
"""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()
lowerCAmelCase__				=					logging.get_logger(__name__)



def 							_lowerCamelCase			(			__a     ):
		print('''Loading config file...'''     )

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

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

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



def 							_lowerCamelCase			(			__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			MobileViTVaConfig()

		SCREAMING_SNAKE_CASE_    			=			False

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

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

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

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

		return config



def 							_lowerCamelCase			(			__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			dct.pop(__a     )
		SCREAMING_SNAKE_CASE_    			=			val



def 							_lowerCamelCase			(			__a,				__a=False     ):
		if base_model:
				SCREAMING_SNAKE_CASE_    			=			''''''
		else:
				SCREAMING_SNAKE_CASE_    			=			'''mobilevitv2.'''

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

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

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

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

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

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

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

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



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			[]
		for k in state_dict.keys():
				if k.startswith('''seg_head.aux_head.'''     ):
						keys_to_ignore.append(__a     )
		for k in keys_to_ignore:
				state_dict.pop(__a,				__a     )



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



@torch.no_grad()
def 							_lowerCamelCase			(			__a,				__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			get_mobilevitva_config(__a,				__a     )

		# load original state_dict
		SCREAMING_SNAKE_CASE_    			=			torch.load(__a,				map_location='''cpu'''     )

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

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

		# load modified state_dict
		model.load_state_dict(__a     )

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

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

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


if __name__ == "__main__":
						lowerCAmelCase__				=					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 . '
						        '\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            '
						    ),
						    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.'
						)

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

"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
						import numpy as np
						import tensorflow as tf

						from transformers import TFCamembertModel





@require_tf
@require_sentencepiece
@require_tokenizers
class 			snake_case  (				unittest.TestCase ):

							@slow
							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''     )

									SCREAMING_SNAKE_CASE_    			=			tf.convert_to_tensor(
									    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]							,							dtype=tf.intaa							,							)  # J'aime le camembert !"

									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_     )['''last_hidden_state''']
									SCREAMING_SNAKE_CASE_    			=			tf.TensorShape((1, 10, 7_68)     )
									self.assertEqual(output.shape							,							SCREAMING_SNAKE_CASE_     )
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE_    			=			tf.convert_to_tensor(
									    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]							,							dtype=tf.floataa							,							)
									# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
									# camembert.eval()
									# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

									self.assertTrue(np.allclose(output[:, :3, :3].numpy()							,							expected_slice.numpy()							,							atol=1e-4     )     )