



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     SCREAMING_SNAKE_CASE(      __UpperCamelCase )	->						Tuple:
				a__	:							List[str]      						= MobileViTConfig()

				# size of the architecture
				if "mobilevit_s" in mobilevit_name:
								a__	:							List[str]      						= [1_44, 1_92, 2_40]
								a__	:							Dict      						= [16, 32, 64, 96, 1_28, 1_60, 6_40]
				elif "mobilevit_xs" in mobilevit_name:
								a__	:							Optional[int]      						= [96, 1_20, 1_44]
								a__	:							List[str]      						= [16, 32, 48, 64, 80, 96, 3_84]
				elif "mobilevit_xxs" in mobilevit_name:
								a__	:							Dict      						= [64, 80, 96]
								a__	:							List[str]      						= [16, 16, 24, 48, 64, 80, 3_20]
								a__	:							Optional[int]      						= 0.0_5
								a__	:							Tuple      						= 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__	:							Any      						= 10_00
								a__	:							Any      						= "imagenet-1k-id2label.json"

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

				return config






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase=False )	->						Any:
				for i in range(1	,				6 ):
								if F'layer_{i}.' in name:
												a__	:							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__	:							Optional[int]      						= name.replace(".block."	,				"." )

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

				for i in range(2	,				5 ):
								if F'.global_rep.{i}.weight' in name:
												a__	:							str      						= name.replace(F'.global_rep.{i}.weight'	,				".layernorm.weight" )
								if F'.global_rep.{i}.bias' in name:
												a__	:							Tuple      						= 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__	:							Any      						= name.replace(".pre_norm_mha.0."	,				".layernorm_before." )
				if ".pre_norm_mha.1.out_proj." in name:
								a__	:							Optional[int]      						= name.replace(".pre_norm_mha.1.out_proj."	,				".attention.output.dense." )
				if ".pre_norm_ffn.0." in name:
								a__	:							int      						= name.replace(".pre_norm_ffn.0."	,				".layernorm_after." )
				if ".pre_norm_ffn.1." in name:
								a__	:							List[str]      						= name.replace(".pre_norm_ffn.1."	,				".intermediate.dense." )
				if ".pre_norm_ffn.4." in name:
								a__	:							Dict      						= name.replace(".pre_norm_ffn.4."	,				".output.dense." )
				if ".transformer." in name:
								a__	:							Union[str, Any]      						= name.replace(".transformer."	,				".transformer.layer." )

				if ".aspp_layer." in name:
								a__	:							Any      						= name.replace(".aspp_layer."	,				"." )
				if ".aspp_pool." in name:
								a__	:							Union[str, Any]      						= name.replace(".aspp_pool."	,				"." )
				if "seg_head." in name:
								a__	:							Any      						= name.replace("seg_head."	,				"segmentation_head." )
				if "segmentation_head.classifier.classifier." in name:
								a__	:							Tuple      						= name.replace("segmentation_head.classifier.classifier."	,				"segmentation_head.classifier." )

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

				return name






def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=False )	->						str:
				if base_model:
								a__	:							int      						= ""
				else:
								a__	:							Dict      						= "mobilevit."

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

								if key[:8] == "encoder.":
												a__	:							Any      						= key[8:]

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

				return orig_state_dict






def     SCREAMING_SNAKE_CASE(      )	->						List[str]:
				a__	:							Optional[Any]      						= "http://images.cocodataset.org/val2017/000000039769.jpg"
				a__	:							Dict      						= Image.open(requests.get(__UpperCamelCase	,				stream=__UpperCamelCase ).raw )
				return im






@torch.no_grad()
def     SCREAMING_SNAKE_CASE(      __UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase	,				__UpperCamelCase=False )	->						Tuple:
				a__	:							Dict      						= get_mobilevit_config(__UpperCamelCase )

				# load original state_dict
				a__	:							Tuple      						= torch.load(__UpperCamelCase	,				map_location="cpu" )

				# load 🤗 model
				if mobilevit_name.startswith("deeplabv3_" ):
								a__	:							List[Any]      						= MobileViTForSemanticSegmentation(__UpperCamelCase ).eval()
				else:
								a__	:							Tuple      						= MobileViTForImageClassification(__UpperCamelCase ).eval()

				a__	:							Any      						= convert_state_dict(__UpperCamelCase	,				__UpperCamelCase )
				model.load_state_dict(__UpperCamelCase )

				# Check outputs on an image, prepared by MobileViTImageProcessor
				a__	:							Union[str, Any]      						= MobileViTImageProcessor(crop_size=config.image_size	,				size=config.image_size + 32 )
				a__	:							Optional[Any]      						= image_processor(images=prepare_img()	,				return_tensors="pt" )
				a__	:							Tuple      						= model(**__UpperCamelCase )
				a__	:							Dict      						= 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__	:							Optional[int]      						= 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__	:							Tuple      						= 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]	,				__UpperCamelCase	,				atol=1e-4 )
				else:
								assert logits.shape == (1, 10_00)

								if mobilevit_name == "mobilevit_s":
												a__	:							List[Any]      						= torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1] )
								elif mobilevit_name == "mobilevit_xs":
												a__	:							List[str]      						= torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7] )
								elif mobilevit_name == "mobilevit_xxs":
												a__	:							Tuple      						= 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]	,				__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:
								a__	:							Tuple      						= {
								    "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__	:							Any      						= 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
						)







import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 				_a						(       SCREAMING_SNAKE_CASE			):





