code: 
  
  
  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()
  lowerCAmelCase					 =	logging.get_logger(__name__)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__=False     ,				lowercase__=False			)		->			Union[str, Any]:
  
  						'''simple docstring'''
  
  
  
  
  
  
  
  						__lowercase=    'backbone.' if is_semantic else ''
  
  						__lowercase=    []
  						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  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__=False     ,				lowercase__=False			)		->			Dict:
  
  						'''simple docstring'''
  
  
  
  
  
  
  
  						for i in range(config.num_hidden_layers			):
  												__lowercase=    'backbone.' if is_semantic else ''
  												# queries, keys and values
  												__lowercase=    state_dict.pop(F'{prefix}blocks.{i}.attn.qkv.weight'			)
  												__lowercase=    state_dict.pop(F'{prefix}blocks.{i}.attn.q_bias'			)
  												__lowercase=    state_dict.pop(F'{prefix}blocks.{i}.attn.v_bias'			)
  
  												__lowercase=    in_proj_weight[
  												    : config.hidden_size, :
  												]
  												__lowercase=    q_bias
  												__lowercase=    in_proj_weight[
  												    config.hidden_size : config.hidden_size * 2, :
  												]
  												__lowercase=    in_proj_weight[
  												    -config.hidden_size :, :
  												]
  												__lowercase=    v_bias
  
  												# gamma_1 and gamma_2
  												# we call them lambda because otherwise they are renamed when using .from_pretrained
  												__lowercase=    state_dict.pop(F'{prefix}blocks.{i}.gamma_1'			)
  												__lowercase=    state_dict.pop(F'{prefix}blocks.{i}.gamma_2'			)
  
  												__lowercase=    gamma_a
  												__lowercase=    gamma_a
  
  												# relative_position bias table + index
  												if not has_lm_head:
  																		# each layer has its own relative position bias
  																		__lowercase=    state_dict.pop(F'{prefix}blocks.{i}.attn.relative_position_bias_table'			)
  																		__lowercase=    state_dict.pop(F'{prefix}blocks.{i}.attn.relative_position_index'			)
  
  																		__lowercase=    table
  																		__lowercase=    index
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__			)		->			List[str]:
  
  						'''simple docstring'''
  
  
  
  
  
  
  
  						__lowercase=    dct.pop(_SCREAMING_SNAKE_CASE			)
  						__lowercase=    val
  
  
  
  
  
  
  
  def  _lowerCamelCase(			)		->			int:
  
  						'''simple docstring'''
  
  
  
  
  
  
  
  						__lowercase=    'http://images.cocodataset.org/val2017/000000039769.jpg'
  						__lowercase=    Image.open(requests.get(_SCREAMING_SNAKE_CASE     ,				stream=_SCREAMING_SNAKE_CASE			).raw			)
  						return im
  
  
  
  
  
  
  
  @torch.no_grad()
  def  _lowerCamelCase(			lowercase__     ,				lowercase__			)		->			Optional[int]:
  
  						'''simple docstring'''
  
  
  
  
  
  
  
  						__lowercase=    BeitConfig()
  						__lowercase=    False
  						__lowercase=    False
  						__lowercase=    'huggingface/label-files'
  						# set config parameters based on URL
  						if checkpoint_url[-9:-4] == "pt22k":
  												# masked image modeling
  												__lowercase=    True
  												__lowercase=    True
  												__lowercase=    True
  						elif checkpoint_url[-9:-4] == "ft22k":
  												# intermediate fine-tuning on ImageNet-22k
  												__lowercase=    True
  												__lowercase=    2_1_8_4_1
  												__lowercase=    'imagenet-22k-id2label.json'
  												__lowercase=    json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				repo_type='dataset'			)     ,				'r'			)			)
  												__lowercase=    {int(_SCREAMING_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[9_2_0_5]
  												del idalabel[1_5_0_2_7]
  												__lowercase=    idalabel
  												__lowercase=    {v: k for k, v in idalabel.items()}
  						elif checkpoint_url[-8:-4] == "to1k":
  												# fine-tuning on ImageNet-1k
  												__lowercase=    True
  												__lowercase=    1_0_0_0
  												__lowercase=    'imagenet-1k-id2label.json'
  												__lowercase=    json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				repo_type='dataset'			)     ,				'r'			)			)
  												__lowercase=    {int(_SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}
  												__lowercase=    idalabel
  												__lowercase=    {v: k for k, v in idalabel.items()}
  												if "384" in checkpoint_url:
  																		__lowercase=    3_8_4
  												if "512" in checkpoint_url:
  																		__lowercase=    5_1_2
  						elif "ade20k" in checkpoint_url:
  												# fine-tuning
  												__lowercase=    True
  												__lowercase=    1_5_0
  												__lowercase=    'ade20k-id2label.json'
  												__lowercase=    json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				repo_type='dataset'			)     ,				'r'			)			)
  												__lowercase=    {int(_SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}
  												__lowercase=    idalabel
  												__lowercase=    {v: k for k, v in idalabel.items()}
  												__lowercase=    6_4_0
  												__lowercase=    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:
  												__lowercase=    1_0_2_4
  												__lowercase=    4_0_9_6
  												__lowercase=    2_4
  												__lowercase=    1_6
  												if "ade20k" in checkpoint_url:
  																		__lowercase=    6_4_0
  																		__lowercase=    [7, 1_1, 1_5, 2_3]
  						else:
  												raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'			)
  
  						# load state_dict of original model, remove and rename some keys
  						__lowercase=    torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE     ,				map_location='cpu'     ,				check_hash=_SCREAMING_SNAKE_CASE			)
  						__lowercase=    state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']
  
