




'''simple docstring'''






import argparse
import json
from pathlib import Path

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

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


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

def 			__lowerCAmelCase					(    snake_case__					,      snake_case__=False					,      snake_case__=False						):
			__UpperCamelCase      :			List[str]									=   "backbone." if is_semantic else ""

			__UpperCamelCase      :			int									=   []
			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"),
			        (F"{prefix}pos_embed", "beit.embeddings.position_embeddings"),
			    ]						)

			if has_lm_head:
						# mask token + layernorm
						rename_keys.extend(
						    [
						        ("mask_token", "beit.embeddings.mask_token"),
						        ("norm.weight", "layernorm.weight"),
						        ("norm.bias", "layernorm.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 			__lowerCAmelCase					(    snake_case__					,      snake_case__					,      snake_case__=False					,      snake_case__=False						):





			for i in range(config.num_hidden_layers						):
						__UpperCamelCase      :			Any									=   "backbone." if is_semantic else ""
						# queries, keys and values
						__UpperCamelCase      :			str									=   state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"						)
						__UpperCamelCase      :			Dict									=   state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"						)
						__UpperCamelCase      :			List[Any]									=   state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"						)

						__UpperCamelCase      :			List[Any]									=   in_proj_weight[
						    : config.hidden_size, :
						]
						__UpperCamelCase      :			Union[str, Any]									=   q_bias
						__UpperCamelCase      :			Optional[Any]									=   in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						__UpperCamelCase      :			List[Any]									=   in_proj_weight[
						    -config.hidden_size :, :
						]
						__UpperCamelCase      :			List[Any]									=   v_bias

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

						__UpperCamelCase      :			str									=   gamma_a
						__UpperCamelCase      :			str									=   gamma_a

def 			__lowerCAmelCase					(    snake_case__					,      snake_case__					,      snake_case__						):
			__UpperCamelCase      :			List[Any]									=   dct.pop(snake_case__						)
			__UpperCamelCase      :			Optional[Any]									=   val

def 			__lowerCAmelCase					(    ):
			__UpperCamelCase      :			List[Any]									=   "http://images.cocodataset.org/val2017/000000039769.jpg"
			__UpperCamelCase      :			List[Any]									=   Image.open(requests.get(snake_case__					,      stream=snake_case__						).raw						)
			return im

@torch.no_grad()
def 			__lowerCAmelCase					(    snake_case__					,      snake_case__					,      snake_case__=False						):
			__UpperCamelCase      :			Any									=   False if "rvlcdip" in checkpoint_url else True
			__UpperCamelCase      :			Optional[Any]									=   BeitConfig(use_absolute_position_embeddings=snake_case__					,      use_mask_token=snake_case__						)

			# size of the architecture
			if "large" in checkpoint_url or "dit-l" in checkpoint_url:
						__UpperCamelCase      :			Union[str, Any]									=   1_024
						__UpperCamelCase      :			Optional[Any]									=   4_096
						__UpperCamelCase      :			Any									=   24
						__UpperCamelCase      :			Any									=   16

			# labels
			if "rvlcdip" in checkpoint_url:
						__UpperCamelCase      :			Optional[Any]									=   16
						__UpperCamelCase      :			Optional[int]									=   "huggingface/label-files"
						__UpperCamelCase      :			List[str]									=   "rvlcdip-id2label.json"
						__UpperCamelCase      :			Optional[int]									=   json.load(open(hf_hub_download(snake_case__					,      snake_case__					,      repo_type="dataset"						)					,      "r"						)						)
						__UpperCamelCase      :			Any									=   {int(snake_case__						): v for k, v in idalabel.items()}
						__UpperCamelCase      :			int									=   idalabel
						__UpperCamelCase      :			Union[str, Any]									=   {v: k for k, v in idalabel.items()}

			# load state_dict of original model, remove and rename some keys
			__UpperCamelCase      :			str									=   torch.hub.load_state_dict_from_url(snake_case__					,      map_location="cpu"						)["model"]

			__UpperCamelCase      :			Optional[Any]									=   create_rename_keys(snake_case__					,      has_lm_head=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__						)

			# load HuggingFace model
			__UpperCamelCase      :			Union[str, Any]									=   BeitForMaskedImageModeling(snake_case__						) if has_lm_head else BeitForImageClassification(snake_case__						)
			model.eval()
			model.load_state_dict(snake_case__						)

			# Check outputs on an image
			__UpperCamelCase      :			List[Any]									=   BeitImageProcessor(
			    size=config.image_size					,      resample=PILImageResampling.BILINEAR					,      do_center_crop=snake_case__						)
			__UpperCamelCase      :			int									=   prepare_img()

			__UpperCamelCase      :			Optional[Any]									=   image_processor(images=snake_case__					,      return_tensors="pt"						)
			__UpperCamelCase      :			Tuple									=   encoding["pixel_values"]

			__UpperCamelCase      :			Any									=   model(snake_case__						)
			__UpperCamelCase      :			Optional[Any]									=   outputs.logits

			# verify logits
			__UpperCamelCase      :			List[str]									=   [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8_192]
			assert logits.shape == torch.Size(snake_case__						), "Shape of logits 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 push_to_hub:
						if has_lm_head:
									__UpperCamelCase      :			Tuple									=   "dit-base" if "base" in checkpoint_url else "dit-large"
						else:
									__UpperCamelCase      :			Optional[Any]									=   "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
						image_processor.push_to_hub(
						    repo_path_or_name=Path(snake_case__					,      snake_case__						)					,      organization="nielsr"					,      commit_message="Add image processor"					,      use_temp_dir=snake_case__					,      )
						model.push_to_hub(
						    repo_path_or_name=Path(snake_case__					,      snake_case__						)					,      organization="nielsr"					,      commit_message="Add model"					,      use_temp_dir=snake_case__					,      )


if __name__ == "__main__":
				_lowerCAmelCase      	=					argparse.ArgumentParser()

				parser.add_argument(
				    '''--checkpoint_url''',
				    default='''https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.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.'''
				)
				parser.add_argument(
				    '''--push_to_hub''',
				    action='''store_true''',
				)
				_lowerCAmelCase      	=					parser.parse_args()
				convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""



def   __lowerCAmelCase						(						lowercase  :  Union[str, Any] )	->						List[str]:


    """simple docstring"""





    snake_case    :      List[str]      					=    len(lowercase )
    for i in range(length - 1 ):
        snake_case    :      List[str]      					=    i
        for k in range(i + 1						,							lowercase ):
            if collection[k] < collection[least]:
                snake_case    :      List[str]      					=    k
        if least != i:
            snake_case					,snake_case    :      Union[str, Any]      					=    (collection[i], collection[least])
    return collection


if __name__ == "__main__":
  __snake_case   			=    input("""Enter numbers separated by a comma:\n""").strip()
  __snake_case   			=    [int(item) for item in user_input.split(""",""")]
  print(selection_sort(unsorted))





