

'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    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.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
UpperCamelCase_    						=       [
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]






def 				_lowerCAmelCase			(   __magic_name__						:     Any   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     Tuple						)				->  str:
						for attribute in key.split('''.'''						):
												lowercase :							Tuple    		=getattr(__magic_name__   , __magic_name__						)

						if weight_type is not None:
												lowercase :							Optional[int]    		=getattr(__magic_name__   , __magic_name__						).shape
						else:
												lowercase :							List[Any]    		=hf_pointer.shape

						if hf_shape != value.shape:
												raise ValueError(
												    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 :							Any    		=value
						elif weight_type == "weight_g":
												lowercase :							List[Any]    		=value
						elif weight_type == "weight_v":
												lowercase :							Union[str, Any]    		=value
						elif weight_type == "bias":
												lowercase :							Tuple    		=value
						elif weight_type == "running_mean":
												lowercase :							Union[str, Any]    		=value
						elif weight_type == "running_var":
												lowercase :							str    		=value
						elif weight_type == "num_batches_tracked":
												lowercase :							Tuple    		=value
						elif weight_type == "inv_freq":
												lowercase :							Optional[Any]    		=value
						else:
												lowercase :							Tuple    		=value

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






def 				_lowerCAmelCase			(   __magic_name__						:     List[Any]   , __magic_name__						:     Union[str, Any]   , __magic_name__						:     str						)				->  Union[str, Any]:
						lowercase :							Optional[int]    		=[]
						lowercase :							Tuple    		=fairseq_model.state_dict()

						lowercase :							List[Any]    		=hf_model.wavaveca_conformer.feature_extractor

						for name, value in fairseq_dict.items():
												lowercase :							Tuple    		=False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __magic_name__   , __magic_name__   , __magic_name__   , __magic_name__   , hf_model.config.feat_extract_norm == '''group'''   , )
																		lowercase :							List[Any]    		=True
												else:
																		for key, mapped_key in MAPPING.items():
																								lowercase :							Optional[int]    		='''wav2vec2_conformer.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																								if key in name or key.split('''w2v_model.'''						)[-1] == name.split('''.'''						)[0]:
																														lowercase :							Union[str, Any]    		=True
																														if "*" in mapped_key:
																																				lowercase :							Optional[int]    		=name.split(__magic_name__						)[0].split('''.'''						)[-2]
																																				lowercase :							List[str]    		=mapped_key.replace('''*'''   , __magic_name__						)
																														if "pos_bias_u" in name:
																																				lowercase :							Optional[Any]    		=None
																														elif "pos_bias_v" in name:
																																				lowercase :							Union[str, Any]    		=None
																														elif "weight_g" in name:
																																				lowercase :							Any    		='''weight_g'''
																														elif "weight_v" in name:
																																				lowercase :							Tuple    		='''weight_v'''
																														elif "bias" in name:
																																				lowercase :							Optional[int]    		='''bias'''
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				lowercase :							Optional[int]    		='''weight'''
																														elif "running_mean" in name:
																																				lowercase :							Union[str, Any]    		='''running_mean'''
																														elif "inv_freq" in name:
																																				lowercase :							Any    		='''inv_freq'''
																														elif "running_var" in name:
																																				lowercase :							Tuple    		='''running_var'''
																														elif "num_batches_tracked" in name:
																																				lowercase :							Dict    		='''num_batches_tracked'''
																														else:
																																				lowercase :							str    		=None
																														set_recursively(__magic_name__   , __magic_name__   , __magic_name__   , __magic_name__   , __magic_name__						)
																								continue
												if not is_used:
																		unused_weights.append(__magic_name__						)

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






def 				_lowerCAmelCase			(   __magic_name__						:     int   , __magic_name__						:     Optional[Any]   , __magic_name__						:     List[Any]   , __magic_name__						:     Optional[Any]   , __magic_name__						:     Tuple						)				->  int:
						lowercase :							Optional[Any]    		=full_name.split('''conv_layers.'''						)[-1]
						lowercase :							Any    		=name.split('''.'''						)
						lowercase :							List[str]    		=int(items[0]						)
						lowercase :							Union[str, Any]    		=int(items[1]						)

						if type_id == 0:
												if "bias" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''						)
																		lowercase :							Union[str, Any]    		=value
																		logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''						)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''						)
																		lowercase :							Optional[Any]    		=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:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''						)
																		lowercase :							Optional[int]    		=value
																		logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
												elif "weight" in name:
																		if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																								raise ValueError(
																								    f'''{full_name} has size {value.shape}, but'''
																								    f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''						)
																		lowercase :							str    		=value
																		logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
						else:
												unused_weights.append(__magic_name__						)






