





"""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()
lowerCamelCase_	:		Union[str, Any]         =   logging.get_logger(__name__)





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase=False					,   _UpperCAmelCase=False   ):






  """simple docstring"""





  A_    :			Optional[Any]									=    'backbone.' if is_semantic else ''

  A_    :			str									=    []
  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 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase=False					,   _UpperCAmelCase=False   ):






  """simple docstring"""





  for i in range(config.num_hidden_layers   ):
    A_    :			Optional[int]									=    'backbone.' if is_semantic else ''
    # queries, keys and values
    A_    :			Dict									=    state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight"""   )
    A_    :			Optional[Any]									=    state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias"""   )
    A_    :			Tuple									=    state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias"""   )

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

    # gamma_1 and gamma_2
    # we call them lambda because otherwise they are renamed when using .from_pretrained
    A_    :			List[str]									=    state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""   )
    A_    :			Any									=    state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""   )

    A_    :			str									=    gamma_a
    A_    :			Any									=    gamma_a

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

      A_    :			Tuple									=    table
      A_    :			Any									=    index





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			int									=    dct.pop(_UpperCAmelCase   )
  A_    :			List[str]									=    val





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			Any									=    'http://images.cocodataset.org/val2017/000000039769.jpg'
  A_    :			int									=    Image.open(requests.get(_UpperCAmelCase					,   stream=_UpperCAmelCase   ).raw   )
  return im





@torch.no_grad()
def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			List[Any]									=    BeitConfig()
  A_    :			Optional[int]									=    False
  A_    :			Any									=    False
  A_    :			Any									=    'huggingface/label-files'
  # set config parameters based on URL
  if checkpoint_url[-9:-4] == "pt22k":
    # masked image modeling
    A_    :			str									=    True
    A_    :			Tuple									=    True
    A_    :			Optional[int]									=    True
  elif checkpoint_url[-9:-4] == "ft22k":
    # intermediate fine-tuning on ImageNet-22k
    A_    :			Any									=    True
    A_    :			Optional[int]									=    21841
    A_    :			Any									=    'imagenet-22k-id2label.json'
    A_    :			List[Any]									=    json.load(open(hf_hub_download(_UpperCAmelCase					,   _UpperCAmelCase					,   repo_type='dataset'   )					,   'r'   )   )
    A_    :			Any									=    {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[9205]
    del idalabel[15027]
    A_    :			str									=    idalabel
    A_    :			int									=    {v: k for k, v in idalabel.items()}
  elif checkpoint_url[-8:-4] == "to1k":
    # fine-tuning on ImageNet-1k
    A_    :			Tuple									=    True
    A_    :			Union[str, Any]									=    1000
    A_    :			List[str]									=    'imagenet-1k-id2label.json'
    A_    :			str									=    json.load(open(hf_hub_download(_UpperCAmelCase					,   _UpperCAmelCase					,   repo_type='dataset'   )					,   'r'   )   )
    A_    :			Optional[Any]									=    {int(_UpperCAmelCase   ): v for k, v in idalabel.items()}
    A_    :			List[str]									=    idalabel
    A_    :			List[str]									=    {v: k for k, v in idalabel.items()}
    if "384" in checkpoint_url:
      A_    :			int									=    384
    if "512" in checkpoint_url:
      A_    :			Any									=    512
  elif "ade20k" in checkpoint_url:
    # fine-tuning
    A_    :			Any									=    True
    A_    :			Union[str, Any]									=    150
    A_    :			List[str]									=    'ade20k-id2label.json'
    A_    :			Tuple									=    json.load(open(hf_hub_download(_UpperCAmelCase					,   _UpperCAmelCase					,   repo_type='dataset'   )					,   'r'   )   )
    A_    :			Union[str, Any]									=    {int(_UpperCAmelCase   ): v for k, v in idalabel.items()}
    A_    :			Dict									=    idalabel
    A_    :			Union[str, Any]									=    {v: k for k, v in idalabel.items()}
    A_    :			List[Any]									=    640
    A_    :			List[Any]									=    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_    :			Optional[int]									=    1024
    A_    :			int									=    4096
    A_    :			str									=    24
    A_    :			Union[str, Any]									=    16
    if "ade20k" in checkpoint_url:
      A_    :			Optional[int]									=    640
      A_    :			str									=    [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_    :			List[Any]									=    torch.hub.load_state_dict_from_url(_UpperCAmelCase					,   map_location='cpu'					,   check_hash=_UpperCAmelCase   )
  A_    :			Any									=    state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

  A_    :			Any									=    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_    :			str									=    state_dict.pop(_UpperCAmelCase   )
      if key.startswith('backbone.fpn'   ):
        A_    :			List[Any]									=    key.replace('backbone.fpn'					,   'fpn'   )
      A_    :			int									=    val

