


'''simple docstring'''

import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowerCamelCase							:       List[Any]  						=     logging.get_logger(__name__)

lowerCamelCase							:       str  						=     {
    '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 _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A  ,			A  ,			A	)	->	Tuple:






       """simple docstring"""







       for attribute in key.split('''.'''	):
              lowercase__				      =			getattr(A  ,			A	)

       if weight_type is not None:
              lowercase__				      =			getattr(A  ,			A	).shape
       else:
              lowercase__				      =			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":
              lowercase__				      =			value
       elif weight_type == "weight_g":
              lowercase__				      =			value
       elif weight_type == "weight_v":
              lowercase__				      =			value
       elif weight_type == "bias":
              lowercase__				      =			value
       else:
              lowercase__				      =			value

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

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






       """simple docstring"""







       lowercase__				      =			[]
       lowercase__				      =			fairseq_model.state_dict()

       lowercase__				      =			hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              lowercase__				      =			False
              if "conv_layers" in name:
                     load_conv_layer(
                         A  ,			A  ,			A  ,			A  ,			hf_model.config.feat_extract_norm == '''group'''  ,			)
                     lowercase__				      =			True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowercase__				      =			mapped_key

                            if key in name:
                                   lowercase__				      =			True
                                   if "*" in mapped_key:
                                          lowercase__				      =			name.split(A	)[0].split('''.'''	)[-2]
                                          lowercase__				      =			mapped_key.replace('''*'''  ,			A	)
                                   if "weight_g" in name:
                                          lowercase__				      =			'''weight_g'''
                                   elif "weight_v" in name:
                                          lowercase__				      =			'''weight_v'''
                                   elif "weight" in name:
                                          lowercase__				      =			'''weight'''
                                   elif "bias" in name:
                                          lowercase__				      =			'''bias'''
                                   else:
                                          lowercase__				      =			None
                                   set_recursively(A  ,			A  ,			A  ,			A  ,			A	)
                            continue
              if not is_used:
                     unused_weights.append(A	)

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

def _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A  ,			A  ,			A	)	->	Any:






       """simple docstring"""







       lowercase__				      =			full_name.split('''conv_layers.'''	)[-1]
       lowercase__				      =			name.split('''.'''	)
       lowercase__				      =			int(items[0]	)
       lowercase__				      =			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."
                     )
                     lowercase__				      =			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."
                     )
                     lowercase__				      =			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."
                     )
                     lowercase__				      =			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."
                     )
                     lowercase__				      =			value
                     logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."	)
       else:
              unused_weights.append(A	)

def _SCREAMING_SNAKE_CASE				(A	)	->	Optional[int]:






       """simple docstring"""







       lowercase__				      =			HubertConfig()
       lowercase__				      =			model.config

       lowercase__				      =			fs_config.activation_dropout
       lowercase__				      =			False
       lowercase__				      =			fs_config.attention_dropout
       lowercase__				      =			False
       lowercase__				      =			eval(fs_config.extractor_conv_feature_layers	)
       lowercase__				      =			[x[0] for x in conv_layers]
       lowercase__				      =			[x[1] for x in conv_layers]
       lowercase__				      =			[x[2] for x in conv_layers]
       lowercase__				      =			'''gelu'''
       lowercase__				      =			'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
       lowercase__				      =			False
       lowercase__				      =			0.0
       lowercase__				      =			0.0
       lowercase__				      =			fs_config.activation_fn
       lowercase__				      =			fs_config.dropout
       lowercase__				      =			fs_config.encoder_embed_dim
       lowercase__				      =			0.02
       lowercase__				      =			fs_config.encoder_ffn_embed_dim
       lowercase__				      =			1E-5
       lowercase__				      =			0.0
       lowercase__				      =			fs_config.encoder_attention_heads
       lowercase__				      =			fs_config.conv_pos_groups
       lowercase__				      =			fs_config.conv_pos
       lowercase__				      =			len(A	)
       lowercase__				      =			fs_config.encoder_layers

       return config

@torch.no_grad()
def _SCREAMING_SNAKE_CASE				(A  ,			A=None	)	->	int:






       """simple docstring"""







       lowercase__				      =			distilhubert().model.model

       if config_path is not None:
              lowercase__				      =			HubertConfig.from_pretrained(A	)
       else:
              lowercase__				      =			convert_config(A	)
       lowercase__				      =			model.eval()

       lowercase__				      =			WavaVecaFeatureExtractor(
           feature_size=1  ,			sampling_rate=16_000  ,			padding_value=0  ,			do_normalize=A  ,			return_attention_mask=A  ,			)
       lowercase__				      =			HubertModel(A	)

       recursively_load_weights(A  ,			A	)

       feature_extractor.save_pretrained(A	)
       hf_model.save_pretrained(A	)


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








'''simple docstring'''

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							:       Dict  						=     logging.get_logger(__name__)

def _SCREAMING_SNAKE_CASE				(A	)	->	Optional[Any]:






       """simple docstring"""







       lowercase__				      =			MobileNetVaConfig(layer_norm_eps=0.001	)

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

       lowercase__				      =			re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''  ,			A	)
       if matches:
              lowercase__				      =			float(matches[1]	)
              lowercase__				      =			int(matches[2]	)

       if model_name.startswith('''deeplabv3_'''	):
              lowercase__				      =			8
              lowercase__				      =			21
              lowercase__				      =			'''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".
              lowercase__				      =			1_001
              lowercase__				      =			'''imagenet-1k-id2label.json'''

       lowercase__				      =			'''huggingface/label-files'''
       lowercase__				      =			json.load(open(hf_hub_download(A  ,			A  ,			repo_type='''dataset'''	)  ,			'''r'''	)	)

       if config.num_labels == 1_001:
              lowercase__				      =			{int(A	) + 1: v for k, v in idalabel.items()}
              lowercase__				      =			'''background'''
       else:
              lowercase__				      =			{int(A	): v for k, v in idalabel.items()}

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

       return config

def _SCREAMING_SNAKE_CASE				()	->	List[Any]:






       """simple docstring"""







       lowercase__				      =			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       lowercase__				      =			Image.open(requests.get(A  ,			stream=A	).raw	)
       return im

@torch.no_grad()
def _SCREAMING_SNAKE_CASE				(A  ,			A  ,			A  ,			A=False	)	->	Any:






       """simple docstring"""







       lowercase__				      =			get_mobilenet_va_config(A	)

       # Load 🤗 model
       if model_name.startswith('''deeplabv3_'''	):
              lowercase__				      =			MobileNetVaForSemanticSegmentation(A	).eval()
       else:
              lowercase__				      =			MobileNetVaForImageClassification(A	).eval()

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

       # Check outputs on an image, prepared by MobileNetV2ImageProcessor
       lowercase__				      =			MobileNetVaImageProcessor(
           crop_size={'''width''': config.image_size, '''height''': config.image_size}  ,			size={'''shortest_edge''': config.image_size + 32}  ,			)
       lowercase__				      =			image_processor(images=prepare_img()  ,			return_tensors='''pt'''	)
       lowercase__				      =			model(**A	)
       lowercase__				      =			outputs.logits

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

              if model_name == "deeplabv3_mobilenet_v2_1.0_513":
                     lowercase__				      =			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]  ,			A  ,			atol=1E-4	)
       else:
              assert logits.shape == (1, 1_001)

              if model_name == "mobilenet_v2_1.4_224":
                     lowercase__				      =			torch.tensor([0.0_181, -1.0_015, 0.4_688]	)
              elif model_name == "mobilenet_v2_1.0_224":
                     lowercase__				      =			torch.tensor([0.2_445, -1.1_993, 0.1_905]	)
              elif model_name == "mobilenet_v2_0.75_160":
                     lowercase__				      =			torch.tensor([0.2_482, 0.4_136, 0.6_669]	)
              elif model_name == "mobilenet_v2_0.35_96":
                     lowercase__				      =			torch.tensor([0.1_451, -0.4_624, 0.7_192]	)
              else:
                     lowercase__				      =			None

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

       Path(A	).mkdir(exist_ok=A	)
       print(f"Saving model {model_name} to {pytorch_dump_folder_path}"	)
       model.save_pretrained(A	)
       print(f"Saving image processor to {pytorch_dump_folder_path}"	)
       image_processor.save_pretrained(A	)

       if push_to_hub:
              print('''Pushing to the hub...'''	)
              lowercase__				      =			'''google/''' + model_name
              image_processor.push_to_hub(A	)
              model.push_to_hub(A	)


if __name__ == "__main__":
 lowerCamelCase							:       int  						=     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							:       int  						=     parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )




