np.mean(np.nan_to_num(lowercase__	)	)),
  				            ('''mean_without_zeros''', lambda snake_case_	: x.replace(0,	np.NaN	).mean()),
  				            (
  				                '''mean_25-75''',
  				                lambda snake_case_	: np.mean(
  				                    np.nan_to_num(
  				                        sorted(lowercase__	)[
  				                            round(len(lowercase__	) * 2_5 / 1_0_0	) : round(len(lowercase__	) * 7_5 / 1_0_0	)
  				                        ]	)	),
  				            ),
  				            ('''mean_with_na''', np.mean),
  				            ('''min''', lambda snake_case_	: x.min()),
  				            ('''5%''', lambda snake_case_	: x.quantile(0.0_5	)),
  				            ('''25%''', lambda snake_case_	: x.quantile(0.2_5	)),
  				            ('''50%''', lambda snake_case_	: x.quantile(0.5_0	)),
  				            ('''75%''', lambda snake_case_	: x.quantile(0.7_5	)),
  				            ('''95%''', lambda snake_case_	: x.quantile(0.9_5	)),
  				            ('''max''', lambda snake_case_	: x.max()),
  				            ('''count''', lambda snake_case_	: x.count()),
  				            ('''stdev''', lambda snake_case_	: x.std()),
  				            ('''mode''', lambda snake_case_	: x.mode()[0]),
  				            ('''median''', lambda snake_case_	: x.median()),
  				            ('''# > 0''', lambda snake_case_	: (x > 0).sum()),
  				        ]	)
  				    .T.reset_index()
  				    .rename(index=lowercase__,	columns={'''level_0''': '''Features''', '''level_1''': '''Type'''}	)
  				)  # rename columns
  				# calculate the size of cluster(count of clientID's)
  				A__     :							Dict   				=report[
  				    (report['''Features'''] == '''dummy''') & (report['''Type'''] == '''count''')
  				].copy()  # avoid SettingWithCopyWarning
  				A__     :							Optional[Any]   				=(
  				    '''ClusterSize'''  # rename created cluster df to match report column names
  				)
  				A__     :							Optional[Any]   				='''# of Customers'''
  				A__     :							int   				=pd.DataFrame(
  				    clustersize.iloc[:, 2:].values
  				    / clustersize.iloc[:, 2:].values.sum()	)
  				A__     :							Tuple   				='''% of Customers'''  # rename created cluster df to match report column names
  				A__     :							Tuple   				='''ClusterProportion'''
  				A__     :							Dict   				=clusterproportion.columns.tolist()
  				A__     :							List[Any]   				=cols[-2:] + cols[:-2]
  				A__     :							str   				=clusterproportion[cols]  # rearrange columns to match report
  				A__     :							Any   				=report.columns
  				A__     :							Tuple   				=pd.DataFrame(
  				    abs(
  				        report[report['''Type'''] == '''count'''].iloc[:, 2:].values
  				        - clustersize.iloc[:, 2:].values	)	)  # generating df with count of nan values
  				A__     :							str   				=0
  				A__     :							Any   				='''# of nan'''
  				A__     :							List[str]   				=report[
  				    report['''Type'''] == '''count'''
  				].Features.tolist()  # filling values in order to match report
  				A__     :							Any   				=a.columns.tolist()
  				A__     :							Optional[Any]   				=cols[-2:] + cols[:-2]
  				A__     :							Optional[Any]   				=a[cols]  # rearrange columns to match report
  				A__     :							Any   				=report.columns  # rename columns to match report
  				A__     :							int   				=report.drop(
  				    report[report.Type == '''count'''].index	)  # drop count values except cluster size
  				A__     :							Dict   				=pd.concat(
  				    [report, a, clustersize, clusterproportion],	axis=0	)  # concat report with clustert size and nan values
  				A__     :							str   				=report['''Features'''].isin(lowercase__	)
  				A__     :							str   				=report.columns.tolist()
  				A__     :							int   				=cols[0:2] + cols[-1:] + cols[2:-1]
  				A__     :							int   				=report[cols]
  				A__     :							List[str]   				={
  				    '''ClusterSize''': 9,
  				    '''ClusterProportion''': 8,
  				    '''mean_with_zeros''': 7,
  				    '''mean_with_na''': 6,
  				    '''max''': 5,
  				    '''50%''': 4,
  				    '''min''': 3,
  				    '''25%''': 2,
  				    '''75%''': 1,
  				    '''# of nan''': 0,
  				    '''# > 0''': -1,
  				    '''sum_with_na''': -2,
  				}
  				A__     :							Dict   				=(
  				    report.assign(
  				        Sortera=lambda snake_case_	: x.Type.map(lowercase__	),	Sortera=lambda snake_case_	: list(reversed(range(len(lowercase__	)	)	)	),	)
  				    .sort_values(['''Sorter1''', '''Mark''', '''Sorter2'''],	ascending=lowercase__	)
  				    .drop(['''Sorter1''', '''Sorter2'''],	axis=1	)
  				)
  				A__     :							Union[str, Any]   				=''''''
  				A__     :							int   				=report.reset_index()
  				A__     :							Union[str, Any]   				=report.drop(columns=['''index''']	)
  				return report
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  
  
style_context: 
  
  
  import argparse
  import json
  import os
  import re
  from collections import OrderedDict
  from dataclasses import dataclass, field
  from functools import partial
  from pathlib import Path
  from pprint import pprint
  from typing import Dict, List, Tuple
  
  import torch
  import torch.nn as nn
  from classy_vision.models.regnet import RegNet, RegNetParams
  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.modeling_utils import PreTrainedModel
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  __lowerCamelCase       :       Union[str, Any]				   =			logging.get_logger()
  
  
  
  @dataclass
  class 			a      :
  		__lowercase													=		42
  		__lowercase													=		field(default_factory=UpperCamelCase_   )
  		__lowercase													=		field(default_factory=UpperCamelCase_   )
  		__lowercase													=		field(default_factory=UpperCamelCase_   )
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)->			List[Any]:
  						'''simple docstring'''
  
  
  						A__     :							Tuple   				=len(list(m.modules()							)							) == 1 or isinstance(__UpperCamelCase	,     nn.Convad							) or isinstance(__UpperCamelCase	,     nn.BatchNormad							)
  						if has_not_submodules:
  										self.traced.append(__UpperCamelCase							)
  										A__     :							Union[str, Any]   				=m
  
  
  
  
  
  
  		def __call__(  self	,     __UpperCamelCase							)->			Any:
  						'''simple docstring'''
  
  
  						for name, m in self.module.named_modules():
  										self.handles.append(m.register_forward_hook(partial(self._forward_hook	,     name=__UpperCamelCase							)							)							)
  						self.module(__UpperCamelCase							)
  						[x.remove() for x in self.handles]
  						return self
  
  
  
  
  
  
  		@property
  		def       lowerCAmelCase_  (  self							)->			str:
  						'''simple docstring'''
  
  
  						return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()							)							) > 0}
  
  
  
  
  
  
  
  class 			a      (      nn.Module   ):
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						super().__init__()
  
  						A__     :							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}'
  										A__     :							Any   				=len(__UpperCamelCase							) + 1
  										feature_blocks.append((F'res{block_index}', v)							)
  
  						A__     :							Tuple   				=nn.ModuleDict(__UpperCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			List[str]:
  						'''simple docstring'''
  
  
  						return get_trunk_forward_outputs(
  						    __UpperCamelCase	,     out_feat_keys=__UpperCamelCase	,     feature_blocks=self._feature_blocks	,     )
  
  
  
  
  
  
  
  class 			a      (      UpperCamelCase_   ):
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			int:
  						'''simple docstring'''
  
  
  						return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]
  
  
  
  
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Dict[str, str]:
  				A__     :							Any   				=RegNetConfig(depths=[2, 7, 1_7, 1],	hidden_sizes=[8, 8, 8, 8],	groups_width=8	)
  				if "in1k" in model_name:
  								A__     :							Any   				=RegNetForImageClassification(snake_case_	)
  				else:
  								A__     :							Tuple   				=RegNetModel(snake_case_	)
  				# create from model (with small weights)
  				A__     :							List[str]   				=FakeRegNetVisslWrapper(
  				    RegNet(FakeRegNetParams(depth=2_7,	group_width=1_0_1_0,	w_a=1_7_4_4,	w_a=6_2_0.8_3,	w_m=2.5_2	)	)	)
  
  				with torch.no_grad():
  								A__     :							str   				=from_model.eval()
  								A__     :							int   				=our_model.eval()
  
  								A__     :							Optional[int]   				=torch.randn((1, 3, 3_2, 3_2)	)
  								# trace both
  								A__     :							int   				=Tracker(snake_case_	)
  								A__     :							Optional[int]   				=dest_tracker(snake_case_	).parametrized
  
  								pprint(dest_tracker.nameamodule	)
  								A__     :							Optional[int]   				=Tracker(snake_case_	)
  								A__     :							Any   				=src_tracker(snake_case_	).parametrized
  
  				# convert the keys -> module dict to keys -> params
  				def to_params_dict(snake_case_	):
  								A__     :							Optional[Any]   				=OrderedDict()
  								for name, module in dict_with_modules.items():
  												for param_name, param in module.state_dict().items():
  																A__     :							Union[str, Any]   				=param
  								return params_dict
  
  				A__     :							Optional[int]   				={}
  
  				A__     :							int   				=to_params_dict(snake_case_	)
  				A__     :							int   				=to_params_dict(snake_case_	)
  
  				for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items(),	dst_state_dict.items()	):
  								A__     :							int   				=dest_key
  								logger.info(f'{src_key} -> {dest_key}'	)
  				# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
  				if "in1k" in model_name:
  								A__     :							str   				='''classifier.1.weight'''
  								A__     :							Union[str, Any]   				='''classifier.1.bias'''
  
  				return from_to_ours_keys
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_ = None,	snake_case_ = True	)			->							int:
  				A__     :							Tuple   				='''imagenet-1k-id2label.json'''
  				A__     :							Any   				=1_0_0_0
  
