

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       UpperCAmelCase__    (      _A				:			str     ,				_A				:			List[str]=False     ,				_A				:			List[Any]=False							):





  '''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       UpperCAmelCase__    (      _A				:			List[Any]     ,				_A				:			Optional[Any]     ,				_A				:			Optional[Any]=False     ,				_A				:			Optional[Any]=False							):





  '''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       UpperCAmelCase__    (      _A				:			List[str]     ,				_A				:			int     ,				_A				:			str							):





  '''simple docstring'''







  a__  =dct.pop(_A							)
  a__  =val




def       UpperCAmelCase__    (      ):





  '''simple docstring'''







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




@torch.no_grad()
def       UpperCAmelCase__    (      _A				:			Optional[Any]     ,				_A				:			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(_A     ,				_A     ,				repo_type='''dataset'''							)     ,				'''r'''							)							)
    a__  ={int(_A							): 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(_A     ,				_A     ,				repo_type='''dataset'''							)     ,				'''r'''							)							)
    a__  ={int(_A							): 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(_A     ,				_A     ,				repo_type='''dataset'''							)     ,				'''r'''							)							)
    a__  ={int(_A							): 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(_A     ,				map_location='''cpu'''     ,				check_hash=_A							)
  a__  =state_dict['''model'''] if '''ade20k''' not in checkpoint_url else state_dict['''state_dict''']

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

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

  # Check outputs on an image
  if is_semantic:
    a__  =BeitImageProcessor(size=config.image_size     ,				do_center_crop=_A							)
    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=_A							)
    a__  =prepare_img()

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

  a__  =model(_A							)
  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([2.2_288, 2.4_671, 0.7_395]							)
    a__  =23_97
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22k'''							):
    a__  =torch.Size([1, 2_18_41]							)
    a__  =torch.tensor([1.6_881, -0.2_787, 0.5_901]							)
    a__  =23_96
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft1k'''							):
    a__  =torch.tensor([0.1_241, 0.0_798, -0.6_569]							)
    a__  =2_85
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_224_pt22k_ft22kto1k'''							):
    a__  =torch.tensor([-1.2_385, -1.0_987, -1.0_108]							)
    a__  =2_81
  elif checkpoint_url[:-4].endswith('''beit_base_patch16_384_pt22k_ft22kto1k'''							):
    a__  =torch.tensor([-1.5_303, -0.9_484, -0.3_147]							)
    a__  =7_61
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft1k'''							):
    a__  =torch.tensor([0.4_610, -0.0_928, 0.2_086]							)
    a__  =7_61
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_224_pt22k_ft22kto1k'''							):
    a__  =torch.tensor([-0.4_804, 0.6_257, -0.1_837]							)
    a__  =7_61
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_384_pt22k_ft22kto1k'''							):
    a__  =torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]							)
    a__  =7_61
  elif checkpoint_url[:-4].endswith('''beit_large_patch16_512_pt22k_ft22kto1k'''							):
    a__  =torch.tensor([-0.3_062, 0.7_261, 0.4_852]							)
    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(
        [
            [[-4.9_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__  =(1, 1_50, 1_60, 1_60)
    a__  =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]     ,				_A     ,				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]     ,				_A     ,				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(_A							).mkdir(exist_ok=_A							)
  print(F"""Saving model to {pytorch_dump_folder_path}"""							)
  model.save_pretrained(_A							)
  print(F"""Saving image processor to {pytorch_dump_folder_path}"""							)
  image_processor.save_pretrained(_A							)


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)



import os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

lowerCamelCase									= [(1,)]




def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  a__  =bool(os.getenv('''RUN_SLOW'''     ,				0							)							)
  if run_slow:
    assert True
  else:
    assert False, "should have been skipped"




def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  a__  =bool(os.getenv('''RUN_SLOW'''     ,				0							)							)
  if run_slow and torch_device == "cuda":
    assert True
  else:
    assert False, "should have been skipped"







@require_torch
class 						__magic_name__      (							unittest.TestCase							):

   '''simple docstring'''




   @slow
   @require_torch_gpu
   def 			_UpperCAmelCase			(    self	)							->   Union[str, Any]:






     """simple docstring"""






     check_slow_torch_cuda()



   @require_torch_gpu
   @slow
   def 			_UpperCAmelCase			(    self	)							->   List[str]:






     """simple docstring"""






     check_slow_torch_cuda()



   @parameterized.expand(lowercase_	)
   @slow
   def 			_UpperCAmelCase			(    self,   lowercase_=None	)							->   Tuple:






     """simple docstring"""






     check_slow()





@slow
@require_torch_gpu
def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  check_slow_torch_cuda()




@require_torch_gpu
@slow
def       UpperCAmelCase__    (      ):





  '''simple docstring'''







  check_slow_torch_cuda()




@slow
@pytest.mark.parametrize('''param'''     ,				[1]							)
def       UpperCAmelCase__    (      _A				:			str							):





  '''simple docstring'''







  check_slow()




@pytest.mark.parametrize('''param'''     ,				[1]							)
@slow
def       UpperCAmelCase__    (      _A				:			Tuple							):





  '''simple docstring'''







  check_slow()
