





"""simple docstring"""






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			      =	logging.get_logger(__name__)

def      __A   (      a_    :int)		->		Optional[Any]:
				__a			:    Optional[Any]      							=	MobileViTConfig()

				# size of the architecture
				if "mobilevit_s" in mobilevit_name:
								__a			:    Any      							=	[1_44, 1_92, 2_40]
								__a			:    List[str]      							=	[16, 32, 64, 96, 1_28, 1_60, 6_40]
				elif "mobilevit_xs" in mobilevit_name:
								__a			:    Dict      							=	[96, 1_20, 1_44]
								__a			:    Union[str, Any]      							=	[16, 32, 48, 64, 80, 96, 3_84]
				elif "mobilevit_xxs" in mobilevit_name:
								__a			:    Optional[int]      							=	[64, 80, 96]
								__a			:    Optional[Any]      							=	[16, 16, 24, 48, 64, 80, 3_20]
								__a			:    Dict      							=	0.0_5
								__a			:    Union[str, Any]      							=	2.0

				if mobilevit_name.startswith('''deeplabv3_'''):
								__a			:    str      							=	5_12
								__a			:    Union[str, Any]      							=	16
								__a			:    Dict      							=	21
								__a			:    Any      							=	'''pascal-voc-id2label.json'''
				else:
								__a			:    List[str]      							=	10_00
								__a			:    str      							=	'''imagenet-1k-id2label.json'''

				__a			:    Optional[Any]      							=	'''huggingface/label-files'''
				__a			:    Optional[int]      							=	json.load(open(hf_hub_download(lowerCAmelCase_ ,							lowerCAmelCase_ ,							repo_type='''dataset''') ,							'''r'''))
				__a			:    int      							=	{int(lowerCAmelCase_): v for k, v in idalabel.items()}
				__a			:    Optional[Any]      							=	idalabel
				__a			:    Union[str, Any]      							=	{v: k for k, v in idalabel.items()}

				return config

def      __A   (      a_    :Dict ,							a_    :List[Any]=False)		->		Union[str, Any]:
				for i in range(1 ,							6):
								if F"""layer_{i}.""" in name:
												__a			:    Optional[Any]      							=	name.replace(F"""layer_{i}.""" ,							F"""encoder.layer.{i - 1}.""")

				if "conv_1." in name:
								__a			:    Optional[Any]      							=	name.replace('''conv_1.''' ,							'''conv_stem.''')
				if ".block." in name:
								__a			:    str      							=	name.replace('''.block.''' ,							'''.''')

				if "exp_1x1" in name:
								__a			:    Dict      							=	name.replace('''exp_1x1''' ,							'''expand_1x1''')
				if "red_1x1" in name:
								__a			:    List[str]      							=	name.replace('''red_1x1''' ,							'''reduce_1x1''')
				if ".local_rep.conv_3x3." in name:
								__a			:    Union[str, Any]      							=	name.replace('''.local_rep.conv_3x3.''' ,							'''.conv_kxk.''')
				if ".local_rep.conv_1x1." in name:
								__a			:    str      							=	name.replace('''.local_rep.conv_1x1.''' ,							'''.conv_1x1.''')
				if ".norm." in name:
								__a			:    str      							=	name.replace('''.norm.''' ,							'''.normalization.''')
				if ".conv." in name:
								__a			:    Any      							=	name.replace('''.conv.''' ,							'''.convolution.''')
				if ".conv_proj." in name:
								__a			:    int      							=	name.replace('''.conv_proj.''' ,							'''.conv_projection.''')

				for i in range(0 ,							2):
								for j in range(0 ,							4):
												if F""".{i}.{j}.""" in name:
																__a			:    Dict      							=	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:
																__a			:    int      							=	name.replace(F""".{i}.{j}.""" ,							F""".{i}.""")
																if "expand_1x1" in name:
																				__a			:    int      							=	name.replace('''expand_1x1''' ,							'''downsampling_layer.expand_1x1''')
																if "conv_3x3" in name:
																				__a			:    Optional[Any]      							=	name.replace('''conv_3x3''' ,							'''downsampling_layer.conv_3x3''')
																if "reduce_1x1" in name:
																				__a			:    Tuple      							=	name.replace('''reduce_1x1''' ,							'''downsampling_layer.reduce_1x1''')

				for i in range(2 ,							5):
								if F""".global_rep.{i}.weight""" in name:
												__a			:    Any      							=	name.replace(F""".global_rep.{i}.weight""" ,							'''.layernorm.weight''')
								if F""".global_rep.{i}.bias""" in name:
												__a			:    List[Any]      							=	name.replace(F""".global_rep.{i}.bias""" ,							'''.layernorm.bias''')

				if ".global_rep." in name:
								__a			:    Dict      							=	name.replace('''.global_rep.''' ,							'''.transformer.''')
				if ".pre_norm_mha.0." in name:
								__a			:    Optional[Any]      							=	name.replace('''.pre_norm_mha.0.''' ,							'''.layernorm_before.''')
				if ".pre_norm_mha.1.out_proj." in name:
								__a			:    Optional[Any]      							=	name.replace('''.pre_norm_mha.1.out_proj.''' ,							'''.attention.output.dense.''')
				if ".pre_norm_ffn.0." in name:
								__a			:    Tuple      							=	name.replace('''.pre_norm_ffn.0.''' ,							'''.layernorm_after.''')
				if ".pre_norm_ffn.1." in name:
								__a			:    Optional[int]      							=	name.replace('''.pre_norm_ffn.1.''' ,							'''.intermediate.dense.''')
				if ".pre_norm_ffn.4." in name:
								__a			:    Optional[int]      							=	name.replace('''.pre_norm_ffn.4.''' ,							'''.output.dense.''')
				if ".transformer." in name:
								__a			:    Optional[Any]      							=	name.replace('''.transformer.''' ,							'''.transformer.layer.''')

