





"""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 					__A  (_SCREAMING_SNAKE_CASE     )   ->Union[str, Any]:





  """simple docstring"""


  lowerCAmelCase__     :Dict											=				SwinvaConfig()
  lowerCAmelCase__     :List[str]											=				swinva_name.split('_'     )

  lowerCAmelCase__     :Tuple											=				name_split[1]
  if "to" in name_split[3]:
    lowerCAmelCase__     :Optional[Any]											=				int(name_split[3][-3:]     )
  else:
    lowerCAmelCase__     :Optional[Any]											=				int(name_split[3]     )
  if "to" in name_split[2]:
    lowerCAmelCase__     :Optional[int]											=				int(name_split[2][-2:]     )
  else:
    lowerCAmelCase__     :List[Any]											=				int(name_split[2][6:]     )

  if model_size == "tiny":
    lowerCAmelCase__     :Tuple											=				96
    lowerCAmelCase__     :List[Any]											=				(2, 2, 6, 2)
    lowerCAmelCase__     :Union[str, Any]											=				(3, 6, 12, 24)
  elif model_size == "small":
    lowerCAmelCase__     :Union[str, Any]											=				96
    lowerCAmelCase__     :List[str]											=				(2, 2, 18, 2)
    lowerCAmelCase__     :str											=				(3, 6, 12, 24)
  elif model_size == "base":
    lowerCAmelCase__     :List[Any]											=				128
    lowerCAmelCase__     :Union[str, Any]											=				(2, 2, 18, 2)
    lowerCAmelCase__     :List[str]											=				(4, 8, 16, 32)
  else:
    lowerCAmelCase__     :List[str]											=				192
    lowerCAmelCase__     :int											=				(2, 2, 18, 2)
    lowerCAmelCase__     :int											=				(6, 12, 24, 48)

  if "to" in swinva_name:
    lowerCAmelCase__     :Optional[Any]											=				(12, 12, 12, 6)

  if ("22k" in swinva_name) and ("to" not in swinva_name):
    lowerCAmelCase__     :Union[str, Any]											=				2_1841
    lowerCAmelCase__     :Any											=				'huggingface/label-files'
    lowerCAmelCase__     :Any											=				'imagenet-22k-id2label.json'
    lowerCAmelCase__     :Optional[int]											=				json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  repo_type='dataset'     )	,  'r'     )     )
    lowerCAmelCase__     :Any											=				{int(_SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
    lowerCAmelCase__     :Dict											=				idalabel
    lowerCAmelCase__     :str											=				{v: k for k, v in idalabel.items()}

  else:
    lowerCAmelCase__     :Any											=				1000
    lowerCAmelCase__     :str											=				'huggingface/label-files'
    lowerCAmelCase__     :str											=				'imagenet-1k-id2label.json'
    lowerCAmelCase__     :Any											=				json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE	,  repo_type='dataset'     )	,  'r'     )     )
    lowerCAmelCase__     :List[Any]											=				{int(_SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}
    lowerCAmelCase__     :Union[str, Any]											=				idalabel
    lowerCAmelCase__     :List[Any]											=				{v: k for k, v in idalabel.items()}

  lowerCAmelCase__     :Any											=				img_size
  lowerCAmelCase__     :Dict											=				num_classes
  lowerCAmelCase__     :List[str]											=				embed_dim
  lowerCAmelCase__     :Dict											=				depths
  lowerCAmelCase__     :Optional[Any]											=				num_heads
  lowerCAmelCase__     :Optional[int]											=				window_size

  return config
def 					__A  (_SCREAMING_SNAKE_CASE     )   ->Optional[Any]:





  """simple docstring"""


