



import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__A   			=False
__A   			=True
__A   			=False


if __name__ == "__main__":
				__A   			=argparse.ArgumentParser()

				parser.add_argument(
				    "--repo_path",
				    default=None,
				    type=str,
				    required=True,
				    help="The config json file corresponding to the architecture.",
				)

				parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

				__A   			=parser.parse_args()

				__A   			={
				    "image_size": "sample_size",
				    "num_res_blocks": "layers_per_block",
				    "block_channels": "block_out_channels",
				    "down_blocks": "down_block_types",
				    "up_blocks": "up_block_types",
				    "downscale_freq_shift": "freq_shift",
				    "resnet_num_groups": "norm_num_groups",
				    "resnet_act_fn": "act_fn",
				    "resnet_eps": "norm_eps",
				    "num_head_channels": "attention_head_dim",
				}

				__A   			={
				    "time_steps": "time_proj",
				    "mid": "mid_block",
				    "downsample_blocks": "down_blocks",
				    "upsample_blocks": "up_blocks",
				}

				__A   			="" if has_file(args.repo_path, "config.json") else "unet"

				with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
								__A   			=reader.read()
								__A   			=json.loads(text)

				if do_only_config:
								for key in config_parameters_to_change.keys():
												config.pop(key, None)

				if has_file(args.repo_path, "config.json"):
								__A   			=UNetaDModel(**config)
				else:
								__A   			=UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
								__A   			=class_name(**config)

				if do_only_config:
								model.save_config(os.path.join(args.repo_path, subfolder))

				__A   			=dict(model.config)

				if do_only_renaming:
								for key, value in config_parameters_to_change.items():
												if key in config:
																__A   			=config[key]
																del config[key]

								__A   			=[k.replace("UNetRes", "") for k in config["down_block_types"]]
								__A   			=[k.replace("UNetRes", "") for k in config["up_block_types"]]

				if do_only_weights:
								__A   			=torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

								__A   			={}
								for param_key, param_value in state_dict.items():
												if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
																continue
												__A   			=False
												for key, new_key in key_parameters_to_change.items():
																if not has_changed and param_key.split(".")[0] == key:
																				__A   			=param_value
																				__A   			=True
												if not has_changed:
																__A   			=param_value

								model.load_state_dict(new_state_dict)
								model.save_pretrained(os.path.join(args.repo_path, subfolder))










import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
				from transformers.models.mobilevitva.modeling_mobilevitva import (
				    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
				    make_divisible,
				)


if is_vision_available():
				from PIL import Image

				from transformers import MobileViTImageProcessor

class 		UpperCAmelCase__			(			__UpperCamelCase   ):




			'''simple docstring'''






			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	self.config_class(**self.inputs_dict )
					self.parent.assertTrue(hasattr(a_       ,       '''width_multiplier''' ) )

class 		UpperCAmelCase__			:




			'''simple docstring'''






			def __init__(   self							: str       ,       a_							: Any       ,       a_							: Tuple=13       ,       a_							: Tuple=64       ,       a_							: Optional[int]=2       ,       a_							: List[str]=3       ,       a_							: Optional[Any]="swish"       ,       a_							: Optional[Any]=3       ,       a_							: str=32       ,       a_							: Dict=0.1       ,       a_							: int=0.0_2       ,       a_							: Tuple=True       ,       a_							: List[Any]=True       ,       a_							: Optional[int]=10       ,       a_							: Optional[int]=None       ,       a_							: Dict=0.2_5       ,       a_							: Tuple=0.0       ,       a_							: List[Any]=0.0       ,       ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	parent
					__UpperCAmelCase    :     str													=	batch_size
					__UpperCAmelCase    :     Dict													=	image_size
					__UpperCAmelCase    :     List[Any]													=	patch_size
					__UpperCAmelCase    :     Union[str, Any]													=	num_channels
					__UpperCAmelCase    :     Any													=	make_divisible(5_12 * width_multiplier       ,       divisor=8 )
					__UpperCAmelCase    :     Tuple													=	hidden_act
					__UpperCAmelCase    :     Dict													=	conv_kernel_size
					__UpperCAmelCase    :     Optional[Any]													=	output_stride
					__UpperCAmelCase    :     Dict													=	classifier_dropout_prob
					__UpperCAmelCase    :     Optional[Any]													=	use_labels
					__UpperCAmelCase    :     List[Any]													=	is_training
					__UpperCAmelCase    :     Tuple													=	num_labels
					__UpperCAmelCase    :     List[str]													=	initializer_range
					__UpperCAmelCase    :     Optional[Any]													=	scope
					__UpperCAmelCase    :     Optional[Any]													=	width_multiplier
					__UpperCAmelCase    :     List[str]													=	ffn_dropout
					__UpperCAmelCase    :     Dict													=	attn_dropout