@torch.no_grad()
def 				_lowerCAmelCase			(   __magic_name__						:     List[str]   , __magic_name__						:     Optional[Any]   , __magic_name__						:     Tuple=None   , __magic_name__						:     Optional[Any]=None   , __magic_name__						:     Union[str, Any]=True						)				->  Union[str, Any]:
						if config_path is not None:
												lowercase :							Optional[Any]    		=WavaVecaConformerConfig.from_pretrained(__magic_name__   , hidden_act='''swish'''						)
						else:
												lowercase :							Optional[int]    		=WavaVecaConformerConfig()

						if "rope" in checkpoint_path:
												lowercase :							Dict    		='''rotary'''

						if is_finetuned:
												if dict_path:
																		lowercase :							Optional[Any]    		=Dictionary.load(__magic_name__						)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		lowercase :							str    		=target_dict.pad_index
																		lowercase :							Union[str, Any]    		=target_dict.bos_index
																		lowercase :							Any    		=target_dict.eos_index
																		lowercase :							Tuple    		=len(target_dict.symbols						)
																		lowercase :							str    		=os.path.join(__magic_name__   , '''vocab.json'''						)
																		if not os.path.isdir(__magic_name__						):
																								logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__magic_name__						)						)
																								return
																		os.makedirs(__magic_name__   , exist_ok=__magic_name__						)
																		lowercase :							Dict    		=target_dict.indices

																		# fairseq has the <pad> and <s> switched
																		lowercase :							str    		=0
																		lowercase :							List[Any]    		=1
																		with open(__magic_name__   , '''w'''   , encoding='''utf-8'''						) as vocab_handle:
																								json.dump(__magic_name__   , __magic_name__						)
																		lowercase :							List[str]    		=WavaVecaCTCTokenizer(
																		    __magic_name__   , unk_token=target_dict.unk_word   , pad_token=target_dict.pad_word   , bos_token=target_dict.bos_word   , eos_token=target_dict.eos_word   , word_delimiter_token='''|'''   , do_lower_case=__magic_name__   , )
																		lowercase :							Optional[int]    		=True if config.feat_extract_norm == '''layer''' else False
																		lowercase :							str    		=WavaVecaFeatureExtractor(
																		    feature_size=1   , sampling_rate=16000   , padding_value=0   , do_normalize=__magic_name__   , return_attention_mask=__magic_name__   , )
																		lowercase :							Tuple    		=WavaVecaProcessor(feature_extractor=__magic_name__   , tokenizer=__magic_name__						)
																		processor.save_pretrained(__magic_name__						)

												lowercase :							str    		=WavaVecaConformerForCTC(__magic_name__						)
						else:
												lowercase :							Tuple    		=WavaVecaConformerForPreTraining(__magic_name__						)

						if is_finetuned:
												lowercase		,	lowercase		,	lowercase :							Optional[int]    		=fairseq.checkpoint_utils.load_model_ensemble_and_task(
												    [checkpoint_path]   , arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''						)[:-1]						)}						)
						else:
												lowercase :							Dict    		=argparse.Namespace(task='''audio_pretraining'''						)
												lowercase :							Optional[int]    		=fairseq.tasks.setup_task(__magic_name__						)

												lowercase		,	lowercase		,	lowercase :							str    		=fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   , task=__magic_name__						)

						lowercase :							List[Any]    		=model[0].eval()

						recursively_load_weights(__magic_name__   , __magic_name__   , not is_finetuned						)

						hf_wavavec.save_pretrained(__magic_name__						)


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("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
							parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
							parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
							parser.add_argument(
							    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
							)
							UpperCamelCase_    						=       parser.parse_args()
							convert_wavaveca_conformer_checkpoint(
							    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
							)












import argparse
import json
from pathlib import Path

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

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


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


def 							__snake_case							(    lowerCAmelCase_	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			MobileViTConfig()

