






'''simple docstring'''







import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


logging.set_verbosity_info()
_A			:    Union[str, Any]					  =logging.get_logger(__name__)

set_seed(770)


_A			:    Optional[Any]					  ={
    '''c_attn''': '''att_proj''',
    '''c_proj''': '''out_proj''',
    '''c_fc''': '''in_proj''',
    '''transformer.''': '''''',
    '''h.''': '''layers.''',
    '''ln_1''': '''layernorm_1''',
    '''ln_2''': '''layernorm_2''',
    '''ln_f''': '''layernorm_final''',
    '''wpe''': '''position_embeds_layer''',
    '''wte''': '''input_embeds_layer''',
}


_A			:    Union[str, Any]					  ={
    '''text_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text.pt''',
    },
    '''coarse_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse.pt''',
    },
    '''fine_small''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine.pt''',
    },
    '''text''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''text_2.pt''',
    },
    '''coarse''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''coarse_2.pt''',
    },
    '''fine''': {
        '''repo_id''': '''suno/bark''',
        '''file_name''': '''fine_2.pt''',
    },
}

_A			:    Any					  =os.path.dirname(os.path.abspath(__file__))
_A			:    Tuple					  =os.path.join(os.path.expanduser('''~'''), '''.cache''')
_A			:    Dict					  =os.path.join(os.getenv('''XDG_CACHE_HOME''', default_cache_dir), '''suno''', '''bark_v0''')





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase=False   )   -> Optional[Any]:
							lowerCamelCase__      :			List[str] 						= model_type
							if use_small:
														key += "_small"
							return os.path.join(UpperCamelCase       ,    REMOTE_MODEL_PATHS[key]["""file_name"""]   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> Union[str, Any]:
							os.makedirs(UpperCamelCase       ,    exist_ok=UpperCamelCase   )
							hf_hub_download(repo_id=UpperCamelCase       ,    filename=UpperCamelCase       ,    local_dir=UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase=False       ,    UpperCamelCase="text"   )   -> Union[str, Any]:
							if model_type == "text":
														lowerCamelCase__      :			Any 						= BarkSemanticModel
														lowerCamelCase__      :			Union[str, Any] 						= BarkSemanticConfig
														lowerCamelCase__      :			List[Any] 						= BarkSemanticGenerationConfig
							elif model_type == "coarse":
														lowerCamelCase__      :			List[Any] 						= BarkCoarseModel
														lowerCamelCase__      :			Union[str, Any] 						= BarkCoarseConfig
														lowerCamelCase__      :			Dict 						= BarkCoarseGenerationConfig
							elif model_type == "fine":
														lowerCamelCase__      :			int 						= BarkFineModel
														lowerCamelCase__      :			Optional[int] 						= BarkFineConfig
														lowerCamelCase__      :			Dict 						= BarkFineGenerationConfig
							else:
														raise NotImplementedError()
							lowerCamelCase__      :			Dict 						= f'''{model_type}_small''' if use_small else model_type
							lowerCamelCase__      :			Any 						= REMOTE_MODEL_PATHS[model_key]
							if not os.path.exists(UpperCamelCase   ):
														logger.info(f'''{model_type} model not found, downloading into `{CACHE_DIR}`.'''   )
														_download(model_info["""repo_id"""]       ,    model_info["""file_name"""]   )
							lowerCamelCase__      :			int 						= torch.load(UpperCamelCase       ,    map_location=UpperCamelCase   )
							# this is a hack
							lowerCamelCase__      :			Any 						= checkpoint["""model_args"""]
							if "input_vocab_size" not in model_args:
														lowerCamelCase__      :			Union[str, Any] 						= model_args["""vocab_size"""]
														lowerCamelCase__      :			Any 						= model_args["""vocab_size"""]
														del model_args["vocab_size"]