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     int													=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

					__UpperCAmelCase    :     List[str]													=	None
					__UpperCAmelCase    :     List[str]													=	None
					if self.use_labels:
							__UpperCAmelCase    :     Optional[int]													=	ids_tensor([self.batch_size]       ,       self.num_labels )
							__UpperCAmelCase    :     str													=	ids_tensor([self.batch_size, self.image_size, self.image_size]       ,       self.num_labels )

					__UpperCAmelCase    :     List[str]													=	self.get_config()

					return config, pixel_values, labels, pixel_labels




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					return MobileViTVaConfig(
					    image_size=self.image_size       ,       patch_size=self.patch_size       ,       num_channels=self.num_channels       ,       hidden_act=self.hidden_act       ,       conv_kernel_size=self.conv_kernel_size       ,       output_stride=self.output_stride       ,       classifier_dropout_prob=self.classifier_dropout_prob       ,       initializer_range=self.initializer_range       ,       width_multiplier=self.width_multiplier       ,       ffn_dropout=self.ffn_dropout_prob       ,       attn_dropout=self.attn_dropout_prob       ,       )




			def  snake_case__     (   self							: Optional[Any]       ,       a_							: Dict       ,       a_							: List[Any]       ,       a_							: Dict       ,       a_							: Tuple ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[str]													=	MobileViTVaModel(config=a_ )
					model.to(a_ )
					model.eval()
					__UpperCAmelCase    :     Tuple													=	model(a_ )
					self.parent.assertEqual(
					    result.last_hidden_state.shape       ,       (
					        self.batch_size,
					        self.last_hidden_size,
					        self.image_size // self.output_stride,
					        self.image_size // self.output_stride,
					    )       ,       )




			def  snake_case__     (   self							: Union[str, Any]       ,       a_							: Dict       ,       a_							: Union[str, Any]       ,       a_							: str       ,       a_							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	self.num_labels
					__UpperCAmelCase    :     List[str]													=	MobileViTVaForImageClassification(a_ )
					model.to(a_ )
					model.eval()
					__UpperCAmelCase    :     List[Any]													=	model(a_       ,       labels=a_ )
					self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.num_labels) )




			def  snake_case__     (   self							: str       ,       a_							: List[str]       ,       a_							: Optional[Any]       ,       a_							: List[str]       ,       a_							: int ):
					'''simple docstring'''





					__UpperCAmelCase    :     Any													=	self.num_labels
					__UpperCAmelCase    :     List[Any]													=	MobileViTVaForSemanticSegmentation(a_ )
					model.to(a_ )
					model.eval()
					__UpperCAmelCase    :     Tuple													=	model(a_ )
					self.parent.assertEqual(
					    result.logits.shape       ,       (
					        self.batch_size,
					        self.num_labels,
					        self.image_size // self.output_stride,
					        self.image_size // self.output_stride,
					    )       ,       )
					__UpperCAmelCase    :     List[str]													=	model(a_       ,       labels=a_ )
					self.parent.assertEqual(
					    result.logits.shape       ,       (
					        self.batch_size,
					        self.num_labels,
					        self.image_size // self.output_stride,
					        self.image_size // self.output_stride,
					    )       ,       )




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[int]													=	self.prepare_config_and_inputs()
					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase    :     str													=	config_and_inputs
					__UpperCAmelCase    :     Optional[Any]													=	{'''pixel_values''': pixel_values}
					return config, inputs_dict

