






"""simple docstring"""




import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification






def     _SCREAMING_SNAKE_CASE				(			UpperCamelCase  :						Any     ):
  A__           =				SwinConfig()
  A__           =				swin_name.split("""_"""     )

  A__           =				name_split[1]
  A__           =				int(name_split[4]     )
  A__           =				int(name_split[3][-1]     )

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

  if "in22k" in swin_name:
    A__           =				2_1841
  else:
    A__           =				1000
    A__           =				"""huggingface/label-files"""
    A__           =				"""imagenet-1k-id2label.json"""
    A__           =				json.load(open(hf_hub_download(__UpperCAmelCase		,					__UpperCAmelCase		,					repo_type="""dataset"""     )		,					"""r"""     )     )
    A__           =				{int(__UpperCAmelCase     ): v for k, v in idalabel.items()}
    A__           =				idalabel
    A__           =				{v: k for k, v in idalabel.items()}

  A__           =				img_size
  A__           =				num_classes
  A__           =				embed_dim
  A__           =				depths
  A__           =				num_heads
  A__           =				window_size

  return config






def     _SCREAMING_SNAKE_CASE				(			UpperCamelCase  :						List[str]     ):







  if "patch_embed.proj" in name:
    A__           =				name.replace("""patch_embed.proj"""		,					"""embeddings.patch_embeddings.projection"""     )
  if "patch_embed.norm" in name:
    A__           =				name.replace("""patch_embed.norm"""		,					"""embeddings.norm"""     )
  if "layers" in name:
    A__           =				"""encoder.""" + name
  if "attn.proj" in name:
    A__           =				name.replace("""attn.proj"""		,					"""attention.output.dense"""     )
  if "attn" in name:
    A__           =				name.replace("""attn"""		,					"""attention.self"""     )
  if "norm1" in name:
    A__           =				name.replace("""norm1"""		,					"""layernorm_before"""     )
  if "norm2" in name:
    A__           =				name.replace("""norm2"""		,					"""layernorm_after"""     )
  if "mlp.fc1" in name:
    A__           =				name.replace("""mlp.fc1"""		,					"""intermediate.dense"""     )
  if "mlp.fc2" in name:
    A__           =				name.replace("""mlp.fc2"""		,					"""output.dense"""     )

  if name == "norm.weight":
    A__           =				"""layernorm.weight"""
  if name == "norm.bias":
    A__           =				"""layernorm.bias"""

  if "head" in name:
    A__           =				name.replace("""head"""		,					"""classifier"""     )
  else:
    A__           =				"""swin.""" + name

  return name






def     _SCREAMING_SNAKE_CASE				(			UpperCamelCase  :						Union[str, Any]		,					UpperCamelCase  :						Optional[Any]     ):







  for key in orig_state_dict.copy().keys():
    A__           =				orig_state_dict.pop(__UpperCAmelCase     )

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

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

  return orig_state_dict






def     _SCREAMING_SNAKE_CASE				(			UpperCamelCase  :						Union[str, Any]		,					UpperCamelCase  :						int     ):
  A__           =				timm.create_model(__UpperCAmelCase		,					pretrained=__UpperCAmelCase     )
  timm_model.eval()

  A__           =				get_swin_config(__UpperCAmelCase     )
  A__           =				SwinForImageClassification(__UpperCAmelCase     )
  model.eval()

  A__           =				convert_state_dict(timm_model.state_dict()		,					__UpperCAmelCase     )
  model.load_state_dict(__UpperCAmelCase     )

  A__           =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""

  A__           =				AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_"""		,					"""-"""     )     )     )
  A__           =				Image.open(requests.get(__UpperCAmelCase		,					stream=__UpperCAmelCase     ).raw     )
  A__           =				image_processor(images=__UpperCAmelCase		,					return_tensors="""pt"""     )

  A__           =				timm_model(inputs["""pixel_values"""]     )
  A__           =				model(**__UpperCAmelCase     ).logits

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

  print(F"""Saving model {swin_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     )


if __name__ == "__main__":
      lowerCamelCase__      = argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--swin_name",
          default="swin_tiny_patch4_window7_224",
          type=str,
          help="Name of the Swin 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."
      )

      lowerCamelCase__      = parser.parse_args()
      convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)











'''simple docstring'''

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
    from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


a   	=    logging.get_logger(__name__)

a   	=    {
    "bigscience/bloom": "https://huggingface.co/bigscience/bloom/resolve/main/config.json",
    "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/config.json",
    "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json",
    "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json",
    "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/config.json",
    "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json",
}







class __a			( _snake_case      ):
   __UpperCamelCase :			List[str]						   =						'bloom'
   __UpperCamelCase :			Optional[Any]						   =						['past_key_values']
   __UpperCamelCase :			Optional[int]						   =						{
       'num_hidden_layers': 'n_layer',
       'num_attention_heads': 'n_head',
   }