							# convert Bark model arguments to HF Bark model arguments
							lowerCamelCase__      :			Optional[Any] 						= model_args.pop("""n_head"""   )
							lowerCamelCase__      :			List[Any] 						= model_args.pop("""n_embd"""   )
							lowerCamelCase__      :			Tuple 						= model_args.pop("""n_layer"""   )

							lowerCamelCase__      :			str 						= ConfigClass(**checkpoint["""model_args"""]   )
							lowerCamelCase__      :			Dict 						= ModelClass(config=UpperCamelCase   )
							lowerCamelCase__      :			Dict 						= GenerationConfigClass()

							lowerCamelCase__      :			Dict 						= model_generation_config
							lowerCamelCase__      :			List[Any] 						= checkpoint["""model"""]
							# fixup checkpoint
							lowerCamelCase__      :			str 						= """_orig_mod."""
							for k, v in list(state_dict.items()   ):
														if k.startswith(UpperCamelCase   ):
																					# replace part of the key with corresponding layer name in HF implementation
																					lowerCamelCase__      :			Any 						= k[len(UpperCamelCase   ) :]
																					for old_layer_name in new_layer_name_dict:
																												lowerCamelCase__      :			int 						= new_k.replace(UpperCamelCase       ,    new_layer_name_dict[old_layer_name]   )

																					lowerCamelCase__      :			List[str] 						= state_dict.pop(UpperCamelCase   )

							lowerCamelCase__      :			str 						= set(state_dict.keys()   ) - set(model.state_dict().keys()   )
							lowerCamelCase__      :			str 						= {k for k in extra_keys if not k.endswith(""".attn.bias"""   )}
							lowerCamelCase__      :			Optional[int] 						= set(model.state_dict().keys()   ) - set(state_dict.keys()   )
							lowerCamelCase__      :			str 						= {k for k in missing_keys if not k.endswith(""".attn.bias"""   )}
							if len(UpperCamelCase   ) != 0:
														raise ValueError(f'''extra keys found: {extra_keys}'''   )
							if len(UpperCamelCase   ) != 0:
														raise ValueError(f'''missing keys: {missing_keys}'''   )
							model.load_state_dict(UpperCamelCase       ,    strict=UpperCamelCase   )
							lowerCamelCase__      :			Any 						= model.num_parameters(exclude_embeddings=UpperCamelCase   )
							lowerCamelCase__      :			List[Any] 						= checkpoint["""best_val_loss"""].item()
							logger.info(f'''model loaded: {round(n_params/1E6       ,    1   )}M params, {round(UpperCamelCase       ,    3   )} loss'''   )
							model.eval()
							model.to(UpperCamelCase   )
							del checkpoint, state_dict

							return model





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase=False       ,    UpperCamelCase="text"   )   -> Any:
							if model_type not in ("text", "coarse", "fine"):
														raise NotImplementedError()

							lowerCamelCase__      :			List[str] 						= """cpu"""  # do conversion on cpu

							lowerCamelCase__      :			Optional[int] 						= _get_ckpt_path(UpperCamelCase       ,    use_small=UpperCamelCase   )
							lowerCamelCase__      :			Any 						= _load_model(UpperCamelCase       ,    UpperCamelCase       ,    model_type=UpperCamelCase       ,    use_small=UpperCamelCase   )

							# load bark initial model
							lowerCamelCase__      :			List[str] 						= _bark_load_model(UpperCamelCase       ,    """cpu"""       ,    model_type=UpperCamelCase       ,    use_small=UpperCamelCase   )

							if model_type == "text":
														lowerCamelCase__      :			Tuple 						= bark_model["""model"""]

							if model.num_parameters(exclude_embeddings=UpperCamelCase   ) != bark_model.get_num_params():
														raise ValueError("""initial and new models don't have the same number of parameters"""   )

							# check if same output as the bark model
							lowerCamelCase__      :			Union[str, Any] 						= 5
							lowerCamelCase__      :			List[Any] 						= 10

							if model_type in ["text", "coarse"]:
														lowerCamelCase__      :			Optional[int] 						= torch.randint(256       ,    (batch_size, sequence_length)       ,    dtype=torch.int   )
														lowerCamelCase__      :			Tuple 						= bark_model(UpperCamelCase   )[0]

														lowerCamelCase__      :			List[str] 						= model(UpperCamelCase   )

														# take last logits
														lowerCamelCase__      :			List[str] 						= output_new_model_total.logits[:, [-1], :]

							else:
														lowerCamelCase__      :			Dict 						= 3
														lowerCamelCase__      :			Optional[int] 						= 8
														lowerCamelCase__      :			Any 						= torch.randint(256       ,    (batch_size, sequence_length, n_codes_total)       ,    dtype=torch.int   )

														lowerCamelCase__      :			Dict 						= model(UpperCamelCase       ,    UpperCamelCase   )
														lowerCamelCase__      :			List[str] 						= bark_model(UpperCamelCase       ,    UpperCamelCase   )

														lowerCamelCase__      :			Optional[Any] 						= output_new_model_total.logits

							# output difference should come from the difference of self-attention implementation design
							if output_new_model.shape != output_old_model.shape:
														raise ValueError("""initial and new outputs don't have the same shape"""   )
							if (output_new_model - output_old_model).abs().max().item() > 1E-3:
														raise ValueError("""initial and new outputs are not equal"""   )

							Path(UpperCamelCase   ).mkdir(exist_ok=UpperCamelCase   )
							model.save_pretrained(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    )   -> Dict:
							lowerCamelCase__      :			List[str] 						= os.path.join(UpperCamelCase       ,    UpperCamelCase   )

							lowerCamelCase__      :			int 						= BarkSemanticConfig.from_pretrained(os.path.join(UpperCamelCase       ,    """config.json"""   )   )
							lowerCamelCase__      :			Union[str, Any] 						= BarkCoarseConfig.from_pretrained(os.path.join(UpperCamelCase       ,    """config.json"""   )   )
							lowerCamelCase__      :			int 						= BarkFineConfig.from_pretrained(os.path.join(UpperCamelCase       ,    """config.json"""   )   )
							lowerCamelCase__      :			Tuple 						= EncodecConfig.from_pretrained("""facebook/encodec_24khz"""   )

							lowerCamelCase__      :			Any 						= BarkSemanticModel.from_pretrained(UpperCamelCase   )
							lowerCamelCase__      :			Dict 						= BarkCoarseModel.from_pretrained(UpperCamelCase   )
							lowerCamelCase__      :			str 						= BarkFineModel.from_pretrained(UpperCamelCase   )
							lowerCamelCase__      :			Optional[Any] 						= EncodecModel.from_pretrained("""facebook/encodec_24khz"""   )

							lowerCamelCase__      :			int 						= BarkConfig.from_sub_model_configs(
							    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )

							lowerCamelCase__      :			Tuple 						= BarkGenerationConfig.from_sub_model_configs(
							    semantic.generation_config       ,    coarseAcoustic.generation_config       ,    fineAcoustic.generation_config   )

							lowerCamelCase__      :			Any 						= BarkModel(UpperCamelCase   )

							lowerCamelCase__      :			List[Any] 						= semantic
							lowerCamelCase__      :			List[Any] 						= coarseAcoustic
							lowerCamelCase__      :			str 						= fineAcoustic
							lowerCamelCase__      :			List[str] 						= codec

							lowerCamelCase__      :			Tuple 						= bark_generation_config

							Path(UpperCamelCase   ).mkdir(exist_ok=UpperCamelCase   )
							bark.save_pretrained(UpperCamelCase       ,    repo_id=UpperCamelCase       ,    push_to_hub=UpperCamelCase   )


if __name__ == "__main__":
			_A			:    List[Any]					  =argparse.ArgumentParser()
			# Required parameters

			parser.add_argument('''model_type''', type=str, help='''text, coarse or fine.''')
			parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
			parser.add_argument('''--is_small''', action='''store_true''', help='''convert the small version instead of the large.''')

			_A			:    Dict					  =parser.parse_args()

			load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)









'''simple docstring'''







import argparse
import collections
import json
from pathlib import Path

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

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


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





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> str:
							print("""Loading config file..."""   )

							def flatten_yaml_as_dict(UpperCamelCase       ,    UpperCamelCase=""       ,    UpperCamelCase="."   ):
														lowerCamelCase__      :			Optional[int] 						= []
														for k, v in d.items():
																					lowerCamelCase__      :			Optional[int] 						= parent_key + sep + k if parent_key else k
																					if isinstance(UpperCamelCase       ,    collections.abc.MutableMapping   ):
																												items.extend(flatten_yaml_as_dict(UpperCamelCase       ,    UpperCamelCase       ,    sep=UpperCamelCase   ).items()   )
																					else:
																												items.append((new_key, v)   )
														return dict(UpperCamelCase   )

							lowerCamelCase__      :			Any 						= argparse.Namespace()
							with open(UpperCamelCase       ,    """r"""   ) as yaml_file:
														try:
																					lowerCamelCase__      :			int 						= yaml.load(UpperCamelCase       ,    Loader=yaml.FullLoader   )

																					lowerCamelCase__      :			Tuple 						= flatten_yaml_as_dict(UpperCamelCase   )
																					for k, v in flat_cfg.items():
																												setattr(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )
														except yaml.YAMLError as exc:
																					logger.error("""Error while loading config file: {}. Error message: {}""".format(UpperCamelCase       ,    str(UpperCamelCase   )   )   )
							return config





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> Optional[Any]:
							lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaConfig()

							lowerCamelCase__      :			str 						= False

							# dataset
							if task_name.startswith("""imagenet1k_"""   ):
														lowerCamelCase__      :			Optional[Any] 						= 1000
														if int(task_name.strip().split("""_"""   )[-1]   ) == 384:
																					lowerCamelCase__      :			int 						= 384
														else:
																					lowerCamelCase__      :			Optional[int] 						= 256
														lowerCamelCase__      :			str 						= """imagenet-1k-id2label.json"""
							elif task_name.startswith("""imagenet21k_to_1k_"""   ):
														lowerCamelCase__      :			Tuple 						= 21000
														if int(task_name.strip().split("""_"""   )[-1]   ) == 384:
																					lowerCamelCase__      :			str 						= 384
														else:
																					lowerCamelCase__      :			Any 						= 256
														lowerCamelCase__      :			int 						= """imagenet-22k-id2label.json"""
							elif task_name.startswith("""ade20k_"""   ):
														lowerCamelCase__      :			Dict 						= 151
														lowerCamelCase__      :			str 						= 512
														lowerCamelCase__      :			List[Any] 						= """ade20k-id2label.json"""
														lowerCamelCase__      :			Union[str, Any] 						= True
							elif task_name.startswith("""voc_"""   ):
														lowerCamelCase__      :			Tuple 						= 21
														lowerCamelCase__      :			Optional[int] 						= 512
														lowerCamelCase__      :			List[Any] 						= """pascal-voc-id2label.json"""
														lowerCamelCase__      :			Tuple 						= True

							# orig_config
							lowerCamelCase__      :			Optional[int] 						= load_orig_config_file(UpperCamelCase   )
							assert getattr(UpperCamelCase       ,    """model.classification.name"""       ,    -1   ) == "mobilevit_v2", "Invalid model"
							lowerCamelCase__      :			int 						= getattr(UpperCamelCase       ,    """model.classification.mitv2.width_multiplier"""       ,    1.0   )
							assert (
							    getattr(UpperCamelCase       ,    """model.classification.mitv2.attn_norm_layer"""       ,    -1   ) == "layer_norm_2d"
							), "Norm layers other than layer_norm_2d is not supported"
							lowerCamelCase__      :			Tuple 						= getattr(UpperCamelCase       ,    """model.classification.activation.name"""       ,    """swish"""   )
							# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

							if is_segmentation_model:
														lowerCamelCase__      :			Any 						= getattr(UpperCamelCase       ,    """model.segmentation.output_stride"""       ,    16   )
														if "_deeplabv3" in task_name:
																					lowerCamelCase__      :			str 						= getattr(UpperCamelCase       ,    """model.segmentation.deeplabv3.aspp_rates"""       ,    [12, 24, 36]   )
																					lowerCamelCase__      :			Tuple 						= getattr(UpperCamelCase       ,    """model.segmentation.deeplabv3.aspp_out_channels"""       ,    512   )
																					lowerCamelCase__      :			List[Any] 						= getattr(UpperCamelCase       ,    """model.segmentation.deeplabv3.aspp_dropout"""       ,    0.1   )

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

							return config





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> Any:
							lowerCamelCase__      :			List[Any] 						= dct.pop(UpperCamelCase   )
							lowerCamelCase__      :			Dict 						= val





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase=False   )   -> Tuple:
							if base_model:
														lowerCamelCase__      :			Optional[int] 						= """"""
							else:
														lowerCamelCase__      :			Optional[Any] 						= """mobilevitv2."""

							lowerCamelCase__      :			List[str] 						= []
							for k in state_dict.keys():
														if k[:8] == "encoder.":
																					lowerCamelCase__      :			Optional[Any] 						= k[8:]
														else:
																					lowerCamelCase__      :			Optional[Any] 						= k

														if ".block." in k:
																					lowerCamelCase__      :			Dict 						= k_new.replace(""".block."""       ,    """."""   )
														if ".conv." in k:
																					lowerCamelCase__      :			List[Any] 						= k_new.replace(""".conv."""       ,    """.convolution."""   )
														if ".norm." in k:
																					lowerCamelCase__      :			str 						= k_new.replace(""".norm."""       ,    """.normalization."""   )

														if "conv_1." in k:
																					lowerCamelCase__      :			Any 						= k_new.replace("""conv_1."""       ,    f'''{model_prefix}conv_stem.'''   )
														for i in [1, 2]:
																					if f'''layer_{i}.''' in k:
																												lowerCamelCase__      :			Optional[Any] 						= k_new.replace(f'''layer_{i}.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.layer.'''   )
														if ".exp_1x1." in k:
																					lowerCamelCase__      :			Dict 						= k_new.replace(""".exp_1x1."""       ,    """.expand_1x1."""   )
														if ".red_1x1." in k:
																					lowerCamelCase__      :			str 						= k_new.replace(""".red_1x1."""       ,    """.reduce_1x1."""   )

														for i in [3, 4, 5]:
																					if f'''layer_{i}.0.''' in k:
																												lowerCamelCase__      :			List[str] 						= k_new.replace(f'''layer_{i}.0.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.'''   )
																					if f'''layer_{i}.1.local_rep.0.''' in k:
																												lowerCamelCase__      :			Optional[Any] 						= k_new.replace(f'''layer_{i}.1.local_rep.0.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.conv_kxk.'''   )
																					if f'''layer_{i}.1.local_rep.1.''' in k:
																												lowerCamelCase__      :			Dict 						= k_new.replace(f'''layer_{i}.1.local_rep.1.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.conv_1x1.'''   )

														for i in [3, 4, 5]:
																					if i == 3:
																												lowerCamelCase__      :			int 						= [0, 1]
																					elif i == 4:
																												lowerCamelCase__      :			str 						= [0, 1, 2, 3]
																					elif i == 5:
																												lowerCamelCase__      :			Dict 						= [0, 1, 2]

																					for j in j_in:
																												if f'''layer_{i}.1.global_rep.{j}.''' in k:
																																			lowerCamelCase__      :			List[Any] 						= k_new.replace(
																																			    f'''layer_{i}.1.global_rep.{j}.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'''   )
																					if f'''layer_{i}.1.global_rep.{j+1}.''' in k:
																												lowerCamelCase__      :			Optional[int] 						= k_new.replace(
																												    f'''layer_{i}.1.global_rep.{j+1}.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.layernorm.'''   )

																					if f'''layer_{i}.1.conv_proj.''' in k:
																												lowerCamelCase__      :			Optional[int] 						= k_new.replace(f'''layer_{i}.1.conv_proj.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.conv_projection.'''   )

														if "pre_norm_attn.0." in k:
																					lowerCamelCase__      :			str 						= k_new.replace("""pre_norm_attn.0."""       ,    """layernorm_before."""   )
														if "pre_norm_attn.1." in k:
																					lowerCamelCase__      :			str 						= k_new.replace("""pre_norm_attn.1."""       ,    """attention."""   )
														if "pre_norm_ffn.0." in k:
																					lowerCamelCase__      :			Optional[Any] 						= k_new.replace("""pre_norm_ffn.0."""       ,    """layernorm_after."""   )
														if "pre_norm_ffn.1." in k:
																					lowerCamelCase__      :			Union[str, Any] 						= k_new.replace("""pre_norm_ffn.1."""       ,    """ffn.conv1."""   )
														if "pre_norm_ffn.3." in k:
																					lowerCamelCase__      :			List[Any] 						= k_new.replace("""pre_norm_ffn.3."""       ,    """ffn.conv2."""   )

														if "classifier.1." in k:
																					lowerCamelCase__      :			Tuple 						= k_new.replace("""classifier.1."""       ,    """classifier."""   )

														if "seg_head." in k:
																					lowerCamelCase__      :			Optional[int] 						= k_new.replace("""seg_head."""       ,    """segmentation_head."""   )
														if ".aspp_layer." in k:
																					lowerCamelCase__      :			Any 						= k_new.replace(""".aspp_layer."""       ,    """."""   )
														if ".aspp_pool." in k:
																					lowerCamelCase__      :			Any 						= k_new.replace(""".aspp_pool."""       ,    """."""   )

														rename_keys.append((k, k_new)   )
							return rename_keys





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> List[str]:
							lowerCamelCase__      :			Union[str, Any] 						= []
							for k in state_dict.keys():
														if k.startswith("""seg_head.aux_head."""   ):
																					keys_to_ignore.append(UpperCamelCase   )
							for k in keys_to_ignore:
														state_dict.pop(UpperCamelCase       ,    UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ ()   -> Dict:
							lowerCamelCase__      :			Union[str, Any] 						= """http://images.cocodataset.org/val2017/000000039769.jpg"""
							# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
							lowerCamelCase__      :			Tuple 						= Image.open(requests.get(UpperCamelCase       ,    stream=UpperCamelCase   ).raw   )
							return im





@torch.no_grad()
def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> Dict:
							lowerCamelCase__      :			str 						= get_mobilevitva_config(UpperCamelCase       ,    UpperCamelCase   )

							# load original state_dict
							lowerCamelCase__      :			List[str] 						= torch.load(UpperCamelCase       ,    map_location="""cpu"""   )

							# load huggingface model
							if task_name.startswith("""ade20k_"""   ) or task_name.startswith("""voc_"""   ):
														lowerCamelCase__      :			int 						= MobileViTVaForSemanticSegmentation(UpperCamelCase   ).eval()
														lowerCamelCase__      :			Tuple 						= False
							else:
														lowerCamelCase__      :			int 						= MobileViTVaForImageClassification(UpperCamelCase   ).eval()
														lowerCamelCase__      :			Optional[Any] 						= False

							# remove and rename some keys of load the original model
							lowerCamelCase__      :			Tuple 						= checkpoint
							remove_unused_keys(UpperCamelCase   )
							lowerCamelCase__      :			Union[str, Any] 						= create_rename_keys(UpperCamelCase       ,    base_model=UpperCamelCase   )
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )

							# load modified state_dict
							model.load_state_dict(UpperCamelCase   )

							# Check outputs on an image, prepared by MobileViTImageProcessor
							lowerCamelCase__      :			int 						= MobileViTImageProcessor(crop_size=config.image_size       ,    size=config.image_size + 32   )
							lowerCamelCase__      :			Dict 						= image_processor(images=prepare_img()       ,    return_tensors="""pt"""   )
							lowerCamelCase__      :			str 						= model(**UpperCamelCase   )

							# verify classification model
							if task_name.startswith("""imagenet"""   ):
														lowerCamelCase__      :			Dict 						= outputs.logits
														lowerCamelCase__      :			Optional[Any] 						= logits.argmax(-1   ).item()
														print("""Predicted class:"""       ,    model.config.idalabel[predicted_class_idx]   )
														if task_name.startswith("""imagenet1k_256"""   ) and config.width_multiplier == 1.0:
																					# expected_logits for base variant
																					lowerCamelCase__      :			Optional[Any] 						= torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-01]   )
																					assert torch.allclose(logits[0, :3]       ,    UpperCamelCase       ,    atol=1E-4   )

							Path(UpperCamelCase   ).mkdir(exist_ok=UpperCamelCase   )
							print(f'''Saving model {task_name} to {pytorch_dump_folder_path}'''   )
							model.save_pretrained(UpperCamelCase   )
							print(f'''Saving image processor to {pytorch_dump_folder_path}'''   )
							image_processor.save_pretrained(UpperCamelCase   )


if __name__ == "__main__":
			_A			:    Optional[int]					  =argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--task''',
			    default='''imagenet1k_256''',
			    type=str,
			    help=(
			        '''Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '''
			        '''
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            '''
			    ),
			    choices=[
			        '''imagenet1k_256''',
			        '''imagenet1k_384''',
			        '''imagenet21k_to_1k_256''',
			        '''imagenet21k_to_1k_384''',
			        '''ade20k_deeplabv3''',
			        '''voc_deeplabv3''',
			    ],
			)

			parser.add_argument(
			    '''--orig_checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
			)
			parser.add_argument('''--orig_config_path''', required=True, type=str, help='''Path to the original config file.''')
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
			)

			_A			:    Dict					  =parser.parse_args()
			convert_mobilevitva_checkpoint(
			    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
			)