@require_torch
class 		UpperCAmelCase__			(			__UpperCamelCase						,__UpperCamelCase						,unittest.TestCase   ):




			'''simple docstring'''






			UpperCamelCase									=       (
			    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
			    if is_torch_available()
			    else ()
			)

			UpperCamelCase									=       (
			    {
			        """feature-extraction""": MobileViTVaModel,
			        """image-classification""": MobileViTVaForImageClassification,
			        """image-segmentation""": MobileViTVaForSemanticSegmentation,
			    }
			    if is_torch_available()
			    else {}
			)

			UpperCamelCase									=       False
			UpperCamelCase									=       False
			UpperCamelCase									=       False
			UpperCamelCase									=       False




			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Dict													=	MobileViTVaModelTester(self )
					__UpperCAmelCase    :     Union[str, Any]													=	MobileViTVaConfigTester(self       ,       config_class=a_       ,       has_text_modality=a_ )




			def  snake_case__     (   self							: str ):
					'''simple docstring'''





					self.config_tester.run_common_tests()




			@unittest.skip(reason='''MobileViTV2 does not use inputs_embeds''' )
			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					pass




			@unittest.skip(reason='''MobileViTV2 does not support input and output embeddings''' )
			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					pass




			@unittest.skip(reason='''MobileViTV2 does not output attentions''' )
			def  snake_case__     (   self							: Tuple ):
					'''simple docstring'''





					pass




			@require_torch_multi_gpu
			@unittest.skip(reason='''Got `CUDA error: misaligned address` for tests after this one being run.''' )
			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					pass




			@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					pass




			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					__UpperCAmelCase							,					__UpperCAmelCase    :     str													=	self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							__UpperCAmelCase    :     Any													=	model_class(a_ )
							__UpperCAmelCase    :     List[Any]													=	inspect.signature(model.forward )
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							__UpperCAmelCase    :     Any													=	[*signature.parameters.keys()]

							__UpperCAmelCase    :     List[str]													=	['''pixel_values''']
							self.assertListEqual(arg_names[:1]       ,       a_ )




			def  snake_case__     (   self							: Union[str, Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*a_ )




			def  snake_case__     (   self							: int ):
					'''simple docstring'''





					def check_hidden_states_output(a_							: List[Any]       ,       a_							: List[Any]       ,       a_							: Union[str, Any] ):
							__UpperCAmelCase    :     Optional[int]													=	model_class(a_ )
							model.to(a_ )
							model.eval()

							with torch.no_grad():
									__UpperCAmelCase    :     Dict													=	model(**self._prepare_for_class(a_       ,       a_ ) )

							__UpperCAmelCase    :     List[Any]													=	outputs.hidden_states

							__UpperCAmelCase    :     str													=	5
							self.assertEqual(len(a_ )       ,       a_ )

							# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
							# with the width and height being successively divided by 2.
							__UpperCAmelCase    :     Any													=	2
							for i in range(len(a_ ) ):
									self.assertListEqual(
									    list(hidden_states[i].shape[-2:] )       ,       [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]       ,       )
									divisor *= 2

							self.assertEqual(self.model_tester.output_stride       ,       divisor // 2 )

					__UpperCAmelCase							,					__UpperCAmelCase    :     Tuple													=	self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							__UpperCAmelCase    :     Dict													=	True
							check_hidden_states_output(a_       ,       a_       ,       a_ )

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							__UpperCAmelCase    :     Optional[Any]													=	True

							check_hidden_states_output(a_       ,       a_       ,       a_ )




			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[Any]													=	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*a_ )




			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					__UpperCAmelCase    :     Union[str, Any]													=	self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_semantic_segmentation(*a_ )




			@slow
			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__UpperCAmelCase    :     Optional[int]													=	MobileViTVaModel.from_pretrained(a_ )
							self.assertIsNotNone(a_ )

def        a						(      ):







		'''simple docstring'''





		__UpperCAmelCase    :     int													=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
		return image

@require_torch
@require_vision
class 		UpperCAmelCase__			(			unittest.TestCase   ):




			'''simple docstring'''






