





'''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   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				SwinConfig()
      _lowerCAmelCase					=				swin_name.split("""_"""    )

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

      if model_size == "tiny":
            _lowerCAmelCase					=				96
            _lowerCAmelCase					=				(2, 2, 6, 2)
            _lowerCAmelCase					=				(3, 6, 12, 24)
      elif model_size == "small":
            _lowerCAmelCase					=				96
            _lowerCAmelCase					=				(2, 2, 18, 2)
            _lowerCAmelCase					=				(3, 6, 12, 24)
      elif model_size == "base":
            _lowerCAmelCase					=				1_28
            _lowerCAmelCase					=				(2, 2, 18, 2)
            _lowerCAmelCase					=				(4, 8, 16, 32)
      else:
            _lowerCAmelCase					=				1_92
            _lowerCAmelCase					=				(2, 2, 18, 2)
            _lowerCAmelCase					=				(6, 12, 24, 48)

      if "in22k" in swin_name:
            _lowerCAmelCase					=				2_18_41
      else:
            _lowerCAmelCase					=				10_00
            _lowerCAmelCase					=				"""huggingface/label-files"""
            _lowerCAmelCase					=				"""imagenet-1k-id2label.json"""
            _lowerCAmelCase					=				json.load(open(hf_hub_download(lowerCAmelCase				,      lowerCAmelCase				,      repo_type="""dataset"""    )				,      """r"""    )    )
            _lowerCAmelCase					=				{int(lowerCAmelCase    ): v for k, v in idalabel.items()}
            _lowerCAmelCase					=				idalabel
            _lowerCAmelCase					=				{v: k for k, v in idalabel.items()}

      _lowerCAmelCase					=				img_size
      _lowerCAmelCase					=				num_classes
      _lowerCAmelCase					=				embed_dim
      _lowerCAmelCase					=				depths
      _lowerCAmelCase					=				num_heads
      _lowerCAmelCase					=				window_size

      return config
def   UpperCamelCase__ (						lowerCAmelCase    ):





      """simple docstring"""


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

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

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

      return name
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      for key in orig_state_dict.copy().keys():
            _lowerCAmelCase					=				orig_state_dict.pop(lowerCAmelCase    )

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

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

      return orig_state_dict
def   UpperCamelCase__ (						lowerCAmelCase				,      lowerCAmelCase    ):





      """simple docstring"""


      _lowerCAmelCase					=				timm.create_model(lowerCAmelCase				,      pretrained=lowerCAmelCase    )
      timm_model.eval()

      _lowerCAmelCase					=				get_swin_config(lowerCAmelCase    )
      _lowerCAmelCase					=				SwinForImageClassification(lowerCAmelCase    )
      model.eval()

      _lowerCAmelCase					=				convert_state_dict(timm_model.state_dict()				,      lowerCAmelCase    )
      model.load_state_dict(lowerCAmelCase    )

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

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

      _lowerCAmelCase					=				timm_model(inputs["""pixel_values"""]    )
      _lowerCAmelCase					=				model(**lowerCAmelCase    ).logits

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

      print(f"Saving model {swin_name} to {pytorch_dump_folder_path}"    )
      model.save_pretrained(lowerCAmelCase    )

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


if __name__ == "__main__":
      A__							:  Any    =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.'''
      )

      A__							:  List[str]    =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, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, TensorType

A__							:  Tuple    =logging.get_logger(__name__)

A__							:  Any    ={
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Tuple			=  '''imagegpt'''
      _lowercase:     Optional[int]			=  ['''past_key_values''']
      _lowercase:     str			=  {
          '''hidden_size''': '''n_embd''',
          '''max_position_embeddings''': '''n_positions''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }




      def __init__(     self     :					Optional[int]     ,     __snake_case     :					Optional[Any]=5_12 + 1     ,     __snake_case     :					Union[str, Any]=32 * 32     ,     __snake_case     :					List[Any]=5_12     ,     __snake_case     :					Any=24     ,     __snake_case     :					Optional[Any]=8     ,     __snake_case     :					List[Any]=None     ,     __snake_case     :					str="quick_gelu"     ,     __snake_case     :					List[Any]=0.1     ,     __snake_case     :					str=0.1     ,     __snake_case     :					Optional[int]=0.1     ,     __snake_case     :					Tuple=1E-5     ,     __snake_case     :					Optional[int]=0.02     ,     __snake_case     :					str=True     ,     __snake_case     :					Dict=True     ,     __snake_case     :					Union[str, Any]=False     ,     __snake_case     :					Dict=False     ,     __snake_case     :					Union[str, Any]=False     ,     **__snake_case     :					Union[str, Any]     ,     )							-> List[Any]:
            _lowerCAmelCase					=				vocab_size
            _lowerCAmelCase					=				n_positions
            _lowerCAmelCase					=				n_embd
            _lowerCAmelCase					=				n_layer
            _lowerCAmelCase					=				n_head
            _lowerCAmelCase					=				n_inner
            _lowerCAmelCase					=				activation_function
            _lowerCAmelCase					=				resid_pdrop
            _lowerCAmelCase					=				embd_pdrop
            _lowerCAmelCase					=				attn_pdrop
            _lowerCAmelCase					=				layer_norm_epsilon
            _lowerCAmelCase					=				initializer_range
            _lowerCAmelCase					=				scale_attn_weights
            _lowerCAmelCase					=				use_cache
            _lowerCAmelCase					=				scale_attn_by_inverse_layer_idx
            _lowerCAmelCase					=				reorder_and_upcast_attn
            _lowerCAmelCase					=				tie_word_embeddings

            super().__init__(tie_word_embeddings=__snake_case     ,     **__snake_case    )




class 					UpperCAmelCase	(     snake_case_  ):




      @property
      def 							lowercase__       (     self     :					Optional[Any]    )							-> Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ]    )




      def 							lowercase__       (     self     :					Union[str, Any]     ,     __snake_case     :					"FeatureExtractionMixin"     ,     __snake_case     :					int = 1     ,     __snake_case     :					int = -1     ,     __snake_case     :					bool = False     ,     __snake_case     :					Optional["TensorType"] = None     ,     __snake_case     :					int = 3     ,     __snake_case     :					int = 32     ,     __snake_case     :					int = 32     ,     )							-> Mapping[str, Any]:
            _lowerCAmelCase					=				self._generate_dummy_images(__snake_case     ,     __snake_case     ,     __snake_case     ,     __snake_case    )
            _lowerCAmelCase					=				dict(preprocessor(images=__snake_case     ,     return_tensors=__snake_case    )    )

            return inputs






