




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


__a							     =					logging.get_logger(__name__)

__a							     =					{
    'facebook/s2t-small-librispeech-asr': (
        'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''speech_to_text'''
    lowerCAmelCase         = ['''past_key_values''']
    lowerCAmelCase         = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





    def __init__( self  ,_SCREAMING_SNAKE_CASE=10_000  ,_SCREAMING_SNAKE_CASE=12  ,_SCREAMING_SNAKE_CASE=2_048  ,_SCREAMING_SNAKE_CASE=4  ,_SCREAMING_SNAKE_CASE=6  ,_SCREAMING_SNAKE_CASE=2_048  ,_SCREAMING_SNAKE_CASE=4  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE="relu"  ,_SCREAMING_SNAKE_CASE=256  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=0.02  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=1  ,_SCREAMING_SNAKE_CASE=0  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=6_000  ,_SCREAMING_SNAKE_CASE=1_024  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=(5, 5)  ,_SCREAMING_SNAKE_CASE=1_024  ,_SCREAMING_SNAKE_CASE=80  ,_SCREAMING_SNAKE_CASE=1  ,**_SCREAMING_SNAKE_CASE  ,)					->						Union[str, Any]:
         UpperCAmelCase_     :				Dict  =     vocab_size
         UpperCAmelCase_     :				Any  =     d_model
         UpperCAmelCase_     :				Union[str, Any]  =     encoder_ffn_dim
         UpperCAmelCase_     :				List[str]  =     encoder_layers
         UpperCAmelCase_     :				Tuple  =     encoder_attention_heads
         UpperCAmelCase_     :				List[str]  =     decoder_ffn_dim
         UpperCAmelCase_     :				int  =     decoder_layers
         UpperCAmelCase_     :				int  =     decoder_attention_heads
         UpperCAmelCase_     :				Tuple  =     dropout
         UpperCAmelCase_     :				Any  =     attention_dropout
         UpperCAmelCase_     :				Dict  =     activation_dropout
         UpperCAmelCase_     :				Optional[Any]  =     activation_function
         UpperCAmelCase_     :				int  =     init_std
         UpperCAmelCase_     :				Dict  =     encoder_layerdrop
         UpperCAmelCase_     :				List[str]  =     decoder_layerdrop
         UpperCAmelCase_     :				Optional[int]  =     use_cache
         UpperCAmelCase_     :				List[str]  =     encoder_layers
         UpperCAmelCase_     :				Optional[int]  =     scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCAmelCase_     :				str  =     max_source_positions
         UpperCAmelCase_     :				Any  =     max_target_positions
         UpperCAmelCase_     :				Optional[int]  =     num_conv_layers
         UpperCAmelCase_     :				Optional[int]  =     list(_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				str  =     conv_channels
         UpperCAmelCase_     :				List[Any]  =     input_feat_per_channel
         UpperCAmelCase_     :				List[str]  =     input_channels

         if len(self.conv_kernel_sizes    ) != self.num_conv_layers:
              raise ValueError(
                  '''Configuration for convolutional module is incorrect. '''
                  '''It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '''
                  f'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes    )}`, '''
                  f'''`config.num_conv_layers = {self.num_conv_layers}`.'''    )

         super().__init__(
             pad_token_id=_SCREAMING_SNAKE_CASE  ,bos_token_id=_SCREAMING_SNAKE_CASE  ,eos_token_id=_SCREAMING_SNAKE_CASE  ,is_encoder_decoder=_SCREAMING_SNAKE_CASE  ,decoder_start_token_id=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE  ,)






'''simple docstring'''




import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE							     =			logging.get_logger()





def 	__lowerCamelCase			(      __lowerCAmelCase       :							int      ,    __lowerCAmelCase       :							str      ,    __lowerCAmelCase       :							LevitConfig      ,    __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							bool = True		)		->  int:
   print(F'''Converting {name}...'''		)

   with torch.no_grad():
      if hidden_sizes == 1_28:
         if name[-1] == "S":
            snake_case 						=   timm.create_model("""levit_128s"""      ,    pretrained=__lowerCAmelCase		)
         else:
            snake_case 						=   timm.create_model("""levit_128"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 1_92:
         snake_case 						=   timm.create_model("""levit_192"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 2_56:
         snake_case 						=   timm.create_model("""levit_256"""      ,    pretrained=__lowerCAmelCase		)
      if hidden_sizes == 3_84:
         snake_case 						=   timm.create_model("""levit_384"""      ,    pretrained=__lowerCAmelCase		)

      from_model.eval()
      snake_case 						=   LevitForImageClassificationWithTeacher(__lowerCAmelCase		).eval()
      snake_case 						=   OrderedDict()

      snake_case 						=   from_model.state_dict()
      snake_case 						=   list(from_model.state_dict().keys()		)
      snake_case 						=   list(our_model.state_dict().keys()		)
      print(len(__lowerCAmelCase		)      ,    len(__lowerCAmelCase		)		)
      for i in range(len(__lowerCAmelCase		)		):
         snake_case 						=   weights[og_keys[i]]
      our_model.load_state_dict(__lowerCAmelCase		)

      snake_case 						=   torch.randn((2, 3, 2_24, 2_24)		)
      snake_case 						=   from_model(__lowerCAmelCase		)
      snake_case 						=   our_model(__lowerCAmelCase		).logits

   assert torch.allclose(__lowerCAmelCase      ,    __lowerCAmelCase		), "The model logits don't match the original one."

   snake_case 						=   name
   print(__lowerCAmelCase		)

   if push_to_hub:
      our_model.save_pretrained(save_directory / checkpoint_name		)
      snake_case 						=   LevitImageProcessor()
      image_processor.save_pretrained(save_directory / checkpoint_name		)

      print(F'''Pushed {checkpoint_name}'''		)





def 	__lowerCamelCase			(      __lowerCAmelCase       :							Path      ,    __lowerCAmelCase       :							str = None      ,    __lowerCAmelCase       :							bool = True		)		->  List[Any]:
   snake_case 						=   """imagenet-1k-id2label.json"""
   snake_case 						=   10_00
   snake_case 						=   (1, num_labels)

   snake_case 						=   """huggingface/label-files"""
   snake_case 						=   num_labels
   snake_case 						=   json.load(open(hf_hub_download(__lowerCAmelCase      ,    __lowerCAmelCase      ,    repo_type="""dataset"""		)      ,    """r"""		)		)
   snake_case 						=   {int(__lowerCAmelCase		): v for k, v in idalabel.items()}

   snake_case 						=   idalabel
   snake_case 						=   {v: k for k, v in idalabel.items()}

   snake_case 						=   partial(__lowerCAmelCase      ,    num_labels=__lowerCAmelCase      ,    idalabel=__lowerCAmelCase      ,    labelaid=__lowerCAmelCase		)

   snake_case 						=   {
       """levit-128S""": 1_28,
       """levit-128""": 1_28,
       """levit-192""": 1_92,
       """levit-256""": 2_56,
       """levit-384""": 3_84,
   }

   snake_case 						=   {
       """levit-128S""": ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]      ,    num_attention_heads=[4, 6, 8]      ,    depths=[2, 3, 4]      ,    key_dim=[16, 16, 16]      ,    drop_path_rate=0      ,    ),
       """levit-128""": ImageNetPreTrainedConfig(
           hidden_sizes=[1_28, 2_56, 3_84]      ,    num_attention_heads=[4, 8, 12]      ,    depths=[4, 4, 4]      ,    key_dim=[16, 16, 16]      ,    drop_path_rate=0      ,    ),
       """levit-192""": ImageNetPreTrainedConfig(
           hidden_sizes=[1_92, 2_88, 3_84]      ,    num_attention_heads=[3, 5, 6]      ,    depths=[4, 4, 4]      ,    key_dim=[32, 32, 32]      ,    drop_path_rate=0      ,    ),
       """levit-256""": ImageNetPreTrainedConfig(
           hidden_sizes=[2_56, 3_84, 5_12]      ,    num_attention_heads=[4, 6, 8]      ,    depths=[4, 4, 4]      ,    key_dim=[32, 32, 32]      ,    drop_path_rate=0      ,    ),
       """levit-384""": ImageNetPreTrainedConfig(
           hidden_sizes=[3_84, 5_12, 7_68]      ,    num_attention_heads=[6, 9, 12]      ,    depths=[4, 4, 4]      ,    key_dim=[32, 32, 32]      ,    drop_path_rate=0.1      ,    ),
   }

   if model_name:
      convert_weight_and_push(
          names_to_hidden_sizes[model_name]      ,    __lowerCAmelCase      ,    names_to_config[model_name]      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   else:
      for model_name, config in names_to_config.items():
         convert_weight_and_push(names_to_hidden_sizes[model_name]      ,    __lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase      ,    __lowerCAmelCase		)
   return config, expected_shape


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE							     =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default=None,
     type=str,
     help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default="levit-dump-folder/",
     type=Path,
     required=False,
     help="Path to the output PyTorch model directory.",
 )
 parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
 parser.add_argument(
     "--no-push_to_hub",
     dest="push_to_hub",
     action="store_false",
     help="Do not push model and image processor to the hub",
 )

 _SCREAMING_SNAKE_CASE							     =			parser.parse_args()
 _SCREAMING_SNAKE_CASE							     =			args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)

