



'''simple docstring'''


import collections
import importlib.util
import os
import re
from pathlib import Path


lowercase_      :				Union[str, Any]   					=    '''src/transformers'''


# Matches is_xxx_available()
lowercase_      :				Tuple   					=    re.compile(r'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
lowercase_      :				str   					=    re.compile(r'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowercase_      :				List[Any]   					=    re.compile(r'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
lowercase_      :				Dict   					=    re.compile(r'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
lowercase_      :				Dict   					=    re.compile(r'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowercase_      :				str   					=    re.compile(r'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma:     "MyModel",
lowercase_      :				List[Any]   					=    re.compile('''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only:    ["foo", "bar"],
lowercase_      :				Tuple   					=    re.compile('''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
lowercase_      :				Tuple   					=    re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
lowercase_      :				str   					=    re.compile(r'''^\s*try:''')
# Catches a line with else:
lowercase_      :				str   					=    re.compile(r'''^\s*else:''')







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str						):
						if _re_test_backend.search(lowercase_						) is None:
												return None
						lowercase        =							[b[0] for b in _re_backend.findall(lowercase_						)]
						backends.sort()
						return "_and_".join(lowercase_						)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      int						):
						with open(lowercase_ ,  """r""" ,  encoding="""utf-8""" ,  newline="""\n"""						) as f:
												lowercase        =							f.readlines()

						lowercase        =							0
						while line_index < len(lowercase_						) and not lines[line_index].startswith("""_import_structure = {"""						):
												line_index += 1

						# If this is a traditional init, just return.
						if line_index >= len(lowercase_						):
												return None

						# First grab the objects without a specific backend in _import_structure
						lowercase        =							[]
						while not lines[line_index].startswith("""if TYPE_CHECKING"""						) and find_backend(lines[line_index]						) is None:
												lowercase        =							lines[line_index]
												# If we have everything on a single line, let's deal with it.
												if _re_one_line_import_struct.search(lowercase_						):
																		lowercase        =							_re_one_line_import_struct.search(lowercase_						).groups()[0]
																		lowercase        =							re.findall("""\[([^\]]+)\]""" ,  lowercase_						)
																		for imp in imports:
																								objects.extend([obj[1:-1] for obj in imp.split(""", """						)]						)
																		line_index += 1
																		continue
												lowercase        =							_re_import_struct_key_value.search(lowercase_						)
												if single_line_import_search is not None:
																		lowercase        =							[obj[1:-1] for obj in single_line_import_search.groups()[0].split(""", """						) if len(lowercase_						) > 0]
																		objects.extend(lowercase_						)
												elif line.startswith(""" """ * 8 + """\""""						):
																		objects.append(line[9:-3]						)
												line_index += 1

						lowercase        =							{"""none""": objects}
						# Let's continue with backend-specific objects in _import_structure
						while not lines[line_index].startswith("""if TYPE_CHECKING"""						):
												# If the line is an if not is_backend_available, we grab all objects associated.
												lowercase        =							find_backend(lines[line_index]						)
												# Check if the backend declaration is inside a try block:
												if _re_try.search(lines[line_index - 1]						) is None:
																		lowercase        =							None

												if backend is not None:
																		line_index += 1

																		# Scroll until we hit the else block of try-except-else
																		while _re_else.search(lines[line_index]						) is None:
																								line_index += 1

