"""simple docstring"""



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()
__magic_name__    				=					logging.get_logger(__name__)





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__=False,UpperCAmelCase__=False					)							->     Union[str, Any]:


   '''simple docstring'''


   a__     				=		'backbone.' if is_semantic else ''

   a__     				=		[]
   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 ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__=False,UpperCAmelCase__=False					)							->     Union[str, Any]:


   '''simple docstring'''


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

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

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

      a__     				=		gamma_a
      a__     				=		gamma_a

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

         a__     				=		table
         a__     				=		index





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)							->     int:


   '''simple docstring'''


   a__     				=		dct.pop(UpperCAmelCase__					)
   a__     				=		val





def   _lowerCamelCase ( )							->     List[str]:


   '''simple docstring'''


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





@torch.no_grad()
def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__					)							->     Optional[int]:


   '''simple docstring'''


   a__     				=		BeitConfig()
   a__     				=		False
   a__     				=		False
   a__     				=		'huggingface/label-files'
   # set config parameters based on URL
   if checkpoint_url[-9:-4] == "pt22k":
      # masked image modeling
      a__     				=		True
      a__     				=		True
      a__     				=		True
   elif checkpoint_url[-9:-4] == "ft22k":
      # intermediate fine-tuning on ImageNet-22k
      a__     				=		True
      a__     				=		2_18_41
      a__     				=		'imagenet-22k-id2label.json'
      a__     				=		json.load(open(hf_hub_download(UpperCAmelCase__,UpperCAmelCase__,repo_type='dataset'					),'r'					)					)
      a__     				=		{int(UpperCAmelCase__					): 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[92_05]
      del idalabel[1_50_27]
      a__     				=		idalabel
      a__     				=		{v: k for k, v in idalabel.items()}
   elif checkpoint_url[-8:-4] == "to1k":
      # fine-tuning on ImageNet-1k
      a__     				=		True
      a__     				=		10_00
      a__     				=		'imagenet-1k-id2label.json'
      a__     				=		json.load(open(hf_hub_download(UpperCAmelCase__,UpperCAmelCase__,repo_type='dataset'					),'r'					)					)
      a__     				=		{int(UpperCAmelCase__					): v for k, v in idalabel.items()}
      a__     				=		idalabel
      a__     				=		{v: k for k, v in idalabel.items()}
      if "384" in checkpoint_url:
         a__     				=		3_84
      if "512" in checkpoint_url:
         a__     				=		5_12
   elif "ade20k" in checkpoint_url:
      # fine-tuning
      a__     				=		True
      a__     				=		1_50
      a__     				=		'ade20k-id2label.json'
      a__     				=		json.load(open(hf_hub_download(UpperCAmelCase__,UpperCAmelCase__,repo_type='dataset'					),'r'					)					)
      a__     				=		{int(UpperCAmelCase__					): v for k, v in idalabel.items()}
      a__     				=		idalabel
      a__     				=		{v: k for k, v in idalabel.items()}
      a__     				=		6_40
      a__     				=		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:
      a__     				=		10_24
      a__     				=		40_96
      a__     				=		24
      a__     				=		16
      if "ade20k" in checkpoint_url:
         a__     				=		6_40
         a__     				=		[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
   a__     				=		torch.hub.load_state_dict_from_url(UpperCAmelCase__,map_location='cpu',check_hash=UpperCAmelCase__					)
   a__     				=		state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

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

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

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

   a__     				=		image_processor(images=UpperCAmelCase__,return_tensors='pt'					)
   a__     				=		encoding['pixel_values']

   a__     				=		model(UpperCAmelCase__					)
   a__     				=		outputs.logits

   # verify logits
   a__     				=		torch.Size([1, 10_00]					)
   if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'					):
      a__     				=		torch.Size([1, 1_96, 81_92]					)
   elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'					):
      a__     				=		torch.Size([1, 1_96, 81_92]					)
   elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'					):
      a__     				=		torch.Size([1, 2_18_41]					)
      a__     				=		torch.tensor([2.2288, 2.4671, 0.7395]					)
      a__     				=		23_97
   elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'					):
      a__     				=		torch.Size([1, 2_18_41]					)
      a__     				=		torch.tensor([1.6881, -0.2787, 0.5901]					)
      a__     				=		23_96
   elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'					):
      a__     				=		torch.tensor([0.1241, 0.0798, -0.6569]					)
      a__     				=		2_85
   elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'					):
      a__     				=		torch.tensor([-1.2385, -1.0987, -1.0108]					)
      a__     				=		2_81
   elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'					):
      a__     				=		torch.tensor([-1.5303, -0.9484, -0.3147]					)
      a__     				=		7_61
   elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'					):
      a__     				=		torch.tensor([0.4610, -0.0928, 0.2086]					)
      a__     				=		7_61
   elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'					):
      a__     				=		torch.tensor([-0.4804, 0.6257, -0.1837]					)
      a__     				=		7_61
   elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'					):
      a__     				=		torch.tensor([[-0.5122, 0.5117, -0.2113]]					)
      a__     				=		7_61
   elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'					):
      a__     				=		torch.tensor([-0.3062, 0.7261, 0.4852]					)
      a__     				=		7_61
   elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'					):
      a__     				=		(1, 1_50, 1_60, 1_60)
      a__     				=		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'					):
      a__     				=		(1, 1_50, 1_60, 1_60)
      a__     				=		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],UpperCAmelCase__,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],UpperCAmelCase__,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(UpperCAmelCase__					).mkdir(exist_ok=UpperCAmelCase__					)
   print(f'''Saving model to {pytorch_dump_folder_path}'''					)
   model.save_pretrained(UpperCAmelCase__					)
   print(f'''Saving image processor to {pytorch_dump_folder_path}'''					)
   image_processor.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     __magic_name__    				=					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."
     )
     __magic_name__    				=					parser.parse_args()
     convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



"""simple docstring"""



import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__					)							->     Optional[int]:


   '''simple docstring'''


   a__     				=		CanineConfig()
   a__     				=		CanineModel(UpperCAmelCase__					)
   model.eval()

   print(f'''Building PyTorch model from configuration: {config}'''					)

   # Load weights from tf checkpoint
   load_tf_weights_in_canine(UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)

   # Save pytorch-model (weights and configuration)
   print(f'''Save PyTorch model to {pytorch_dump_path}'''					)
   model.save_pretrained(UpperCAmelCase__					)

   # Save tokenizer files
   a__     				=		CanineTokenizer()
   print(f'''Save tokenizer files to {pytorch_dump_path}'''					)
   tokenizer.save_pretrained(UpperCAmelCase__					)


if __name__ == "__main__":
     __magic_name__    				=					argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--tf_checkpoint_path",
         default=None,
         type=str,
         required=True,
         help="Path to the TensorFlow checkpoint. Should end with model.ckpt",
     )
     parser.add_argument(
         "--pytorch_dump_path",
         default=None,
         type=str,
         required=True,
         help="Path to a folder where the PyTorch model will be placed.",
     )
     __magic_name__    				=					parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)