			@cached_property
			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					return (
					    MobileViTImageProcessor.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256''' )
					    if is_vision_available()
					    else None
					)




			@slow
			def  snake_case__     (   self							: Optional[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Optional[Any]													=	MobileViTVaForImageClassification.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256''' ).to(
					    a_ )

					__UpperCAmelCase    :     Optional[int]													=	self.default_image_processor
					__UpperCAmelCase    :     int													=	prepare_img()
					__UpperCAmelCase    :     Union[str, Any]													=	image_processor(images=a_       ,       return_tensors='''pt''' ).to(a_ )

					# forward pass
					with torch.no_grad():
							__UpperCAmelCase    :     Union[str, Any]													=	model(**a_ )

					# verify the logits
					__UpperCAmelCase    :     str													=	torch.Size((1, 10_00) )
					self.assertEqual(outputs.logits.shape       ,       a_ )

					__UpperCAmelCase    :     Any													=	torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01] ).to(a_ )

					self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,       a_       ,       atol=1e-4 ) )




			@slow
			def  snake_case__     (   self							: List[Any] ):
					'''simple docstring'''





					__UpperCAmelCase    :     int													=	MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )
					__UpperCAmelCase    :     int													=	model.to(a_ )

					__UpperCAmelCase    :     str													=	MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )

					__UpperCAmelCase    :     List[str]													=	prepare_img()
					__UpperCAmelCase    :     Tuple													=	image_processor(images=a_       ,       return_tensors='''pt''' ).to(a_ )

					# forward pass
					with torch.no_grad():
							__UpperCAmelCase    :     Dict													=	model(**a_ )
					__UpperCAmelCase    :     int													=	outputs.logits

					# verify the logits
					__UpperCAmelCase    :     Union[str, Any]													=	torch.Size((1, 21, 32, 32) )
					self.assertEqual(logits.shape       ,       a_ )

					__UpperCAmelCase    :     List[Any]													=	torch.tensor(
					    [
					        [[7.0_8_6_3, 7.1_5_2_5, 6.8_2_0_1], [6.6_9_3_1, 6.8_7_7_0, 6.8_9_3_3], [6.2_9_7_8, 7.0_3_6_6, 6.9_6_3_6]],
					        [[-3.7_1_3_4, -3.6_7_1_2, -3.6_6_7_5], [-3.5_8_2_5, -3.3_5_4_9, -3.4_7_7_7], [-3.3_4_3_5, -3.3_9_7_9, -3.2_8_5_7]],
					        [[-2.9_3_2_9, -2.8_0_0_3, -2.7_3_6_9], [-3.0_5_6_4, -2.4_7_8_0, -2.0_2_0_7], [-2.6_8_8_9, -1.9_2_9_8, -1.7_6_4_0]],
					    ]       ,       device=a_       ,       )

					self.assertTrue(torch.allclose(logits[0, :3, :3, :3]       ,       a_       ,       atol=1e-4 ) )




			@slow
			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )
					__UpperCAmelCase    :     Optional[int]													=	model.to(a_ )

					__UpperCAmelCase    :     List[Any]													=	MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )

					__UpperCAmelCase    :     Optional[int]													=	prepare_img()
					__UpperCAmelCase    :     Dict													=	image_processor(images=a_       ,       return_tensors='''pt''' ).to(a_ )

					# forward pass
					with torch.no_grad():
							__UpperCAmelCase    :     Union[str, Any]													=	model(**a_ )

					__UpperCAmelCase    :     Optional[Any]													=	outputs.logits.detach().cpu()

					__UpperCAmelCase    :     List[Any]													=	image_processor.post_process_semantic_segmentation(outputs=a_       ,       target_sizes=[(50, 60)] )
					__UpperCAmelCase    :     Tuple													=	torch.Size((50, 60) )
					self.assertEqual(segmentation[0].shape       ,       a_ )

					__UpperCAmelCase    :     List[Any]													=	image_processor.post_process_semantic_segmentation(outputs=a_ )
					__UpperCAmelCase    :     Optional[int]													=	torch.Size((32, 32) )
					self.assertEqual(segmentation[0].shape       ,       a_ )