				if ".aspp_layer." in name:
								__a			:    Optional[Any]      							=	name.replace('''.aspp_layer.''' ,							'''.''')
				if ".aspp_pool." in name:
								__a			:    str      							=	name.replace('''.aspp_pool.''' ,							'''.''')
				if "seg_head." in name:
								__a			:    int      							=	name.replace('''seg_head.''' ,							'''segmentation_head.''')
				if "segmentation_head.classifier.classifier." in name:
								__a			:    Optional[int]      							=	name.replace('''segmentation_head.classifier.classifier.''' ,							'''segmentation_head.classifier.''')

				if "classifier.fc." in name:
								__a			:    Any      							=	name.replace('''classifier.fc.''' ,							'''classifier.''')
				elif (not base_model) and ("segmentation_head." not in name):
								__a			:    List[str]      							=	'''mobilevit.''' + name

				return name

def      __A   (      a_    :Any ,							a_    :List[str] ,							a_    :Tuple=False)		->		Optional[Any]:
				if base_model:
								__a			:    List[Any]      							=	''''''
				else:
								__a			:    List[Any]      							=	'''mobilevit.'''

				for key in orig_state_dict.copy().keys():
								__a			:    List[Any]      							=	orig_state_dict.pop(lowerCAmelCase_)

								if key[:8] == "encoder.":
												__a			:    List[str]      							=	key[8:]

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

				return orig_state_dict

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

@torch.no_grad()
def      __A   (      a_    :Any ,							a_    :int ,							a_    :Any ,							a_    :List[Any]=False)		->		Any:
				__a			:    Any      							=	get_mobilevit_config(lowerCAmelCase_)

				# load original state_dict
				__a			:    Any      							=	torch.load(lowerCAmelCase_ ,							map_location='''cpu''')

				# load 🤗 model
				if mobilevit_name.startswith('''deeplabv3_'''):
								__a			:    List[Any]      							=	MobileViTForSemanticSegmentation(lowerCAmelCase_).eval()
				else:
								__a			:    Optional[int]      							=	MobileViTForImageClassification(lowerCAmelCase_).eval()

				__a			:    Any      							=	convert_state_dict(lowerCAmelCase_ ,							lowerCAmelCase_)
				model.load_state_dict(lowerCAmelCase_)

				# Check outputs on an image, prepared by MobileViTImageProcessor
				__a			:    Tuple      							=	MobileViTImageProcessor(crop_size=config.image_size ,							size=config.image_size + 32)
				__a			:    Optional[int]      							=	image_processor(images=prepare_img() ,							return_tensors='''pt''')
				__a			:    Dict      							=	model(**lowerCAmelCase_)
				__a			:    Union[str, Any]      							=	outputs.logits

				if mobilevit_name.startswith('''deeplabv3_'''):
								assert logits.shape == (1, 21, 32, 32)

								if mobilevit_name == "deeplabv3_mobilevit_s":
												__a			:    str      							=	torch.tensor(
												    [
												        [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
												        [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
												        [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
												    ])
								elif mobilevit_name == "deeplabv3_mobilevit_xs":
												__a			:    Tuple      							=	torch.tensor(
												    [
												        [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
												        [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
												        [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
												    ])
								elif mobilevit_name == "deeplabv3_mobilevit_xxs":
												__a			:    List[Any]      							=	torch.tensor(
												    [
												        [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
												        [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
												        [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
												    ])
								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, 10_00)

								if mobilevit_name == "mobilevit_s":
												__a			:    List[str]      							=	torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1])
								elif mobilevit_name == "mobilevit_xs":
												__a			:    Tuple      							=	torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7])
								elif mobilevit_name == "mobilevit_xxs":
												__a			:    Any      							=	torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3])
								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:
								__a			:    Dict      							=	{
								    '''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...''')
								__a			:    Dict      							=	model_mapping[mobilevit_name]
								image_processor.push_to_hub(lowerCAmelCase_ ,							organization='''apple''')
								model.push_to_hub(lowerCAmelCase_ ,							organization='''apple''')


if __name__ == "__main__":
		A			      =	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			      =	parser.parse_args()
		convert_movilevit_checkpoint(
		    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A					:				List[str]				=			{"""configuration_wavlm""": ["""WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """WavLMConfig"""]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _A					:				Dict				=			[
      """WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """WavLMForAudioFrameClassification""",
      """WavLMForCTC""",
      """WavLMForSequenceClassification""",
      """WavLMForXVector""",
      """WavLMModel""",
      """WavLMPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

  _A					:				List[Any]				=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
