"""simple docstring"""







import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)



def     lowercase__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]   ):
     lowercase_			:       List[str]									=			XCLIPTextConfig()

     # derive patch size from model name
     lowercase_			:       Union[str, Any]									=			model_name.find('patch'   )
     lowercase_			:       Any									=			int(model_name[start_idx + len('patch'   ) : start_idx + len('patch'   ) + 2]   )
     lowercase_			:       str									=			XCLIPVisionConfig(patch_size=__SCREAMING_SNAKE_CASE  ,    num_frames=__SCREAMING_SNAKE_CASE   )

     if "large" in model_name:
          lowercase_			:       List[str]									=			7_68
          lowercase_			:       str									=			30_72
          lowercase_			:       Tuple									=			12

          lowercase_			:       int									=			10_24
          lowercase_			:       int									=			40_96
          lowercase_			:       str									=			16
          lowercase_			:       Union[str, Any]									=			24
          lowercase_			:       List[str]									=			7_68
          lowercase_			:       Optional[int]									=			30_72

     if model_name == "xclip-large-patch14-16-frames":
          lowercase_			:       List[str]									=			3_36

     lowercase_			:       List[str]									=			XCLIPConfig.from_text_vision_configs(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )

     if "large" in model_name:
          lowercase_			:       Tuple									=			7_68

     return config



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[int]   ):
     # text encoder
     if name == "token_embedding.weight":
          lowercase_			:       int									=			name.replace('token_embedding.weight'  ,    'text_model.embeddings.token_embedding.weight'   )
     if name == "positional_embedding":
          lowercase_			:       Optional[Any]									=			name.replace('positional_embedding'  ,    'text_model.embeddings.position_embedding.weight'   )
     if "ln_1" in name:
          lowercase_			:       Dict									=			name.replace('ln_1'  ,    'layer_norm1'   )
     if "ln_2" in name:
          lowercase_			:       Any									=			name.replace('ln_2'  ,    'layer_norm2'   )
     if "c_fc" in name:
          lowercase_			:       List[str]									=			name.replace('c_fc'  ,    'fc1'   )
     if "c_proj" in name:
          lowercase_			:       List[Any]									=			name.replace('c_proj'  ,    'fc2'   )
     if name.startswith('transformer.resblocks'   ):
          lowercase_			:       str									=			name.replace('transformer.resblocks'  ,    'text_model.encoder.layers'   )
     if "attn.out_proj" in name and "message" not in name:
          lowercase_			:       str									=			name.replace('attn.out_proj'  ,    'self_attn.out_proj'   )
     if "ln_final" in name:
          lowercase_			:       Any									=			name.replace('ln_final'  ,    'text_model.final_layer_norm'   )
     # visual encoder
     if name == "visual.class_embedding":
          lowercase_			:       Tuple									=			name.replace('visual.class_embedding'  ,    'vision_model.embeddings.class_embedding'   )
     if name == "visual.positional_embedding":
          lowercase_			:       Optional[int]									=			name.replace('visual.positional_embedding'  ,    'vision_model.embeddings.position_embedding.weight'   )
     if name.startswith('visual.transformer.resblocks'   ):
          lowercase_			:       Optional[int]									=			name.replace('visual.transformer.resblocks'  ,    'vision_model.encoder.layers'   )
     if "visual.conv1" in name:
          lowercase_			:       List[str]									=			name.replace('visual.conv1'  ,    'vision_model.embeddings.patch_embedding'   )
     if "visual.ln_pre" in name:
          lowercase_			:       List[str]									=			name.replace('visual.ln_pre'  ,    'vision_model.pre_layernorm'   )
     if "visual.ln_post" in name:
          lowercase_			:       List[str]									=			name.replace('visual.ln_post'  ,    'vision_model.post_layernorm'   )
     if "visual.proj" in name:
          lowercase_			:       str									=			name.replace('visual.proj'  ,    'visual_projection.weight'   )
     if "text_projection" in name:
          lowercase_			:       str									=			name.replace('text_projection'  ,    'text_projection.weight'   )
     # things on top
     if "prompts_visual_proj" in name:
          lowercase_			:       List[Any]									=			name.replace('prompts_visual_proj'  ,    'prompts_visual_projection'   )
     if "prompts_visual_ln" in name:
          lowercase_			:       Any									=			name.replace('prompts_visual_ln'  ,    'prompts_visual_layernorm'   )
     # mit
     if name == "mit.positional_embedding":
          lowercase_			:       Any									=			name.replace('positional'  ,    'position'   )
     if name.startswith('mit.resblocks'   ):
          lowercase_			:       int									=			name.replace('mit.resblocks'  ,    'mit.encoder.layers'   )
     # prompts generator
     if name.startswith('prompts_generator.norm'   ):
          lowercase_			:       Any									=			name.replace('prompts_generator.norm'  ,    'prompts_generator.layernorm'   )

