



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()
SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


def A    (							__UpperCamelCase       )    ->   List[Any]:
			print('Loading config file...'       )

			def flatten_yaml_as_dict(__UpperCamelCase			,						__UpperCamelCase=""			,						__UpperCamelCase="."       ):
						A__	  =     []
						for k, v in d.items():
									A__	  =     parent_key + sep + k if parent_key else k
									if isinstance(__UpperCamelCase			,						collections.abc.MutableMapping       ):
												items.extend(flatten_yaml_as_dict(__UpperCamelCase			,						__UpperCamelCase			,						sep=__UpperCamelCase       ).items()       )
									else:
												items.append((new_key, v)       )
						return dict(__UpperCamelCase       )

			A__	  =     argparse.Namespace()
			with open(__UpperCamelCase			,						'r'       ) as yaml_file:
						try:
									A__	  =     yaml.load(__UpperCamelCase			,						Loader=yaml.FullLoader       )

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


def A    (							__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[Any]:
			A__	  =     MobileViTVaConfig()

			A__	  =     False

			# dataset
			if task_name.startswith('imagenet1k_'       ):
						A__	  =     1_000
						if int(task_name.strip().split('_'       )[-1]       ) == 384:
									A__	  =     384
						else:
									A__	  =     256
						A__	  =     'imagenet-1k-id2label.json'
			elif task_name.startswith('imagenet21k_to_1k_'       ):
						A__	  =     21_000
						if int(task_name.strip().split('_'       )[-1]       ) == 384:
									A__	  =     384
						else:
									A__	  =     256
						A__	  =     'imagenet-22k-id2label.json'
			elif task_name.startswith('ade20k_'       ):
						A__	  =     151
						A__	  =     512
						A__	  =     'ade20k-id2label.json'
						A__	  =     True
			elif task_name.startswith('voc_'       ):
						A__	  =     21
						A__	  =     512
						A__	  =     'pascal-voc-id2label.json'
						A__	  =     True

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

			if is_segmentation_model:
						A__	  =     getattr(__UpperCamelCase			,						'model.segmentation.output_stride'			,						16       )
						if "_deeplabv3" in task_name:
									A__	  =     getattr(__UpperCamelCase			,						'model.segmentation.deeplabv3.aspp_rates'			,						[12, 24, 36]       )
									A__	  =     getattr(__UpperCamelCase			,						'model.segmentation.deeplabv3.aspp_out_channels'			,						512       )
									A__	  =     getattr(__UpperCamelCase			,						'model.segmentation.deeplabv3.aspp_dropout'			,						0.1       )

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

			return config


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   List[str]:
			A__	  =     dct.pop(__UpperCamelCase       )
			A__	  =     val


def A    (							__UpperCamelCase			,						__UpperCamelCase=False       )    ->   Dict:
			if base_model:
						A__	  =     ''
			else:
						A__	  =     'mobilevitv2.'

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

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

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

						for i in [3, 4, 5]:
									if f'''layer_{i}.0.''' in k:
												A__	  =     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:
												A__	  =     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:
												A__	  =     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:
												A__	  =     [0, 1]
									elif i == 4:
												A__	  =     [0, 1, 2, 3]
									elif i == 5:
												A__	  =     [0, 1, 2]

									for j in j_in:
												if f'''layer_{i}.1.global_rep.{j}.''' in k:
															A__	  =     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:
												A__	  =     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:
												A__	  =     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:
									A__	  =     k_new.replace('pre_norm_attn.0.'			,						'layernorm_before.'       )
						if "pre_norm_attn.1." in k:
									A__	  =     k_new.replace('pre_norm_attn.1.'			,						'attention.'       )
						if "pre_norm_ffn.0." in k:
									A__	  =     k_new.replace('pre_norm_ffn.0.'			,						'layernorm_after.'       )
						if "pre_norm_ffn.1." in k:
									A__	  =     k_new.replace('pre_norm_ffn.1.'			,						'ffn.conv1.'       )
						if "pre_norm_ffn.3." in k:
									A__	  =     k_new.replace('pre_norm_ffn.3.'			,						'ffn.conv2.'       )

