"""simple docstring"""


import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      int:
     snake_case_					       =					SwinvaConfig()
     snake_case_					       =					swinva_name.split('_'					)

     snake_case_					       =					name_split[1]
     if "to" in name_split[3]:
          snake_case_					       =					int(name_split[3][-3:]					)
     else:
          snake_case_					       =					int(name_split[3]					)
     if "to" in name_split[2]:
          snake_case_					       =					int(name_split[2][-2:]					)
     else:
          snake_case_					       =					int(name_split[2][6:]					)

     if model_size == "tiny":
          snake_case_					       =					96
          snake_case_					       =					(2, 2, 6, 2)
          snake_case_					       =					(3, 6, 12, 24)
     elif model_size == "small":
          snake_case_					       =					96
          snake_case_					       =					(2, 2, 18, 2)
          snake_case_					       =					(3, 6, 12, 24)
     elif model_size == "base":
          snake_case_					       =					128
          snake_case_					       =					(2, 2, 18, 2)
          snake_case_					       =					(4, 8, 16, 32)
     else:
          snake_case_					       =					192
          snake_case_					       =					(2, 2, 18, 2)
          snake_case_					       =					(6, 12, 24, 48)

     if "to" in swinva_name:
          snake_case_					       =					(12, 12, 12, 6)

     if ("22k" in swinva_name) and ("to" not in swinva_name):
          snake_case_					       =					21841
          snake_case_					       =					'huggingface/label-files'
          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()}
          snake_case_					       =					idalabel
          snake_case_					       =					{v: k for k, v in idalabel.items()}

     else:
          snake_case_					       =					1000
          snake_case_					       =					'huggingface/label-files'
          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()}

     snake_case_					       =					img_size
     snake_case_					       =					num_classes
     snake_case_					       =					embed_dim
     snake_case_					       =					depths
     snake_case_					       =					num_heads
     snake_case_					       =					window_size

     return config







def 					UpperCAmelCase    (		UpperCAmelCase					)				->      Dict:
     if "patch_embed.proj" in name:
          snake_case_					       =					name.replace('patch_embed.proj'						,					'embeddings.patch_embeddings.projection'					)
     if "patch_embed.norm" in name:
          snake_case_					       =					name.replace('patch_embed.norm'						,					'embeddings.norm'					)
     if "layers" in name:
          snake_case_					       =					'encoder.' + name
     if "attn.proj" in name:
          snake_case_					       =					name.replace('attn.proj'						,					'attention.output.dense'					)
     if "attn" in name:
          snake_case_					       =					name.replace('attn'						,					'attention.self'					)
     if "norm1" in name:
          snake_case_					       =					name.replace('norm1'						,					'layernorm_before'					)
     if "norm2" in name:
          snake_case_					       =					name.replace('norm2'						,					'layernorm_after'					)
     if "mlp.fc1" in name:
          snake_case_					       =					name.replace('mlp.fc1'						,					'intermediate.dense'					)
     if "mlp.fc2" in name:
          snake_case_					       =					name.replace('mlp.fc2'						,					'output.dense'					)
     if "q_bias" in name:
          snake_case_					       =					name.replace('q_bias'						,					'query.bias'					)
     if "k_bias" in name:
          snake_case_					       =					name.replace('k_bias'						,					'key.bias'					)
     if "v_bias" in name:
          snake_case_					       =					name.replace('v_bias'						,					'value.bias'					)
     if "cpb_mlp" in name:
          snake_case_					       =					name.replace('cpb_mlp'						,					'continuous_position_bias_mlp'					)
     if name == "norm.weight":
          snake_case_					       =					'layernorm.weight'
     if name == "norm.bias":
          snake_case_					       =					'layernorm.bias'

     if "head" in name:
          snake_case_					       =					name.replace('head'						,					'classifier'					)
     else:
          snake_case_					       =					'swinv2.' + name

     return name







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase					)				->      Union[str, Any]:
     for key in orig_state_dict.copy().keys():
          snake_case_					       =					orig_state_dict.pop(UpperCAmelCase					)

