

"""simple docstring"""




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')

__A	:       Optional[Any]        =						'K-MEANS-CLUST/ '
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__=None ):
					"""simple docstring"""
					if seed is not None:  # useful for obtaining consistent results
										np.random.seed(lowercase__ )
					A     =   data.shape[0]  # number of data points

					# Pick K indices from range [0, N).
					A     =   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     =   data[rand_indices, :]

					return centroids
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					return pairwise_distances(lowercase__     ,				lowercase__     ,				metric="euclidean" )
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					# Compute distances between each data point and the set of centroids:
					# Fill in the blank (RHS only)
					A     =   centroid_pairwise_dist(lowercase__     ,				lowercase__ )

					# Compute cluster assignments for each data point:
					# Fill in the blank (RHS only)
					A     =   np.argmin(lowercase__     ,				axis=1 )

					return cluster_assignment
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   []
					for i in range(lowercase__ ):
										# Select all data points that belong to cluster i. Fill in the blank (RHS only)
										A     =   data[cluster_assignment == i]
										# Compute the mean of the data points. Fill in the blank (RHS only)
										A     =   member_data_points.mean(axis=0 )
										new_centroids.append(lowercase__ )
					A     =   np.array(lowercase__ )

					return new_centroids
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					A     =   0.0
					for i in range(lowercase__ ):
										# Select all data points that belong to cluster i. Fill in the blank (RHS only)
										A     =   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     =   pairwise_distances(
															    lowercase__     ,				[centroids[i]]     ,				metric="euclidean" )
															A     =   distances**2
															heterogeneity += np.sum(lowercase__ )

					return heterogeneity
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					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": 16} )
					plt.show()
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__=500     ,				lowercase__=None     ,				lowercase__=False ):
					"""simple docstring"""
					A     =   initial_centroids[:]
					A     =   None

					for itr in range(lowercase__ ):
										if verbose:
															print(lowercase__     ,				end="" )

										# 1. Make cluster assignments using nearest centroids
										A     =   assign_clusters(lowercase__     ,				lowercase__ )

										# 2. Compute a new centroid for each of the k clusters, averaging all data
										#    points assigned to that cluster.
										A     =   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     =   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     =   compute_heterogeneity(lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ )
															record_heterogeneity.append(lowercase__ )