																		line_index += 1

																		lowercase        =							[]
																		# Until we unindent, add backend objects to the list
																		while len(lines[line_index]						) <= 1 or lines[line_index].startswith(""" """ * 4						):
																								lowercase        =							lines[line_index]
																								if _re_import_struct_add_one.search(lowercase_						) is not None:
																														objects.append(_re_import_struct_add_one.search(lowercase_						).groups()[0]						)
																								elif _re_import_struct_add_many.search(lowercase_						) is not None:
																														lowercase        =							_re_import_struct_add_many.search(lowercase_						).groups()[0].split(""", """						)
																														lowercase        =							[obj[1:-1] for obj in imports if len(lowercase_						) > 0]
																														objects.extend(lowercase_						)
																								elif _re_between_brackets.search(lowercase_						) is not None:
																														lowercase        =							_re_between_brackets.search(lowercase_						).groups()[0].split(""", """						)
																														lowercase        =							[obj[1:-1] for obj in imports if len(lowercase_						) > 0]
																														objects.extend(lowercase_						)
																								elif _re_quote_object.search(lowercase_						) is not None:
																														objects.append(_re_quote_object.search(lowercase_						).groups()[0]						)
																								elif line.startswith(""" """ * 8 + """\""""						):
																														objects.append(line[9:-3]						)
																								elif line.startswith(""" """ * 12 + """\""""						):
																														objects.append(line[13:-3]						)
																								line_index += 1

																		lowercase        =							objects
												else:
																		line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
						lowercase        =							[]
						while (
						    line_index < len(lowercase_						)
						    and find_backend(lines[line_index]						) is None
						    and not lines[line_index].startswith("""else"""						)
						):
												lowercase        =							lines[line_index]
												lowercase        =							_re_import.search(lowercase_						)
												if single_line_import_search is not None:
																		objects.extend(single_line_import_search.groups()[0].split(""", """						)						)
												elif line.startswith(""" """ * 8						):
																		objects.append(line[8:-2]						)
												line_index += 1

						lowercase        =							{"""none""": objects}
						# Let's continue with backend-specific objects
						while line_index < len(lowercase_						):
												# If the line is an if is_backend_available, we grab all objects associated.
												lowercase        =							find_backend(lines[line_index]						)
												# Check if the backend declaration is inside a try block:
												if _re_try.search(lines[line_index - 1]						) is None:
																		lowercase        =							None

												if backend is not None:
																		line_index += 1

																		# Scroll until we hit the else block of try-except-else
																		while _re_else.search(lines[line_index]						) is None:
																								line_index += 1

																		line_index += 1

																		lowercase        =							[]
																		# Until we unindent, add backend objects to the list
																		while len(lines[line_index]						) <= 1 or lines[line_index].startswith(""" """ * 8						):
																								lowercase        =							lines[line_index]
																								lowercase        =							_re_import.search(lowercase_						)
																								if single_line_import_search is not None:
																														objects.extend(single_line_import_search.groups()[0].split(""", """						)						)
																								elif line.startswith(""" """ * 12						):
																														objects.append(line[12:-2]						)
																								line_index += 1

																		lowercase        =							objects
												else:
																		line_index += 1

						return import_dict_objects, type_hint_objects







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[Any] ,  lowercase_    :      str						):

						def find_duplicates(lowercase_    :      List[str]						):
												return [k for k, v in collections.Counter(lowercase_						).items() if v > 1]

						if list(import_dict_objects.keys()						) != list(type_hint_objects.keys()						):
												return ["Both sides of the init do not have the same backends!"]

						lowercase        =							[]
						for key in import_dict_objects.keys():
												lowercase        =							find_duplicates(import_dict_objects[key]						)
												if duplicate_imports:
																		errors.append(F"""Duplicate _import_structure definitions for: {duplicate_imports}"""						)
												lowercase        =							find_duplicates(type_hint_objects[key]						)
												if duplicate_type_hints:
																		errors.append(F"""Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}"""						)

												if sorted(set(import_dict_objects[key]						)						) != sorted(set(type_hint_objects[key]						)						):
																		lowercase        =							"""base imports""" if key == """none""" else F"""{key} backend"""
																		errors.append(F"""Differences for {name}:"""						)
																		for a in type_hint_objects[key]:
																								if a not in import_dict_objects[key]:
																														errors.append(F"""  {a} in TYPE_HINT but not in _import_structure."""						)
																		for a in import_dict_objects[key]:
																								if a not in type_hint_objects[key]:
																														errors.append(F"""  {a} in _import_structure but not in TYPE_HINT."""						)
						return errors







