



import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__UpperCAmelCase    	=  logging.get_logger(__name__)

__UpperCAmelCase    	=  {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''mask_emb''': '''masked_spec_embed''',
}





def     __lowerCamelCase							(   __magic_name__						:	Union[str, Any]  ,			__magic_name__						:	Optional[int]  ,			__magic_name__						:	List[Any]  ,			__magic_name__						:	str  ,			__magic_name__						:	Tuple    ):





							for attribute in key.split("."    ):
														a__:							Any									=getattr(__lowerCamelCase  ,			__lowerCamelCase    )

							if weight_type is not None:
														a__:							List[Any]									=getattr(__lowerCamelCase  ,			__lowerCamelCase    ).shape
							else:
														a__:							Dict									=hf_pointer.shape

							assert hf_shape == value.shape, (
							    F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
							    F" {value.shape} for {full_name}"
							)

							if weight_type == "weight":
														a__:							str									=value
							elif weight_type == "weight_g":
														a__:							str									=value
							elif weight_type == "weight_v":
														a__:							Optional[Any]									=value
							elif weight_type == "bias":
														a__:							int									=value
							else:
														a__:							Optional[Any]									=value

							logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."    )





def     __lowerCamelCase							(   __magic_name__						:	int  ,			__magic_name__						:	Tuple    ):
							a__:							Dict									=[]
							a__:							str									=fairseq_model.state_dict()

							a__:							List[Any]									=hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														a__:							str									=False
														if "conv_layers" in name:
																					load_conv_layer(
																					    __lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			hf_model.config.feat_extract_norm == "group"  ,			)
																					a__:							Optional[int]									=True
														else:
																					for key, mapped_key in MAPPING.items():
																												a__:							List[str]									=mapped_key

																												if key in name:
																																			a__:							Union[str, Any]									=True
																																			if "*" in mapped_key:
																																										a__:							List[Any]									=name.split(__lowerCamelCase    )[0].split("."    )[-2]
																																										a__:							int									=mapped_key.replace("*"  ,			__lowerCamelCase    )
																																			if "weight_g" in name:
																																										a__:							int									="weight_g"
																																			elif "weight_v" in name:
																																										a__:							str									="weight_v"
																																			elif "weight" in name:
																																										a__:							Tuple									="weight"
																																			elif "bias" in name:
																																										a__:							Optional[int]									="bias"
																																			else:
																																										a__:							int									=None
																																			set_recursively(__lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase  ,			__lowerCamelCase    )
																												continue
														if not is_used:
																					unused_weights.append(__lowerCamelCase    )

							logger.warning(F"Unused weights: {unused_weights}"    )





def     __lowerCamelCase							(   __magic_name__						:	List[Any]  ,			__magic_name__						:	Optional[Any]  ,			__magic_name__						:	str  ,			__magic_name__						:	str  ,			__magic_name__						:	Union[str, Any]    ):
							a__:							int									=full_name.split("conv_layers."    )[-1]
							a__:							Union[str, Any]									=name.split("."    )
							a__:							Dict									=int(items[0]    )
							a__:							Dict									=int(items[1]    )

							if type_id == 0:
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
																					    F"{full_name} has size {value.shape}, but"
																					    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
																					)
																					a__:							Dict									=value
																					logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."    )
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
																					    F"{full_name} has size {value.shape}, but"
																					    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
																					)
																					a__:							int									=value
																					logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."    )
							elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
														if "bias" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
																					    F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
																					    " found."
																					)
																					a__:							Union[str, Any]									=value
																					logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
														elif "weight" in name:
																					assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
																					    F"{full_name} has size {value.shape}, but"
																					    F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
																					)
																					a__:							Optional[Any]									=value
																					logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."    )
							else:
														unused_weights.append(__lowerCamelCase    )





def     __lowerCamelCase							(   __magic_name__						:	List[Any]    ):
							a__:							Optional[Any]									=HubertConfig()
							a__:							List[str]									=model.config