   def __init__(       self					:      Union[str, Any]					,lowerCamelCase					:      str=25_0880					,lowerCamelCase					:      List[Any]=64					,lowerCamelCase					:      Optional[Any]=2					,lowerCamelCase					:      Tuple=8					,lowerCamelCase					:      Union[str, Any]=1E-5					,lowerCamelCase					:      Optional[Any]=0.02					,lowerCamelCase					:      str=True					,lowerCamelCase					:      List[Any]=1					,lowerCamelCase					:      Union[str, Any]=2					,lowerCamelCase					:      Optional[int]=False					,lowerCamelCase					:      Optional[int]=0.0					,lowerCamelCase					:      List[Any]=0.0					,lowerCamelCase					:      Optional[Any]=1					,lowerCamelCase					:      Tuple=False					,**lowerCamelCase					:      str					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							vocab_size
      # Backward compatibility with n_embed kwarg
      __SCREAMING_SNAKE_CASE   				=							kwargs.pop("""n_embed"""					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							hidden_size if n_embed is None else n_embed
      __SCREAMING_SNAKE_CASE   				=							n_layer
      __SCREAMING_SNAKE_CASE   				=							n_head
      __SCREAMING_SNAKE_CASE   				=							layer_norm_epsilon
      __SCREAMING_SNAKE_CASE   				=							initializer_range
      __SCREAMING_SNAKE_CASE   				=							use_cache
      __SCREAMING_SNAKE_CASE   				=							pretraining_tp
      __SCREAMING_SNAKE_CASE   				=							apply_residual_connection_post_layernorm
      __SCREAMING_SNAKE_CASE   				=							hidden_dropout
      __SCREAMING_SNAKE_CASE   				=							attention_dropout

      __SCREAMING_SNAKE_CASE   				=							bos_token_id
      __SCREAMING_SNAKE_CASE   				=							eos_token_id
      __SCREAMING_SNAKE_CASE   				=							slow_but_exact

      super().__init__(bos_token_id=lowerCamelCase					,eos_token_id=lowerCamelCase					,**lowerCamelCase     )







class __a			( _snake_case      ):
   __UpperCamelCase :			int						   =						version.parse('1.12'      )





   def __init__(       self					:      Optional[int]					,lowerCamelCase					:      PretrainedConfig					,lowerCamelCase					:      str = "default"					,lowerCamelCase					:      List[PatchingSpec] = None					,lowerCamelCase					:      bool = False					,):

      '''simple docstring'''

      super().__init__(lowerCamelCase					,task=lowerCamelCase					,patching_specs=lowerCamelCase					,use_past=lowerCamelCase     )
      if not getattr(self._config					,"""pad_token_id"""					,lowerCamelCase     ):
         # TODO: how to do that better?
         __SCREAMING_SNAKE_CASE   				=							0





   @property
   def   UpperCAmelCase__  (       self					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}     )
      if self.use_past:
         # BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
         self.fill_with_past_key_values_(lowerCamelCase					,direction="""inputs"""					,inverted_values_shape=lowerCamelCase     )
         __SCREAMING_SNAKE_CASE   				=							{0: """batch""", 1: """past_sequence + sequence"""}
      else:
         __SCREAMING_SNAKE_CASE   				=							{0: """batch""", 1: """sequence"""}

      return common_inputs





   @property
   def   UpperCAmelCase__  (       self					:      Tuple     ):

      '''simple docstring'''

      return self._config.n_layer





   @property
   def   UpperCAmelCase__  (       self					:      Dict     ):

      '''simple docstring'''

      return self._config.n_head





   @property
   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      return 1E-3





   def   UpperCAmelCase__  (       self					:      str					,lowerCamelCase					:      "PreTrainedTokenizer"					,lowerCamelCase					:      int = -1					,lowerCamelCase					:      int = -1					,lowerCamelCase					:      bool = False					,lowerCamelCase					:      Optional["TensorType"] = None					,):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							super(lowerCamelCase					,self     ).generate_dummy_inputs(
          lowerCamelCase					,batch_size=lowerCamelCase					,seq_length=lowerCamelCase					,is_pair=lowerCamelCase					,framework=lowerCamelCase     )

      # We need to order the input in the way they appears in the forward()
      __SCREAMING_SNAKE_CASE   				=							OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}     )

      # Need to add the past_keys
      if self.use_past:
         if not is_torch_available():
            raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""     )
         else:
            import torch

            __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							common_inputs["""input_ids"""].shape
            # Not using the same length for past_key_values
            __SCREAMING_SNAKE_CASE   				=							seqlen + 2
            __SCREAMING_SNAKE_CASE   				=							self._config.hidden_size // self.num_attention_heads
            __SCREAMING_SNAKE_CASE   				=							(
                batch * self.num_attention_heads,
                head_dim,
                past_key_values_length,
            )
            __SCREAMING_SNAKE_CASE   				=							(
                batch * self.num_attention_heads,
                past_key_values_length,
                head_dim,
            )
            __SCREAMING_SNAKE_CASE   				=							[
                (torch.zeros(lowerCamelCase     ), torch.zeros(lowerCamelCase     )) for _ in range(self.num_layers     )
            ]

      __SCREAMING_SNAKE_CASE   				=							common_inputs["""attention_mask"""]
      if self.use_past:
         __SCREAMING_SNAKE_CASE   				=							ordered_inputs["""attention_mask"""].dtype
         __SCREAMING_SNAKE_CASE   				=							torch.cat(
             [ordered_inputs["""attention_mask"""], torch.ones(lowerCamelCase					,lowerCamelCase					,dtype=lowerCamelCase     )]					,dim=1     )

      return ordered_inputs





   @property
   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      return 13





