


import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:str   )      ->   List[Any]:
 __lowerCAmelCase					:	List[str]      =       SwinvaConfig()
 __lowerCAmelCase					:	int      =       swinva_name.split("""_"""   )

 __lowerCAmelCase					:	str      =       name_split[1]
 if "to" in name_split[3]:
  __lowerCAmelCase					:	Any      =       int(name_split[3][-3:]   )
 else:
  __lowerCAmelCase					:	Optional[int]      =       int(name_split[3]   )
 if "to" in name_split[2]:
  __lowerCAmelCase					:	Dict      =       int(name_split[2][-2:]   )
 else:
  __lowerCAmelCase					:	List[Any]      =       int(name_split[2][6:]   )

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

 if "to" in swinva_name:
  __lowerCAmelCase					:	Tuple      =       (12, 12, 12, 6)

 if ("22k" in swinva_name) and ("to" not in swinva_name):
  __lowerCAmelCase					:	Optional[Any]      =       21_841
  __lowerCAmelCase					:	Optional[Any]      =       '''huggingface/label-files'''
  __lowerCAmelCase					:	List[str]      =       '''imagenet-22k-id2label.json'''
  __lowerCAmelCase					:	str      =       json.load(open(hf_hub_download(__lowerCAmelCase   ,   __lowerCAmelCase   ,   repo_type="""dataset"""   )   ,   """r"""   )   )
  __lowerCAmelCase					:	List[Any]      =       {int(__lowerCAmelCase   ): v for k, v in idalabel.items()}
  __lowerCAmelCase					:	int      =       idalabel
  __lowerCAmelCase					:	Any      =       {v: k for k, v in idalabel.items()}

 else:
  __lowerCAmelCase					:	Tuple      =       1_000
  __lowerCAmelCase					:	int      =       '''huggingface/label-files'''
  __lowerCAmelCase					:	Union[str, Any]      =       '''imagenet-1k-id2label.json'''
  __lowerCAmelCase					:	Optional[int]      =       json.load(open(hf_hub_download(__lowerCAmelCase   ,   __lowerCAmelCase   ,   repo_type="""dataset"""   )   ,   """r"""   )   )
  __lowerCAmelCase					:	str      =       {int(__lowerCAmelCase   ): v for k, v in idalabel.items()}
  __lowerCAmelCase					:	Union[str, Any]      =       idalabel
  __lowerCAmelCase					:	int      =       {v: k for k, v in idalabel.items()}

 __lowerCAmelCase					:	int      =       img_size
 __lowerCAmelCase					:	Any      =       num_classes
 __lowerCAmelCase					:	Tuple      =       embed_dim
 __lowerCAmelCase					:	Any      =       depths
 __lowerCAmelCase					:	str      =       num_heads
 __lowerCAmelCase					:	Any      =       window_size

 return config






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:List[str]   )      ->   Tuple:

 if "patch_embed.proj" in name:
  __lowerCAmelCase					:	List[Any]      =       name.replace("""patch_embed.proj"""   ,   """embeddings.patch_embeddings.projection"""   )
 if "patch_embed.norm" in name:
  __lowerCAmelCase					:	Tuple      =       name.replace("""patch_embed.norm"""   ,   """embeddings.norm"""   )
 if "layers" in name:
  __lowerCAmelCase					:	List[Any]      =       '''encoder.''' + name
 if "attn.proj" in name:
  __lowerCAmelCase					:	Optional[int]      =       name.replace("""attn.proj"""   ,   """attention.output.dense"""   )
 if "attn" in name:
  __lowerCAmelCase					:	int      =       name.replace("""attn"""   ,   """attention.self"""   )
 if "norm1" in name:
  __lowerCAmelCase					:	Any      =       name.replace("""norm1"""   ,   """layernorm_before"""   )
 if "norm2" in name:
  __lowerCAmelCase					:	List[Any]      =       name.replace("""norm2"""   ,   """layernorm_after"""   )
 if "mlp.fc1" in name:
  __lowerCAmelCase					:	Union[str, Any]      =       name.replace("""mlp.fc1"""   ,   """intermediate.dense"""   )
 if "mlp.fc2" in name:
  __lowerCAmelCase					:	Union[str, Any]      =       name.replace("""mlp.fc2"""   ,   """output.dense"""   )
 if "q_bias" in name:
  __lowerCAmelCase					:	str      =       name.replace("""q_bias"""   ,   """query.bias"""   )
 if "k_bias" in name:
  __lowerCAmelCase					:	Union[str, Any]      =       name.replace("""k_bias"""   ,   """key.bias"""   )
 if "v_bias" in name:
  __lowerCAmelCase					:	Dict      =       name.replace("""v_bias"""   ,   """value.bias"""   )
 if "cpb_mlp" in name:
  __lowerCAmelCase					:	Tuple      =       name.replace("""cpb_mlp"""   ,   """continuous_position_bias_mlp"""   )
 if name == "norm.weight":
  __lowerCAmelCase					:	Tuple      =       '''layernorm.weight'''
 if name == "norm.bias":
  __lowerCAmelCase					:	Optional[Any]      =       '''layernorm.bias'''

 if "head" in name:
  __lowerCAmelCase					:	Union[str, Any]      =       name.replace("""head"""   ,   """classifier"""   )
 else:
  __lowerCAmelCase					:	Tuple      =       '''swinv2.''' + name

 return name






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:List[str]   ,   SCREAMING_SNAKE_CASE		:List[str]   )      ->   List[Any]:

 for key in orig_state_dict.copy().keys():
  __lowerCAmelCase					:	Optional[int]      =       orig_state_dict.pop(__lowerCAmelCase   )

