





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 DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__						:			Optional[int]									=logging.get_logger(__name__)





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase=False				)						->  Union[str, Any]:
				lowerCamelCase	 =[]
				for i in range(config.num_hidden_layers				):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""")				)
								rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")				)
								rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")				)
								rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")				)
								rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")				)
								rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")				)
								rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")				)
								rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")				)
								rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")				)
								rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")				)

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        ("""cls_token""", """vit.embeddings.cls_token"""),
				        ("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
				        ("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
				        ("""pos_embed""", """vit.embeddings.position_embeddings"""),
				    ]				)

				if base_model:
								# layernorm + pooler
								rename_keys.extend(
								    [
								        ("""norm.weight""", """layernorm.weight"""),
								        ("""norm.bias""", """layernorm.bias"""),
								        ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
								        ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
								    ]				)

								# if just the base model, we should remove "vit" from all keys that start with "vit"
								lowerCamelCase	 =[(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""				) else pair for pair in rename_keys]
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ("""norm.weight""", """vit.layernorm.weight"""),
								        ("""norm.bias""", """vit.layernorm.bias"""),
								        ("""head.weight""", """classifier.weight"""),
								        ("""head.bias""", """classifier.bias"""),
								    ]				)

				return rename_keys





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase=False				)						->  Dict:
				for i in range(config.num_hidden_layers				):
								if base_model:
												lowerCamelCase	 =""""""
								else:
												lowerCamelCase	 ="""vit."""
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								lowerCamelCase	 =state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""				)
								lowerCamelCase	 =state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""				)
								# next, add query, keys and values (in that order) to the state dict
								lowerCamelCase	 =in_proj_weight[
								    : config.hidden_size, :
								]
								lowerCamelCase	 =in_proj_bias[: config.hidden_size]
								lowerCamelCase	 =in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								lowerCamelCase	 =in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								lowerCamelCase	 =in_proj_weight[
								    -config.hidden_size :, :
								]
								lowerCamelCase	 =in_proj_bias[-config.hidden_size :]





def      _lowercase					(						_UpperCAmelCase				)						->  List[str]:
				lowerCamelCase	 =["""head.weight""", """head.bias"""]
				for k in ignore_keys:
								state_dict.pop(_snake_case			,      _snake_case				)





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Union[str, Any]:
				lowerCamelCase	 =dct.pop(_snake_case				)
				lowerCamelCase	 =val





def      _lowercase					(						)						->  Tuple:
				lowerCamelCase	 ="""http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowerCamelCase	 =Image.open(requests.get(_snake_case			,      stream=_snake_case				).raw				)
				return im





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase				)						->  Dict:
				lowerCamelCase	 =ViTConfig()
				lowerCamelCase	 =False
				# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
				if vit_name[-5:] == "in21k":
								lowerCamelCase	 =True
								lowerCamelCase	 =int(vit_name[-12:-10]				)
								lowerCamelCase	 =int(vit_name[-9:-6]				)
				else:
								lowerCamelCase	 =10_00
								lowerCamelCase	 ="""huggingface/label-files"""
								lowerCamelCase	 ="""imagenet-1k-id2label.json"""
								lowerCamelCase	 =json.load(open(hf_hub_download(_snake_case			,      _snake_case			,      repo_type="""dataset"""				)			,      """r"""				)				)
								lowerCamelCase	 ={int(_snake_case				): v for k, v in idalabel.items()}
								lowerCamelCase	 =idalabel
								lowerCamelCase	 ={v: k for k, v in idalabel.items()}
								lowerCamelCase	 =int(vit_name[-6:-4]				)
								lowerCamelCase	 =int(vit_name[-3:]				)
				# size of the architecture
				if "deit" in vit_name:
								if vit_name[9:].startswith("""tiny"""				):
												lowerCamelCase	 =1_92
												lowerCamelCase	 =7_68
												lowerCamelCase	 =12
												lowerCamelCase	 =3
								elif vit_name[9:].startswith("""small"""				):
												lowerCamelCase	 =3_84
												lowerCamelCase	 =15_36
												lowerCamelCase	 =12
												lowerCamelCase	 =6
								else:
												pass
				else:
								if vit_name[4:].startswith("""small"""				):
												lowerCamelCase	 =7_68
												lowerCamelCase	 =23_04
												lowerCamelCase	 =8
												lowerCamelCase	 =8
								elif vit_name[4:].startswith("""base"""				):
												pass
								elif vit_name[4:].startswith("""large"""				):
												lowerCamelCase	 =10_24
												lowerCamelCase	 =40_96
												lowerCamelCase	 =24
												lowerCamelCase	 =16
								elif vit_name[4:].startswith("""huge"""				):
												lowerCamelCase	 =12_80
												lowerCamelCase	 =51_20
												lowerCamelCase	 =32
												lowerCamelCase	 =16