										A     =   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

	__A	:       Union[str, Any]        =						ds.load_iris()
	__A	:       List[Any]        =						3
	__A	:       Optional[Any]        =						[]
	__A	:       Optional[int]        =						get_initial_centroids(dataset['data'], k, seed=0)
	__A    ,       __A	:       Dict        =						kmeans(
	    dataset['data'],
	    k,
	    initial_centroids,
	    maxiter=4_00,
	    record_heterogeneity=heterogeneity,
	    verbose=True,
	)
	plot_heterogeneity(heterogeneity, k)
def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__     ,				lowercase__=None ):
					"""simple docstring"""
					# Fill missing values with given rules
					if fill_missing_report:
										A     =   df.fillna(value=lowercase__ )
					A     =   1
					A     =   df.select_dtypes(np.number ).columns
					A     =   (
					    df.groupby(["Cluster"] )[  # construct report dataframe
					        numeric_cols
					    ]  # group by cluster number
					    .agg(
					        [
					            ("sum", np.sum),
					            ("mean_with_zeros", lambda lowercase__ : np.mean(np.nan_to_num(lowercase__ ) )),
					            ("mean_without_zeros", lambda lowercase__ : x.replace(0     ,				np.NaN ).mean()),
					            (
					                "mean_25-75",
					                lambda lowercase__ : np.mean(
					                    np.nan_to_num(
					                        sorted(lowercase__ )[
					                            round(len(lowercase__ ) * 25 / 100 ) : round(len(lowercase__ ) * 75 / 100 )
					                        ] ) ),
					            ),
					            ("mean_with_na", np.mean),
					            ("min", lambda lowercase__ : x.min()),
					            ("5%", lambda lowercase__ : x.quantile(0.05 )),
					            ("25%", lambda lowercase__ : x.quantile(0.25 )),
					            ("50%", lambda lowercase__ : x.quantile(0.50 )),
					            ("75%", lambda lowercase__ : x.quantile(0.75 )),
					            ("95%", lambda lowercase__ : x.quantile(0.95 )),
					            ("max", lambda lowercase__ : x.max()),
					            ("count", lambda lowercase__ : x.count()),
					            ("stdev", lambda lowercase__ : x.std()),
					            ("mode", lambda lowercase__ : x.mode()[0]),
					            ("median", lambda lowercase__ : x.median()),
					            ("# > 0", lambda lowercase__ : (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     =   report[
					    (report["Features"] == "dummy") & (report["Type"] == "count")
					].copy()  # avoid SettingWithCopyWarning
					A     =   (
					    "ClusterSize"  # rename created cluster df to match report column names
					)
					A     =   "# of Customers"
					A     =   pd.DataFrame(
					    clustersize.iloc[:, 2:].values
					    / clustersize.iloc[:, 2:].values.sum() )
					A     =   "% of Customers"  # rename created cluster df to match report column names
					A     =   "ClusterProportion"
					A     =   clusterproportion.columns.tolist()
					A     =   cols[-2:] + cols[:-2]
					A     =   clusterproportion[cols]  # rearrange columns to match report
					A     =   report.columns
					A     =   pd.DataFrame(
					    abs(
					        report[report["Type"] == "count"].iloc[:, 2:].values
					        - clustersize.iloc[:, 2:].values ) )  # generating df with count of nan values
					A     =   0
					A     =   "# of nan"
					A     =   report[
					    report["Type"] == "count"
					].Features.tolist()  # filling values in order to match report
					A     =   a.columns.tolist()
					A     =   cols[-2:] + cols[:-2]
					A     =   a[cols]  # rearrange columns to match report
					A     =   report.columns  # rename columns to match report
					A     =   report.drop(
					    report[report.Type == "count"].index )  # drop count values except cluster size
					A     =   pd.concat(
					    [report, a, clustersize, clusterproportion]     ,				axis=0 )  # concat report with clustert size and nan values
					A     =   report["Features"].isin(lowercase__ )
					A     =   report.columns.tolist()
					A     =   cols[0:2] + cols[-1:] + cols[2:-1]
					A     =   report[cols]
					A     =   {
					    "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     =   (
					    report.assign(
					        Sortera=lambda lowercase__ : x.Type.map(lowercase__ )     ,				Sortera=lambda lowercase__ : list(reversed(range(len(lowercase__ ) ) ) )     ,				)
					    .sort_values(["Sorter1", "Mark", "Sorter2"]     ,				ascending=lowercase__ )
					    .drop(["Sorter1", "Sorter2"]     ,				axis=1 )
					)
					A     =   ""
					A     =   report.reset_index()
					A     =   report.drop(columns=["index"] )
					return report


if __name__ == "__main__":
	import doctest

	doctest.testmod()







import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__				:				List[Any]						=      logging.get_logger(__name__)




def 						A_			(				snake_case				: int			,   snake_case				: Tuple=False			,   snake_case				: List[Any]=False			)   ->     List[str]:



						'''simple docstring'''

						__UpperCamelCase  	=      '''backbone.''' if is_semantic else ''''''

						__UpperCamelCase  	=      []
						for i in range(config.num_hidden_layers			):
												# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
												rename_keys.append((f"{prefix}blocks.{i}.norm1.weight", f"beit.encoder.layer.{i}.layernorm_before.weight")			)
												rename_keys.append((f"{prefix}blocks.{i}.norm1.bias", f"beit.encoder.layer.{i}.layernorm_before.bias")			)
												rename_keys.append(
												    (f"{prefix}blocks.{i}.attn.proj.weight", f"beit.encoder.layer.{i}.attention.output.dense.weight")			)
												rename_keys.append(
												    (f"{prefix}blocks.{i}.attn.proj.bias", f"beit.encoder.layer.{i}.attention.output.dense.bias")			)
												rename_keys.append((f"{prefix}blocks.{i}.norm2.weight", f"beit.encoder.layer.{i}.layernorm_after.weight")			)
												rename_keys.append((f"{prefix}blocks.{i}.norm2.bias", f"beit.encoder.layer.{i}.layernorm_after.bias")			)
												rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.weight", f"beit.encoder.layer.{i}.intermediate.dense.weight")			)
												rename_keys.append((f"{prefix}blocks.{i}.mlp.fc1.bias", f"beit.encoder.layer.{i}.intermediate.dense.bias")			)
												rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.weight", f"beit.encoder.layer.{i}.output.dense.weight")			)
												rename_keys.append((f"{prefix}blocks.{i}.mlp.fc2.bias", f"beit.encoder.layer.{i}.output.dense.bias")			)

						# projection layer + position embeddings
						rename_keys.extend(
						    [
						        (f"{prefix}cls_token", '''beit.embeddings.cls_token'''),
						        (f"{prefix}patch_embed.proj.weight", '''beit.embeddings.patch_embeddings.projection.weight'''),
						        (f"{prefix}patch_embed.proj.bias", '''beit.embeddings.patch_embeddings.projection.bias'''),
						    ]			)

						if has_lm_head:
												# mask token + shared relative position bias + layernorm
												rename_keys.extend(
												    [
												        ('''mask_token''', '''beit.embeddings.mask_token'''),
												        (
												            '''rel_pos_bias.relative_position_bias_table''',
												            '''beit.encoder.relative_position_bias.relative_position_bias_table''',
												        ),
												        (
												            '''rel_pos_bias.relative_position_index''',
												            '''beit.encoder.relative_position_bias.relative_position_index''',
												        ),
												        ('''norm.weight''', '''layernorm.weight'''),
												        ('''norm.bias''', '''layernorm.bias'''),
												    ]			)
						elif is_semantic:
												# semantic segmentation classification heads
												rename_keys.extend(
												    [
												        ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
												        ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
												        ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
												        ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
												    ]			)
						else:
												# layernorm + classification head
												rename_keys.extend(
												    [
												        ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
												        ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
												        ('''head.weight''', '''classifier.weight'''),
												        ('''head.bias''', '''classifier.bias'''),
												    ]			)

						return rename_keys




def 						A_			(				snake_case				: Union[str, Any]			,   snake_case				: List[Any]			,   snake_case				: Union[str, Any]=False			,   snake_case				: List[str]=False			)   ->     Union[str, Any]:



						'''simple docstring'''

						for i in range(config.num_hidden_layers			):
												__UpperCamelCase  	=      '''backbone.''' if is_semantic else ''''''
												# queries, keys and values
												__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.attn.qkv.weight"			)
												__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.attn.q_bias"			)
												__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.attn.v_bias"			)

												__UpperCamelCase  	=      in_proj_weight[
												    : config.hidden_size, :
												]
												__UpperCamelCase  	=      q_bias
												__UpperCamelCase  	=      in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												__UpperCamelCase  	=      in_proj_weight[
												    -config.hidden_size :, :
												]
												__UpperCamelCase  	=      v_bias

												# gamma_1 and gamma_2
												# we call them lambda because otherwise they are renamed when using .from_pretrained
												__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.gamma_1"			)
												__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.gamma_2"			)

												__UpperCamelCase  	=      gamma_a
												__UpperCamelCase  	=      gamma_a

												# relative_position bias table + index
												if not has_lm_head:
																		# each layer has its own relative position bias
																		__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_bias_table"			)
																		__UpperCamelCase  	=      state_dict.pop(f"{prefix}blocks.{i}.attn.relative_position_index"			)

																		__UpperCamelCase  	=      table
																		__UpperCamelCase  	=      index




def 						A_			(				snake_case				: Dict			,   snake_case				: Optional[int]			,   snake_case				: int			)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      dct.pop(snake_case			)
						__UpperCamelCase  	=      val




def 						A_			(				)   ->     Optional[Any]:



						'''simple docstring'''

						__UpperCamelCase  	=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__UpperCamelCase  	=      Image.open(requests.get(snake_case			,   stream=snake_case			).raw			)
						return im




@torch.no_grad()
def 						A_			(				snake_case				: Optional[Any]			,   snake_case				: Any			)   ->     Tuple:



						'''simple docstring'''