  if "patch_embed.proj" in name:
    lowerCAmelCase__     :Dict											=				name.replace('patch_embed.proj'	,  'embeddings.patch_embeddings.projection'     )
  if "patch_embed.norm" in name:
    lowerCAmelCase__     :int											=				name.replace('patch_embed.norm'	,  'embeddings.norm'     )
  if "layers" in name:
    lowerCAmelCase__     :int											=				'encoder.' + name
  if "attn.proj" in name:
    lowerCAmelCase__     :List[str]											=				name.replace('attn.proj'	,  'attention.output.dense'     )
  if "attn" in name:
    lowerCAmelCase__     :List[str]											=				name.replace('attn'	,  'attention.self'     )
  if "norm1" in name:
    lowerCAmelCase__     :int											=				name.replace('norm1'	,  'layernorm_before'     )
  if "norm2" in name:
    lowerCAmelCase__     :int											=				name.replace('norm2'	,  'layernorm_after'     )
  if "mlp.fc1" in name:
    lowerCAmelCase__     :List[str]											=				name.replace('mlp.fc1'	,  'intermediate.dense'     )
  if "mlp.fc2" in name:
    lowerCAmelCase__     :Tuple											=				name.replace('mlp.fc2'	,  'output.dense'     )
  if "q_bias" in name:
    lowerCAmelCase__     :int											=				name.replace('q_bias'	,  'query.bias'     )
  if "k_bias" in name:
    lowerCAmelCase__     :Any											=				name.replace('k_bias'	,  'key.bias'     )
  if "v_bias" in name:
    lowerCAmelCase__     :Dict											=				name.replace('v_bias'	,  'value.bias'     )
  if "cpb_mlp" in name:
    lowerCAmelCase__     :Optional[int]											=				name.replace('cpb_mlp'	,  'continuous_position_bias_mlp'     )
  if name == "norm.weight":
    lowerCAmelCase__     :Any											=				'layernorm.weight'
  if name == "norm.bias":
    lowerCAmelCase__     :Optional[Any]											=				'layernorm.bias'

  if "head" in name:
    lowerCAmelCase__     :int											=				name.replace('head'	,  'classifier'     )
  else:
    lowerCAmelCase__     :Optional[Any]											=				'swinv2.' + name

  return name
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Union[str, Any]:





  """simple docstring"""


  for key in orig_state_dict.copy().keys():
    lowerCAmelCase__     :Tuple											=				orig_state_dict.pop(_SCREAMING_SNAKE_CASE     )

    if "mask" in key:
      continue
    elif "qkv" in key:
      lowerCAmelCase__     :List[Any]											=				key.split('.'     )
      lowerCAmelCase__     :Union[str, Any]											=				int(key_split[1]     )
      lowerCAmelCase__     :Tuple											=				int(key_split[3]     )
      lowerCAmelCase__     :Tuple											=				model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        lowerCAmelCase__     :Union[str, Any]											=				val[:dim, :]
        lowerCAmelCase__     :Any											=				val[dim : dim * 2, :]
        lowerCAmelCase__     :Dict											=				val[-dim:, :]
      else:
        lowerCAmelCase__     :Dict											=				val[:dim]
        lowerCAmelCase__     :Optional[int]											=				val[
            dim : dim * 2
        ]
        lowerCAmelCase__     :str											=				val[-dim:]
    else:
      lowerCAmelCase__     :Tuple											=				val

  return orig_state_dict
def 					__A  (_SCREAMING_SNAKE_CASE	,  _SCREAMING_SNAKE_CASE     )   ->Optional[int]:





  """simple docstring"""


  lowerCAmelCase__     :Any											=				timm.create_model(_SCREAMING_SNAKE_CASE	,  pretrained=_SCREAMING_SNAKE_CASE     )
  timm_model.eval()

  lowerCAmelCase__     :Tuple											=				get_swinva_config(_SCREAMING_SNAKE_CASE     )
  lowerCAmelCase__     :List[Any]											=				SwinvaForImageClassification(_SCREAMING_SNAKE_CASE     )
  model.eval()

  lowerCAmelCase__     :Dict											=				convert_state_dict(timm_model.state_dict()	,  _SCREAMING_SNAKE_CASE     )
  model.load_state_dict(_SCREAMING_SNAKE_CASE     )

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

  lowerCAmelCase__     :List[str]											=				AutoImageProcessor.from_pretrained('microsoft/{}'.format(swinva_name.replace('_'	,  '-'     )     )     )
  lowerCAmelCase__     :Tuple											=				Image.open(requests.get(_SCREAMING_SNAKE_CASE	,  stream=_SCREAMING_SNAKE_CASE     ).raw     )
  lowerCAmelCase__     :str											=				image_processor(images=_SCREAMING_SNAKE_CASE	,  return_tensors='pt'     )

  lowerCAmelCase__     :Optional[Any]											=				timm_model(inputs['pixel_values']     )
  lowerCAmelCase__     :List[Any]											=				model(**_SCREAMING_SNAKE_CASE     ).logits

