"""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    SCREAMING_SNAKE_CASE       (     snake_case):
     __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				=							1_28
          __snake_case				=							(2, 2, 18, 2)
          __snake_case				=							(4, 8, 16, 32)
     else:
          __snake_case				=							1_92
          __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				=							2_18_41
          __snake_case				=							'''huggingface/label-files'''
          __snake_case				=							'''imagenet-22k-id2label.json'''
          __snake_case				=							json.load(open(hf_hub_download(snake_case,      snake_case,      repo_type='''dataset'''),      '''r'''))
          __snake_case				=							{int(snake_case): v for k, v in idalabel.items()}
          __snake_case				=							idalabel
          __snake_case				=							{v: k for k, v in idalabel.items()}

     else:
          __snake_case				=							10_00
          __snake_case				=							'''huggingface/label-files'''
          __snake_case				=							'''imagenet-1k-id2label.json'''
          __snake_case				=							json.load(open(hf_hub_download(snake_case,      snake_case,      repo_type='''dataset'''),      '''r'''))
          __snake_case				=							{int(snake_case): 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    SCREAMING_SNAKE_CASE       (     snake_case):
     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    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     for key in orig_state_dict.copy().keys():
          __snake_case				=							orig_state_dict.pop(snake_case)

          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    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
     __snake_case				=							timm.create_model(snake_case,      pretrained=snake_case)
     timm_model.eval()

     __snake_case				=							get_swinva_config(snake_case)
     __snake_case				=							SwinvaForImageClassification(snake_case)
     model.eval()

     __snake_case				=							convert_state_dict(timm_model.state_dict(),      snake_case)
     model.load_state_dict(snake_case)

     __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(snake_case,      stream=snake_case).raw)
     __snake_case				=							image_processor(images=snake_case,      return_tensors='''pt''')

     __snake_case				=							timm_model(inputs['''pixel_values'''])
     __snake_case				=							model(**snake_case).logits

     assert torch.allclose(snake_case,      snake_case,      atol=1E-3)

     print(f"Saving model {swinva_name} to {pytorch_dump_folder_path}")
     model.save_pretrained(snake_case)

     print(f"Saving image processor to {pytorch_dump_folder_path}")
     image_processor.save_pretrained(snake_case)

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


if __name__ == "__main__":
       __lowercase		:		int														=							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."
       )

       __lowercase		:		List[Any]														=							parser.parse_args()
       convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
"""simple docstring"""



from typing import TYPE_CHECKING

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


__lowercase		:		Optional[Any]														=							{
    "configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		str														=							[
           "FALCON_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FalconForCausalLM",
           "FalconModel",
           "FalconPreTrainedModel",
           "FalconForSequenceClassification",
           "FalconForTokenClassification",
           "FalconForQuestionAnswering",
       ]


if TYPE_CHECKING:
       from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_falcon import (
                  FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FalconForCausalLM,
                  FalconForQuestionAnswering,
                  FalconForSequenceClassification,
                  FalconForTokenClassification,
                  FalconModel,
                  FalconPreTrainedModel,
              )


else:
       import sys

       __lowercase		:		List[Any]														=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)