     return name



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  Tuple   ):
     for key in orig_state_dict.copy().keys():
          lowercase_			:       int									=			orig_state_dict.pop(__SCREAMING_SNAKE_CASE   )

          if "attn.in_proj" in key:
               lowercase_			:       Optional[Any]									=			key.split('.'   )
               if key.startswith('visual'   ):
                    lowercase_			:       Optional[Any]									=			key_split[3]
                    lowercase_			:       Optional[Any]									=			config.vision_config.hidden_size
                    if "message_attn" in key:
                         if "weight" in key:
                              lowercase_			:       Optional[int]									=			val[
                                  :dim, :
                              ]
                              lowercase_			:       List[str]									=			val[
                                  dim : dim * 2, :
                              ]
                              lowercase_			:       Optional[int]									=			val[
                                  -dim:, :
                              ]
                         else:
                              lowercase_			:       Optional[Any]									=			val[
                                  :dim
                              ]
                              lowercase_			:       Optional[int]									=			val[
                                  dim : dim * 2
                              ]
                              lowercase_			:       Union[str, Any]									=			val[
                                  -dim:
                              ]
                    else:
                         if "weight" in key:
                              lowercase_			:       Any									=			val[
                                  :dim, :
                              ]
                              lowercase_			:       int									=			val[
                                  dim : dim * 2, :
                              ]
                              lowercase_			:       Dict									=			val[
                                  -dim:, :
                              ]
                         else:
                              lowercase_			:       Optional[Any]									=			val[:dim]
                              lowercase_			:       Any									=			val[
                                  dim : dim * 2
                              ]
                              lowercase_			:       Dict									=			val[-dim:]
               elif key.startswith('mit'   ):
                    lowercase_			:       List[str]									=			key_split[2]
                    lowercase_			:       Union[str, Any]									=			config.vision_config.mit_hidden_size
                    if "weight" in key:
                         lowercase_			:       Any									=			val[:dim, :]
                         lowercase_			:       Union[str, Any]									=			val[dim : dim * 2, :]
                         lowercase_			:       Dict									=			val[-dim:, :]
                    else:
                         lowercase_			:       Dict									=			val[:dim]
                         lowercase_			:       List[Any]									=			val[dim : dim * 2]
                         lowercase_			:       Any									=			val[-dim:]
               else:
                    lowercase_			:       Union[str, Any]									=			key_split[2]
                    lowercase_			:       Dict									=			config.text_config.hidden_size
                    if "weight" in key:
                         lowercase_			:       Optional[Any]									=			val[:dim, :]
                         lowercase_			:       int									=			val[
                             dim : dim * 2, :
                         ]
                         lowercase_			:       Tuple									=			val[-dim:, :]
                    else:
                         lowercase_			:       Union[str, Any]									=			val[:dim]
                         lowercase_			:       Union[str, Any]									=			val[
                             dim : dim * 2
                         ]
                         lowercase_			:       str									=			val[-dim:]
          else:
               lowercase_			:       Union[str, Any]									=			rename_key(__SCREAMING_SNAKE_CASE   )
               if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                    lowercase_			:       Dict									=			val.T
               lowercase_			:       Optional[int]									=			val