							'''simple docstring'''

							A				:int =       ["image_processor", "tokenizer"]
							A				:Any =       "LayoutLMv3ImageProcessor"
							A				:str =       ("LayoutLMv3Tokenizer", "LayoutLMv3TokenizerFast")



							def __init__(							self ,					__UpperCAmelCase=None ,					__UpperCAmelCase=None ,					**__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							str      						= None
											if "feature_extractor" in kwargs:
															warnings.warn(
															    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
															    " instead." ,					__UpperCAmelCase ,					)
															a__	:							List[str]      						= kwargs.pop("feature_extractor"				)

											a__	:							Optional[int]      						= image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
															raise ValueError("You need to specify an `image_processor`."				)
											if tokenizer is None:
															raise ValueError("You need to specify a `tokenizer`."				)

											super().__init__(__UpperCAmelCase ,					__UpperCAmelCase				)



							def __call__(							self ,					__UpperCAmelCase ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = True ,					__UpperCAmelCase = False ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = 0 ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = None ,					__UpperCAmelCase = False ,					__UpperCAmelCase = False ,					__UpperCAmelCase = False ,					__UpperCAmelCase = False ,					__UpperCAmelCase = True ,					__UpperCAmelCase = None ,					**__UpperCAmelCase ,					):







											"""simple docstring"""



											if self.image_processor.apply_ocr and (boxes is not None):
															raise ValueError(
															    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."				)

											if self.image_processor.apply_ocr and (word_labels is not None):
															raise ValueError(
															    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."				)

											# first, apply the image processor
											a__	:							List[str]      						= self.image_processor(images=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase				)

											# second, apply the tokenizer
											if text is not None and self.image_processor.apply_ocr and text_pair is None:
															if isinstance(__UpperCAmelCase ,					__UpperCAmelCase				):
																			a__	:							Union[str, Any]      						= [text]  # add batch dimension (as the image processor always adds a batch dimension)
															a__	:							Optional[Any]      						= features["words"]

											a__	:							Union[str, Any]      						= self.tokenizer(
											    text=text if text is not None else features["words"] ,					text_pair=text_pair if text_pair is not None else None ,					boxes=boxes if boxes is not None else features["boxes"] ,					word_labels=__UpperCAmelCase ,					add_special_tokens=__UpperCAmelCase ,					padding=__UpperCAmelCase ,					truncation=__UpperCAmelCase ,					max_length=__UpperCAmelCase ,					stride=__UpperCAmelCase ,					pad_to_multiple_of=__UpperCAmelCase ,					return_token_type_ids=__UpperCAmelCase ,					return_attention_mask=__UpperCAmelCase ,					return_overflowing_tokens=__UpperCAmelCase ,					return_special_tokens_mask=__UpperCAmelCase ,					return_offsets_mapping=__UpperCAmelCase ,					return_length=__UpperCAmelCase ,					verbose=__UpperCAmelCase ,					return_tensors=__UpperCAmelCase ,					**__UpperCAmelCase ,					)

											# add pixel values
											a__	:							Dict      						= features.pop("pixel_values"				)
											if return_overflowing_tokens is True:
															a__	:							str      						= self.get_overflowing_images(__UpperCAmelCase ,					encoded_inputs["overflow_to_sample_mapping"]				)
											a__	:							Tuple      						= images

											return encoded_inputs



							def 		_A	(							self ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Any      						= []
											for sample_idx in overflow_to_sample_mapping:
															images_with_overflow.append(images[sample_idx]				)

											if len(__UpperCAmelCase				) != len(__UpperCAmelCase				):
															raise ValueError(
															    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
															    f' {len(__UpperCAmelCase				)} and {len(__UpperCAmelCase				)}'				)

											return images_with_overflow



							def 		_A	(							self ,					*__UpperCAmelCase ,					**__UpperCAmelCase				):







											"""simple docstring"""



											return self.tokenizer.batch_decode(*__UpperCAmelCase ,					**__UpperCAmelCase				)



							def 		_A	(							self ,					*__UpperCAmelCase ,					**__UpperCAmelCase				):







											"""simple docstring"""



											return self.tokenizer.decode(*__UpperCAmelCase ,					**__UpperCAmelCase				)



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											return ["input_ids", "bbox", "attention_mask", "pixel_values"]



							@property
							def 		_A	(							self				):







											"""simple docstring"""



											warnings.warn(
											    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." ,					__UpperCAmelCase ,					)
											return self.image_processor_class




							@property
							def 		_A	(							self				):







											"""simple docstring"""



											warnings.warn(
											    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." ,					__UpperCAmelCase ,					)
											return self.image_processor


