



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()
__UpperCAmelCase    	=  logging.get_logger(__name__)





def     __lowerCamelCase							(   __magic_name__						:	Union[str, Any]    ):
							a__:							Dict									=MobileNetVaConfig(layer_norm_eps=0.001    )

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

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

							if model_name.startswith("deeplabv3_"    ):
														a__:							Union[str, Any]									=8
														a__:							Dict									=21
														a__:							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".
														a__:							int									=1_001
														a__:							Dict									="imagenet-1k-id2label.json"

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

							if config.num_labels == 1_001:
														a__:							int									={int(__lowerCamelCase    ) + 1: v for k, v in idalabel.items()}
														a__:							int									="background"
							else:
														a__:							Any									={int(__lowerCamelCase    ): v for k, v in idalabel.items()}

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

							return config





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





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

							# Load 🤗 model
							if model_name.startswith("deeplabv3_"    ):
														a__:							Union[str, Any]									=MobileNetVaForSemanticSegmentation(__lowerCamelCase    ).eval()
							else:
														a__:							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
							a__:							Tuple									=MobileNetVaImageProcessor(
							    crop_size={"width": config.image_size, "height": config.image_size}  ,			size={"shortest_edge": config.image_size + 32}  ,			)
							a__:							Any									=image_processor(images=prepare_img()  ,			return_tensors="pt"    )
							a__:							List[str]									=model(**__lowerCamelCase    )
							a__:							Any									=outputs.logits

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

														if model_name == "deeplabv3_mobilenet_v2_1.0_513":
																					a__:							List[Any]									=torch.tensor(
																					    [
																					        [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
																					        [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
																					        [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
																					    ]    )

														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, 1_001)

														if model_name == "mobilenet_v2_1.4_224":
																					a__:							Optional[Any]									=torch.tensor([0.0_181, -1.0_015, 0.4_688]    )
														elif model_name == "mobilenet_v2_1.0_224":
																					a__:							str									=torch.tensor([0.2_445, -1.1_993, 0.1_905]    )
														elif model_name == "mobilenet_v2_0.75_160":
																					a__:							int									=torch.tensor([0.2_482, 0.4_136, 0.6_669]    )
														elif model_name == "mobilenet_v2_0.35_96":
																					a__:							Any									=torch.tensor([0.1_451, -0.4_624, 0.7_192]    )
														else:
																					a__:							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..."    )
														a__:							Tuple									="google/" + model_name
														image_processor.push_to_hub(__lowerCamelCase    )
														model.push_to_hub(__lowerCamelCase    )


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

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










import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

_lowerCamelCase         =	{
    """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    (					__lowerCamelCase :  Union[str, Any]		,						__lowerCamelCase :  Optional[int]		,						__lowerCamelCase :  List[Any]		,						__lowerCamelCase :  str		,						__lowerCamelCase :  Tuple  ):



 """simple docstring"""





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

 if weight_type is not None:
  __SCREAMING_SNAKE_CASE							:     List[Any]							=						getattr(__lowerCamelCase		,						__lowerCamelCase  ).shape
 else:
  __SCREAMING_SNAKE_CASE							:     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":
  __SCREAMING_SNAKE_CASE							:     str							=						value
 elif weight_type == "weight_g":
  __SCREAMING_SNAKE_CASE							:     str							=						value
 elif weight_type == "weight_v":
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						value
 elif weight_type == "bias":
  __SCREAMING_SNAKE_CASE							:     int							=						value
 else:
  __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						value

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






def 						_lowerCAmelCase    (					__lowerCamelCase :  int		,						__lowerCamelCase :  Tuple  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						[]
 __SCREAMING_SNAKE_CASE							:     str							=						fairseq_model.state_dict()

 __SCREAMING_SNAKE_CASE							:     List[Any]							=						hf_model.feature_extractor

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

    if key in name:
     __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						True
     if "*" in mapped_key:
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						name.split(__lowerCamelCase  )[0].split("."  )[-2]
      __SCREAMING_SNAKE_CASE							:     int							=						mapped_key.replace("*"		,						__lowerCamelCase  )
     if "weight_g" in name:
      __SCREAMING_SNAKE_CASE							:     int							=						"weight_g"
     elif "weight_v" in name:
      __SCREAMING_SNAKE_CASE							:     str							=						"weight_v"
     elif "weight" in name:
      __SCREAMING_SNAKE_CASE							:     Tuple							=						"weight"
     elif "bias" in name:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						"bias"
     else:
      __SCREAMING_SNAKE_CASE							:     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    (					__lowerCamelCase :  List[Any]		,						__lowerCamelCase :  Optional[Any]		,						__lowerCamelCase :  str		,						__lowerCamelCase :  str		,						__lowerCamelCase :  Union[str, Any]  ):



 """simple docstring"""





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



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						HubertConfig()
 __SCREAMING_SNAKE_CASE							:     List[str]							=						model.config

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

 return config






@torch.no_grad()
def 						_lowerCAmelCase    (					__lowerCamelCase :  Any		,						__lowerCamelCase :  Optional[Any]=None  ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE							:     Dict							=						distilhubert().model.model

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

 __SCREAMING_SNAKE_CASE							:     Optional[int]							=						WavaVecaFeatureExtractor(
     feature_size=1		,						sampling_rate=16000		,						padding_value=0		,						do_normalize=__lowerCamelCase		,						return_attention_mask=__lowerCamelCase		,						)
 __SCREAMING_SNAKE_CASE							:     List[Any]							=						HubertModel(__lowerCamelCase  )

 recursively_load_weights(__lowerCamelCase		,						__lowerCamelCase  )

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


if __name__ == "__main__":
      _lowerCamelCase         =	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""")
      _lowerCamelCase         =	parser.parse_args()
      convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)






