


'''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()
__snake_case							      =     logging.get_logger(__name__)


def 			a      (			__a   )  ->	int:



					'''simple docstring'''






					print('''Loading config file...'''   )

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

					UpperCamelCase__   :str												= argparse.Namespace()
					with open(lowercase__ ,					'''r'''   ) as yaml_file:
										try:
															UpperCamelCase__   :str												= yaml.load(lowercase__ ,					Loader=yaml.FullLoader   )

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


def 			a      (			__a ,					__a   )  ->	str:



					'''simple docstring'''






					UpperCamelCase__   :Union[str, Any]												= MobileViTVaConfig()

					UpperCamelCase__   :Optional[int]												= False

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

					# orig_config
					UpperCamelCase__   :Optional[Any]												= load_orig_config_file(lowercase__   )
					assert getattr(lowercase__ ,					'''model.classification.name''' ,					-1   ) == "mobilevit_v2", "Invalid model"
					UpperCamelCase__   :Union[str, Any]												= getattr(lowercase__ ,					'''model.classification.mitv2.width_multiplier''' ,					1.0   )
					assert (
					    getattr(lowercase__ ,					'''model.classification.mitv2.attn_norm_layer''' ,					-1   ) == "layer_norm_2d"
					), "Norm layers other than layer_norm_2d is not supported"
					UpperCamelCase__   :Dict												= getattr(lowercase__ ,					'''model.classification.activation.name''' ,					'''swish'''   )
					# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

					if is_segmentation_model:
										UpperCamelCase__   :Union[str, Any]												= getattr(lowercase__ ,					'''model.segmentation.output_stride''' ,					16   )
										if "_deeplabv3" in task_name:
															UpperCamelCase__   :Optional[int]												= getattr(lowercase__ ,					'''model.segmentation.deeplabv3.aspp_rates''' ,					[12, 24, 36]   )
															UpperCamelCase__   :List[str]												= getattr(lowercase__ ,					'''model.segmentation.deeplabv3.aspp_out_channels''' ,					512   )
															UpperCamelCase__   :Any												= getattr(lowercase__ ,					'''model.segmentation.deeplabv3.aspp_dropout''' ,					0.1   )

    # id2label
					UpperCamelCase__   :Any												= '''huggingface/label-files'''
					UpperCamelCase__   :Optional[int]												= json.load(open(hf_hub_download(lowercase__ ,					lowercase__ ,					repo_type='''dataset'''   ) ,					'''r'''   )   )
					UpperCamelCase__   :Dict												= {int(lowercase__   ): v for k, v in idalabel.items()}
					UpperCamelCase__   :List[Any]												= idalabel
					UpperCamelCase__   :int												= {v: k for k, v in idalabel.items()}

					return config


def 			a      (			__a ,					__a ,					__a   )  ->	str:



					'''simple docstring'''






					UpperCamelCase__   :str												= dct.pop(lowercase__   )
					UpperCamelCase__   :List[Any]												= val


def 			a      (			__a ,					__a=False   )  ->	Optional[Any]:



					'''simple docstring'''






					if base_model:
										UpperCamelCase__   :Tuple												= ''''''
					else:
										UpperCamelCase__   :List[Any]												= '''mobilevitv2.'''

					UpperCamelCase__   :int												= []
					for k in state_dict.keys():
										if k[:8] == "encoder.":
															UpperCamelCase__   :Optional[Any]												= k[8:]
										else:
															UpperCamelCase__   :Tuple												= k

										if ".block." in k:
															UpperCamelCase__   :List[Any]												= k_new.replace('''.block.''' ,					'''.'''   )
										if ".conv." in k:
															UpperCamelCase__   :Tuple												= k_new.replace('''.conv.''' ,					'''.convolution.'''   )
										if ".norm." in k:
															UpperCamelCase__   :Any												= k_new.replace('''.norm.''' ,					'''.normalization.'''   )

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

										for i in [3, 4, 5]:
															if f'''layer_{i}.0.''' in k:
																				UpperCamelCase__   :Any												= 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:
																				UpperCamelCase__   :int												= 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:
																				UpperCamelCase__   :Optional[int]												= 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:
																				UpperCamelCase__   :Optional[Any]												= [0, 1]
															elif i == 4:
																				UpperCamelCase__   :Union[str, Any]												= [0, 1, 2, 3]
															elif i == 5:
																				UpperCamelCase__   :Dict												= [0, 1, 2]

															for j in j_in:
																				if f'''layer_{i}.1.global_rep.{j}.''' in k:
																									UpperCamelCase__   :Any												= 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:
																				UpperCamelCase__   :int												= 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:
																				UpperCamelCase__   :Dict												= 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:
															UpperCamelCase__   :int												= k_new.replace('''pre_norm_attn.0.''' ,					'''layernorm_before.'''   )
										if "pre_norm_attn.1." in k:
															UpperCamelCase__   :Any												= k_new.replace('''pre_norm_attn.1.''' ,					'''attention.'''   )
										if "pre_norm_ffn.0." in k:
															UpperCamelCase__   :Optional[Any]												= k_new.replace('''pre_norm_ffn.0.''' ,					'''layernorm_after.'''   )
										if "pre_norm_ffn.1." in k:
															UpperCamelCase__   :Union[str, Any]												= k_new.replace('''pre_norm_ffn.1.''' ,					'''ffn.conv1.'''   )
										if "pre_norm_ffn.3." in k:
															UpperCamelCase__   :Any												= k_new.replace('''pre_norm_ffn.3.''' ,					'''ffn.conv2.'''   )

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

										if "seg_head." in k:
															UpperCamelCase__   :Tuple												= k_new.replace('''seg_head.''' ,					'''segmentation_head.'''   )
										if ".aspp_layer." in k:
															UpperCamelCase__   :Dict												= k_new.replace('''.aspp_layer.''' ,					'''.'''   )
										if ".aspp_pool." in k:
															UpperCamelCase__   :Any												= k_new.replace('''.aspp_pool.''' ,					'''.'''   )

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


def 			a      (			__a   )  ->	Tuple:



					'''simple docstring'''






					UpperCamelCase__   :int												= []
					for k in state_dict.keys():
										if k.startswith('''seg_head.aux_head.'''   ):
															keys_to_ignore.append(lowercase__   )
					for k in keys_to_ignore:
										state_dict.pop(lowercase__ ,					lowercase__   )


def 			a      (			)  ->	Optional[int]:



					'''simple docstring'''






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


@torch.no_grad()
def 			a      (			__a ,					__a ,					__a ,					__a   )  ->	Any:



					'''simple docstring'''






					UpperCamelCase__   :Tuple												= get_mobilevitva_config(lowercase__ ,					lowercase__   )

					# load original state_dict
					UpperCamelCase__   :Tuple												= torch.load(lowercase__ ,					map_location='''cpu'''   )

					# load huggingface model
					if task_name.startswith('''ade20k_'''   ) or task_name.startswith('''voc_'''   ):
										UpperCamelCase__   :int												= MobileViTVaForSemanticSegmentation(lowercase__   ).eval()
										UpperCamelCase__   :Dict												= False
					else:
										UpperCamelCase__   :Dict												= MobileViTVaForImageClassification(lowercase__   ).eval()
										UpperCamelCase__   :List[Any]												= False

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

					# load modified state_dict
					model.load_state_dict(lowercase__   )

					# Check outputs on an image, prepared by MobileViTImageProcessor
					UpperCamelCase__   :List[str]												= MobileViTImageProcessor(crop_size=config.image_size ,					size=config.image_size + 32   )
					UpperCamelCase__   :Dict												= image_processor(images=prepare_img() ,					return_tensors='''pt'''   )
					UpperCamelCase__   :Union[str, Any]												= model(**lowercase__   )

					# verify classification model
					if task_name.startswith('''imagenet'''   ):
										UpperCamelCase__   :Dict												= outputs.logits
										UpperCamelCase__   :Tuple												= 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
															UpperCamelCase__   :str												= torch.tensor([-1.6_336e00, -7.3_204e-02, -5.1_883e-01]   )
															assert torch.allclose(logits[0, :3] ,					lowercase__ ,					atol=1e-4   )

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


if __name__ == "__main__":
		__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.'''
		)

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



import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()





class       A    (   A_					,				unittest.TestCase ):
							UpperCamelCase_   :							Any       =PriorTransformer
							UpperCamelCase_   :							List[str]       ='''hidden_states'''


							@property
							def 			_A       (self					):
									__lowercase=			4
									__lowercase=			8
									__lowercase=			7

									__lowercase=			floats_tensor((batch_size, embedding_dim)					).to(lowerCAmelCase					)

									__lowercase=			floats_tensor((batch_size, embedding_dim)					).to(lowerCAmelCase					)
									__lowercase=			floats_tensor((batch_size, num_embeddings, embedding_dim)					).to(lowerCAmelCase					)

									return {
									    "hidden_states": hidden_states,
									    "timestep": 2,
									    "proj_embedding": proj_embedding,
									    "encoder_hidden_states": encoder_hidden_states,
									}


							def 			_A       (self      ,				lowerCAmelCase=0					):
									torch.manual_seed(lowerCAmelCase					)
									__lowercase=			4
									__lowercase=			8
									__lowercase=			7

									__lowercase=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)

									__lowercase=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)
									__lowercase=			torch.randn((batch_size, num_embeddings, embedding_dim)					).to(lowerCAmelCase					)

									return {
									    "hidden_states": hidden_states,
									    "timestep": 2,
									    "proj_embedding": proj_embedding,
									    "encoder_hidden_states": encoder_hidden_states,
									}


							@property
							def 			_A       (self					):
									return (4, 8)


							@property
							def 			_A       (self					):
									return (4, 8)


							def 			_A       (self					):
									__lowercase=			{
									    'num_attention_heads': 2,
									    'attention_head_dim': 4,
									    'num_layers': 2,
									    'embedding_dim': 8,
									    'num_embeddings': 7,
									    'additional_embeddings': 4,
									}
									__lowercase=			self.dummy_input
									return init_dict, inputs_dict


							def 			_A       (self					):
									__lowercase,							__lowercase=			PriorTransformer.from_pretrained(
									    'hf-internal-testing/prior-dummy'      ,				output_loading_info=lowerCAmelCase					)
									self.assertIsNotNone(lowerCAmelCase					)
									self.assertEqual(len(loading_info['missing_keys']					)      ,				0					)

									model.to(lowerCAmelCase					)
									__lowercase=			model(**self.dummy_input					)[0]

									assert hidden_states is not None, "Make sure output is not None"


							def 			_A       (self					):
									__lowercase,							__lowercase=			self.prepare_init_args_and_inputs_for_common()

									__lowercase=			self.model_class(**lowerCAmelCase					)
									__lowercase=			inspect.signature(model.forward					)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									__lowercase=			[*signature.parameters.keys()]

									__lowercase=			['hidden_states', 'timestep']
									self.assertListEqual(arg_names[:2]      ,				lowerCAmelCase					)







							def 			_A       (self					):
									__lowercase=			PriorTransformer.from_pretrained('hf-internal-testing/prior-dummy'					)
									__lowercase=			model.to(lowerCAmelCase					)

									if hasattr(lowerCAmelCase      ,				'set_default_attn_processor'					):
											model.set_default_attn_processor()

									__lowercase=			self.get_dummy_seed_input()

									with torch.no_grad():
											__lowercase=			model(**lowerCAmelCase					)[0]

									__lowercase=			output[0, :5].flatten().cpu()
									print(lowerCAmelCase					)

									# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
									# the expected output slices are not the same for CPU and GPU.
									__lowercase=			torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]					)
									self.assertTrue(torch_all_close(lowerCAmelCase      ,				lowerCAmelCase      ,				rtol=1E-2					)					)





@slow
class       A    (   unittest.TestCase ):


							def 			_A       (self      ,				lowerCAmelCase=1      ,				lowerCAmelCase=7_6_8      ,				lowerCAmelCase=7_7      ,				lowerCAmelCase=0					):
									torch.manual_seed(lowerCAmelCase					)
									__lowercase=			batch_size
									__lowercase=			embedding_dim
									__lowercase=			num_embeddings

									__lowercase=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)

									__lowercase=			torch.randn((batch_size, embedding_dim)					).to(lowerCAmelCase					)
									__lowercase=			torch.randn((batch_size, num_embeddings, embedding_dim)					).to(lowerCAmelCase					)

									return {
									    "hidden_states": hidden_states,
									    "timestep": 2,
									    "proj_embedding": proj_embedding,
									    "encoder_hidden_states": encoder_hidden_states,
									}


							def 			_A       (self					):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







							@parameterized.expand(
							    [
							        # fmt: off
							        [1_3, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
							        [3_7, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
							        # fmt: on
							    ]					)
							def 			_A       (self      ,				lowerCAmelCase      ,				lowerCAmelCase					):
									__lowercase=			PriorTransformer.from_pretrained('kandinsky-community/kandinsky-2-1-prior'      ,				subfolder='prior'					)
									model.to(lowerCAmelCase					)
									__lowercase=			self.get_dummy_seed_input(seed=lowerCAmelCase					)

									with torch.no_grad():
											__lowercase=			model(**lowerCAmelCase					)[0]

									assert list(sample.shape					) == [1, 7_6_8]

									__lowercase=			sample[0, :8].flatten().cpu()
									print(lowerCAmelCase					)
									__lowercase=			torch.tensor(lowerCAmelCase					)

									assert torch_all_close(lowerCAmelCase      ,				lowerCAmelCase      ,				atol=1E-3					)