     return orig_state_dict



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int   ):
     if num_frames == 8:
          lowercase_			:       Union[str, Any]									=			'eating_spaghetti_8_frames.npy'
     elif num_frames == 16:
          lowercase_			:       Tuple									=			'eating_spaghetti.npy'
     elif num_frames == 32:
          lowercase_			:       str									=			'eating_spaghetti_32_frames.npy'
     lowercase_			:       Union[str, Any]									=			hf_hub_download(
         repo_id='hf-internal-testing/spaghetti-video'  ,    filename=__SCREAMING_SNAKE_CASE  ,    repo_type='dataset'  ,    )
     lowercase_			:       int									=			np.load(__SCREAMING_SNAKE_CASE   )
     return list(__SCREAMING_SNAKE_CASE   )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  Dict=None  ,    __SCREAMING_SNAKE_CASE							:  Union[str, Any]=False   ):
     lowercase_			:       int									=			{
         # fully supervised kinetics-400 checkpoints
         'xclip-base-patch32': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth',
         'xclip-base-patch32-16-frames': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'
         ),
         'xclip-base-patch16': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth',
         'xclip-base-patch16-16-frames': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'
         ),
         'xclip-large-patch14': 'https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb',
         'xclip-large-patch14-16-frames': 'https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f',
         # fully supervised kinetics-600 checkpoints
         'xclip-base-patch16-kinetics-600': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'
         ),
         'xclip-base-patch16-kinetics-600-16-frames': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'
         ),
         'xclip-large-patch14-kinetics-600': 'https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be',
         # few shot
         'xclip-base-patch16-hmdb-2-shot': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'
         ),
         'xclip-base-patch16-hmdb-4-shot': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'
         ),
         'xclip-base-patch16-hmdb-8-shot': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'
         ),
         'xclip-base-patch16-hmdb-16-shot': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'
         ),
         'xclip-base-patch16-ucf-2-shot': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'
         ),
         'xclip-base-patch16-ucf-4-shot': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'
         ),
         'xclip-base-patch16-ucf-8-shot': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'
         ),
         'xclip-base-patch16-ucf-16-shot': (
             'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'
         ),
         # zero shot
         'xclip-base-patch16-zero-shot': 'https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth',
     }

     lowercase_			:       List[str]									=			model_to_url[model_name]
     lowercase_			:       Dict									=			8
     if "16-frames" in model_name:
          lowercase_			:       List[str]									=			16
     elif "shot" in model_name:
          lowercase_			:       Tuple									=			32

     lowercase_			:       str									=			get_xclip_config(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     lowercase_			:       Optional[Any]									=			XCLIPModel(__SCREAMING_SNAKE_CASE   )
     model.eval()

     if "drive" in checkpoint_url:
          lowercase_			:       List[str]									=			'pytorch_model.bin'
          gdown.cached_download(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    quiet=__SCREAMING_SNAKE_CASE   )
          lowercase_			:       Union[str, Any]									=			torch.load(__SCREAMING_SNAKE_CASE  ,    map_location='cpu'   )['model']
     else:
          lowercase_			:       Optional[int]									=			torch.hub.load_state_dict_from_url(__SCREAMING_SNAKE_CASE   )['model']

     lowercase_			:       Optional[Any]									=			convert_state_dict(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )

     lowercase_			:       Optional[Any]									=			XCLIPModel(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Dict									=			model.load_state_dict(__SCREAMING_SNAKE_CASE  ,    strict=__SCREAMING_SNAKE_CASE   )
     assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
     model.eval()

     lowercase_			:       Tuple									=			3_36 if model_name == 'xclip-large-patch14-16-frames' else 2_24
     lowercase_			:       Optional[Any]									=			VideoMAEImageProcessor(size=__SCREAMING_SNAKE_CASE   )
     lowercase_			:       int									=			CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'   )
     lowercase_			:       List[Any]									=			CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32'   )
     lowercase_			:       Tuple									=			XCLIPProcessor(image_processor=__SCREAMING_SNAKE_CASE  ,    tokenizer=__SCREAMING_SNAKE_CASE   )

     lowercase_			:       Union[str, Any]									=			prepare_video(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       List[str]									=			processor(
         text=['playing sports', 'eating spaghetti', 'go shopping']  ,    videos=__SCREAMING_SNAKE_CASE  ,    return_tensors='pt'  ,    padding=__SCREAMING_SNAKE_CASE   )

     print('Shape of pixel values:'  ,    inputs.pixel_values.shape   )

     with torch.no_grad():
          lowercase_			:       Dict									=			model(**__SCREAMING_SNAKE_CASE   )

