






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()
UpperCAmelCase_ :						Optional[Any]      =      logging.get_logger(__name__)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[int]					)		->			Union[str, Any]:






	"""simple docstring"""



	UpperCamelCase     :List[str]          =	MobileViTConfig()

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

	if mobilevit_name.startswith("""deeplabv3_"""					):
		UpperCamelCase     :str          =	512
		UpperCamelCase     :int          =	16
		UpperCamelCase     :List[str]          =	21
		UpperCamelCase     :Tuple          =	"""pascal-voc-id2label.json"""
	else:
		UpperCamelCase     :Optional[int]          =	1000
		UpperCamelCase     :Any          =	"""imagenet-1k-id2label.json"""

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

	return config

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[int]	,			__magic_name__					:  List[str]=False					)		->			Optional[Any]:






	"""simple docstring"""



	for i in range(1	,			6					):
		if f"""layer_{i}.""" in name:
			UpperCamelCase     :Optional[int]          =	name.replace(f"""layer_{i}."""	,			f"""encoder.layer.{i - 1}."""					)

	if "conv_1." in name:
		UpperCamelCase     :Tuple          =	name.replace("""conv_1."""	,			"""conv_stem."""					)
	if ".block." in name:
		UpperCamelCase     :Any          =	name.replace(""".block."""	,			"""."""					)

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

	for i in range(0	,			2					):
		for j in range(0	,			4					):
			if f""".{i}.{j}.""" in name:
				UpperCamelCase     :Any          =	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:
				UpperCamelCase     :str          =	name.replace(f""".{i}.{j}."""	,			f""".{i}."""					)
				if "expand_1x1" in name:
					UpperCamelCase     :Any          =	name.replace("""expand_1x1"""	,			"""downsampling_layer.expand_1x1"""					)
				if "conv_3x3" in name:
					UpperCamelCase     :Any          =	name.replace("""conv_3x3"""	,			"""downsampling_layer.conv_3x3"""					)
				if "reduce_1x1" in name:
					UpperCamelCase     :List[str]          =	name.replace("""reduce_1x1"""	,			"""downsampling_layer.reduce_1x1"""					)

	for i in range(2	,			5					):
		if f""".global_rep.{i}.weight""" in name:
			UpperCamelCase     :Tuple          =	name.replace(f""".global_rep.{i}.weight"""	,			""".layernorm.weight"""					)
		if f""".global_rep.{i}.bias""" in name:
			UpperCamelCase     :str          =	name.replace(f""".global_rep.{i}.bias"""	,			""".layernorm.bias"""					)

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

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

	if "classifier.fc." in name:
		UpperCamelCase     :Optional[int]          =	name.replace("""classifier.fc."""	,			"""classifier."""					)
	elif (not base_model) and ("segmentation_head." not in name):
		UpperCamelCase     :Any          =	"""mobilevit.""" + name

	return name

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  str	,			__magic_name__					:  int	,			__magic_name__					:  str=False					)		->			Optional[Any]:






	"""simple docstring"""



	if base_model:
		UpperCamelCase     :Tuple          =	""""""
	else:
		UpperCamelCase     :str          =	"""mobilevit."""

	for key in orig_state_dict.copy().keys():
		UpperCamelCase     :Any          =	orig_state_dict.pop(__magic_name__					)

		if key[:8] == "encoder.":
			UpperCamelCase     :Tuple          =	key[8:]

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

	return orig_state_dict

def    SCREAMING_SNAKE_CASE_							(							)		->			List[str]:






	"""simple docstring"""



	UpperCamelCase     :Optional[int]          =	"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	UpperCamelCase     :Dict          =	Image.open(requests.get(__magic_name__	,			stream=__magic_name__					).raw					)
	return im

@torch.no_grad()
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[str]	,			__magic_name__					:  Optional[int]	,			__magic_name__					:  List[str]	,			__magic_name__					:  Optional[int]=False					)		->			int:






	"""simple docstring"""



	UpperCamelCase     :Optional[int]          =	get_mobilevit_config(__magic_name__					)

	# load original state_dict
	UpperCamelCase     :Optional[Any]          =	torch.load(__magic_name__	,			map_location="""cpu"""					)

	# load 🤗 model
	if mobilevit_name.startswith("""deeplabv3_"""					):
		UpperCamelCase     :Union[str, Any]          =	MobileViTForSemanticSegmentation(__magic_name__					).eval()
	else:
		UpperCamelCase     :Optional[int]          =	MobileViTForImageClassification(__magic_name__					).eval()

	UpperCamelCase     :List[str]          =	convert_state_dict(__magic_name__	,			__magic_name__					)
	model.load_state_dict(__magic_name__					)

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

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

		if mobilevit_name == "deeplabv3_mobilevit_s":
			UpperCamelCase     :Optional[int]          =	torch.tensor(
			    [
			        [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
			        [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
			        [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
			    ]					)
		elif mobilevit_name == "deeplabv3_mobilevit_xs":
			UpperCamelCase     :Tuple          =	torch.tensor(
			    [
			        [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
			        [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
			        [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
			    ]					)
		elif mobilevit_name == "deeplabv3_mobilevit_xxs":
			UpperCamelCase     :Optional[Any]          =	torch.tensor(
			    [
			        [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
			        [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
			        [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
			    ]					)
		else:
			raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}"""					)

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

		if mobilevit_name == "mobilevit_s":
			UpperCamelCase     :str          =	torch.tensor([-0.9866, 0.2392, -1.1241]					)
		elif mobilevit_name == "mobilevit_xs":
			UpperCamelCase     :int          =	torch.tensor([-2.4761, -0.9399, -1.9587]					)
		elif mobilevit_name == "mobilevit_xxs":
			UpperCamelCase     :Tuple          =	torch.tensor([-1.9364, -1.2327, -0.4653]					)
		else:
			raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}"""					)

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

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

	if push_to_hub:
		UpperCamelCase     :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..."""					)
		UpperCamelCase     :Dict          =	model_mapping[mobilevit_name]
		image_processor.push_to_hub(__magic_name__	,			organization="""apple"""					)
		model.push_to_hub(__magic_name__	,			organization="""apple"""					)


if __name__ == "__main__":
	UpperCAmelCase_ :						Tuple      =      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.'''
	)

	UpperCAmelCase_ :						int      =      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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_ :						str      =      {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Tuple      =      [
	    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''OPTForCausalLM''',
	    '''OPTModel''',
	    '''OPTPreTrainedModel''',
	    '''OPTForSequenceClassification''',
	    '''OPTForQuestionAnswering''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Any      =      ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase_ :						Optional[Any]      =      [
	    '''FlaxOPTForCausalLM''',
	    '''FlaxOPTModel''',
	    '''FlaxOPTPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_opt import (
		    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OPTForCausalLM,
		    OPTForQuestionAnswering,
		    OPTForSequenceClassification,
		    OPTModel,
		    OPTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
	import sys

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