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

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

 return orig_state_dict






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:str   ,   SCREAMING_SNAKE_CASE		:List[Any]   )      ->   Optional[int]:
 __lowerCAmelCase					:	int      =       timm.create_model(__lowerCAmelCase   ,   pretrained=__lowerCAmelCase   )
 timm_model.eval()

 __lowerCAmelCase					:	List[str]      =       get_swinva_config(__lowerCAmelCase   )
 __lowerCAmelCase					:	int      =       SwinvaForImageClassification(__lowerCAmelCase   )
 model.eval()

 __lowerCAmelCase					:	Dict      =       convert_state_dict(timm_model.state_dict()   ,   __lowerCAmelCase   )
 model.load_state_dict(__lowerCAmelCase   )

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

 __lowerCAmelCase					:	List[Any]      =       AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""   ,   """-"""   )   )   )
 __lowerCAmelCase					:	str      =       Image.open(requests.get(__lowerCAmelCase   ,   stream=__lowerCAmelCase   ).raw   )
 __lowerCAmelCase					:	Tuple      =       image_processor(images=__lowerCAmelCase   ,   return_tensors="""pt"""   )

 __lowerCAmelCase					:	Any      =       timm_model(inputs["""pixel_values"""]   )
 __lowerCAmelCase					:	List[Any]      =       model(**__lowerCAmelCase   ).logits

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

 print(F'''Saving model {swinva_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   )

 model.push_to_hub(
     repo_path_or_name=Path(__lowerCAmelCase   ,   __lowerCAmelCase   )   ,   organization="""nandwalritik"""   ,   commit_message="""Add model"""   ,   )


if __name__ == "__main__":
      _UpperCAmelCase             =							argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--swinv2_name',
          default='swinv2_tiny_patch4_window8_256',
          type=str,
          help='Name of the Swinv2 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.'
      )

      _UpperCAmelCase             =							parser.parse_args()
      convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)






import functools
import operator

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


_UpperCAmelCase             =							logging.get_logger(__name__)

_UpperCAmelCase             =							{
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}






class        snake_case_    (		__lowercase						):
  A_      							=   'unispeech-sat'






  def __init__(							self			:					str       ,  _snake_case			:					List[Any]=32       ,  _snake_case			:					Union[str, Any]=768       ,  _snake_case			:					Tuple=12       ,  _snake_case			:					Optional[int]=12       ,  _snake_case			:					Optional[Any]=3072       ,  _snake_case			:					Tuple="gelu"       ,  _snake_case			:					int=0.1       ,  _snake_case			:					List[Any]=0.1       ,  _snake_case			:					Union[str, Any]=0.1       ,  _snake_case			:					str=0.0       ,  _snake_case			:					List[str]=0.0       ,  _snake_case			:					int=0.1       ,  _snake_case			:					Optional[Any]=0.1       ,  _snake_case			:					Optional[Any]=0.02       ,  _snake_case			:					int=1E-5       ,  _snake_case			:					Dict="group"       ,  _snake_case			:					Optional[Any]="gelu"       ,  _snake_case			:					Optional[Any]=(512, 512, 512, 512, 512, 512, 512)       ,  _snake_case			:					int=(5, 2, 2, 2, 2, 2, 2)       ,  _snake_case			:					int=(10, 3, 3, 3, 3, 2, 2)       ,  _snake_case			:					Any=False       ,  _snake_case			:					Optional[Any]=128       ,  _snake_case			:					Tuple=16       ,  _snake_case			:					str=False       ,  _snake_case			:					Dict=True       ,  _snake_case			:					Tuple=0.05       ,  _snake_case			:					str=10       ,  _snake_case			:					Tuple=2       ,  _snake_case			:					List[Any]=0.0       ,  _snake_case			:					str=10       ,  _snake_case			:					Any=0       ,  _snake_case			:					List[Any]=320       ,  _snake_case			:					Union[str, Any]=2       ,  _snake_case			:					Dict=0.1       ,  _snake_case			:					Dict=100       ,  _snake_case			:					Union[str, Any]=256       ,  _snake_case			:					int=256       ,  _snake_case			:					Union[str, Any]=0.1       ,  _snake_case			:					Optional[Any]="mean"       ,  _snake_case			:					int=False       ,  _snake_case			:					str=False       ,  _snake_case			:					str=256       ,  _snake_case			:					List[Any]=(512, 512, 512, 512, 1500)       ,  _snake_case			:					Optional[int]=(5, 3, 3, 1, 1)       ,  _snake_case			:					Tuple=(1, 2, 3, 1, 1)       ,  _snake_case			:					Dict=512       ,  _snake_case			:					Union[str, Any]=0       ,  _snake_case			:					List[str]=1       ,  _snake_case			:					Optional[Any]=2       ,  _snake_case			:					Optional[int]=504       ,  **_snake_case			:					Optional[int]       ,  )->Union[str, Any]:






   '''simple docstring'''

   super().__init__(**_snake_case       ,  pad_token_id=_snake_case       ,  bos_token_id=_snake_case       ,  eos_token_id=_snake_case      )
   __lowerCAmelCase					:	Dict      =       hidden_size
   __lowerCAmelCase					:	List[Any]      =       feat_extract_norm
   __lowerCAmelCase					:	int      =       feat_extract_activation
   __lowerCAmelCase					:	Union[str, Any]      =       list(_snake_case      )
   __lowerCAmelCase					:	str      =       list(_snake_case      )
   __lowerCAmelCase					:	Optional[Any]      =       list(_snake_case      )
   __lowerCAmelCase					:	Optional[int]      =       conv_bias
   __lowerCAmelCase					:	Dict      =       num_conv_pos_embeddings
   __lowerCAmelCase					:	List[Any]      =       num_conv_pos_embedding_groups
   __lowerCAmelCase					:	Tuple      =       len(self.conv_dim      )
   __lowerCAmelCase					:	int      =       num_hidden_layers
   __lowerCAmelCase					:	str      =       intermediate_size
   __lowerCAmelCase					:	str      =       hidden_act
   __lowerCAmelCase					:	Any      =       num_attention_heads
   __lowerCAmelCase					:	Optional[int]      =       hidden_dropout
   __lowerCAmelCase					:	str      =       attention_dropout
   __lowerCAmelCase					:	int      =       activation_dropout
   __lowerCAmelCase					:	Union[str, Any]      =       feat_proj_dropout
   __lowerCAmelCase					:	List[str]      =       final_dropout
   __lowerCAmelCase					:	Dict      =       layerdrop
   __lowerCAmelCase					:	Tuple      =       layer_norm_eps
   __lowerCAmelCase					:	Optional[Any]      =       initializer_range
   __lowerCAmelCase					:	str      =       vocab_size
   __lowerCAmelCase					:	Optional[int]      =       num_clusters
   __lowerCAmelCase					:	List[Any]      =       do_stable_layer_norm
   __lowerCAmelCase					:	Tuple      =       use_weighted_layer_sum

   if (
       (len(self.conv_stride      ) != self.num_feat_extract_layers)
       or (len(self.conv_kernel      ) != self.num_feat_extract_layers)
       or (len(self.conv_dim      ) != self.num_feat_extract_layers)
   ):
    raise ValueError(
        """Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
        """ `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
        F''' {len(self.conv_dim      )}`, `len(config.conv_stride) = {len(self.conv_stride      )}`,'''
        F''' `len(config.conv_kernel) = {len(self.conv_kernel      )}`.'''      )

   # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
   __lowerCAmelCase					:	Dict      =       apply_spec_augment
   __lowerCAmelCase					:	List[Any]      =       mask_time_prob
   __lowerCAmelCase					:	List[str]      =       mask_time_length
   __lowerCAmelCase					:	Dict      =       mask_time_min_masks
   __lowerCAmelCase					:	Tuple      =       mask_feature_prob
   __lowerCAmelCase					:	List[str]      =       mask_feature_length
   __lowerCAmelCase					:	str      =       mask_feature_min_masks

   # parameters for pretraining with codevector quantized representations
   __lowerCAmelCase					:	Optional[int]      =       num_codevectors_per_group
   __lowerCAmelCase					:	List[Any]      =       num_codevector_groups
   __lowerCAmelCase					:	int      =       contrastive_logits_temperature
   __lowerCAmelCase					:	str      =       feat_quantizer_dropout
   __lowerCAmelCase					:	int      =       num_negatives
   __lowerCAmelCase					:	str      =       codevector_dim
   __lowerCAmelCase					:	Any      =       proj_codevector_dim
   __lowerCAmelCase					:	Any      =       diversity_loss_weight

   # ctc loss
   __lowerCAmelCase					:	Tuple      =       ctc_loss_reduction
   __lowerCAmelCase					:	Any      =       ctc_zero_infinity

   # SequenceClassification-specific parameter. Feel free to ignore for other classes.
   __lowerCAmelCase					:	Any      =       classifier_proj_size

   # XVector-specific parameters. Feel free to ignore for other classes.
   __lowerCAmelCase					:	List[str]      =       list(_snake_case      )
   __lowerCAmelCase					:	List[str]      =       list(_snake_case      )
   __lowerCAmelCase					:	Optional[int]      =       list(_snake_case      )
   __lowerCAmelCase					:	Optional[int]      =       xvector_output_dim






  @property
  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->Any:






   '''simple docstring'''

   return functools.reduce(operator.mul       ,  self.conv_stride       ,  1      )