
"""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()
lowerCamelCase__        =		logging.get_logger(__name__)
def    __lowerCAmelCase    (_UpperCamelCase		):
	__lowerCAmelCase      :						List[str]						      =				MobileViTConfig()

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

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

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

	return config
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase=False		):
	for i in range(1	,  6		):
		if F"layer_{i}." in name:
			__lowerCAmelCase      :						Tuple						      =				name.replace(F"layer_{i}."	,  F"encoder.layer.{i - 1}."		)

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

	if "exp_1x1" in name:
		__lowerCAmelCase      :						List[str]						      =				name.replace('exp_1x1'	,  'expand_1x1'		)
	if "red_1x1" in name:
		__lowerCAmelCase      :						str						      =				name.replace('red_1x1'	,  'reduce_1x1'		)
	if ".local_rep.conv_3x3." in name:
		__lowerCAmelCase      :						Optional[int]						      =				name.replace('.local_rep.conv_3x3.'	,  '.conv_kxk.'		)
	if ".local_rep.conv_1x1." in name:
		__lowerCAmelCase      :						Any						      =				name.replace('.local_rep.conv_1x1.'	,  '.conv_1x1.'		)
	if ".norm." in name:
		__lowerCAmelCase      :						Dict						      =				name.replace('.norm.'	,  '.normalization.'		)
	if ".conv." in name:
		__lowerCAmelCase      :						Dict						      =				name.replace('.conv.'	,  '.convolution.'		)
	if ".conv_proj." in name:
		__lowerCAmelCase      :						Union[str, 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:
				__lowerCAmelCase      :						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:
				__lowerCAmelCase      :						Tuple						      =				name.replace(F".{i}.{j}."	,  F".{i}."		)
				if "expand_1x1" in name:
					__lowerCAmelCase      :						Dict						      =				name.replace('expand_1x1'	,  'downsampling_layer.expand_1x1'		)
				if "conv_3x3" in name:
					__lowerCAmelCase      :						Any						      =				name.replace('conv_3x3'	,  'downsampling_layer.conv_3x3'		)
				if "reduce_1x1" in name:
					__lowerCAmelCase      :						Tuple						      =				name.replace('reduce_1x1'	,  'downsampling_layer.reduce_1x1'		)

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

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

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

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

	return name
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase=False		):
	if base_model:
		__lowerCAmelCase      :						Optional[Any]						      =				''
	else:
		__lowerCAmelCase      :						Dict						      =				'mobilevit.'

	for key in orig_state_dict.copy().keys():
		__lowerCAmelCase      :						Any						      =				orig_state_dict.pop(_UpperCamelCase		)

		if key[:8] == "encoder.":
			__lowerCAmelCase      :						Union[str, Any]						      =				key[8:]

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

	return orig_state_dict
def    __lowerCAmelCase    ():
	__lowerCAmelCase      :						str						      =				'http://images.cocodataset.org/val2017/000000039769.jpg'
	__lowerCAmelCase      :						Any						      =				Image.open(requests.get(_UpperCamelCase	,  stream=_UpperCamelCase		).raw		)
	return im
@torch.no_grad()
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase=False		):
	__lowerCAmelCase      :						Dict						      =				get_mobilevit_config(_UpperCamelCase		)

	# load original state_dict
	__lowerCAmelCase      :						Dict						      =				torch.load(_UpperCamelCase	,  map_location='cpu'		)

	# load 🤗 model
	if mobilevit_name.startswith('deeplabv3_'		):
		__lowerCAmelCase      :						List[str]						      =				MobileViTForSemanticSegmentation(_UpperCamelCase		).eval()
	else:
		__lowerCAmelCase      :						str						      =				MobileViTForImageClassification(_UpperCamelCase		).eval()

	__lowerCAmelCase      :						Optional[Any]						      =				convert_state_dict(_UpperCamelCase	,  _UpperCamelCase		)
	model.load_state_dict(_UpperCamelCase		)

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

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

		if mobilevit_name == "deeplabv3_mobilevit_s":
			__lowerCAmelCase      :						Any						      =				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":
			__lowerCAmelCase      :						List[Any]						      =				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":
			__lowerCAmelCase      :						Union[str, 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]	,  _UpperCamelCase	,  atol=1e-4		)
	else:
		assert logits.shape == (1, 1000)

		if mobilevit_name == "mobilevit_s":
			__lowerCAmelCase      :						Any						      =				torch.tensor([-0.9866, 0.2392, -1.1241]		)
		elif mobilevit_name == "mobilevit_xs":
			__lowerCAmelCase      :						Tuple						      =				torch.tensor([-2.4761, -0.9399, -1.9587]		)
		elif mobilevit_name == "mobilevit_xxs":
			__lowerCAmelCase      :						Optional[int]						      =				torch.tensor([-1.9364, -1.2327, -0.4653]		)
		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:
		__lowerCAmelCase      :						List[Any]						      =				{
		    '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...'		)
		__lowerCAmelCase      :						str						      =				model_mapping[mobilevit_name]
		image_processor.push_to_hub(_UpperCamelCase	,  organization='apple'		)
		model.push_to_hub(_UpperCamelCase	,  organization='apple'		)


if __name__ == "__main__":
							lowerCamelCase__        =		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."""
							)

							lowerCamelCase__        =		parser.parse_args()
							convert_movilevit_checkpoint(
							    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
							)

"""simple docstring"""







from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels