




import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_pytesseract_available():
					from PIL import Image

					from transformers import LayoutLMvaImageProcessor
class     a__					(      unittest.TestCase   ):






						def __init__(     self	,    _A	,    _A=7	,    _A=3	,    _A=1_8	,    _A=3_0	,    _A=4_0_0	,    _A=True	,    _A=None	,    _A=True	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = size if size is not None else {"height": 1_8, "width": 1_8}
											__lowerCAmelCase            = parent
											__lowerCAmelCase            = batch_size
											__lowerCAmelCase            = num_channels
											__lowerCAmelCase            = image_size
											__lowerCAmelCase            = min_resolution
											__lowerCAmelCase            = max_resolution
											__lowerCAmelCase            = do_resize
											__lowerCAmelCase            = size
											__lowerCAmelCase            = apply_ocr






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}






@require_torch
@require_pytesseract
class     a__					(      snake_case__				,       unittest.TestCase   ):
						_a							:					Tuple      =					LayoutLMvaImageProcessor if is_pytesseract_available() else None






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = LayoutLMvaImageProcessingTester(self    )






						@property
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											return self.image_processor_tester.prepare_image_processor_dict()






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.image_processing_class(**self.image_processor_dict    )
											self.assertTrue(hasattr(_A	,    "do_resize"    )    )
											self.assertTrue(hasattr(_A	,    "size"    )    )
											self.assertTrue(hasattr(_A	,    "apply_ocr"    )    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.image_processing_class.from_dict(self.image_processor_dict    )
											self.assertEqual(image_processor.size	,    {"height": 1_8, "width": 1_8}    )

											__lowerCAmelCase            = self.image_processing_class.from_dict(self.image_processor_dict	,    size=4_2    )
											self.assertEqual(image_processor.size	,    {"height": 4_2, "width": 4_2}    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											pass






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.image_processing_class(**self.image_processor_dict    )
											# create random PIL images
											__lowerCAmelCase            = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=_A    )
											for image in image_inputs:
																self.assertIsInstance(_A	,    Image.Image    )

											# Test not batched input
											__lowerCAmelCase            = image_processing(image_inputs[0]	,    return_tensors="pt"    )
											self.assertEqual(
											    encoding.pixel_values.shape	,    (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )	,    )

											self.assertIsInstance(encoding.words	,    _A    )
											self.assertIsInstance(encoding.boxes	,    _A    )

											# Test batched
											__lowerCAmelCase            = image_processing(_A	,    return_tensors="pt"    ).pixel_values
											self.assertEqual(
											    encoded_images.shape	,    (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )	,    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.image_processing_class(**self.image_processor_dict    )
											# create random numpy tensors
											__lowerCAmelCase            = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=_A	,    numpify=_A    )
											for image in image_inputs:
																self.assertIsInstance(_A	,    np.ndarray    )

											# Test not batched input
											__lowerCAmelCase            = image_processing(image_inputs[0]	,    return_tensors="pt"    ).pixel_values
											self.assertEqual(
											    encoded_images.shape	,    (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )	,    )

											# Test batched
											__lowerCAmelCase            = image_processing(_A	,    return_tensors="pt"    ).pixel_values
											self.assertEqual(
											    encoded_images.shape	,    (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )	,    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.image_processing_class(**self.image_processor_dict    )
											# create random PyTorch tensors
											__lowerCAmelCase            = prepare_image_inputs(self.image_processor_tester	,    equal_resolution=_A	,    torchify=_A    )
											for image in image_inputs:
																self.assertIsInstance(_A	,    torch.Tensor    )

											# Test not batched input
											__lowerCAmelCase            = image_processing(image_inputs[0]	,    return_tensors="pt"    ).pixel_values
											self.assertEqual(
											    encoded_images.shape	,    (
											        1,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )	,    )

											# Test batched
											__lowerCAmelCase            = image_processing(_A	,    return_tensors="pt"    ).pixel_values
											self.assertEqual(
											    encoded_images.shape	,    (
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        self.image_processor_tester.size["height"],
											        self.image_processor_tester.size["width"],
											    )	,    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = LayoutLMvaImageProcessor()

											from datasets import load_dataset

											__lowerCAmelCase            = load_dataset("hf-internal-testing/fixtures_docvqa"	,    split="test"    )

											__lowerCAmelCase            = Image.open(ds[0]["file"]    ).convert("RGB"    )

											__lowerCAmelCase            = image_processing(_A	,    return_tensors="pt"    )

											self.assertEqual(encoding.pixel_values.shape	,    (1, 3, 2_2_4, 2_2_4)    )
											self.assertEqual(len(encoding.words    )	,    len(encoding.boxes    )    )