    # size of the architecture
    if "mobilevit_s" in mobilevit_name:
        SCREAMING_SNAKE_CASE__											=			[1_4_4, 1_9_2, 2_4_0]
        SCREAMING_SNAKE_CASE__											=			[1_6, 3_2, 6_4, 9_6, 1_2_8, 1_6_0, 6_4_0]
    elif "mobilevit_xs" in mobilevit_name:
        SCREAMING_SNAKE_CASE__											=			[9_6, 1_2_0, 1_4_4]
        SCREAMING_SNAKE_CASE__											=			[1_6, 3_2, 4_8, 6_4, 8_0, 9_6, 3_8_4]
    elif "mobilevit_xxs" in mobilevit_name:
        SCREAMING_SNAKE_CASE__											=			[6_4, 8_0, 9_6]
        SCREAMING_SNAKE_CASE__											=			[1_6, 1_6, 2_4, 4_8, 6_4, 8_0, 3_2_0]
        SCREAMING_SNAKE_CASE__											=			0.05
        SCREAMING_SNAKE_CASE__											=			2.0

    if mobilevit_name.startswith('''deeplabv3_'''	):
        SCREAMING_SNAKE_CASE__											=			5_1_2
        SCREAMING_SNAKE_CASE__											=			1_6
        SCREAMING_SNAKE_CASE__											=			2_1
        SCREAMING_SNAKE_CASE__											=			'''pascal-voc-id2label.json'''
    else:
        SCREAMING_SNAKE_CASE__											=			1_0_0_0
        SCREAMING_SNAKE_CASE__											=			'''imagenet-1k-id2label.json'''

    SCREAMING_SNAKE_CASE__											=			'''huggingface/label-files'''
    SCREAMING_SNAKE_CASE__											=			json.load(open(hf_hub_download(lowerCAmelCase_ ,							lowerCAmelCase_ ,							repo_type='''dataset'''	) ,							'''r'''	)	)
    SCREAMING_SNAKE_CASE__											=			{int(lowerCAmelCase_	): v for k, v in idalabel.items()}
    SCREAMING_SNAKE_CASE__											=			idalabel
    SCREAMING_SNAKE_CASE__											=			{v: k for k, v in idalabel.items()}

    return config


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_=False	)				->						Union[str, Any]:
    for i in range(1 ,							6	):
        if f'''layer_{i}.''' in name:
            SCREAMING_SNAKE_CASE__											=			name.replace(f'''layer_{i}.''' ,							f'''encoder.layer.{i - 1}.'''	)

    if "conv_1." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''conv_1.''' ,							'''conv_stem.'''	)
    if ".block." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.block.''' ,							'''.'''	)

    if "exp_1x1" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''exp_1x1''' ,							'''expand_1x1'''	)
    if "red_1x1" in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''red_1x1''' ,							'''reduce_1x1'''	)
    if ".local_rep.conv_3x3." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.local_rep.conv_3x3.''' ,							'''.conv_kxk.'''	)
    if ".local_rep.conv_1x1." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.local_rep.conv_1x1.''' ,							'''.conv_1x1.'''	)
    if ".norm." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.norm.''' ,							'''.normalization.'''	)
    if ".conv." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.conv.''' ,							'''.convolution.'''	)
    if ".conv_proj." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.conv_proj.''' ,							'''.conv_projection.'''	)

    for i in range(0 ,							2	):
        for j in range(0 ,							4	):
            if f'''.{i}.{j}.''' in name:
                SCREAMING_SNAKE_CASE__											=			name.replace(f'''.{i}.{j}.''' ,							f'''.{i}.layer.{j}.'''	)

    for i in range(2 ,							6	):
        for j in range(0 ,							4	):
            if f'''.{i}.{j}.''' in name:
                SCREAMING_SNAKE_CASE__											=			name.replace(f'''.{i}.{j}.''' ,							f'''.{i}.'''	)
                if "expand_1x1" in name:
                    SCREAMING_SNAKE_CASE__											=			name.replace('''expand_1x1''' ,							'''downsampling_layer.expand_1x1'''	)
                if "conv_3x3" in name:
                    SCREAMING_SNAKE_CASE__											=			name.replace('''conv_3x3''' ,							'''downsampling_layer.conv_3x3'''	)
                if "reduce_1x1" in name:
                    SCREAMING_SNAKE_CASE__											=			name.replace('''reduce_1x1''' ,							'''downsampling_layer.reduce_1x1'''	)