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

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

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


def A    (							__UpperCamelCase       )    ->   Tuple:
			A__	  =     []
			for k in state_dict.keys():
						if k.startswith('seg_head.aux_head.'       ):
									keys_to_ignore.append(__UpperCamelCase       )
			for k in keys_to_ignore:
						state_dict.pop(__UpperCamelCase			,						__UpperCamelCase       )


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


@torch.no_grad()
def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Optional[Any]:
			A__	  =     get_mobilevitva_config(__UpperCamelCase			,						__UpperCamelCase       )

			# load original state_dict
			A__	  =     torch.load(__UpperCamelCase			,						map_location='cpu'       )

			# load huggingface model
			if task_name.startswith('ade20k_'       ) or task_name.startswith('voc_'       ):
						A__	  =     MobileViTVaForSemanticSegmentation(__UpperCamelCase       ).eval()
						A__	  =     False
			else:
						A__	  =     MobileViTVaForImageClassification(__UpperCamelCase       ).eval()
						A__	  =     False

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

			# load modified state_dict
			model.load_state_dict(__UpperCamelCase       )

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

			# verify classification model
			if task_name.startswith('imagenet'       ):
						A__	  =     outputs.logits
						A__	  =     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
									A__	  =     torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01]       )
									assert torch.allclose(logits[0, :3]			,						__UpperCamelCase			,						atol=1E-4       )

			Path(__UpperCamelCase       ).mkdir(exist_ok=__UpperCamelCase       )
			print(f'''Saving model {task_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 __name__ == "__main__":
							SCREAMING_SNAKE_CASE__   			=   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.'''
							)

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











import json
import os
from pathlib import Path

import pytest

from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename


SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   '''http://www.mocksite.com/file1.txt'''
SCREAMING_SNAKE_CASE_   :      str			=   '''"text": ["foo", "foo"]'''
SCREAMING_SNAKE_CASE_   :      Optional[int]			=   '''6d8ce9aa78a471c7477201efbeabd3bb01ac2e7d100a6dc024ba1608361f90a8'''

class 		snake_case_   :







	'''simple docstring'''
	__UpperCamelCase   =		2_00
	__UpperCamelCase   =		{'''Content-Length''': '''100'''}
	__UpperCamelCase   =		{}




	def    UpperCAmelCase			(						self							:     str					,      **__lowerCamelCase							:     Optional[int]   )       -> str:

			'''simple docstring'''




			return [bytes(__lowerCamelCase					,      'utf-8'   )]





def  SCREAMING_SNAKE_CASE				(      *snake_case						,   **snake_case							)		->    int:
		return MockResponse()



@pytest.mark.parametrize('urls_type'						,   [str, list, dict]							)
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Optional[Any]:
		import requests

		monkeypatch.setattr(snake_case						,   'request'						,   snake_case							)

		__lowercase											=     URL
		if issubclass(snake_case						,   snake_case							):
				__lowercase											=     url
		elif issubclass(snake_case						,   snake_case							):
				__lowercase											=     [url]
		elif issubclass(snake_case						,   snake_case							):
				__lowercase											=     {'train': url}
		__lowercase											=     'dummy'
		__lowercase											=     'downloads'
		__lowercase											=     tmp_path
		__lowercase											=     DownloadConfig(
		    cache_dir=os.path.join(snake_case						,   snake_case							)						,   use_etag=snake_case						,   )
		__lowercase											=     DownloadManager(dataset_name=snake_case						,   download_config=snake_case							)
		__lowercase											=     dl_manager.download(snake_case							)
		__lowercase											=     urls
		for downloaded_paths in [downloaded_paths]:
				if isinstance(snake_case						,   snake_case							):
						__lowercase											=     [downloaded_paths]
						__lowercase											=     [urls]
				elif isinstance(snake_case						,   snake_case							):
						assert "train" in downloaded_paths.keys()
						__lowercase											=     downloaded_paths.values()
						__lowercase											=     urls.values()
				assert downloaded_paths
				for downloaded_path, input_url in zip(snake_case						,   snake_case							):
						assert downloaded_path == dl_manager.downloaded_paths[input_url]
						__lowercase											=     Path(snake_case							)
						__lowercase											=     downloaded_path.parts
						assert parts[-1] == HASH
						assert parts[-2] == cache_subdir
						assert downloaded_path.exists()
						__lowercase											=     downloaded_path.read_text()
						assert content == CONTENT
						__lowercase											=     downloaded_path.with_suffix('.json'							)
						assert metadata_downloaded_path.exists()
						__lowercase											=     json.loads(metadata_downloaded_path.read_text()							)
						assert metadata_content == {"url": URL, "etag": None}



@pytest.mark.parametrize('paths_type'						,   [str, list, dict]							)
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Union[str, Any]:
		__lowercase											=     str(snake_case							)
		if issubclass(snake_case						,   snake_case							):
				__lowercase											=     filename
		elif issubclass(snake_case						,   snake_case							):
				__lowercase											=     [filename]
		elif issubclass(snake_case						,   snake_case							):
				__lowercase											=     {'train': filename}
		__lowercase											=     'dummy'
		__lowercase											=     xz_file.parent
		__lowercase											=     'extracted'
		__lowercase											=     DownloadConfig(
		    cache_dir=snake_case						,   use_etag=snake_case						,   )
		__lowercase											=     DownloadManager(dataset_name=snake_case						,   download_config=snake_case							)
		__lowercase											=     dl_manager.extract(snake_case							)
		__lowercase											=     paths
		for extracted_paths in [extracted_paths]:
				if isinstance(snake_case						,   snake_case							):
						__lowercase											=     [extracted_paths]
						__lowercase											=     [paths]
				elif isinstance(snake_case						,   snake_case							):
						assert "train" in extracted_paths.keys()
						__lowercase											=     extracted_paths.values()
						__lowercase											=     paths.values()
				assert extracted_paths
				for extracted_path, input_path in zip(snake_case						,   snake_case							):
						assert extracted_path == dl_manager.extracted_paths[input_path]
						__lowercase											=     Path(snake_case							)
						__lowercase											=     extracted_path.parts
						assert parts[-1] == hash_url_to_filename(snake_case						,   etag=snake_case							)
						assert parts[-2] == extracted_subdir
						assert extracted_path.exists()
						__lowercase											=     extracted_path.read_text()
						__lowercase											=     text_file.read_text()
						assert extracted_file_content == expected_file_content



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    int:
		assert path.endswith('.jsonl'							)
		for num_items, line in enumerate(snake_case						,   start=1							):
				__lowercase											=     json.loads(line.decode('utf-8'							)							)
				assert item.keys() == {"col_1", "col_2", "col_3"}
		assert num_items == 4



@pytest.mark.parametrize('archive_jsonl'						,   ['tar_jsonl_path', 'zip_jsonl_path']							)
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    int:
		__lowercase											=     request.getfixturevalue(snake_case							)
		__lowercase											=     DownloadManager()
		for num_jsonl, (path, file) in enumerate(dl_manager.iter_archive(snake_case							)						,   start=1							):
				_test_jsonl(snake_case						,   snake_case							)
		assert num_jsonl == 2



@pytest.mark.parametrize('archive_nested_jsonl'						,   ['tar_nested_jsonl_path', 'zip_nested_jsonl_path']							)
def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Any:
		__lowercase											=     request.getfixturevalue(snake_case							)
		__lowercase											=     DownloadManager()
		for num_tar, (path, file) in enumerate(dl_manager.iter_archive(snake_case							)						,   start=1							):
				for num_jsonl, (subpath, subfile) in enumerate(dl_manager.iter_archive(snake_case							)						,   start=1							):
						_test_jsonl(snake_case						,   snake_case							)
		assert num_tar == 1
		assert num_jsonl == 2



def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Dict:
		__lowercase											=     DownloadManager()
		for num_file, file in enumerate(dl_manager.iter_files(snake_case							)						,   start=1							):
				assert os.path.basename(snake_case							) == ("test.txt" if num_file == 1 else "train.txt")
		assert num_file == 2




