

'''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()
_UpperCAmelCase	:     Any		       = logging.get_logger(__name__)





def       __lowerCamelCase			(				UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					MobileViTConfig()

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

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

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

							return config





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__=False   ):







							'''simple docstring'''






							for i in range(1     ,							6   ):
														if F'''layer_{i}.''' in name:
																					snake_case_           =					name.replace(F'''layer_{i}.'''     ,							F'''encoder.layer.{i - 1}.'''   )

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

							if "exp_1x1" in name:
														snake_case_           =					name.replace('exp_1x1'     ,							'expand_1x1'   )
							if "red_1x1" in name:
														snake_case_           =					name.replace('red_1x1'     ,							'reduce_1x1'   )
							if ".local_rep.conv_3x3." in name:
														snake_case_           =					name.replace('.local_rep.conv_3x3.'     ,							'.conv_kxk.'   )
							if ".local_rep.conv_1x1." in name:
														snake_case_           =					name.replace('.local_rep.conv_1x1.'     ,							'.conv_1x1.'   )
							if ".norm." in name:
														snake_case_           =					name.replace('.norm.'     ,							'.normalization.'   )
							if ".conv." in name:
														snake_case_           =					name.replace('.conv.'     ,							'.convolution.'   )
							if ".conv_proj." in name:
														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:
																												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:
																												snake_case_           =					name.replace(F'''.{i}.{j}.'''     ,							F'''.{i}.'''   )
																												if "expand_1x1" in name:
																																			snake_case_           =					name.replace('expand_1x1'     ,							'downsampling_layer.expand_1x1'   )
																												if "conv_3x3" in name:
																																			snake_case_           =					name.replace('conv_3x3'     ,							'downsampling_layer.conv_3x3'   )
																												if "reduce_1x1" in name:
																																			snake_case_           =					name.replace('reduce_1x1'     ,							'downsampling_layer.reduce_1x1'   )

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

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

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

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

							return name





def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=False   ):







							'''simple docstring'''






							if base_model:
														snake_case_           =					''
							else:
														snake_case_           =					'mobilevit.'

							for key in orig_state_dict.copy().keys():
														snake_case_           =					orig_state_dict.pop(UpperCamelCase__   )

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

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

							return orig_state_dict





def       __lowerCamelCase			(				):







							'''simple docstring'''






							snake_case_           =					'http://images.cocodataset.org/val2017/000000039769.jpg'
							snake_case_           =					Image.open(requests.get(UpperCamelCase__     ,							stream=UpperCamelCase__   ).raw   )
							return im





@torch.no_grad()
def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__     ,							UpperCamelCase__=False   ):







							'''simple docstring'''






							snake_case_           =					get_mobilevit_config(UpperCamelCase__   )

							# load original state_dict
							snake_case_           =					torch.load(UpperCamelCase__     ,							map_location='cpu'   )

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

							snake_case_           =					convert_state_dict(UpperCamelCase__     ,							UpperCamelCase__   )
							model.load_state_dict(UpperCamelCase__   )

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

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

														if mobilevit_name == "deeplabv3_mobilevit_s":
																					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":
																					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":
																					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]     ,							UpperCamelCase__     ,							atol=1E-4   )
							else:
														assert logits.shape == (1, 1000)

														if mobilevit_name == "mobilevit_s":
																					snake_case_           =					torch.tensor([-0.98_66, 0.23_92, -1.12_41]   )
														elif mobilevit_name == "mobilevit_xs":
																					snake_case_           =					torch.tensor([-2.47_61, -0.93_99, -1.95_87]   )
														elif mobilevit_name == "mobilevit_xxs":
																					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]     ,							UpperCamelCase__     ,							atol=1E-4   )

							Path(UpperCamelCase__   ).mkdir(exist_ok=UpperCamelCase__   )
							print(F'''Saving model {mobilevit_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 push_to_hub:
														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...'   )
														snake_case_           =					model_mapping[mobilevit_name]
														image_processor.push_to_hub(UpperCamelCase__     ,							organization='apple'   )
														model.push_to_hub(UpperCamelCase__     ,							organization='apple'   )


if __name__ == "__main__":
					_UpperCAmelCase	:     Optional[Any]		       = 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	:     Optional[Any]		       = 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_torch_available


_UpperCAmelCase	:     Any		       = {
    """configuration_longt5""": ["""LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LongT5Config""", """LongT5OnnxConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase	:     Any		       = [
					    """LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """LongT5EncoderModel""",
					    """LongT5ForConditionalGeneration""",
					    """LongT5Model""",
					    """LongT5PreTrainedModel""",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase	:     Optional[int]		       = [
					    """FlaxLongT5ForConditionalGeneration""",
					    """FlaxLongT5Model""",
					    """FlaxLongT5PreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_longta import (
										    LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LongTaEncoderModel,
										    LongTaForConditionalGeneration,
										    LongTaModel,
										    LongTaPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_longta import (
										    FlaxLongTaForConditionalGeneration,
										    FlaxLongTaModel,
										    FlaxLongTaPreTrainedModel,
										)


else:
					import sys

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