    for i in range(2 ,							5	):
        if f'''.global_rep.{i}.weight''' in name:
            SCREAMING_SNAKE_CASE__											=			name.replace(f'''.global_rep.{i}.weight''' ,							'''.layernorm.weight'''	)
        if f'''.global_rep.{i}.bias''' in name:
            SCREAMING_SNAKE_CASE__											=			name.replace(f'''.global_rep.{i}.bias''' ,							'''.layernorm.bias'''	)

    if ".global_rep." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.global_rep.''' ,							'''.transformer.'''	)
    if ".pre_norm_mha.0." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_mha.0.''' ,							'''.layernorm_before.'''	)
    if ".pre_norm_mha.1.out_proj." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_mha.1.out_proj.''' ,							'''.attention.output.dense.'''	)
    if ".pre_norm_ffn.0." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_ffn.0.''' ,							'''.layernorm_after.'''	)
    if ".pre_norm_ffn.1." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_ffn.1.''' ,							'''.intermediate.dense.'''	)
    if ".pre_norm_ffn.4." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.pre_norm_ffn.4.''' ,							'''.output.dense.'''	)
    if ".transformer." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.transformer.''' ,							'''.transformer.layer.'''	)

    if ".aspp_layer." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.aspp_layer.''' ,							'''.'''	)
    if ".aspp_pool." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''.aspp_pool.''' ,							'''.'''	)
    if "seg_head." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''seg_head.''' ,							'''segmentation_head.'''	)
    if "segmentation_head.classifier.classifier." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''segmentation_head.classifier.classifier.''' ,							'''segmentation_head.classifier.'''	)

    if "classifier.fc." in name:
        SCREAMING_SNAKE_CASE__											=			name.replace('''classifier.fc.''' ,							'''classifier.'''	)
    elif (not base_model) and ("segmentation_head." not in name):
        SCREAMING_SNAKE_CASE__											=			'''mobilevit.''' + name

    return name


def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=False	)				->						Optional[Any]:
    if base_model:
        SCREAMING_SNAKE_CASE__											=			''''''
    else:
        SCREAMING_SNAKE_CASE__											=			'''mobilevit.'''

    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE__											=			orig_state_dict.pop(lowerCAmelCase_	)

        if key[:8] == "encoder.":
            SCREAMING_SNAKE_CASE__											=			key[8:]

        if "qkv" in key:
            SCREAMING_SNAKE_CASE__											=			key.split('''.'''	)
            SCREAMING_SNAKE_CASE__											=			int(key_split[0][6:]	) - 1
            SCREAMING_SNAKE_CASE__											=			int(key_split[3]	)
            SCREAMING_SNAKE_CASE__											=			model.get_submodule(f'''{model_prefix}encoder.layer.{layer_num}'''	)
            SCREAMING_SNAKE_CASE__											=			layer.transformer.layer[transformer_num].attention.attention.all_head_size
            SCREAMING_SNAKE_CASE__											=			(
                f'''{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'''
            )
            if "weight" in key:
                SCREAMING_SNAKE_CASE__											=			val[:dim, :]
                SCREAMING_SNAKE_CASE__											=			val[dim : dim * 2, :]
                SCREAMING_SNAKE_CASE__											=			val[-dim:, :]
            else:
                SCREAMING_SNAKE_CASE__											=			val[:dim]
                SCREAMING_SNAKE_CASE__											=			val[dim : dim * 2]
                SCREAMING_SNAKE_CASE__											=			val[-dim:]
        else:
            SCREAMING_SNAKE_CASE__											=			val

    return orig_state_dict


def 							__snake_case							(    )				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    SCREAMING_SNAKE_CASE__											=			Image.open(requests.get(lowerCAmelCase_ ,							stream=lowerCAmelCase_	).raw	)
    return im