    # load HuggingFace model
  if checkpoint_url[-9:-4] == "pt22k":
    A_    :			int									=    BeitForMaskedImageModeling(_UpperCAmelCase   )
  elif "ade20k" in checkpoint_url:
    A_    :			str									=    BeitForSemanticSegmentation(_UpperCAmelCase   )
  else:
    A_    :			Tuple									=    BeitForImageClassification(_UpperCAmelCase   )
  model.eval()
  model.load_state_dict(_UpperCAmelCase   )

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

  A_    :			Dict									=    image_processor(images=_UpperCAmelCase					,   return_tensors='pt'   )
  A_    :			Union[str, Any]									=    encoding['pixel_values']

  A_    :			int									=    model(_UpperCAmelCase   )
  A_    :			Union[str, Any]									=    outputs.logits

  # verify logits
  A_    :			List[str]									=    torch.Size([1, 1000]   )
  if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'   ):
    A_    :			Optional[int]									=    torch.Size([1, 196, 8192]   )
  elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'   ):
    A_    :			int									=    torch.Size([1, 196, 8192]   )
  elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'   ):
    A_    :			List[str]									=    torch.Size([1, 21841]   )
    A_    :			str									=    torch.tensor([2.2_288, 2.4_671, 0.7_395]   )
    A_    :			Tuple									=    2397
  elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'   ):
    A_    :			Union[str, Any]									=    torch.Size([1, 21841]   )
    A_    :			Dict									=    torch.tensor([1.6_881, -0.2_787, 0.5_901]   )
    A_    :			Dict									=    2396
  elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'   ):
    A_    :			Tuple									=    torch.tensor([0.1_241, 0.0_798, -0.6_569]   )
    A_    :			Union[str, Any]									=    285
  elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'   ):
    A_    :			Union[str, Any]									=    torch.tensor([-1.2_385, -1.0_987, -1.0_108]   )
    A_    :			Tuple									=    281
  elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'   ):
    A_    :			Tuple									=    torch.tensor([-1.5_303, -0.9_484, -0.3_147]   )
    A_    :			Optional[int]									=    761
  elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'   ):
    A_    :			List[str]									=    torch.tensor([0.4_610, -0.0_928, 0.2_086]   )
    A_    :			str									=    761
  elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'   ):
    A_    :			str									=    torch.tensor([-0.4_804, 0.6_257, -0.1_837]   )
    A_    :			Any									=    761
  elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'   ):
    A_    :			Optional[int]									=    torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]   )
    A_    :			Any									=    761
  elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'   ):
    A_    :			Any									=    torch.tensor([-0.3_062, 0.7_261, 0.4_852]   )
    A_    :			List[str]									=    761
  elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'   ):
    A_    :			int									=    (1, 150, 160, 160)
    A_    :			List[Any]									=    torch.tensor(
        [
            [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
            [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
            [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
        ]   )
  elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'   ):
    A_    :			str									=    (1, 150, 160, 160)
    A_    :			Optional[int]									=    torch.tensor(
        [
            [[-4.3_305, -2.3_049, -3.0_161], [-2.9_591, -1.5_305, -2.2_251], [-3.4_198, -1.8_004, -2.9_062]],
            [[-5.8_922, -3.7_435, -4.3_978], [-4.2_063, -2.7_872, -3.4_755], [-4.2_791, -3.1_874, -4.1_681]],
            [[0.9_895, 4.3_467, 4.7_663], [4.2_476, 5.6_830, 6.1_518], [4.5_550, 6.2_495, 6.5_154]],
        ]   )
  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__":
   lowerCamelCase_	:		str         =   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_	:		Optional[int]         =   parser.parse_args()
   convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



'''simple docstring'''



import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir



@dataclass
class       a							:


     """simple docstring"""






     SCREAMING_SNAKE_CASE			:					str



class       a							(						unittest.TestCase			):


     """simple docstring"""






     def 					lowerCamelCase__					(							self	:						str     )		->				Dict:
          __UpperCAmelCase      :					str    				=     get_tests_dir('''fixtures/test_sentencepiece.model'''     )
          __UpperCAmelCase      :					List[Any]    				=     get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model'''     )

          __UpperCAmelCase      :					Tuple    				=     FakeOriginalTokenizer(vocab_file=snake_case     )

          with warnings.catch_warnings(record=snake_case     ) as w:
               __UpperCAmelCase      :					Optional[int]    				=     SpmConverter(snake_case     )
          self.assertEqual(len(snake_case     )				,					0     )

          __UpperCAmelCase      :					str    				=     FakeOriginalTokenizer(vocab_file=snake_case     )

          with warnings.catch_warnings(record=snake_case     ) as w:
               __UpperCAmelCase      :					Optional[int]    				=     SpmConverter(snake_case     )
          self.assertEqual(len(snake_case     )				,					1     )

          self.assertIn(
              '''The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'''
              ''' which is not implemented in the fast tokenizers.'''				,					str(w[0].message     )				,					)