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							[]
						for root, _, files in os.walk(lowercase_						):
												if "__init__.py" in files:
																		lowercase        =							os.path.join(lowercase_ ,  """__init__.py"""						)
																		lowercase        =							parse_init(lowercase_						)
																		if objects is not None:
																								lowercase        =							analyze_results(*lowercase_						)
																								if len(lowercase_						) > 0:
																														lowercase        =							F"""Problem in {fname}, both halves do not define the same objects.\n{errors[0]}"""
																														failures.append("""\n""".join(lowercase_						)						)
						if len(lowercase_						) > 0:
												raise ValueError("""\n\n""".join(lowercase_						)						)







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							[]
						for path, directories, files in os.walk(lowercase_						):
												for folder in directories:
																		# Ignore private modules
																		if folder.startswith("""_"""						):
																								directories.remove(lowercase_						)
																								continue
																		# Ignore leftovers from branches (empty folders apart from pycache)
																		if len(list((Path(lowercase_						) / folder).glob("""*.py"""						)						)						) == 0:
																								continue
																		lowercase        =							str((Path(lowercase_						) / folder).relative_to(lowercase_						)						)
																		lowercase        =							short_path.replace(os.path.sep ,  """."""						)
																		submodules.append(lowercase_						)
												for fname in files:
																		if fname == "__init__.py":
																								continue
																		lowercase        =							str((Path(lowercase_						) / fname).relative_to(lowercase_						)						)
																		lowercase        =							short_path.replace(""".py""" ,  """"""						).replace(os.path.sep ,  """."""						)
																		if len(submodule.split("""."""						)						) == 1:
																								submodules.append(lowercase_						)
						return submodules


lowercase_      :				Tuple   					=    [
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
]







def      SCREAMING_SNAKE_CASE				(				):
						# This is to make sure the transformers module imported is the one in the repo.
						lowercase        =							importlib.util.spec_from_file_location(
						    """transformers""" ,  os.path.join(lowercase_ ,  """__init__.py"""						) ,  submodule_search_locations=[PATH_TO_TRANSFORMERS] ,  )
						lowercase        =							spec.loader.load_module()

						lowercase        =							[
						    module
						    for module in get_transformers_submodules()
						    if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
						]
						if len(lowercase_						) > 0:
												lowercase        =							"""\n""".join(F"""- {module}""" for module in module_not_registered						)
												raise ValueError(
												    """The following submodules are not properly registered in the main init of Transformers:\n"""
												    F"""{list_of_modules}\n"""
												    """Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."""						)


if __name__ == "__main__":
						check_all_inits()
						check_submodules()







'''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()
lowercase_      :				List[str]   					=    logging.get_logger(__name__)







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      int						):
						lowercase        =							MobileViTConfig()

						# size of the architecture
						if "mobilevit_s" in mobilevit_name:
												lowercase        =							[144, 192, 240]
												lowercase        =							[16, 32, 64, 96, 128, 160, 640]
						elif "mobilevit_xs" in mobilevit_name:
												lowercase        =							[96, 120, 144]
												lowercase        =							[16, 32, 48, 64, 80, 96, 384]
						elif "mobilevit_xxs" in mobilevit_name:
												lowercase        =							[64, 80, 96]
												lowercase        =							[16, 16, 24, 48, 64, 80, 320]
												lowercase        =							0.05
												lowercase        =							2.0

						if mobilevit_name.startswith("""deeplabv3_"""						):
												lowercase        =							512
												lowercase        =							16
												lowercase        =							21
												lowercase        =							"""pascal-voc-id2label.json"""
						else:
												lowercase        =							1000
												lowercase        =							"""imagenet-1k-id2label.json"""

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

						return config







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Any ,  lowercase_    :      Any=False						):
						for i in range(1 ,  6						):
												if F"""layer_{i}.""" in name:
																		lowercase        =							name.replace(F"""layer_{i}.""" ,  F"""encoder.layer.{i - 1}."""						)