@torch.no_grad()
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_=False	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			get_mobilevit_config(lowerCAmelCase_	)

    # load original state_dict
    SCREAMING_SNAKE_CASE__											=			torch.load(lowerCAmelCase_ ,							map_location='''cpu'''	)

    # load 🤗 model
    if mobilevit_name.startswith('''deeplabv3_'''	):
        SCREAMING_SNAKE_CASE__											=			MobileViTForSemanticSegmentation(lowerCAmelCase_	).eval()
    else:
        SCREAMING_SNAKE_CASE__											=			MobileViTForImageClassification(lowerCAmelCase_	).eval()

    SCREAMING_SNAKE_CASE__											=			convert_state_dict(lowerCAmelCase_ ,							lowerCAmelCase_	)
    model.load_state_dict(lowerCAmelCase_	)

    # Check outputs on an image, prepared by MobileViTImageProcessor
    SCREAMING_SNAKE_CASE__											=			MobileViTImageProcessor(crop_size=config.image_size ,							size=config.image_size + 3_2	)
    SCREAMING_SNAKE_CASE__											=			image_processor(images=prepare_img() ,							return_tensors='''pt'''	)
    SCREAMING_SNAKE_CASE__											=			model(**lowerCAmelCase_	)
    SCREAMING_SNAKE_CASE__											=			outputs.logits

    if mobilevit_name.startswith('''deeplabv3_'''	):
        assert logits.shape == (1, 2_1, 3_2, 3_2)

        if mobilevit_name == "deeplabv3_mobilevit_s":
            SCREAMING_SNAKE_CASE__											=			torch.tensor(
                [
                    [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
                    [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
                    [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
                ]	)
        elif mobilevit_name == "deeplabv3_mobilevit_xs":
            SCREAMING_SNAKE_CASE__											=			torch.tensor(
                [
                    [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
                    [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
                    [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
                ]	)
        elif mobilevit_name == "deeplabv3_mobilevit_xxs":
            SCREAMING_SNAKE_CASE__											=			torch.tensor(
                [
                    [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
                    [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
                    [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
                ]	)
        else:
            raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''	)

        assert torch.allclose(logits[0, :3, :3, :3] ,							lowerCAmelCase_ ,							atol=1e-4	)
    else:
        assert logits.shape == (1, 1_0_0_0)

        if mobilevit_name == "mobilevit_s":
            SCREAMING_SNAKE_CASE__											=			torch.tensor([-0.98_66, 0.23_92, -1.12_41]	)
        elif mobilevit_name == "mobilevit_xs":
            SCREAMING_SNAKE_CASE__											=			torch.tensor([-2.47_61, -0.93_99, -1.95_87]	)
        elif mobilevit_name == "mobilevit_xxs":
            SCREAMING_SNAKE_CASE__											=			torch.tensor([-1.93_64, -1.23_27, -0.46_53]	)
        else:
            raise ValueError(f'''Unknown mobilevit_name: {mobilevit_name}'''	)

        assert torch.allclose(logits[0, :3] ,							lowerCAmelCase_ ,							atol=1e-4	)

    Path(lowerCAmelCase_	).mkdir(exist_ok=lowerCAmelCase_	)
    print(f'''Saving model {mobilevit_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:
        SCREAMING_SNAKE_CASE__											=			{
            '''mobilevit_s''': '''mobilevit-small''',
            '''mobilevit_xs''': '''mobilevit-x-small''',
            '''mobilevit_xxs''': '''mobilevit-xx-small''',
            '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
            '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
            '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
        }

        print('''Pushing to the hub...'''	)
        SCREAMING_SNAKE_CASE__											=			model_mapping[mobilevit_name]
        image_processor.push_to_hub(lowerCAmelCase_ ,							organization='''apple'''	)
        model.push_to_hub(lowerCAmelCase_ ,							organization='''apple'''	)


if __name__ == "__main__":
  _A					:				str				=			argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--mobilevit_name""",
      default="""mobilevit_s""",
      type=str,
      help=(
          """Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
          """ 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
      ),
  )
  parser.add_argument(
      """--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt 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."""
  )

  _A					:				str				=			parser.parse_args()
  convert_movilevit_checkpoint(
      args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
  )
