



"""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()
__SCREAMING_SNAKE_CASE  :						Tuple    							=      logging.get_logger(__name__)



def        lowerCAmelCase_(   lowercase_     :							Optional[Any]							)	->       int:
				_lowerCamelCase        =				MobileViTConfig()

				# size of the architecture
				if "mobilevit_s" in mobilevit_name:
								_lowerCamelCase        =				[1_44, 1_92, 2_40]
								_lowerCamelCase        =				[16, 32, 64, 96, 1_28, 1_60, 6_40]
				elif "mobilevit_xs" in mobilevit_name:
								_lowerCamelCase        =				[96, 1_20, 1_44]
								_lowerCamelCase        =				[16, 32, 48, 64, 80, 96, 3_84]
				elif "mobilevit_xxs" in mobilevit_name:
								_lowerCamelCase        =				[64, 80, 96]
								_lowerCamelCase        =				[16, 16, 24, 48, 64, 80, 3_20]
								_lowerCamelCase        =				0.0_5
								_lowerCamelCase        =				2.0

				if mobilevit_name.startswith('''deeplabv3_'''							):
								_lowerCamelCase        =				5_12
								_lowerCamelCase        =				16
								_lowerCamelCase        =				21
								_lowerCamelCase        =				"""pascal-voc-id2label.json"""
				else:
								_lowerCamelCase        =				10_00
								_lowerCamelCase        =				"""imagenet-1k-id2label.json"""

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

				return config



def        lowerCAmelCase_(   lowercase_     :							str						,   lowercase_     :							Optional[Any]=False							)	->       Optional[int]:
				for i in range(1						,   6							):
								if F"""layer_{i}.""" in name:
												_lowerCamelCase        =				name.replace(F"""layer_{i}."""						,   F"""encoder.layer.{i - 1}."""							)

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

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

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

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

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

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

				return name



def        lowerCAmelCase_(   lowercase_     :							int						,   lowercase_     :							Union[str, Any]						,   lowercase_     :							str=False							)	->       Optional[Any]:
				if base_model:
								_lowerCamelCase        =				""""""
				else:
								_lowerCamelCase        =				"""mobilevit."""

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

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

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

				return orig_state_dict



def        lowerCAmelCase_(   )	->       int:
				_lowerCamelCase        =				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				_lowerCamelCase        =				Image.open(requests.get(UpperCamelCase__						,   stream=UpperCamelCase__							).raw							)
				return im



@torch.no_grad()
def        lowerCAmelCase_(   lowercase_     :							str						,   lowercase_     :							Optional[Any]						,   lowercase_     :							Union[str, Any]						,   lowercase_     :							int=False							)	->       str:
				_lowerCamelCase        =				get_mobilevit_config(UpperCamelCase__							)

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

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

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

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

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

								if mobilevit_name == "deeplabv3_mobilevit_s":
												_lowerCamelCase        =				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":
												_lowerCamelCase        =				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":
												_lowerCamelCase        =				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]],
												        [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.8_6_2_4, -9.5_9_6_4], [-10.88_40, -10.81_58, -10.66_59]],
												        [[-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":
												_lowerCamelCase        =				torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1]							)
								elif mobilevit_name == "mobilevit_xs":
												_lowerCamelCase        =				torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7]							)
								elif mobilevit_name == "mobilevit_xxs":
												_lowerCamelCase        =				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:
								_lowerCamelCase        =				{
								    """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        =				model_mapping[mobilevit_name]
								image_processor.push_to_hub(UpperCamelCase__						,   organization='''apple'''							)
								model.push_to_hub(UpperCamelCase__						,   organization='''apple'''							)


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE  :						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.'''
			)

			__SCREAMING_SNAKE_CASE  :						int    							=      parser.parse_args()
			convert_movilevit_checkpoint(
			    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
			)





"""simple docstring"""
import numpy as np



def        lowerCAmelCase_(   lowercase_     :							np.ndarray						,   lowercase_     :							np.ndarray						,   lowercase_     :							float = 1e-12						,   lowercase_     :							int = 1_00						,   )	->       tuple[float, np.ndarray]:
				assert np.shape(lowercase_							)[0] == np.shape(lowercase_							)[1]
				# Ensure proper dimensionality.
				assert np.shape(lowercase_							)[0] == np.shape(lowercase_							)[0]
				# Ensure inputs are either both complex or both real
				assert np.iscomplexobj(lowercase_							) == np.iscomplexobj(lowercase_							)
				_lowerCamelCase        =				np.iscomplexobj(lowercase_							)
				if is_complex:
								# Ensure complex input_matrix is Hermitian
								assert np.array_equal(lowercase_						,   input_matrix.conj().T							)

				# Set convergence to False. Will define convergence when we exceed max_iterations
				# or when we have small changes from one iteration to next.

				_lowerCamelCase        =				False
				_lowerCamelCase        =				0
				_lowerCamelCase        =				0
				_lowerCamelCase        =				1e12

				while not convergence:
								# Multiple matrix by the vector.
								_lowerCamelCase        =				np.dot(lowercase_						,   lowercase_							)
								# Normalize the resulting output vector.
								_lowerCamelCase        =				w / np.linalg.norm(lowercase_							)
								# Find rayleigh quotient
								# (faster than usual b/c we know vector is normalized already)
								_lowerCamelCase        =				vector.conj().T if is_complex else vector.T
								_lowerCamelCase        =				np.dot(lowercase_						,   np.dot(lowercase_						,   lowercase_							)							)

								# Check convergence.
								_lowerCamelCase        =				np.abs(lambda_ - lambda_previous							) / lambda_
								iterations += 1

								if error <= error_tol or iterations >= max_iterations:
												_lowerCamelCase        =				True

								_lowerCamelCase        =				lambda_

				if is_complex:
								_lowerCamelCase        =				np.real(lambda_							)

				return lambda_, vector



def        lowerCAmelCase_(   )	->       None:
				_lowerCamelCase        =				np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]							)
				_lowerCamelCase        =				np.array([41, 4, 20]							)
				_lowerCamelCase        =				real_input_matrix.astype(np.complexaaa							)
				_lowerCamelCase        =				np.triu(1j * complex_input_matrix						,   1							)
				complex_input_matrix += imag_matrix
				complex_input_matrix += -1 * imag_matrix.T
				_lowerCamelCase        =				np.array([41, 4, 20]							).astype(np.complexaaa							)

				for problem_type in ["real", "complex"]:
								if problem_type == "real":
												_lowerCamelCase        =				real_input_matrix
												_lowerCamelCase        =				real_vector
								elif problem_type == "complex":
												_lowerCamelCase        =				complex_input_matrix
												_lowerCamelCase        =				complex_vector

								# Our implementation.
								_lowerCamelCase  ,     _lowerCamelCase        =				power_iteration(lowercase_						,   lowercase_							)

								# Numpy implementation.

								# Get eigenvalues and eigenvectors using built-in numpy
								# eigh (eigh used for symmetric or hermetian matrices).
								_lowerCamelCase  ,     _lowerCamelCase        =				np.linalg.eigh(lowercase_							)
								# Last eigenvalue is the maximum one.
								_lowerCamelCase        =				eigen_values[-1]
								# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
								_lowerCamelCase        =				eigen_vectors[:, -1]

								# Check our implementation and numpy gives close answers.
								assert np.abs(eigen_value - eigen_value_max							) <= 1e-6
								# Take absolute values element wise of each eigenvector.
								# as they are only unique to a minus sign.
								assert np.linalg.norm(np.abs(lowercase_							) - np.abs(lowercase_							)							) <= 1e-6


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			test_power_iteration()
