
'''simple docstring'''



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()
_lowercase       =		logging.get_logger(__name__)


def A				(__lowerCamelCase				:Optional[int]		):
						print("""Loading config file..."""		)

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

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

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


def A				(__lowerCamelCase				:Union[str, Any]			,     __lowerCamelCase				:Dict		):
						_lowerCAmelCase  						=			MobileViTVaConfig()

						_lowerCAmelCase  						=			False

						# dataset
						if task_name.startswith("""imagenet1k_"""		):
												_lowerCAmelCase  						=			1000
												if int(task_name.strip().split("""_"""		)[-1]		) == 384:
																		_lowerCAmelCase  						=			384
												else:
																		_lowerCAmelCase  						=			256
												_lowerCAmelCase  						=			"""imagenet-1k-id2label.json"""
						elif task_name.startswith("""imagenet21k_to_1k_"""		):
												_lowerCAmelCase  						=			21000
												if int(task_name.strip().split("""_"""		)[-1]		) == 384:
																		_lowerCAmelCase  						=			384
												else:
																		_lowerCAmelCase  						=			256
												_lowerCAmelCase  						=			"""imagenet-22k-id2label.json"""
						elif task_name.startswith("""ade20k_"""		):
												_lowerCAmelCase  						=			151
												_lowerCAmelCase  						=			512
												_lowerCAmelCase  						=			"""ade20k-id2label.json"""
												_lowerCAmelCase  						=			True
						elif task_name.startswith("""voc_"""		):
												_lowerCAmelCase  						=			21
												_lowerCAmelCase  						=			512
												_lowerCAmelCase  						=			"""pascal-voc-id2label.json"""
												_lowerCAmelCase  						=			True

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

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

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

						return config


def A				(__lowerCamelCase				:Any			,     __lowerCamelCase				:Tuple			,     __lowerCamelCase				:Optional[Any]		):
						_lowerCAmelCase  						=			dct.pop(__lowerCamelCase		)
						_lowerCAmelCase  						=			val


def A				(__lowerCamelCase				:List[Any]			,     __lowerCamelCase				:str=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				(__lowerCamelCase				:Tuple		):
						_lowerCAmelCase  						=			[]
						for k in state_dict.keys():
												if k.startswith("""seg_head.aux_head."""		):
																		keys_to_ignore.append(__lowerCamelCase		)
						for k in keys_to_ignore:
												state_dict.pop(__lowerCamelCase			,     __lowerCamelCase		)


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(__lowerCamelCase			,     stream=__lowerCamelCase		).raw		)
						return im


@torch.no_grad()
def A				(__lowerCamelCase				:Optional[int]			,     __lowerCamelCase				:List[str]			,     __lowerCamelCase				:Any			,     __lowerCamelCase				:int		):
						_lowerCAmelCase  						=			get_mobilevitva_config(__lowerCamelCase			,     __lowerCamelCase		)

						# load original state_dict
						_lowerCAmelCase  						=			torch.load(__lowerCamelCase			,     map_location="""cpu"""		)

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

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

						# load modified state_dict
						model.load_state_dict(__lowerCamelCase		)

						# 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(**__lowerCamelCase		)

						# 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([-1.6336e00, -7.3204e-02, -5.1883e-01]		)
																		assert torch.allclose(logits[0, :3]			,     __lowerCamelCase			,     atol=1e-4		)

						Path(__lowerCamelCase		).mkdir(exist_ok=__lowerCamelCase		)
						print(f'Saving model {task_name} to {pytorch_dump_folder_path}'		)
						model.save_pretrained(__lowerCamelCase		)
						print(f'Saving image processor to {pytorch_dump_folder_path}'		)
						image_processor.save_pretrained(__lowerCamelCase		)


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

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


'''simple docstring'''



import inspect
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
_lowercase    =		"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
_lowercase    =		direct_transformers_import(PATH_TO_TRANSFORMERS)

_lowercase    =		transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
_lowercase    =		re.compile(R"""\[(.+?)\]\((https://huggingface\.co/.+?)\)""")


_lowercase    =		{
    """DecisionTransformerConfig""",
    """EncoderDecoderConfig""",
    """MusicgenConfig""",
    """RagConfig""",
    """SpeechEncoderDecoderConfig""",
    """TimmBackboneConfig""",
    """VisionEncoderDecoderConfig""",
    """VisionTextDualEncoderConfig""",
    """LlamaConfig""",
}


def A				(__lowerCamelCase				:str		):
						_lowerCAmelCase  						=			None

						# source code of `config_class`
						_lowerCAmelCase  						=			inspect.getsource(__lowerCamelCase		)
						_lowerCAmelCase  						=			_re_checkpoint.findall(__lowerCamelCase		)

						# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
						# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
						for ckpt_name, ckpt_link in checkpoints:
												# allow the link to end with `/`
												if ckpt_link.endswith("""/"""		):
																		_lowerCAmelCase  						=			ckpt_link[:-1]

												# verify the checkpoint name corresponds to the checkpoint link
												_lowerCAmelCase  						=			f'https://huggingface.co/{ckpt_name}'
												if ckpt_link == ckpt_link_from_name:
																		_lowerCAmelCase  						=			ckpt_name
																		break

						return checkpoint


def A				():
						_lowerCAmelCase  						=			[]

						for config_class in list(CONFIG_MAPPING.values()		):
												# Skip deprecated models
												if "models.deprecated" in config_class.__module__:
																		continue
												_lowerCAmelCase  						=			get_checkpoint_from_config_class(__lowerCamelCase		)

												_lowerCAmelCase  						=			config_class.__name__
												if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
																		configs_without_checkpoint.append(__lowerCamelCase		)

						if len(__lowerCamelCase		) > 0:
												_lowerCAmelCase  						=			"""\n""".join(sorted(__lowerCamelCase		)		)
												raise ValueError(f'The following configurations don\'t contain any valid checkpoint:\n{message}'		)


if __name__ == "__main__":
						check_config_docstrings_have_checkpoints()
