


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__					(  SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Tuple		 =						SwinConfig()
 __lowerCamelCase		:							Dict		 =						swin_name.split('_'						)

 __lowerCamelCase		:							List[str]		 =						name_split[1]
 __lowerCamelCase		:							Optional[Any]		 =						int(name_split[4]						)
 __lowerCamelCase		:							Tuple		 =						int(name_split[3][-1]						)

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

 if "in22k" in swin_name:
  __lowerCamelCase		:							List[str]		 =						21_841
 else:
  __lowerCamelCase		:							Optional[int]		 =						1_000
  __lowerCamelCase		:							int		 =						'huggingface/label-files'
  __lowerCamelCase		:							Any		 =						'imagenet-1k-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		:							Union[str, Any]		 =						idalabel
  __lowerCamelCase		:							Optional[int]		 =						{v: k for k, v in idalabel.items()}

 __lowerCamelCase		:							Dict		 =						img_size
 __lowerCamelCase		:							List[Any]		 =						num_classes
 __lowerCamelCase		:							Tuple		 =						embed_dim
 __lowerCamelCase		:							str		 =						depths
 __lowerCamelCase		:							Dict		 =						num_heads
 __lowerCamelCase		:							str		 =						window_size

 return config
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__						):
 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		:							List[Any]		 =						name.replace('patch_embed.norm'      ,	'embeddings.norm'						)
 if "layers" in name:
  __lowerCamelCase		:							List[Any]		 =						'encoder.' + name
 if "attn.proj" in name:
  __lowerCamelCase		:							List[Any]		 =						name.replace('attn.proj'      ,	'attention.output.dense'						)
 if "attn" in name:
  __lowerCamelCase		:							Optional[Any]		 =						name.replace('attn'      ,	'attention.self'						)
 if "norm1" in name:
  __lowerCamelCase		:							Union[str, Any]		 =						name.replace('norm1'      ,	'layernorm_before'						)
 if "norm2" in name:
  __lowerCamelCase		:							Any		 =						name.replace('norm2'      ,	'layernorm_after'						)
 if "mlp.fc1" in name:
  __lowerCamelCase		:							Tuple		 =						name.replace('mlp.fc1'      ,	'intermediate.dense'						)
 if "mlp.fc2" in name:
  __lowerCamelCase		:							List[Any]		 =						name.replace('mlp.fc2'      ,	'output.dense'						)

 if name == "norm.weight":
  __lowerCamelCase		:							Any		 =						'layernorm.weight'
 if name == "norm.bias":
  __lowerCamelCase		:							List[Any]		 =						'layernorm.bias'

 if "head" in name:
  __lowerCamelCase		:							str		 =						name.replace('head'      ,	'classifier'						)
 else:
  __lowerCamelCase		:							Dict		 =						'swin.' + name

 return name
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 for key in orig_state_dict.copy().keys():
  __lowerCamelCase		:							Optional[Any]		 =						orig_state_dict.pop(SCREAMING_SNAKE_CASE__						)

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

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

 return orig_state_dict
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Tuple		 =						timm.create_model(SCREAMING_SNAKE_CASE__      ,	pretrained=SCREAMING_SNAKE_CASE__						)
 timm_model.eval()

 __lowerCamelCase		:							Dict		 =						get_swin_config(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							List[str]		 =						SwinForImageClassification(SCREAMING_SNAKE_CASE__						)
 model.eval()

 __lowerCamelCase		:							List[Any]		 =						convert_state_dict(timm_model.state_dict()      ,	SCREAMING_SNAKE_CASE__						)
 model.load_state_dict(SCREAMING_SNAKE_CASE__						)

 __lowerCamelCase		:							Any		 =						'http://images.cocodataset.org/val2017/000000039769.jpg'

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

 __lowerCamelCase		:							List[Any]		 =						timm_model(inputs['pixel_values']						)
 __lowerCamelCase		:							Dict		 =						model(**SCREAMING_SNAKE_CASE__						).logits

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

 print(f'Saving model {swin_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__						)


if __name__ == "__main__":
       lowercase_	   =     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.'
       )

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





import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
       from ...processing_utils import ProcessorMixin
       from ...utils import TensorType

