"""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()
__UpperCamelCase										=     logging.get_logger(__name__)







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase=False		,   UpperCAmelCase=False   )							-> List[Any]:
     snake_case_   			=   'backbone.' if is_semantic else ''

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

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

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

          snake_case_   			=   gamma_a
          snake_case_   			=   gamma_a

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

               snake_case_   			=   table
               snake_case_   			=   index







def    UpperCAmelCase       (						UpperCAmelCase		,   UpperCAmelCase		,   UpperCAmelCase   )							-> int:
     snake_case_   			=   dct.pop(UpperCAmelCase   )
     snake_case_   			=   val







def    UpperCAmelCase       (						)							-> Optional[int]:
     snake_case_   			=   'http://images.cocodataset.org/val2017/000000039769.jpg'
     snake_case_   			=   Image.open(requests.get(UpperCAmelCase		,   stream=UpperCAmelCase   ).raw   )
     return im







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

     snake_case_   			=   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():
               snake_case_   			=   state_dict.pop(UpperCAmelCase   )
               if key.startswith('backbone.fpn'   ):
                    snake_case_   			=   key.replace('backbone.fpn'		,   'fpn'   )
               snake_case_   			=   val

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

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

     snake_case_   			=   image_processor(images=UpperCAmelCase		,   return_tensors='pt'   )
     snake_case_   			=   encoding['pixel_values']

     snake_case_   			=   model(UpperCAmelCase   )
     snake_case_   			=   outputs.logits

     # verify logits
     snake_case_   			=   torch.Size([1, 1000]   )
     if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'   ):
          snake_case_   			=   torch.Size([1, 196, 8192]   )
     elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'   ):
          snake_case_   			=   torch.Size([1, 196, 8192]   )
     elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'   ):
          snake_case_   			=   torch.Size([1, 21841]   )
          snake_case_   			=   torch.tensor([2.2_288, 2.4_671, 0.7_395]   )
          snake_case_   			=   2397
     elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'   ):
          snake_case_   			=   torch.Size([1, 21841]   )
          snake_case_   			=   torch.tensor([1.6_881, -0.2_787, 0.5_901]   )
          snake_case_   			=   2396
     elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'   ):
          snake_case_   			=   torch.tensor([0.1_241, 0.0_798, -0.6_569]   )
          snake_case_   			=   285
     elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'   ):
          snake_case_   			=   torch.tensor([-1.2_385, -1.0_987, -1.0_108]   )
          snake_case_   			=   281
     elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'   ):
          snake_case_   			=   torch.tensor([-1.5_303, -0.9_484, -0.3_147]   )
          snake_case_   			=   761
     elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'   ):
          snake_case_   			=   torch.tensor([0.4_610, -0.0_928, 0.2_086]   )
          snake_case_   			=   761
     elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'   ):
          snake_case_   			=   torch.tensor([-0.4_804, 0.6_257, -0.1_837]   )
          snake_case_   			=   761
     elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'   ):
          snake_case_   			=   torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]   )
          snake_case_   			=   761
     elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'   ):
          snake_case_   			=   torch.tensor([-0.3_062, 0.7_261, 0.4_852]   )
          snake_case_   			=   761
     elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'   ):
          snake_case_   			=   (1, 150, 160, 160)
          snake_case_   			=   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'   ):
          snake_case_   			=   (1, 150, 160, 160)
          snake_case_   			=   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__":
    __UpperCamelCase										=     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.'''
    )
    __UpperCamelCase										=     parser.parse_args()
    convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase										=     logging.get_logger(__name__)

__UpperCamelCase										=     {
    '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/config.json''',
    '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json''',
    '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/config.json''',
    '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json''',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}




class 					UpperCamelCase   (							lowerCAmelCase__  ):
       SCREAMING_SNAKE_CASE_						      = "splinter"







       def __init__(					self,				lowerCAmelCase__=3_0522,				lowerCAmelCase__=768,				lowerCAmelCase__=12,				lowerCAmelCase__=12,				lowerCAmelCase__=3072,				lowerCAmelCase__="gelu",				lowerCAmelCase__=0.1,				lowerCAmelCase__=0.1,				lowerCAmelCase__=512,				lowerCAmelCase__=2,				lowerCAmelCase__=0.02,				lowerCAmelCase__=1e-12,				lowerCAmelCase__=True,				lowerCAmelCase__=0,				lowerCAmelCase__=104,				**lowerCAmelCase__,				) -> Dict:
            super().__init__(pad_token_id=lowerCAmelCase__,				**lowerCAmelCase__)

            snake_case_   			=   vocab_size
            snake_case_   			=   max_position_embeddings
            snake_case_   			=   hidden_size
            snake_case_   			=   num_hidden_layers
            snake_case_   			=   num_attention_heads
            snake_case_   			=   intermediate_size
            snake_case_   			=   hidden_act
            snake_case_   			=   hidden_dropout_prob
            snake_case_   			=   attention_probs_dropout_prob
            snake_case_   			=   initializer_range
            snake_case_   			=   type_vocab_size
            snake_case_   			=   layer_norm_eps
            snake_case_   			=   use_cache
            snake_case_   			=   question_token_id

