



"""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        snake_case_  (      A_					:		Union[str, Any]						):


       '''simple docstring'''





       _lowerCamelCase						:				Dict    			= SwinConfig()
       _lowerCamelCase						:				str    			= swin_name.split('''_'''						)

       _lowerCamelCase						:				List[str]    			= name_split[1]
       _lowerCamelCase						:				str    			= int(name_split[4]						)
       _lowerCamelCase						:				str    			= int(name_split[3][-1]						)

       if model_size == "tiny":
              _lowerCamelCase						:				int    			= 96
              _lowerCamelCase						:				List[Any]    			= (2, 2, 6, 2)
              _lowerCamelCase						:				Tuple    			= (3, 6, 12, 24)
       elif model_size == "small":
              _lowerCamelCase						:				Union[str, Any]    			= 96
              _lowerCamelCase						:				Tuple    			= (2, 2, 18, 2)
              _lowerCamelCase						:				List[Any]    			= (3, 6, 12, 24)
       elif model_size == "base":
              _lowerCamelCase						:				Union[str, Any]    			= 1_28
              _lowerCamelCase						:				List[Any]    			= (2, 2, 18, 2)
              _lowerCamelCase						:				Tuple    			= (4, 8, 16, 32)
       else:
              _lowerCamelCase						:				Union[str, Any]    			= 1_92
              _lowerCamelCase						:				int    			= (2, 2, 18, 2)
              _lowerCamelCase						:				List[str]    			= (6, 12, 24, 48)

       if "in22k" in swin_name:
              _lowerCamelCase						:				Optional[int]    			= 2_18_41
       else:
              _lowerCamelCase						:				str    			= 10_00
              _lowerCamelCase						:				Optional[int]    			= '''huggingface/label-files'''
              _lowerCamelCase						:				List[str]    			= '''imagenet-1k-id2label.json'''
              _lowerCamelCase						:				Dict    			= json.load(open(hf_hub_download(A_,				A_,				repo_type='''dataset'''						),				'''r'''						)						)
              _lowerCamelCase						:				List[str]    			= {int(A_						): v for k, v in idalabel.items()}
              _lowerCamelCase						:				List[str]    			= idalabel
              _lowerCamelCase						:				Optional[Any]    			= {v: k for k, v in idalabel.items()}

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

       return config






def        snake_case_  (      A_					:		List[str]						):


       '''simple docstring'''





       if "patch_embed.proj" in name:
              _lowerCamelCase						:				List[str]    			= name.replace('''patch_embed.proj''',				'''embeddings.patch_embeddings.projection'''						)
       if "patch_embed.norm" in name:
              _lowerCamelCase						:				Any    			= name.replace('''patch_embed.norm''',				'''embeddings.norm'''						)
       if "layers" in name:
              _lowerCamelCase						:				Dict    			= '''encoder.''' + name
       if "attn.proj" in name:
              _lowerCamelCase						:				Union[str, Any]    			= name.replace('''attn.proj''',				'''attention.output.dense'''						)
       if "attn" in name:
              _lowerCamelCase						:				Optional[int]    			= name.replace('''attn''',				'''attention.self'''						)
       if "norm1" in name:
              _lowerCamelCase						:				Optional[int]    			= name.replace('''norm1''',				'''layernorm_before'''						)
       if "norm2" in name:
              _lowerCamelCase						:				str    			= name.replace('''norm2''',				'''layernorm_after'''						)
       if "mlp.fc1" in name:
              _lowerCamelCase						:				str    			= name.replace('''mlp.fc1''',				'''intermediate.dense'''						)
       if "mlp.fc2" in name:
              _lowerCamelCase						:				Dict    			= name.replace('''mlp.fc2''',				'''output.dense'''						)

       if name == "norm.weight":
              _lowerCamelCase						:				Optional[int]    			= '''layernorm.weight'''
       if name == "norm.bias":
              _lowerCamelCase						:				int    			= '''layernorm.bias'''

       if "head" in name:
              _lowerCamelCase						:				Optional[Any]    			= name.replace('''head''',				'''classifier'''						)
       else:
              _lowerCamelCase						:				Tuple    			= '''swin.''' + name

       return name






def        snake_case_  (      A_					:		Optional[int],				A_					:		int						):


       '''simple docstring'''





       for key in orig_state_dict.copy().keys():
              _lowerCamelCase						:				Optional[Any]    			= orig_state_dict.pop(A_						)

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

                     if "weight" in key:
                            _lowerCamelCase						:				List[Any]    			= val[:dim, :]
                            _lowerCamelCase						:				Dict    			= val[
                                dim : dim * 2, :
                            ]
                            _lowerCamelCase						:				List[str]    			= val[-dim:, :]
                     else:
                            _lowerCamelCase						:				List[Any]    			= val[
                                :dim
                            ]
                            _lowerCamelCase						:				List[Any]    			= val[
                                dim : dim * 2
                            ]
                            _lowerCamelCase						:				List[Any]    			= val[
                                -dim:
                            ]
              else:
                     _lowerCamelCase						:				Optional[Any]    			= val

       return orig_state_dict






def        snake_case_  (      A_					:		Dict,				A_					:		Optional[Any]						):


       '''simple docstring'''





       _lowerCamelCase						:				int    			= timm.create_model(A_,				pretrained=A_						)
       timm_model.eval()

       _lowerCamelCase						:				List[str]    			= get_swin_config(A_						)
       _lowerCamelCase						:				List[str]    			= SwinForImageClassification(A_						)
       model.eval()

       _lowerCamelCase						:				Union[str, Any]    			= convert_state_dict(timm_model.state_dict(),				A_						)
       model.load_state_dict(A_						)

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

       _lowerCamelCase						:				List[Any]    			= AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_''',				'''-'''						)						)						)
       _lowerCamelCase						:				Optional[int]    			= Image.open(requests.get(A_,				stream=A_						).raw						)
       _lowerCamelCase						:				Tuple    			= image_processor(images=A_,				return_tensors='''pt'''						)

       _lowerCamelCase						:				List[str]    			= timm_model(inputs['''pixel_values''']						)
       _lowerCamelCase						:				str    			= model(**A_						).logits

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

       print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}'''						)
       model.save_pretrained(A_						)

       print(F'''Saving image processor to {pytorch_dump_folder_path}'''						)
       image_processor.save_pretrained(A_						)


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 typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__		:				Union[str, Any]          =	logging.get_logger(__name__)

lowercase__		:				List[str]          =	{
    """huggingface/informer-tourism-monthly""": (
        """https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json"""
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}







class       UpperCamelCase__   (	lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE  				=     """informer"""
      _SCREAMING_SNAKE_CASE  				=     {
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
          """num_hidden_layers""": """encoder_layers""",
      }



      def __init__(      self				:      Any    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[int] = None    ,			SCREAMING_SNAKE_CASE_				:      str = "student_t"    ,			SCREAMING_SNAKE_CASE_				:      str = "nll"    ,			SCREAMING_SNAKE_CASE_				:      int = 1    ,			SCREAMING_SNAKE_CASE_				:      List[int] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[Union[str, bool]] = "mean"    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      int = 0    ,			SCREAMING_SNAKE_CASE_				:      Optional[List[int]] = None    ,			SCREAMING_SNAKE_CASE_				:      Optional[List[int]] = None    ,			SCREAMING_SNAKE_CASE_				:      int = 6_4    ,			SCREAMING_SNAKE_CASE_				:      int = 3_2    ,			SCREAMING_SNAKE_CASE_				:      int = 3_2    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			SCREAMING_SNAKE_CASE_				:      int = 2    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			SCREAMING_SNAKE_CASE_				:      str = "gelu"    ,			SCREAMING_SNAKE_CASE_				:      float = 0.05    ,			SCREAMING_SNAKE_CASE_				:      float = 0.1    ,			SCREAMING_SNAKE_CASE_				:      float = 0.1    ,			SCREAMING_SNAKE_CASE_				:      float = 0.1    ,			SCREAMING_SNAKE_CASE_				:      float = 0.1    ,			SCREAMING_SNAKE_CASE_				:      int = 1_0_0    ,			SCREAMING_SNAKE_CASE_				:      float = 0.02    ,			SCREAMING_SNAKE_CASE_				:      str=True    ,			SCREAMING_SNAKE_CASE_				:      str = "prob"    ,			SCREAMING_SNAKE_CASE_				:      int = 5    ,			SCREAMING_SNAKE_CASE_				:      bool = True    ,			**SCREAMING_SNAKE_CASE_				:      int    ,			):
        # time series specific configuration
        lowerCAmelCase_   :				Dict								= prediction_length
        lowerCAmelCase_   :				List[str]								= context_length or prediction_length
        lowerCAmelCase_   :				List[Any]								= distribution_output
        lowerCAmelCase_   :				int								= loss
        lowerCAmelCase_   :				Optional[int]								= input_size
        lowerCAmelCase_   :				Tuple								= num_time_features
        lowerCAmelCase_   :				List[str]								= lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
        lowerCAmelCase_   :				int								= scaling
        lowerCAmelCase_   :				List[Any]								= num_dynamic_real_features
        lowerCAmelCase_   :				Union[str, Any]								= num_static_real_features
        lowerCAmelCase_   :				Optional[int]								= num_static_categorical_features

        # set cardinality
        if cardinality and num_static_categorical_features > 0:
          if len(SCREAMING_SNAKE_CASE_       ) != num_static_categorical_features:
            raise ValueError(
                'The cardinality should be a list of the same length as `num_static_categorical_features`'       )
          lowerCAmelCase_   :				str								= cardinality
        else:
          lowerCAmelCase_   :				Any								= [0]

        # set embedding_dimension
        if embedding_dimension and num_static_categorical_features > 0:
          if len(SCREAMING_SNAKE_CASE_       ) != num_static_categorical_features:
            raise ValueError(
                'The embedding dimension should be a list of the same length as `num_static_categorical_features`'       )
          lowerCAmelCase_   :				Optional[int]								= embedding_dimension
        else:
          lowerCAmelCase_   :				Union[str, Any]								= [min(5_0    ,			(cat + 1) // 2       ) for cat in self.cardinality]

        lowerCAmelCase_   :				Optional[int]								= num_parallel_samples

        # Transformer architecture configuration
        lowerCAmelCase_   :				Any								= input_size * len(self.lags_sequence       ) + self._number_of_features
        lowerCAmelCase_   :				Any								= d_model
        lowerCAmelCase_   :				Union[str, Any]								= encoder_attention_heads
        lowerCAmelCase_   :				Optional[Any]								= decoder_attention_heads
        lowerCAmelCase_   :				Any								= encoder_ffn_dim
        lowerCAmelCase_   :				List[str]								= decoder_ffn_dim
        lowerCAmelCase_   :				Optional[Any]								= encoder_layers
        lowerCAmelCase_   :				Tuple								= decoder_layers

        lowerCAmelCase_   :				Optional[int]								= dropout
        lowerCAmelCase_   :				Dict								= attention_dropout
        lowerCAmelCase_   :				int								= activation_dropout
        lowerCAmelCase_   :				Dict								= encoder_layerdrop
        lowerCAmelCase_   :				str								= decoder_layerdrop

        lowerCAmelCase_   :				Union[str, Any]								= activation_function
        lowerCAmelCase_   :				Union[str, Any]								= init_std

        lowerCAmelCase_   :				Union[str, Any]								= use_cache

        # Informer
        lowerCAmelCase_   :				Optional[int]								= attention_type
        lowerCAmelCase_   :				Any								= sampling_factor
        lowerCAmelCase_   :				int								= distil

        super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE_    ,			**SCREAMING_SNAKE_CASE_       )





      @property
      def      SCREAMING_SNAKE_CASE__ (      self				:      Any       ):
        return (
            sum(self.embedding_dimension       )
            + self.num_dynamic_real_features
            + self.num_time_features
            + self.num_static_real_features
            + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
        )