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


lowercase_	   =     logging.get_logger(__name__)

lowercase_	   =     {
    'google/owlvit-base-patch32': 'https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json',
    'google/owlvit-base-patch16': 'https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json',
    'google/owlvit-large-patch14': 'https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json',
}


class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """owlvit_text_model"""




      def __init__(    self:	Optional[int]						,				a:	Dict=4_9408						,				a:	Optional[Any]=512						,				a:	Dict=2048						,				a:	Optional[Any]=12						,				a:	Tuple=8						,				a:	Union[str, Any]=16						,				a:	str="quick_gelu"						,				a:	List[Any]=1e-5						,				a:	Dict=0.0						,				a:	Optional[int]=0.0_2						,				a:	Dict=1.0						,				a:	Any=0						,				a:	Union[str, Any]=4_9406						,				a:	Any=4_9407						,				**a:	Dict						,				):
       super().__init__(pad_token_id=a						,				bos_token_id=a						,				eos_token_id=a						,				**a    )

       __lowerCamelCase		:							List[Any]		 =						vocab_size
       __lowerCamelCase		:							int		 =						hidden_size
       __lowerCamelCase		:							Optional[Any]		 =						intermediate_size
       __lowerCamelCase		:							List[Any]		 =						num_hidden_layers
       __lowerCamelCase		:							Any		 =						num_attention_heads
       __lowerCamelCase		:							Union[str, Any]		 =						max_position_embeddings
       __lowerCamelCase		:							List[Any]		 =						hidden_act
       __lowerCamelCase		:							List[str]		 =						layer_norm_eps
       __lowerCamelCase		:							Tuple		 =						attention_dropout
       __lowerCamelCase		:							Optional[int]		 =						initializer_range
       __lowerCamelCase		:							Tuple		 =						initializer_factor




      @classmethod
      def 					_snake_case     (    cls:	Dict						,				a:	Union[str, os.PathLike]						,				**a:	Optional[int]    ):
       cls._set_token_in_kwargs(a    )

       __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						cls.get_config_dict(a						,				**a    )

       # get the text config dict if we are loading from OwlViTConfig
       if config_dict.get('model_type'    ) == "owlvit":
        __lowerCamelCase		:							Optional[Any]		 =						config_dict['text_config']

       if "model_type" in config_dict and hasattr(cls						,				'model_type'    ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
            F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

       return cls.from_dict(a						,				**a    )



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """owlvit_vision_model"""




      def __init__(    self:	int						,				a:	Tuple=768						,				a:	int=3072						,				a:	List[str]=12						,				a:	Optional[Any]=12						,				a:	Optional[int]=3						,				a:	Optional[int]=768						,				a:	Optional[Any]=32						,				a:	Optional[int]="quick_gelu"						,				a:	Union[str, Any]=1e-5						,				a:	Union[str, Any]=0.0						,				a:	Union[str, Any]=0.0_2						,				a:	int=1.0						,				**a:	Union[str, Any]						,				):
       super().__init__(**a    )

       __lowerCamelCase		:							str		 =						hidden_size
       __lowerCamelCase		:							Tuple		 =						intermediate_size
       __lowerCamelCase		:							Dict		 =						num_hidden_layers
       __lowerCamelCase		:							Optional[Any]		 =						num_attention_heads
       __lowerCamelCase		:							int		 =						num_channels
       __lowerCamelCase		:							Optional[Any]		 =						image_size
       __lowerCamelCase		:							Tuple		 =						patch_size
       __lowerCamelCase		:							List[str]		 =						hidden_act
       __lowerCamelCase		:							Tuple		 =						layer_norm_eps
       __lowerCamelCase		:							List[Any]		 =						attention_dropout
       __lowerCamelCase		:							Tuple		 =						initializer_range
       __lowerCamelCase		:							List[Any]		 =						initializer_factor




      @classmethod
      def 					_snake_case     (    cls:	Optional[int]						,				a:	Union[str, os.PathLike]						,				**a:	int    ):
       cls._set_token_in_kwargs(a    )