  				A__     :							int   				='''huggingface/label-files'''
  				A__     :							Tuple   				=num_labels
  				A__     :							Optional[int]   				=json.load(open(cached_download(hf_hub_url(snake_case_,	snake_case_,	repo_type='''dataset'''	)	),	'''r'''	)	)
  				A__     :							str   				={int(snake_case_	): v for k, v in idalabel.items()}
  
  				A__     :							int   				=idalabel
  				A__     :							Tuple   				={v: k for k, v in idalabel.items()}
  
  				A__     :							str   				=partial(snake_case_,	num_labels=snake_case_,	idalabel=snake_case_,	labelaid=snake_case_	)
  
  				A__     :							Tuple   				={
  				    '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
  				        depths=[2, 7, 1_7, 1],	hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0],	groups_width=1_0_1_0	),
  				    # finetuned on imagenet
  				    '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
  				        depths=[2, 7, 1_7, 1],	hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0],	groups_width=1_0_1_0	),
  				}
  
  				# add seer weights logic
  				def load_using_classy_vision(snake_case_	) -> Tuple[Dict, Dict]:
  								A__     :							Optional[int]   				=torch.hub.load_state_dict_from_url(snake_case_,	model_dir=str(snake_case_	),	map_location='''cpu'''	)
  								# check if we have a head, if yes add it
  								A__     :							Dict   				=files['''classy_state_dict''']['''base_model''']['''model''']
  								return model_state_dict["trunk"], model_state_dict["heads"]
  
  				A__     :							Any   				={
  				    '''regnet-y-10b-seer''': partial(
  				        snake_case_,	'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch''',	),
  				    '''regnet-y-10b-seer-in1k''': partial(
  				        snake_case_,	'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch''',	),
  				}
  
  				A__     :							Tuple   				=get_from_to_our_keys(snake_case_	)
  
  				if not (save_directory / f'{model_name}.pth').exists():
  								logger.info('''Loading original state_dict.'''	)
  								A__				,  A__     :							Any   				=names_to_from_model[model_name]()
  								A__     :							int   				=from_state_dict_trunk
  								if "in1k" in model_name:
  												# add the head
  												A__     :							Union[str, Any]   				={**from_state_dict_trunk, **from_state_dict_head}
  								logger.info('''Done!'''	)
  
  								A__     :							List[str]   				={}
  
  								A__     :							int   				=list(from_state_dict.keys()	)
  								A__     :							Optional[Any]   				=R'''\.block.-part.'''
  								# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
  								for key in from_state_dict.keys():
  												# remove the weird "block[0,1]-part" from the key
  												A__     :							Tuple   				=re.sub(snake_case_,	'''''',	snake_case_	)
  												# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
  												A__     :							Any   				=from_to_ours_keys[src_key]
  												# store the parameter with our key
  												A__     :							str   				=from_state_dict[key]
  												not_used_keys.remove(snake_case_	)
  								# check that all keys have been updated
  								assert len(snake_case_	) == 0, f'Some keys where not used {",".join(snake_case_	)}'
  
  								logger.info(f'The following keys were not used: {",".join(snake_case_	)}'	)
  
  								# save our state dict to disk
  								torch.save(snake_case_,	save_directory / f'{model_name}.pth'	)
  
  								del converted_state_dict
  				else:
  								logger.info('''The state_dict was already stored on disk.'''	)
  				if push_to_hub:
  								logger.info(f'Token is {os.environ["HF_TOKEN"]}'	)
  								logger.info('''Loading our model.'''	)
  								# create our model
  								A__     :							Optional[int]   				=names_to_config[model_name]
  								A__     :							Any   				=RegNetModel
  								if "in1k" in model_name:
  												A__     :							Optional[int]   				=RegNetForImageClassification
  								A__     :							Optional[int]   				=our_model_func(snake_case_	)
  								# place our model to the meta device (so remove all the weights)
  								our_model.to(torch.device('''meta'''	)	)
  								logger.info('''Loading state_dict in our model.'''	)
  								# load state dict
  								A__     :							Any   				=our_model.state_dict().keys()
  								PreTrainedModel._load_pretrained_model_low_mem(
  								    snake_case_,	snake_case_,	[save_directory / f'{model_name}.pth']	)
  								logger.info('''Finally, pushing!'''	)
  								# push it to hub
  								our_model.push_to_hub(
  								    repo_path_or_name=save_directory / model_name,	commit_message='''Add model''',	output_dir=save_directory / model_name,	)
  								A__     :							List[Any]   				=3_8_4
  								# we can use the convnext one
  								A__     :							List[Any]   				=AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''',	size=snake_case_	)
  								image_processor.push_to_hub(
  								    repo_path_or_name=save_directory / model_name,	commit_message='''Add image processor''',	output_dir=save_directory / model_name,	)
  
  
  if __name__ == "__main__":
  						__lowerCamelCase       :       Any				   =			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       :       List[str]				   =			parser.parse_args()
  
  						__lowerCamelCase       :       Path				   =			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)
  
  
  
  