     # Verify outputs
     lowercase_			:       Any									=			outputs.logits_per_video
     lowercase_			:       int									=			logits_per_video.softmax(dim=1   )
     print('Probs:'  ,    __SCREAMING_SNAKE_CASE   )
     # kinetics-400
     if model_name == "xclip-base-patch32":
          lowercase_			:       str									=			torch.tensor([[0.0019, 0.9951, 0.0030]]   )
     elif model_name == "xclip-base-patch32-16-frames":
          lowercase_			:       int									=			torch.tensor([[7.0_999E-04, 9.9_883E-01, 4.5_580E-04]]   )
     elif model_name == "xclip-base-patch16":
          lowercase_			:       Any									=			torch.tensor([[0.0083, 0.9681, 0.0236]]   )
     elif model_name == "xclip-base-patch16-16-frames":
          lowercase_			:       List[str]									=			torch.tensor([[7.6_937E-04, 9.9_728E-01, 1.9_473E-03]]   )
     elif model_name == "xclip-large-patch14":
          lowercase_			:       str									=			torch.tensor([[0.0062, 0.9864, 0.0075]]   )
     elif model_name == "xclip-large-patch14-16-frames":
          lowercase_			:       List[str]									=			torch.tensor([[3.3_877E-04, 9.9_937E-01, 2.8_888E-04]]   )
     # kinetics-600
     elif model_name == "xclip-base-patch16-kinetics-600":
          lowercase_			:       List[str]									=			torch.tensor([[0.0555, 0.8914, 0.0531]]   )
     elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
          lowercase_			:       Tuple									=			torch.tensor([[3.8_554E-04, 9.9_929E-01, 3.2_754E-04]]   )
     elif model_name == "xclip-large-patch14-kinetics-600":
          lowercase_			:       List[str]									=			torch.tensor([[0.0036, 0.9920, 0.0045]]   )
     # few shot
     elif model_name == "xclip-base-patch16-hmdb-2-shot":
          lowercase_			:       List[Any]									=			torch.tensor([[7.1_890E-06, 9.9_994E-01, 5.6_559E-05]]   )
     elif model_name == "xclip-base-patch16-hmdb-4-shot":
          lowercase_			:       Union[str, Any]									=			torch.tensor([[1.0_320E-05, 9.9_993E-01, 6.2_435E-05]]   )
     elif model_name == "xclip-base-patch16-hmdb-8-shot":
          lowercase_			:       Tuple									=			torch.tensor([[4.1_377E-06, 9.9_990E-01, 9.8_386E-05]]   )
     elif model_name == "xclip-base-patch16-hmdb-16-shot":
          lowercase_			:       Dict									=			torch.tensor([[4.1_347E-05, 9.9_962E-01, 3.3_411E-04]]   )
     elif model_name == "xclip-base-patch16-ucf-2-shot":
          lowercase_			:       List[Any]									=			torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]]   )
     elif model_name == "xclip-base-patch16-ucf-4-shot":
          lowercase_			:       int									=			torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]]   )
     elif model_name == "xclip-base-patch16-ucf-8-shot":
          lowercase_			:       str									=			torch.tensor([[0.0027, 0.9904, 0.0070]]   )
     elif model_name == "xclip-base-patch16-ucf-16-shot":
          lowercase_			:       str									=			torch.tensor([[9.8_219E-04, 9.9_593E-01, 3.0_863E-03]]   )
     # zero shot
     elif model_name == "xclip-base-patch16-zero-shot":
          lowercase_			:       Optional[int]									=			torch.tensor([[3.5_082E-04, 9.9_785E-01, 1.7_966E-03]]   )
     else:
          raise ValueError(F'''Model name {model_name} not supported'''   )
     assert torch.allclose(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE  ,    atol=1E-3   )
     print('Looks ok!'   )

     if pytorch_dump_folder_path is not None:
          print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''   )
          model.save_pretrained(__SCREAMING_SNAKE_CASE   )

     if push_to_hub:
          print('Pushing model, processor and slow tokenizer files to the hub...'   )
          model.push_to_hub(__SCREAMING_SNAKE_CASE  ,    organization='nielsr'   )
          processor.push_to_hub(__SCREAMING_SNAKE_CASE  ,    organization='nielsr'   )
          slow_tokenizer.push_to_hub(__SCREAMING_SNAKE_CASE  ,    organization='nielsr'   )


if __name__ == "__main__":
       __SCREAMING_SNAKE_CASE 			=argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="xclip-base-patch32",
           type=str,
           help="Name of the model.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
       )
       parser.add_argument(
           "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
       )

       __SCREAMING_SNAKE_CASE 			=parser.parse_args()
       convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


"""simple docstring"""







import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)







class  UpperCamelCase	(    nn.Module						):
   def __init__(    self    )			->   Any:


        '''simple docstring'''


        super().__init__()
        lowercase_			:       Dict									=			nn.Linear(3  ,4    )
        lowercase_			:       Union[str, Any]									=			nn.BatchNormad(4    )
        lowercase_			:       Union[str, Any]									=			nn.Linear(4  ,5    )







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   int:


        '''simple docstring'''


        return self.lineara(self.batchnorm(self.lineara(__UpperCamelCase    )    )    )







class  UpperCamelCase	(    unittest.TestCase						):
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       int									=			ModelForTest()
        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,model.state_dict()    )
             lowercase_			:       str									=			os.path.join(__UpperCamelCase  ,'index.json'    )
             self.assertTrue(os.path.isfile(__UpperCamelCase    )    )
             # TODO: add tests on what is inside the index

             for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
                  lowercase_			:       int									=			os.path.join(__UpperCamelCase  ,f'''{key}.dat'''    )
                  self.assertTrue(os.path.isfile(__UpperCamelCase    )    )
                  # TODO: add tests on the fact weights are properly loaded
   def     _UpperCAmelCase     (    self    )			->   Tuple:


        '''simple docstring'''


        lowercase_			:       List[str]									=			[torch.floataa, torch.floataa, torch.bfloataa]

        for dtype in dtypes:
             lowercase_			:       Tuple									=			torch.randn(2  ,3  ,dtype=__UpperCamelCase    )
             with TemporaryDirectory() as tmp_dir:
                  lowercase_			:       List[str]									=			offload_weight(__UpperCamelCase  ,'weight'  ,__UpperCamelCase  ,{}    )
                  lowercase_			:       str									=			os.path.join(__UpperCamelCase  ,'weight.dat'    )
                  self.assertTrue(os.path.isfile(__UpperCamelCase    )    )
                  self.assertDictEqual(__UpperCamelCase  ,{'weight': {'shape': [2, 3], 'dtype': str(__UpperCamelCase    ).split('.'    )[1]}}    )

                  lowercase_			:       Optional[Any]									=			load_offloaded_weight(__UpperCamelCase  ,index['weight']    )
                  self.assertTrue(torch.equal(__UpperCamelCase  ,__UpperCamelCase    )    )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       List[str]									=			ModelForTest()
        lowercase_			:       Optional[int]									=			model.state_dict()
        lowercase_			:       int									=			{k: v for k, v in state_dict.items() if 'linear2' not in k}
        lowercase_			:       Optional[Any]									=			{k: v for k, v in state_dict.items() if 'linear2' in k}

        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,__UpperCamelCase    )
             lowercase_			:       Tuple									=			OffloadedWeightsLoader(state_dict=__UpperCamelCase  ,save_folder=__UpperCamelCase    )

             # Every key is there with the right value
             self.assertEqual(sorted(__UpperCamelCase    )  ,sorted(state_dict.keys()    )    )
             for key, param in state_dict.items():
                  self.assertTrue(torch.allclose(__UpperCamelCase  ,weight_map[key]    )    )

        lowercase_			:       int									=			{k: v for k, v in state_dict.items() if 'weight' in k}
        lowercase_			:       int									=			{k: v for k, v in state_dict.items() if 'weight' not in k}

        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,__UpperCamelCase    )
             lowercase_			:       int									=			OffloadedWeightsLoader(state_dict=__UpperCamelCase  ,save_folder=__UpperCamelCase    )

             # Every key is there with the right value
             self.assertEqual(sorted(__UpperCamelCase    )  ,sorted(state_dict.keys()    )    )
             for key, param in state_dict.items():
                  self.assertTrue(torch.allclose(__UpperCamelCase  ,weight_map[key]    )    )

        with TemporaryDirectory() as tmp_dir:
             offload_state_dict(__UpperCamelCase  ,__UpperCamelCase    )
             # Duplicates are removed
             lowercase_			:       Tuple									=			OffloadedWeightsLoader(state_dict=__UpperCamelCase  ,save_folder=__UpperCamelCase    )

             # Every key is there with the right value
             self.assertEqual(sorted(__UpperCamelCase    )  ,sorted(state_dict.keys()    )    )
             for key, param in state_dict.items():
                  self.assertTrue(torch.allclose(__UpperCamelCase  ,weight_map[key]    )    )







   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       Optional[int]									=			{'a.1': 0, 'a.10': 1, 'a.2': 2}
        lowercase_			:       int									=			extract_submodules_state_dict(__UpperCamelCase  ,['a.1', 'a.2']    )
        self.assertDictEqual(__UpperCamelCase  ,{'a.1': 0, 'a.2': 2}    )

        lowercase_			:       Union[str, Any]									=			{'a.1.a': 0, 'a.10.a': 1, 'a.2.a': 2}
        lowercase_			:       Optional[int]									=			extract_submodules_state_dict(__UpperCamelCase  ,['a.1', 'a.2']    )
        self.assertDictEqual(__UpperCamelCase  ,{'a.1.a': 0, 'a.2.a': 2}    )

