
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser




def 						A_			(				snake_case				: Optional[Any]=None			,   snake_case				: Optional[Any]=None			)   ->     Optional[Any]:



						'''simple docstring'''

						return field(default_factory=lambda: default			,   metadata=snake_case			)






@dataclass
class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							_snake_case  		=	field(
							    metadata={'help': 'The csv file to plot.'} ,		)
							_snake_case  		=	field(
							    default=SCREAMING_SNAKE_CASE_ ,		metadata={'help': 'Whether to plot along batch size or sequence length. Defaults to sequence length.'} ,		)
							_snake_case  		=	field(
							    default=SCREAMING_SNAKE_CASE_ ,		metadata={'help': 'Whether the csv file has time results or memory results. Defaults to memory results.'} ,		)
							_snake_case  		=	field(
							    default=SCREAMING_SNAKE_CASE_ ,		metadata={'help': 'Disable logarithmic scale when plotting'} ,		)
							_snake_case  		=	field(
							    default=SCREAMING_SNAKE_CASE_ ,		metadata={
							        'help': 'Whether the csv file has training results or inference results. Defaults to inference results.'
							    } ,		)
							_snake_case  		=	field(
							    default=SCREAMING_SNAKE_CASE_ ,		metadata={'help': 'Filename under which the plot will be saved. If unused no plot is saved.'} ,		)
							_snake_case  		=	list_field(
							    default=SCREAMING_SNAKE_CASE_ ,		metadata={'help': 'List of model names that are used instead of the ones in the csv file.'}     )





def 						A_			(				snake_case				: List[Any]			)   ->     str:



						'''simple docstring'''

						try:
												int(snake_case			)
												return True
						except ValueError:
												return False




def 						A_			(				snake_case				: List[str]			)   ->     int:



						'''simple docstring'''

						try:
												float(snake_case			)
												return True
						except ValueError:
												return False






class 							SCREAMING_SNAKE_CASE__     :

							"""simple docstring"""






							def __init__(	self  ,						SCREAMING_SNAKE_CASE_      )-> Any:





													'''simple docstring'''






													__UpperCamelCase  	=      args
													__UpperCamelCase  	=      defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}      )

													with open(self.args.csv_file  ,						newline=''''''      ) as csv_file:
																			__UpperCamelCase  	=      csv.DictReader(SCREAMING_SNAKE_CASE_      )
																			for row in reader:
																									__UpperCamelCase  	=      row['''model''']
																									self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']      )      )
																									self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']      )      )
																									if can_convert_to_int(row['''result''']      ):
																															# value is not None
																															__UpperCamelCase  	=      int(row['''result''']      )
																									elif can_convert_to_float(row['''result''']      ):
																															# value is not None
																															__UpperCamelCase  	=      float(row['''result''']      )



							def      A__		(	self      )-> int:





													'''simple docstring'''






													__UpperCamelCase	,				__UpperCamelCase  	=      plt.subplots()
													__UpperCamelCase  	=      '''Time usage''' if self.args.is_time else '''Memory usage'''
													__UpperCamelCase  	=      title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

													if not self.args.no_log_scale:
																			# set logarithm scales
																			ax.set_xscale('''log'''      )
																			ax.set_yscale('''log'''      )

													for axis in [ax.xaxis, ax.yaxis]:
																			axis.set_major_formatter(ScalarFormatter()      )

													for model_name_idx, model_name in enumerate(self.result_dict.keys()      ):
																			__UpperCamelCase  	=      sorted(set(self.result_dict[model_name]['''bsz''']      )      )
																			__UpperCamelCase  	=      sorted(set(self.result_dict[model_name]['''seq_len''']      )      )
																			__UpperCamelCase  	=      self.result_dict[model_name]['''result''']

																			((__UpperCamelCase)	,				(__UpperCamelCase))  	=      (
																			    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
																			)

																			__UpperCamelCase  	=      (
																			    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
																			)

																			for inner_loop_value in inner_loop_array:
																									if self.args.plot_along_batch:
																															__UpperCamelCase  	=      np.asarray(
																															    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]  ,						dtype=SCREAMING_SNAKE_CASE_  ,						)
																									else:
																															__UpperCamelCase  	=      np.asarray(
																															    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]  ,						dtype=np.floataa  ,						)

																									((__UpperCamelCase)	,				(__UpperCamelCase))  	=      (
																									    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
																									)

																									__UpperCamelCase  	=      np.asarray(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_      )[: len(SCREAMING_SNAKE_CASE_      )]
																									plt.scatter(
																									    SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"      )
																									plt.plot(SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						'''--'''      )

																			title_str += F" {label_model_name} vs."

													__UpperCamelCase  	=      title_str[:-4]
													__UpperCamelCase  	=      '''Time in s''' if self.args.is_time else '''Memory in MB'''

													# plot
													plt.title(SCREAMING_SNAKE_CASE_      )
													plt.xlabel(SCREAMING_SNAKE_CASE_      )
													plt.ylabel(SCREAMING_SNAKE_CASE_      )
													plt.legend()

													if self.args.figure_png_file is not None:
																			plt.savefig(self.args.figure_png_file      )
													else:
																			plt.show()





def 						A_			(				)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      HfArgumentParser(snake_case			)
						__UpperCamelCase  	=      parser.parse_args_into_dataclasses()[0]
						__UpperCamelCase  	=      Plot(args=snake_case			)
						plot.plot()


if __name__ == "__main__":
				main()






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)