						__UpperCamelCase  	=      BeitConfig()
						__UpperCamelCase  	=      False
						__UpperCamelCase  	=      False
						__UpperCamelCase  	=      '''huggingface/label-files'''
						# set config parameters based on URL
						if checkpoint_url[-9:-4] == "pt22k":
												# masked image modeling
												__UpperCamelCase  	=      True
												__UpperCamelCase  	=      True
												__UpperCamelCase  	=      True
						elif checkpoint_url[-9:-4] == "ft22k":
												# intermediate fine-tuning on ImageNet-22k
												__UpperCamelCase  	=      True
												__UpperCamelCase  	=      21841
												__UpperCamelCase  	=      '''imagenet-22k-id2label.json'''
												__UpperCamelCase  	=      json.load(open(hf_hub_download(snake_case			,   snake_case			,   repo_type='''dataset'''			)			,   '''r'''			)			)
												__UpperCamelCase  	=      {int(snake_case			): v for k, v in idalabel.items()}
												# this dataset contains 21843 labels but the model only has 21841
												# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
												del idalabel[9205]
												del idalabel[15027]
												__UpperCamelCase  	=      idalabel
												__UpperCamelCase  	=      {v: k for k, v in idalabel.items()}
						elif checkpoint_url[-8:-4] == "to1k":
												# fine-tuning on ImageNet-1k
												__UpperCamelCase  	=      True
												__UpperCamelCase  	=      1000
												__UpperCamelCase  	=      '''imagenet-1k-id2label.json'''
												__UpperCamelCase  	=      json.load(open(hf_hub_download(snake_case			,   snake_case			,   repo_type='''dataset'''			)			,   '''r'''			)			)
												__UpperCamelCase  	=      {int(snake_case			): v for k, v in idalabel.items()}
												__UpperCamelCase  	=      idalabel
												__UpperCamelCase  	=      {v: k for k, v in idalabel.items()}
												if "384" in checkpoint_url:
																		__UpperCamelCase  	=      384
												if "512" in checkpoint_url:
																		__UpperCamelCase  	=      512
						elif "ade20k" in checkpoint_url:
												# fine-tuning
												__UpperCamelCase  	=      True
												__UpperCamelCase  	=      150
												__UpperCamelCase  	=      '''ade20k-id2label.json'''
												__UpperCamelCase  	=      json.load(open(hf_hub_download(snake_case			,   snake_case			,   repo_type='''dataset'''			)			,   '''r'''			)			)
												__UpperCamelCase  	=      {int(snake_case			): v for k, v in idalabel.items()}
												__UpperCamelCase  	=      idalabel
												__UpperCamelCase  	=      {v: k for k, v in idalabel.items()}
												__UpperCamelCase  	=      640
												__UpperCamelCase  	=      True
						else:
												raise ValueError('''Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''''			)

						# size of the architecture
						if "base" in checkpoint_url:
												pass
						elif "large" in checkpoint_url:
												__UpperCamelCase  	=      1024
												__UpperCamelCase  	=      4096
												__UpperCamelCase  	=      24
												__UpperCamelCase  	=      16
												if "ade20k" in checkpoint_url:
																		__UpperCamelCase  	=      640
																		__UpperCamelCase  	=      [7, 11, 15, 23]
						else:
												raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''			)

						# load state_dict of original model, remove and rename some keys
						__UpperCamelCase  	=      torch.hub.load_state_dict_from_url(snake_case			,   map_location='''cpu'''			,   check_hash=snake_case			)
						__UpperCamelCase  	=      state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

						__UpperCamelCase  	=      create_rename_keys(snake_case			,   has_lm_head=snake_case			,   is_semantic=snake_case			)
						for src, dest in rename_keys:
												rename_key(snake_case			,   snake_case			,   snake_case			)
						read_in_q_k_v(snake_case			,   snake_case			,   has_lm_head=snake_case			,   is_semantic=snake_case			)
						if is_semantic:
												# add prefix to decoder keys
												for key, val in state_dict.copy().items():
																		__UpperCamelCase  	=      state_dict.pop(snake_case			)
																		if key.startswith('''backbone.fpn'''			):
																								__UpperCamelCase  	=      key.replace('''backbone.fpn'''			,   '''fpn'''			)
																		__UpperCamelCase  	=      val

    # load HuggingFace model
						if checkpoint_url[-9:-4] == "pt22k":
												__UpperCamelCase  	=      BeitForMaskedImageModeling(snake_case			)
						elif "ade20k" in checkpoint_url:
												__UpperCamelCase  	=      BeitForSemanticSegmentation(snake_case			)
						else:
												__UpperCamelCase  	=      BeitForImageClassification(snake_case			)
						model.eval()
						model.load_state_dict(snake_case			)

						# Check outputs on an image
						if is_semantic:
												__UpperCamelCase  	=      BeitImageProcessor(size=config.image_size			,   do_center_crop=snake_case			)
												__UpperCamelCase  	=      load_dataset('''hf-internal-testing/fixtures_ade20k'''			,   split='''test'''			)
												__UpperCamelCase  	=      Image.open(ds[0]['''file''']			)
						else:
												__UpperCamelCase  	=      BeitImageProcessor(
												    size=config.image_size			,   resample=PILImageResampling.BILINEAR			,   do_center_crop=snake_case			)
												__UpperCamelCase  	=      prepare_img()

						__UpperCamelCase  	=      image_processor(images=snake_case			,   return_tensors='''pt'''			)
						__UpperCamelCase  	=      encoding['''pixel_values''']

						__UpperCamelCase  	=      model(snake_case			)
						__UpperCamelCase  	=      outputs.logits

						# verify logits
						__UpperCamelCase  	=      torch.Size([1, 1000]			)
						if checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k'''			):
												__UpperCamelCase  	=      torch.Size([1, 196, 8192]			)
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k'''			):
												__UpperCamelCase  	=      torch.Size([1, 196, 8192]			)
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22k'''			):
												__UpperCamelCase  	=      torch.Size([1, 21841]			)
												__UpperCamelCase  	=      torch.tensor([2.2288, 2.4671, 0.7395]			)
												__UpperCamelCase  	=      2397
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''			):
												__UpperCamelCase  	=      torch.Size([1, 21841]			)
												__UpperCamelCase  	=      torch.tensor([1.6881, -0.2787, 0.5901]			)
												__UpperCamelCase  	=      2396
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''			):
												__UpperCamelCase  	=      torch.tensor([0.1241, 0.0798, -0.6569]			)
												__UpperCamelCase  	=      285
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''			):
												__UpperCamelCase  	=      torch.tensor([-1.2385, -1.0987, -1.0108]			)
												__UpperCamelCase  	=      281
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''			):
												__UpperCamelCase  	=      torch.tensor([-1.5303, -0.9484, -0.3147]			)
												__UpperCamelCase  	=      761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''			):
												__UpperCamelCase  	=      torch.tensor([0.4610, -0.0928, 0.2086]			)
												__UpperCamelCase  	=      761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''			):
												__UpperCamelCase  	=      torch.tensor([-0.4804, 0.6257, -0.1837]			)
												__UpperCamelCase  	=      761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''			):
												__UpperCamelCase  	=      torch.tensor([[-0.5122, 0.5117, -0.2113]]			)
												__UpperCamelCase  	=      761
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''			):
												__UpperCamelCase  	=      torch.tensor([-0.3062, 0.7261, 0.4852]			)
												__UpperCamelCase  	=      761
						elif checkpoint_url[:-4].endswith('''beit_base_patch16_640_pt22k_ft22ktoade20k'''			):
												__UpperCamelCase  	=      (1, 150, 160, 160)
												__UpperCamelCase  	=      torch.tensor(
												    [
												        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
												        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
												        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
												    ]			)
						elif checkpoint_url[:-4].endswith('''beit_large_patch16_640_pt22k_ft22ktoade20k'''			):
												__UpperCamelCase  	=      (1, 150, 160, 160)
												__UpperCamelCase  	=      torch.tensor(
												    [
												        [[-4.3305, -2.3049, -3.0161], [-2.9591, -1.5305, -2.2251], [-3.4198, -1.8004, -2.9062]],
												        [[-5.8922, -3.7435, -4.3978], [-4.2063, -2.7872, -3.4755], [-4.2791, -3.1874, -4.1681]],
												        [[0.9895, 4.3467, 4.7663], [4.2476, 5.6830, 6.1518], [4.5550, 6.2495, 6.5154]],
												    ]			)
						else:
												raise ValueError('''Can\'t verify logits as model is not supported'''			)

						assert logits.shape == expected_shape, "Shape of logits not as expected"
						if not has_lm_head:
												if is_semantic:
																		assert torch.allclose(
																		    logits[0, :3, :3, :3]			,   snake_case			,   atol=1e-3			), "First elements of logits not as expected"
												else:
																		print('''Predicted class idx:'''			,   logits.argmax(-1			).item()			)
																		assert torch.allclose(
																		    logits[0, :3]			,   snake_case			,   atol=1e-3			), "First elements of logits not as expected"
																		assert logits.argmax(-1			).item() == expected_class_idx, "Predicted class index not as expected"

						Path(snake_case			).mkdir(exist_ok=snake_case			)
						print(f"Saving model to {pytorch_dump_folder_path}"			)
						model.save_pretrained(snake_case			)
						print(f"Saving image processor to {pytorch_dump_folder_path}"			)
						image_processor.save_pretrained(snake_case			)


if __name__ == "__main__":
				lowercase__				:				Any						=      argparse.ArgumentParser()

				parser.add_argument(
				    "--checkpoint_url",
				    default="https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth",
				    type=str,
				    help="URL to the original PyTorch checkpoint (.pth file).",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
				)
				lowercase__				:				int						=      parser.parse_args()
				convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