       __lowerCamelCase ,					__lowerCamelCase		:							Dict		 =						cls.get_config_dict(a						,				**a    )

       # get the vision config dict if we are loading from OwlViTConfig
       if config_dict.get('model_type'    ) == "owlvit":
        __lowerCamelCase		:							Any		 =						config_dict['vision_config']

       if "model_type" in config_dict and hasattr(cls						,				'model_type'    ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
            F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

       return cls.from_dict(a						,				**a    )



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      __snake_case 	=       """owlvit"""
      __snake_case 	=       True




      def __init__(    self:	Dict						,				a:	int=None						,				a:	str=None						,				a:	Tuple=512						,				a:	Tuple=2.6_5_9_2						,				a:	int=True						,				**a:	int						,				):
       super().__init__(**a    )

       if text_config is None:
        __lowerCamelCase		:							List[str]		 =						{}
        logger.info('text_config is None. Initializing the OwlViTTextConfig with default values.'    )

       if vision_config is None:
        __lowerCamelCase		:							str		 =						{}
        logger.info('vision_config is None. initializing the OwlViTVisionConfig with default values.'    )

       __lowerCamelCase		:							List[Any]		 =						OwlViTTextConfig(**a    )
       __lowerCamelCase		:							str		 =						OwlViTVisionConfig(**a    )

       __lowerCamelCase		:							Union[str, Any]		 =						projection_dim
       __lowerCamelCase		:							Tuple		 =						logit_scale_init_value
       __lowerCamelCase		:							Dict		 =						return_dict
       __lowerCamelCase		:							Tuple		 =						1.0




      @classmethod
      def 					_snake_case     (    cls:	str						,				a:	Union[str, os.PathLike]						,				**a:	List[Any]    ):
       cls._set_token_in_kwargs(a    )

       __lowerCamelCase ,					__lowerCamelCase		:							List[Any]		 =						cls.get_config_dict(a						,				**a    )

       if "model_type" in config_dict and hasattr(cls						,				'model_type'    ) and config_dict["model_type"] != cls.model_type:
        logger.warning(
            F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
            F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'    )

       return cls.from_dict(a						,				**a    )




      @classmethod
      def 					_snake_case     (    cls:	Tuple						,				a:	Dict						,				a:	Dict						,				**a:	str    ):
       __lowerCamelCase		:							List[str]		 =						{}
       __lowerCamelCase		:							List[str]		 =						text_config
       __lowerCamelCase		:							Optional[int]		 =						vision_config

       return cls.from_dict(a						,				**a    )




      def 					_snake_case     (    self:	Optional[int]    ):
       __lowerCamelCase		:							Optional[Any]		 =						copy.deepcopy(self.__dict__    )
       __lowerCamelCase		:							List[Any]		 =						self.text_config.to_dict()
       __lowerCamelCase		:							List[str]		 =						self.vision_config.to_dict()
       __lowerCamelCase		:							Optional[Any]		 =						self.__class__.model_type
       return output



class 	A_ (	__UpperCamelCase   ):




      '''simple docstring'''


      @property
      def 					_snake_case     (    self:	str    ):
       return OrderedDict(
           [
               ('input_ids', {0: 'batch', 1: 'sequence'}),
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
               ('attention_mask', {0: 'batch', 1: 'sequence'}),
           ]    )




      @property
      def 					_snake_case     (    self:	Dict    ):
       return OrderedDict(
           [
               ('logits_per_image', {0: 'batch'}),
               ('logits_per_text', {0: 'batch'}),
               ('text_embeds', {0: 'batch'}),
               ('image_embeds', {0: 'batch'}),
           ]    )




      @property
      def 					_snake_case     (    self:	int    ):
       return 1e-4




      def 					_snake_case     (    self:	Any						,				a:	"ProcessorMixin"						,				a:	int = -1						,				a:	int = -1						,				a:	Optional["TensorType"] = None						,				):
       __lowerCamelCase		:							List[str]		 =						super().generate_dummy_inputs(
           processor.tokenizer						,				batch_size=a						,				seq_length=a						,				framework=a    )
       __lowerCamelCase		:							int		 =						super().generate_dummy_inputs(
           processor.image_processor						,				batch_size=a						,				framework=a    )
       return {**text_input_dict, **image_input_dict}




      @property
      def 					_snake_case     (    self:	int    ):
       return 14