    # load original model from timm
				lowerCamelCase	 =timm.create_model(_snake_case			,      pretrained=_snake_case				)
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				lowerCamelCase	 =timm_model.state_dict()
				if base_model:
								remove_classification_head_(_snake_case				)
				lowerCamelCase	 =create_rename_keys(_snake_case			,      _snake_case				)
				for src, dest in rename_keys:
								rename_key(_snake_case			,      _snake_case			,      _snake_case				)
				read_in_q_k_v(_snake_case			,      _snake_case			,      _snake_case				)

				# load HuggingFace model
				if vit_name[-5:] == "in21k":
								lowerCamelCase	 =ViTModel(_snake_case				).eval()
				else:
								lowerCamelCase	 =ViTForImageClassification(_snake_case				).eval()
				model.load_state_dict(_snake_case				)

				# Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
				if "deit" in vit_name:
								lowerCamelCase	 =DeiTImageProcessor(size=config.image_size				)
				else:
								lowerCamelCase	 =ViTImageProcessor(size=config.image_size				)
				lowerCamelCase	 =image_processor(images=prepare_img()			,      return_tensors="""pt"""				)
				lowerCamelCase	 =encoding["""pixel_values"""]
				lowerCamelCase	 =model(_snake_case				)

				if base_model:
								lowerCamelCase	 =timm_model.forward_features(_snake_case				)
								assert timm_pooled_output.shape == outputs.pooler_output.shape
								assert torch.allclose(_snake_case			,      outputs.pooler_output			,      atol=1e-3				)
				else:
								lowerCamelCase	 =timm_model(_snake_case				)
								assert timm_logits.shape == outputs.logits.shape
								assert torch.allclose(_snake_case			,      outputs.logits			,      atol=1e-3				)

				Path(_snake_case				).mkdir(exist_ok=_snake_case				)
				print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}"""				)
				model.save_pretrained(_snake_case				)
				print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
				image_processor.save_pretrained(_snake_case				)


if __name__ == "__main__":
							UpperCAmelCase__						:			str									=argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--vit_name''',
							    default='''vit_base_patch16_224''',
							    type=str,
							    help='''Name of the ViT 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__						:			Optional[int]									=parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)












import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase__						:			int									=logging.get_logger(__name__)





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Any:
				lowerCamelCase	 =UniSpeechSatForSequenceClassification.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""projector.weight"""]
				lowerCamelCase	 =downstream_dict["""projector.bias"""]
				lowerCamelCase	 =downstream_dict["""model.post_net.linear.weight"""]
				lowerCamelCase	 =downstream_dict["""model.post_net.linear.bias"""]
				return model





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  int:
				lowerCamelCase	 =UniSpeechSatForAudioFrameClassification.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""model.linear.weight"""]
				lowerCamelCase	 =downstream_dict["""model.linear.bias"""]
				return model





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Dict:
				lowerCamelCase	 =UniSpeechSatForXVector.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""connector.weight"""]
				lowerCamelCase	 =downstream_dict["""connector.bias"""]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
								lowerCamelCase	 =downstream_dict[
								    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
								]
								lowerCamelCase	 =downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
				lowerCamelCase	 =downstream_dict["""objective.W"""]
				return model





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  str:
				lowerCamelCase	 =torch.load(_UpperCAmelCase			,      map_location="""cpu"""				)

				lowerCamelCase	 =checkpoint["""Downstream"""]

				lowerCamelCase	 =UniSpeechSatConfig.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =WavaVecaFeatureExtractor.from_pretrained(
				    _UpperCAmelCase			,      return_attention_mask=_UpperCAmelCase			,      do_normalize=_UpperCAmelCase				)

				lowerCamelCase	 =hf_config.architectures[0]
				if arch.endswith("""ForSequenceClassification"""				):
								lowerCamelCase	 =convert_classification(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				elif arch.endswith("""ForAudioFrameClassification"""				):
								lowerCamelCase	 =convert_diarization(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				elif arch.endswith("""ForXVector"""				):
								lowerCamelCase	 =convert_xvector(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				else:
								raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}"""				)

				if hf_config.use_weighted_layer_sum:
								lowerCamelCase	 =checkpoint["""Featurizer"""]["""weights"""]

				hf_feature_extractor.save_pretrained(_UpperCAmelCase				)
				hf_model.save_pretrained(_UpperCAmelCase				)


if __name__ == "__main__":
							UpperCAmelCase__						:			Dict									=argparse.ArgumentParser()
							parser.add_argument(
							    '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
							)
							parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
							parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
							parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
							UpperCAmelCase__						:			List[Any]									=parser.parse_args()
							convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