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

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

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

						for i in range(2 ,  5						):
												if F""".global_rep.{i}.weight""" in name:
																		lowercase        =							name.replace(F""".global_rep.{i}.weight""" ,  """.layernorm.weight"""						)
												if F""".global_rep.{i}.bias""" in name:
																		lowercase        =							name.replace(F""".global_rep.{i}.bias""" ,  """.layernorm.bias"""						)

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

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

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

						return name







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Optional[Any] ,  lowercase_    :      List[Any] ,  lowercase_    :      str=False						):
						if base_model:
												lowercase        =							""""""
						else:
												lowercase        =							"""mobilevit."""

						for key in orig_state_dict.copy().keys():
												lowercase        =							orig_state_dict.pop(lowercase_						)

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

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

						return orig_state_dict







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						lowercase        =							Image.open(requests.get(lowercase_ ,  stream=lowercase_						).raw						)
						return im







@torch.no_grad()
def      SCREAMING_SNAKE_CASE				(				lowercase_    :      Dict ,  lowercase_    :      List[Any] ,  lowercase_    :      Any ,  lowercase_    :      List[str]=False						):
						lowercase        =							get_mobilevit_config(lowercase_						)

						# load original state_dict
						lowercase        =							torch.load(lowercase_ ,  map_location="""cpu"""						)

						# load 🤗 model
						if mobilevit_name.startswith("""deeplabv3_"""						):
												lowercase        =							MobileViTForSemanticSegmentation(lowercase_						).eval()
						else:
												lowercase        =							MobileViTForImageClassification(lowercase_						).eval()

						lowercase        =							convert_state_dict(lowercase_ ,  lowercase_						)
						model.load_state_dict(lowercase_						)

						# Check outputs on an image, prepared by MobileViTImageProcessor
						lowercase        =							MobileViTImageProcessor(crop_size=config.image_size ,  size=config.image_size + 32						)
						lowercase        =							image_processor(images=prepare_img() ,  return_tensors="""pt"""						)
						lowercase        =							model(**lowercase_						)
						lowercase        =							outputs.logits

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

												if mobilevit_name == "deeplabv3_mobilevit_s":
																		lowercase        =							torch.tensor(
																		    [
																		        [[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
																		        [[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
																		        [[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
																		    ]						)
												elif mobilevit_name == "deeplabv3_mobilevit_xs":
																		lowercase        =							torch.tensor(
																		    [
																		        [[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
																		        [[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
																		        [[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
																		    ]						)
												elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																		lowercase        =							torch.tensor(
																		    [
																		        [[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
																		        [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
																		        [[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
																		    ]						)
												else:
																		raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""						)

												assert torch.allclose(logits[0, :3, :3, :3] ,  lowercase_ ,  atol=1E-4						)
						else:
												assert logits.shape == (1, 1000)

												if mobilevit_name == "mobilevit_s":
																		lowercase        =							torch.tensor([-0.9_866, 0.2_392, -1.1_241]						)
												elif mobilevit_name == "mobilevit_xs":
																		lowercase        =							torch.tensor([-2.4_761, -0.9_399, -1.9_587]						)
												elif mobilevit_name == "mobilevit_xxs":
																		lowercase        =							torch.tensor([-1.9_364, -1.2_327, -0.4_653]						)
												else:
																		raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""						)

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

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

						if push_to_hub:
												lowercase        =							{
												    """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..."""						)
												lowercase        =							model_mapping[mobilevit_name]
												image_processor.push_to_hub(lowercase_ ,  organization="""apple"""						)
												model.push_to_hub(lowercase_ ,  organization="""apple"""						)


if __name__ == "__main__":
						lowercase_      :				List[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.'''
						)

						lowercase_      :				List[str]   					=    parser.parse_args()
						convert_movilevit_checkpoint(
						    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)