  						__lowercase=    create_rename_keys(_SCREAMING_SNAKE_CASE     ,				has_lm_head=_SCREAMING_SNAKE_CASE     ,				is_semantic=_SCREAMING_SNAKE_CASE			)
  						for src, dest in rename_keys:
  												rename_key(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE			)
  						read_in_q_k_v(_SCREAMING_SNAKE_CASE     ,				_SCREAMING_SNAKE_CASE     ,				has_lm_head=_SCREAMING_SNAKE_CASE     ,				is_semantic=_SCREAMING_SNAKE_CASE			)
  						if is_semantic:
  												# add prefix to decoder keys
  												for key, val in state_dict.copy().items():
  																		__lowercase=    state_dict.pop(_SCREAMING_SNAKE_CASE			)
  																		if key.startswith('backbone.fpn'			):
  																								__lowercase=    key.replace('backbone.fpn'     ,				'fpn'			)
  																		__lowercase=    val
  
      # load HuggingFace model
  						if checkpoint_url[-9:-4] == "pt22k":
  												__lowercase=    BeitForMaskedImageModeling(_SCREAMING_SNAKE_CASE			)
  						elif "ade20k" in checkpoint_url:
  												__lowercase=    BeitForSemanticSegmentation(_SCREAMING_SNAKE_CASE			)
  						else:
  												__lowercase=    BeitForImageClassification(_SCREAMING_SNAKE_CASE			)
  						model.eval()
  						model.load_state_dict(_SCREAMING_SNAKE_CASE			)
  
  						# Check outputs on an image
  						if is_semantic:
  												__lowercase=    BeitImageProcessor(size=config.image_size     ,				do_center_crop=_SCREAMING_SNAKE_CASE			)
  												__lowercase=    load_dataset('hf-internal-testing/fixtures_ade20k'     ,				split='test'			)
  												__lowercase=    Image.open(ds[0]['file']			)
  						else:
  												__lowercase=    BeitImageProcessor(
  												    size=config.image_size     ,				resample=PILImageResampling.BILINEAR     ,				do_center_crop=_SCREAMING_SNAKE_CASE			)
  												__lowercase=    prepare_img()
  
  						__lowercase=    image_processor(images=_SCREAMING_SNAKE_CASE     ,				return_tensors='pt'			)
  						__lowercase=    encoding['pixel_values']
  
  						__lowercase=    model(_SCREAMING_SNAKE_CASE			)
  						__lowercase=    outputs.logits
  
  						# verify logits
  						__lowercase=    torch.Size([1, 1_0_0_0]			)
  						if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'			):
  												__lowercase=    torch.Size([1, 1_9_6, 8_1_9_2]			)
  						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'			):
  												__lowercase=    torch.Size([1, 1_9_6, 8_1_9_2]			)
  						elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'			):
  												__lowercase=    torch.Size([1, 2_1_8_4_1]			)
  												__lowercase=    torch.tensor([2.2288, 2.4671, 0.7395]			)
  												__lowercase=    2_3_9_7
  						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'			):
  												__lowercase=    torch.Size([1, 2_1_8_4_1]			)
  												__lowercase=    torch.tensor([1.6881, -0.2787, 0.5901]			)
  												__lowercase=    2_3_9_6
  						elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'			):
  												__lowercase=    torch.tensor([0.1241, 0.0798, -0.6569]			)
  												__lowercase=    2_8_5
  						elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'			):
  												__lowercase=    torch.tensor([-1.2385, -1.0987, -1.0108]			)
  												__lowercase=    2_8_1
  						elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'			):
  												__lowercase=    torch.tensor([-1.5303, -0.9484, -0.3147]			)
  												__lowercase=    7_6_1
  						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'			):
  												__lowercase=    torch.tensor([0.4610, -0.0928, 0.2086]			)
  												__lowercase=    7_6_1
  						elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'			):
  												__lowercase=    torch.tensor([-0.4804, 0.6257, -0.1837]			)
  												__lowercase=    7_6_1
  						elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'			):
  												__lowercase=    torch.tensor([[-0.5122, 0.5117, -0.2113]]			)
  												__lowercase=    7_6_1
  						elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'			):
  												__lowercase=    torch.tensor([-0.3062, 0.7261, 0.4852]			)
  												__lowercase=    7_6_1
  						elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'			):
  												__lowercase=    (1, 1_5_0, 1_6_0, 1_6_0)
  												__lowercase=    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'			):
  												__lowercase=    (1, 1_5_0, 1_6_0, 1_6_0)
  												__lowercase=    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]     ,				_SCREAMING_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]     ,				_SCREAMING_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(_SCREAMING_SNAKE_CASE			).mkdir(exist_ok=_SCREAMING_SNAKE_CASE			)
  						print(F'Saving model to {pytorch_dump_folder_path}'			)
  						model.save_pretrained(_SCREAMING_SNAKE_CASE			)
  						print(F'Saving image processor to {pytorch_dump_folder_path}'			)
  						image_processor.save_pretrained(_SCREAMING_SNAKE_CASE			)
  
  
  if __name__ == "__main__":
  	lowerCAmelCase					 =	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.'''
  	)
  	lowerCAmelCase					 =	parser.parse_args()
  	convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
  
  
  
  
  
  
  
style_context: 
  
  
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  @dataclass
  # Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
  class    A					(     A_				):
  							UpperCamelCase_     :							Union[List[PIL.Image.Image], np.ndarray]
  							UpperCamelCase_     :							Optional[List[bool]]
  
  
  if is_transformers_available() and is_torch_available():
  	from .modeling_roberta_series import RobertaSeriesModelWithTransformation
  	from .pipeline_alt_diffusion import AltDiffusionPipeline
  	from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
  
  
  
  
  
  
  