											# fmt: off
											# the words and boxes were obtained with Tesseract 4.1.1
											__lowerCAmelCase            = [["11:14", "to", "11:39", "a.m", "11:39", "to", "11:44", "a.m.", "11:44", "a.m.", "to", "12:25", "p.m.", "12:25", "to", "12:58", "p.m.", "12:58", "to", "4:00", "p.m.", "2:00", "to", "5:00", "p.m.", "Coffee", "Break", "Coffee", "will", "be", "served", "for", "men", "and", "women", "in", "the", "lobby", "adjacent", "to", "exhibit", "area.", "Please", "move", "into", "exhibit", "area.", "(Exhibits", "Open)", "TRRF", "GENERAL", "SESSION", "(PART", "|)", "Presiding:", "Lee", "A.", "Waller", "TRRF", "Vice", "President", "“Introductory", "Remarks”", "Lee", "A.", "Waller,", "TRRF", "Vice", "Presi-", "dent", "Individual", "Interviews", "with", "TRRF", "Public", "Board", "Members", "and", "Sci-", "entific", "Advisory", "Council", "Mem-", "bers", "Conducted", "by", "TRRF", "Treasurer", "Philip", "G.", "Kuehn", "to", "get", "answers", "which", "the", "public", "refrigerated", "warehousing", "industry", "is", "looking", "for.", "Plus", "questions", "from", "the", "floor.", "Dr.", "Emil", "M.", "Mrak,", "University", "of", "Cal-", "ifornia,", "Chairman,", "TRRF", "Board;", "Sam", "R.", "Cecil,", "University", "of", "Georgia", "College", "of", "Agriculture;", "Dr.", "Stanley", "Charm,", "Tufts", "University", "School", "of", "Medicine;", "Dr.", "Robert", "H.", "Cotton,", "ITT", "Continental", "Baking", "Company;", "Dr.", "Owen", "Fennema,", "University", "of", "Wis-", "consin;", "Dr.", "Robert", "E.", "Hardenburg,", "USDA.", "Questions", "and", "Answers", "Exhibits", "Open", "Capt.", "Jack", "Stoney", "Room", "TRRF", "Scientific", "Advisory", "Council", "Meeting", "Ballroom", "Foyer"]]  # noqa: E231
											__lowerCAmelCase            = [[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]]  # noqa: E231
											# fmt: on

											self.assertListEqual(encoding.words	,    _A    )
											self.assertListEqual(encoding.boxes	,    _A    )

											# with apply_OCR = False
											__lowerCAmelCase            = LayoutLMvaImageProcessor(apply_ocr=_A    )

											__lowerCAmelCase            = image_processing(_A	,    return_tensors="pt"    )

											self.assertEqual(encoding.pixel_values.shape	,    (1, 3, 2_2_4, 2_2_4)    )










import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__					    =    logging.get_logger(__name__)







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Tuple  ):
					print("Loading config file..."  )

					def flatten_yaml_as_dict(SCREAMING_SNAKE_CASE_							:       str   ,   SCREAMING_SNAKE_CASE_							:       Optional[Any]=""   ,   SCREAMING_SNAKE_CASE_							:       Dict="."  ):
										__lowerCAmelCase            = []
										for k, v in d.items():
															__lowerCAmelCase            = parent_key + sep + k if parent_key else k
															if isinstance(SCREAMING_SNAKE_CASE_   ,   collections.abc.MutableMapping  ):
																				items.extend(flatten_yaml_as_dict(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   sep=SCREAMING_SNAKE_CASE_  ).items()  )
															else:
																				items.append((new_key, v)  )
										return dict(SCREAMING_SNAKE_CASE_  )

					__lowerCAmelCase            = argparse.Namespace()
					with open(SCREAMING_SNAKE_CASE_   ,   "r"  ) as yaml_file:
										try:
															__lowerCAmelCase            = yaml.load(SCREAMING_SNAKE_CASE_   ,   Loader=yaml.FullLoader  )

															__lowerCAmelCase            = flatten_yaml_as_dict(SCREAMING_SNAKE_CASE_  )
															for k, v in flat_cfg.items():
																				setattr(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )
										except yaml.YAMLError as exc:
															logger.error("Error while loading config file: {}. Error message: {}".format(SCREAMING_SNAKE_CASE_   ,   str(SCREAMING_SNAKE_CASE_  )  )  )
					return config







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Optional[Any]   ,   SCREAMING_SNAKE_CASE_							:       str  ):
					__lowerCAmelCase            = MobileViTVaConfig()

					__lowerCAmelCase            = False

					# dataset
					if task_name.startswith("imagenet1k_"  ):
										__lowerCAmelCase            = 10_00
										if int(task_name.strip().split("_"  )[-1]  ) == 3_84:
															__lowerCAmelCase            = 3_84
										else:
															__lowerCAmelCase            = 2_56
										__lowerCAmelCase            = "imagenet-1k-id2label.json"
					elif task_name.startswith("imagenet21k_to_1k_"  ):
										__lowerCAmelCase            = 2_10_00
										if int(task_name.strip().split("_"  )[-1]  ) == 3_84:
															__lowerCAmelCase            = 3_84
										else:
															__lowerCAmelCase            = 2_56
										__lowerCAmelCase            = "imagenet-22k-id2label.json"
					elif task_name.startswith("ade20k_"  ):
										__lowerCAmelCase            = 1_51
										__lowerCAmelCase            = 5_12
										__lowerCAmelCase            = "ade20k-id2label.json"
										__lowerCAmelCase            = True
					elif task_name.startswith("voc_"  ):
										__lowerCAmelCase            = 21
										__lowerCAmelCase            = 5_12
										__lowerCAmelCase            = "pascal-voc-id2label.json"
										__lowerCAmelCase            = True

					# orig_config
					__lowerCAmelCase            = load_orig_config_file(SCREAMING_SNAKE_CASE_  )
					assert getattr(SCREAMING_SNAKE_CASE_   ,   "model.classification.name"   ,   -1  ) == "mobilevit_v2", "Invalid model"
					__lowerCAmelCase            = getattr(SCREAMING_SNAKE_CASE_   ,   "model.classification.mitv2.width_multiplier"   ,   1.0  )
					assert (
					    getattr(SCREAMING_SNAKE_CASE_   ,   "model.classification.mitv2.attn_norm_layer"   ,   -1  ) == "layer_norm_2d"
					), "Norm layers other than layer_norm_2d is not supported"
					__lowerCAmelCase            = getattr(SCREAMING_SNAKE_CASE_   ,   "model.classification.activation.name"   ,   "swish"  )
					# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

					if is_segmentation_model:
										__lowerCAmelCase            = getattr(SCREAMING_SNAKE_CASE_   ,   "model.segmentation.output_stride"   ,   16  )
										if "_deeplabv3" in task_name:
															__lowerCAmelCase            = getattr(SCREAMING_SNAKE_CASE_   ,   "model.segmentation.deeplabv3.aspp_rates"   ,   [12, 24, 36]  )
															__lowerCAmelCase            = getattr(SCREAMING_SNAKE_CASE_   ,   "model.segmentation.deeplabv3.aspp_out_channels"   ,   5_12  )
															__lowerCAmelCase            = getattr(SCREAMING_SNAKE_CASE_   ,   "model.segmentation.deeplabv3.aspp_dropout"   ,   0.1  )

    # id2label
					__lowerCAmelCase            = "huggingface/label-files"
					__lowerCAmelCase            = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   repo_type="dataset"  )   ,   "r"  )  )
					__lowerCAmelCase            = {int(SCREAMING_SNAKE_CASE_  ): v for k, v in idalabel.items()}
					__lowerCAmelCase            = idalabel
					__lowerCAmelCase            = {v: k for k, v in idalabel.items()}

					return config







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Dict   ,   SCREAMING_SNAKE_CASE_							:       List[str]   ,   SCREAMING_SNAKE_CASE_							:       Any  ):
					__lowerCAmelCase            = dct.pop(SCREAMING_SNAKE_CASE_  )
					__lowerCAmelCase            = val







def 			_a   (		SCREAMING_SNAKE_CASE_							:       Union[str, Any]   ,   SCREAMING_SNAKE_CASE_							:       Dict=False  ):
					if base_model:
										__lowerCAmelCase            = ""
					else:
										__lowerCAmelCase            = "mobilevitv2."

					__lowerCAmelCase            = []
					for k in state_dict.keys():
										if k[:8] == "encoder.":
															__lowerCAmelCase            = k[8:]
										else:
															__lowerCAmelCase            = k

										if ".block." in k:
															__lowerCAmelCase            = k_new.replace(".block."   ,   "."  )
										if ".conv." in k:
															__lowerCAmelCase            = k_new.replace(".conv."   ,   ".convolution."  )
										if ".norm." in k:
															__lowerCAmelCase            = k_new.replace(".norm."   ,   ".normalization."  )

										if "conv_1." in k:
															__lowerCAmelCase            = k_new.replace("conv_1."   ,   F"""{model_prefix}conv_stem."""  )
										for i in [1, 2]:
															if F"""layer_{i}.""" in k:
																				__lowerCAmelCase            = k_new.replace(F"""layer_{i}."""   ,   F"""{model_prefix}encoder.layer.{i-1}.layer."""  )
										if ".exp_1x1." in k:
															__lowerCAmelCase            = k_new.replace(".exp_1x1."   ,   ".expand_1x1."  )
										if ".red_1x1." in k:
															__lowerCAmelCase            = k_new.replace(".red_1x1."   ,   ".reduce_1x1."  )

										for i in [3, 4, 5]:
															if F"""layer_{i}.0.""" in k:
																				__lowerCAmelCase            = k_new.replace(F"""layer_{i}.0."""   ,   F"""{model_prefix}encoder.layer.{i-1}.downsampling_layer."""  )
															if F"""layer_{i}.1.local_rep.0.""" in k:
																				__lowerCAmelCase            = k_new.replace(F"""layer_{i}.1.local_rep.0."""   ,   F"""{model_prefix}encoder.layer.{i-1}.conv_kxk."""  )
															if F"""layer_{i}.1.local_rep.1.""" in k:
																				__lowerCAmelCase            = k_new.replace(F"""layer_{i}.1.local_rep.1."""   ,   F"""{model_prefix}encoder.layer.{i-1}.conv_1x1."""  )

										for i in [3, 4, 5]:
															if i == 3:
																				__lowerCAmelCase            = [0, 1]
															elif i == 4:
																				__lowerCAmelCase            = [0, 1, 2, 3]
															elif i == 5:
																				__lowerCAmelCase            = [0, 1, 2]

															for j in j_in:
																				if F"""layer_{i}.1.global_rep.{j}.""" in k:
																									__lowerCAmelCase            = k_new.replace(
																									    F"""layer_{i}.1.global_rep.{j}."""   ,   F"""{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}."""  )
															if F"""layer_{i}.1.global_rep.{j+1}.""" in k:
																				__lowerCAmelCase            = k_new.replace(
																				    F"""layer_{i}.1.global_rep.{j+1}."""   ,   F"""{model_prefix}encoder.layer.{i-1}.layernorm."""  )

															if F"""layer_{i}.1.conv_proj.""" in k:
																				__lowerCAmelCase            = k_new.replace(F"""layer_{i}.1.conv_proj."""   ,   F"""{model_prefix}encoder.layer.{i-1}.conv_projection."""  )

										if "pre_norm_attn.0." in k:
															__lowerCAmelCase            = k_new.replace("pre_norm_attn.0."   ,   "layernorm_before."  )
										if "pre_norm_attn.1." in k:
															__lowerCAmelCase            = k_new.replace("pre_norm_attn.1."   ,   "attention."  )
										if "pre_norm_ffn.0." in k:
															__lowerCAmelCase            = k_new.replace("pre_norm_ffn.0."   ,   "layernorm_after."  )
										if "pre_norm_ffn.1." in k:
															__lowerCAmelCase            = k_new.replace("pre_norm_ffn.1."   ,   "ffn.conv1."  )
										if "pre_norm_ffn.3." in k:
															__lowerCAmelCase            = k_new.replace("pre_norm_ffn.3."   ,   "ffn.conv2."  )

										if "classifier.1." in k:
															__lowerCAmelCase            = k_new.replace("classifier.1."   ,   "classifier."  )

										if "seg_head." in k:
															__lowerCAmelCase            = k_new.replace("seg_head."   ,   "segmentation_head."  )
										if ".aspp_layer." in k:
															__lowerCAmelCase            = k_new.replace(".aspp_layer."   ,   "."  )
										if ".aspp_pool." in k:
															__lowerCAmelCase            = k_new.replace(".aspp_pool."   ,   "."  )

										rename_keys.append((k, k_new)  )
					return rename_keys







def 			_a   (		SCREAMING_SNAKE_CASE_							:       List[Any]  ):
					__lowerCAmelCase            = []
					for k in state_dict.keys():
										if k.startswith("seg_head.aux_head."  ):
															keys_to_ignore.append(SCREAMING_SNAKE_CASE_  )
					for k in keys_to_ignore:
										state_dict.pop(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )







def 			_a   (		):
					__lowerCAmelCase            = "http://images.cocodataset.org/val2017/000000039769.jpg"
					# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
					__lowerCAmelCase            = Image.open(requests.get(SCREAMING_SNAKE_CASE_   ,   stream=SCREAMING_SNAKE_CASE_  ).raw  )
					return im







@torch.no_grad()
def 			_a   (		SCREAMING_SNAKE_CASE_							:       List[str]   ,   SCREAMING_SNAKE_CASE_							:       Dict   ,   SCREAMING_SNAKE_CASE_							:       Tuple   ,   SCREAMING_SNAKE_CASE_							:       List[Any]  ):
					__lowerCAmelCase            = get_mobilevitva_config(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )

					# load original state_dict
					__lowerCAmelCase            = torch.load(SCREAMING_SNAKE_CASE_   ,   map_location="cpu"  )

					# load huggingface model
					if task_name.startswith("ade20k_"  ) or task_name.startswith("voc_"  ):
										__lowerCAmelCase            = MobileViTVaForSemanticSegmentation(SCREAMING_SNAKE_CASE_  ).eval()
										__lowerCAmelCase            = False
					else:
										__lowerCAmelCase            = MobileViTVaForImageClassification(SCREAMING_SNAKE_CASE_  ).eval()
										__lowerCAmelCase            = False

					# remove and rename some keys of load the original model
					__lowerCAmelCase            = checkpoint
					remove_unused_keys(SCREAMING_SNAKE_CASE_  )
					__lowerCAmelCase            = create_rename_keys(SCREAMING_SNAKE_CASE_   ,   base_model=SCREAMING_SNAKE_CASE_  )
					for rename_key_src, rename_key_dest in rename_keys:
										rename_key(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  )

					# load modified state_dict
					model.load_state_dict(SCREAMING_SNAKE_CASE_  )

					# 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(**SCREAMING_SNAKE_CASE_  )

					# verify classification model
					if task_name.startswith("imagenet"  ):
										__lowerCAmelCase            = outputs.logits
										__lowerCAmelCase            = logits.argmax(-1  ).item()
										print("Predicted class:"   ,   model.config.idalabel[predicted_class_idx]  )
										if task_name.startswith("imagenet1k_256"  ) and config.width_multiplier == 1.0:
															# expected_logits for base variant
															__lowerCAmelCase            = torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-01]  )
															assert torch.allclose(logits[0, :3]   ,   SCREAMING_SNAKE_CASE_   ,   atol=1E-4  )

					Path(SCREAMING_SNAKE_CASE_  ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_  )
					print(F"""Saving model {task_name} to {pytorch_dump_folder_path}"""  )
					model.save_pretrained(SCREAMING_SNAKE_CASE_  )
					print(F"""Saving image processor to {pytorch_dump_folder_path}"""  )
					image_processor.save_pretrained(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
					UpperCamelCase__					    =    argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--task""",
					    default="""imagenet1k_256""",
					    type=str,
					    help=(
					        """Name of the task for which the MobileViTV2 model you'd like to convert is trained on . """
					        """
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            """
					    ),
					    choices=[
					        """imagenet1k_256""",
					        """imagenet1k_384""",
					        """imagenet21k_to_1k_256""",
					        """imagenet21k_to_1k_384""",
					        """ade20k_deeplabv3""",
					        """voc_deeplabv3""",
					    ],
					)

					parser.add_argument(
					    """--orig_checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
					)
					parser.add_argument("""--orig_config_path""", required=True, type=str, help="""Path to the original config file.""")
					parser.add_argument(
					    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
					)

					UpperCamelCase__					    =    parser.parse_args()
					convert_mobilevitva_checkpoint(
					    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
					)