          if "mask" in key:
               continue
          elif "qkv" in key:
               snake_case_					       =					key.split('.'					)
               snake_case_					       =					int(key_split[1]					)
               snake_case_					       =					int(key_split[3]					)
               snake_case_					       =					model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

               if "weight" in key:
                    snake_case_					       =					val[:dim, :]
                    snake_case_					       =					val[dim : dim * 2, :]
                    snake_case_					       =					val[-dim:, :]
               else:
                    snake_case_					       =					val[:dim]
                    snake_case_					       =					val[
                        dim : dim * 2
                    ]
                    snake_case_					       =					val[-dim:]
          else:
               snake_case_					       =					val

     return orig_state_dict







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase					)				->      List[str]:
     snake_case_					       =					timm.create_model(UpperCAmelCase						,					pretrained=UpperCAmelCase					)
     timm_model.eval()

     snake_case_					       =					get_swinva_config(UpperCAmelCase					)
     snake_case_					       =					SwinvaForImageClassification(UpperCAmelCase					)
     model.eval()

     snake_case_					       =					convert_state_dict(timm_model.state_dict()						,					UpperCAmelCase					)
     model.load_state_dict(UpperCAmelCase					)

     snake_case_					       =					'http://images.cocodataset.org/val2017/000000039769.jpg'

     snake_case_					       =					AutoImageProcessor.from_pretrained('microsoft/{}'.format(swinva_name.replace('_'						,					'-'					)					)					)
     snake_case_					       =					Image.open(requests.get(UpperCAmelCase						,					stream=UpperCAmelCase					).raw					)
     snake_case_					       =					image_processor(images=UpperCAmelCase						,					return_tensors='pt'					)

     snake_case_					       =					timm_model(inputs['pixel_values']					)
     snake_case_					       =					model(**UpperCAmelCase					).logits

     assert torch.allclose(UpperCAmelCase						,					UpperCAmelCase						,					atol=1e-3					)

     print(f'Saving model {swinva_name} to {pytorch_dump_folder_path}'					)
     model.save_pretrained(UpperCAmelCase					)

     print(f'Saving image processor to {pytorch_dump_folder_path}'					)
     image_processor.save_pretrained(UpperCAmelCase					)

     model.push_to_hub(
         repo_path_or_name=Path(UpperCAmelCase						,					UpperCAmelCase					)						,					organization='nandwalritik'						,					commit_message='Add model'						,					)


if __name__ == "__main__":
    __UpperCamelCase									=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--swinv2_name''',
        default='''swinv2_tiny_patch4_window8_256''',
        type=str,
        help='''Name of the Swinv2 timm model you\'d like to convert.''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
    )

    __UpperCamelCase									=			parser.parse_args()
    convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase           =					{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					['''DebertaTokenizerFast''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''DebertaForMaskedLM''',
           '''DebertaForQuestionAnswering''',
           '''DebertaForSequenceClassification''',
           '''DebertaForTokenClassification''',
           '''DebertaModel''',
           '''DebertaPreTrainedModel''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase           =					[
           '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''TFDebertaForMaskedLM''',
           '''TFDebertaForQuestionAnswering''',
           '''TFDebertaForSequenceClassification''',
           '''TFDebertaForTokenClassification''',
           '''TFDebertaModel''',
           '''TFDebertaPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
       from .tokenization_deberta import DebertaTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_deberta_fast import DebertaTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_deberta import (
                  DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DebertaForMaskedLM,
                  DebertaForQuestionAnswering,
                  DebertaForSequenceClassification,
                  DebertaForTokenClassification,
                  DebertaModel,
                  DebertaPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_deberta import (
                  TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  TFDebertaForMaskedLM,
                  TFDebertaForQuestionAnswering,
                  TFDebertaForSequenceClassification,
                  TFDebertaForTokenClassification,
                  TFDebertaModel,
                  TFDebertaPreTrainedModel,
              )


else:
       import sys

       lowerCamelCase           =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