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

  print(F"Saving model {swinva_name} to {pytorch_dump_folder_path}"     )
  model.save_pretrained(_SCREAMING_SNAKE_CASE     )

  print(F"Saving image processor to {pytorch_dump_folder_path}"     )
  image_processor.save_pretrained(_SCREAMING_SNAKE_CASE     )

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


if __name__ == "__main__":
  __A											=						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."""
  )

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







"""simple docstring"""





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class    _lowerCAmelCase  (	a			):
       """simple docstring"""




       __magic_name__	:torch.FloatTensor






class    _lowerCAmelCase  (	a    ,  a			):
       """simple docstring"""




       @register_to_config
       def __init__(       self       ,					__UpperCAmelCase = 1_6       ,					__UpperCAmelCase = 8_8       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = 1       ,					__UpperCAmelCase = 0.0       ,					__UpperCAmelCase = 3_2       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = False       ,					__UpperCAmelCase = None       ,					__UpperCAmelCase = "geglu"       ,					__UpperCAmelCase = True       ,					__UpperCAmelCase = True       ,					):



         '''simple docstring'''

         super().__init__()
         lowerCAmelCase__     :Dict											=				num_attention_heads
         lowerCAmelCase__     :Any											=				attention_head_dim
         lowerCAmelCase__     :Optional[int]											=				num_attention_heads * attention_head_dim

         lowerCAmelCase__     :Any											=				in_channels

         lowerCAmelCase__     :str											=				torch.nn.GroupNorm(num_groups=__UpperCAmelCase       ,					num_channels=__UpperCAmelCase       ,					eps=1E-6       ,					affine=__UpperCAmelCase )
         lowerCAmelCase__     :int											=				nn.Linear(__UpperCAmelCase       ,					__UpperCAmelCase )

         # 3. Define transformers blocks
         lowerCAmelCase__     :List[Any]											=				nn.ModuleList(
             [
                 BasicTransformerBlock(
                     __UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					dropout=__UpperCAmelCase       ,					cross_attention_dim=__UpperCAmelCase       ,					activation_fn=__UpperCAmelCase       ,					attention_bias=__UpperCAmelCase       ,					double_self_attention=__UpperCAmelCase       ,					norm_elementwise_affine=__UpperCAmelCase       ,					)
                 for d in range(__UpperCAmelCase )
             ] )

         lowerCAmelCase__     :List[Any]											=				nn.Linear(__UpperCAmelCase       ,					__UpperCAmelCase )






       def 	snake_case		(       self       ,					__UpperCAmelCase       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase=1       ,					__UpperCAmelCase=None       ,					__UpperCAmelCase = True       ,					):



         '''simple docstring'''

         lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__				,							lowerCAmelCase__     :int											=				hidden_states.shape
         lowerCAmelCase__     :Tuple											=				batch_frames // num_frames

         lowerCAmelCase__     :str											=				hidden_states

         lowerCAmelCase__     :Union[str, Any]											=				hidden_states[None, :].reshape(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
         lowerCAmelCase__     :str											=				hidden_states.permute(0       ,					2       ,					1       ,					3       ,					4 )

         lowerCAmelCase__     :Optional[int]											=				self.norm(__UpperCAmelCase )
         lowerCAmelCase__     :Optional[Any]											=				hidden_states.permute(0       ,					3       ,					4       ,					2       ,					1 ).reshape(batch_size * height * width       ,					__UpperCAmelCase       ,					__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				self.proj_in(__UpperCAmelCase )

         # 2. Blocks
         for block in self.transformer_blocks:
           lowerCAmelCase__     :Optional[int]											=				block(
               __UpperCAmelCase       ,					encoder_hidden_states=__UpperCAmelCase       ,					timestep=__UpperCAmelCase       ,					cross_attention_kwargs=__UpperCAmelCase       ,					class_labels=__UpperCAmelCase       ,					)

         # 3. Output
         lowerCAmelCase__     :Any											=				self.proj_out(__UpperCAmelCase )
         lowerCAmelCase__     :Dict											=				(
             hidden_states[None, None, :]
             .reshape(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )
             .permute(0       ,					3       ,					4       ,					1       ,					2 )
             .contiguous()
         )
         lowerCAmelCase__     :Optional[Any]											=				hidden_states.reshape(__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase       ,					__UpperCAmelCase )

         lowerCAmelCase__     :List[Any]											=				hidden_states + residual

         if not return_dict:
           return (output,)

         return TransformerTemporalModelOutput(sample=__UpperCAmelCase )