							a__:							str									=fs_config.activation_dropout
							a__:							Any									=False
							a__:							Optional[Any]									=fs_config.attention_dropout
							a__:							List[str]									=False
							a__:							Optional[int]									=eval(fs_config.extractor_conv_feature_layers    )
							a__:							Optional[int]									=[x[0] for x in conv_layers]
							a__:							Any									=[x[1] for x in conv_layers]
							a__:							Dict									=[x[2] for x in conv_layers]
							a__:							Optional[int]									="gelu"
							a__:							List[Any]									="layer" if fs_config.extractor_mode == "layer_norm" else "group"
							a__:							Any									=False
							a__:							Tuple									=0.0
							a__:							Optional[int]									=0.0
							a__:							Any									=fs_config.activation_fn
							a__:							Optional[int]									=fs_config.dropout
							a__:							List[Any]									=fs_config.encoder_embed_dim
							a__:							int									=0.02
							a__:							Tuple									=fs_config.encoder_ffn_embed_dim
							a__:							List[Any]									=1e-5
							a__:							Any									=0.0
							a__:							Optional[int]									=fs_config.encoder_attention_heads
							a__:							Dict									=fs_config.conv_pos_groups
							a__:							Tuple									=fs_config.conv_pos
							a__:							Tuple									=len(__lowerCamelCase    )
							a__:							Optional[int]									=fs_config.encoder_layers

							return config





@torch.no_grad()
def     __lowerCamelCase							(   __magic_name__						:	Any  ,			__magic_name__						:	Optional[Any]=None    ):
							a__:							Dict									=distilhubert().model.model

							if config_path is not None:
														a__:							List[Any]									=HubertConfig.from_pretrained(__lowerCamelCase    )
							else:
														a__:							Union[str, Any]									=convert_config(__lowerCamelCase    )
							a__:							Optional[Any]									=model.eval()

							a__:							Optional[int]									=WavaVecaFeatureExtractor(
							    feature_size=1  ,			sampling_rate=16_000  ,			padding_value=0  ,			do_normalize=__lowerCamelCase  ,			return_attention_mask=__lowerCamelCase  ,			)
							a__:							List[Any]									=HubertModel(__lowerCamelCase    )

							recursively_load_weights(__lowerCamelCase  ,			__lowerCamelCase    )

							feature_extractor.save_pretrained(__lowerCamelCase    )
							hf_model.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
		__UpperCAmelCase    	=  argparse.ArgumentParser()
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		__UpperCAmelCase    	=  parser.parse_args()
		convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)










import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase         =	logging.get_logger(__name__)






def 						_lowerCAmelCase    (					__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						MobileNetVaConfig(layer_norm_eps=0.001  )

 if "quant" in model_name:
  raise ValueError("Quantized models are not supported."  )

 __SCREAMING_SNAKE_CASE							:     Tuple							=						re.match(r"^.*mobilenet_v2_([^_]*)_([^_]*)$"		,						__lowerCamelCase  )
 if matches:
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						float(matches[1]  )
  __SCREAMING_SNAKE_CASE							:     str							=						int(matches[2]  )

 if model_name.startswith("deeplabv3_"  ):
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						8
  __SCREAMING_SNAKE_CASE							:     Dict							=						21
  __SCREAMING_SNAKE_CASE							:     Tuple							=						"pascal-voc-id2label.json"
 else:
  # The TensorFlow version of MobileNetV2 predicts 1001 classes instead
  # of the usual 1000. The first class (index 0) is "background".
  __SCREAMING_SNAKE_CASE							:     int							=						1001
  __SCREAMING_SNAKE_CASE							:     Dict							=						"imagenet-1k-id2label.json"

 __SCREAMING_SNAKE_CASE							:     List[Any]							=						"huggingface/label-files"
 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						json.load(open(hf_hub_download(__lowerCamelCase		,						__lowerCamelCase		,						repo_type="dataset"  )		,						"r"  )  )

 if config.num_labels == 1001:
  __SCREAMING_SNAKE_CASE							:     int							=						{int(__lowerCamelCase  ) + 1: v for k, v in idalabel.items()}
  __SCREAMING_SNAKE_CASE							:     int							=						"background"
 else:
  __SCREAMING_SNAKE_CASE							:     Any							=						{int(__lowerCamelCase  ): v for k, v in idalabel.items()}

 __SCREAMING_SNAKE_CASE							:     str							=						idalabel
 __SCREAMING_SNAKE_CASE							:     Tuple							=						{v: k for k, v in idalabel.items()}

 return config






def 						_lowerCAmelCase    (					):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						"http://images.cocodataset.org/val2017/000000039769.jpg"
 __SCREAMING_SNAKE_CASE							:     Any							=						Image.open(requests.get(__lowerCamelCase		,						stream=__lowerCamelCase  ).raw  )
 return im






@torch.no_grad()
def 						_lowerCAmelCase    (					__lowerCamelCase :  str		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  Tuple=False  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Any							=						get_mobilenet_va_config(__lowerCamelCase  )

 # Load 🤗 model
 if model_name.startswith("deeplabv3_"  ):
  __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						MobileNetVaForSemanticSegmentation(__lowerCamelCase  ).eval()
 else:
  __SCREAMING_SNAKE_CASE							:     str							=						MobileNetVaForImageClassification(__lowerCamelCase  ).eval()

 # Load weights from TensorFlow checkpoint
 load_tf_weights_in_mobilenet_va(__lowerCamelCase		,						__lowerCamelCase		,						__lowerCamelCase  )

 # Check outputs on an image, prepared by MobileNetV2ImageProcessor
 __SCREAMING_SNAKE_CASE							:     Tuple							=						MobileNetVaImageProcessor(
     crop_size={"width": config.image_size, "height": config.image_size}		,						size={"shortest_edge": config.image_size + 32}		,						)
 __SCREAMING_SNAKE_CASE							:     Any							=						image_processor(images=prepare_img()		,						return_tensors="pt"  )
 __SCREAMING_SNAKE_CASE							:     List[str]							=						model(**__lowerCamelCase  )
 __SCREAMING_SNAKE_CASE							:     Any							=						outputs.logits

 if model_name.startswith("deeplabv3_"  ):
  assert logits.shape == (1, 21, 65, 65)

  if model_name == "deeplabv3_mobilenet_v2_1.0_513":
   __SCREAMING_SNAKE_CASE							:     List[Any]							=						torch.tensor(
       [
           [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
           [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
           [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
       ]  )

  else:
   raise ValueError(F"""Unknown model name: {model_name}"""  )

  assert torch.allclose(logits[0, :3, :3, :3]		,						__lowerCamelCase		,						atol=1E-4  )
 else:
  assert logits.shape == (1, 1001)

  if model_name == "mobilenet_v2_1.4_224":
   __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						torch.tensor([0.0181, -1.0015, 0.4688]  )
  elif model_name == "mobilenet_v2_1.0_224":
   __SCREAMING_SNAKE_CASE							:     str							=						torch.tensor([0.2445, -1.1993, 0.1905]  )
  elif model_name == "mobilenet_v2_0.75_160":
   __SCREAMING_SNAKE_CASE							:     int							=						torch.tensor([0.2482, 0.4136, 0.6669]  )
  elif model_name == "mobilenet_v2_0.35_96":
   __SCREAMING_SNAKE_CASE							:     Any							=						torch.tensor([0.1451, -0.4624, 0.7192]  )
  else:
   __SCREAMING_SNAKE_CASE							:     Optional[int]							=						None

  if expected_logits is not None:
   assert torch.allclose(logits[0, :3]		,						__lowerCamelCase		,						atol=1E-4  )

 Path(__lowerCamelCase  ).mkdir(exist_ok=__lowerCamelCase  )
 print(F"""Saving model {model_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  )

 if push_to_hub:
  print("Pushing to the hub..."  )
  __SCREAMING_SNAKE_CASE							:     Tuple							=						"google/" + model_name
  image_processor.push_to_hub(__lowerCamelCase  )
  model.push_to_hub(__lowerCamelCase  )


if __name__ == "__main__":
      _lowerCamelCase         =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""mobilenet_v2_1.0_224""",
          type=str,
          help="""Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<depth>_<size>'.""",
      )
      parser.add_argument(
          """--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", required=True, 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."""
      )

      _lowerCamelCase         =	parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )






