

'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE							:				int     	=			logging.get_logger(__name__)




def 							_UpperCamelCase				(   lowerCAmelCase__:					str		,lowerCAmelCase__:					List[str]=False   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				[]
							for i in range(config.num_hidden_layers   ):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""")   )
														rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""")   )
														rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""")   )
														rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""")   )
														rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""")   )
														rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""")   )
														rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""")   )
														rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""")   )
														rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""")   )
														rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""")   )

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ('cls_token', 'vit.embeddings.cls_token'),
							        ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
							        ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
							        ('pos_embed', 'vit.embeddings.position_embeddings'),
							    ]   )

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ('norm.weight', 'layernorm.weight'),
														        ('norm.bias', 'layernorm.bias'),
														    ]   )

														# if just the base model, we should remove "vit" from all keys that start with "vit"
														SCREAMING_SNAKE_CASE_				=				[(pair[0], pair[1][4:]) if pair[1].startswith('vit'   ) else pair for pair in rename_keys]
							else:
														# layernorm + classification head
														rename_keys.extend(
														    [
														        ('norm.weight', 'vit.layernorm.weight'),
														        ('norm.bias', 'vit.layernorm.bias'),
														        ('head.weight', 'classifier.weight'),
														        ('head.bias', 'classifier.bias'),
														    ]   )

							return rename_keys




def 							_UpperCamelCase				(   lowerCAmelCase__:					Dict		,lowerCAmelCase__:					Union[str, Any]		,lowerCAmelCase__:					Union[str, Any]=False   )      -> int:






							for i in range(config.num_hidden_layers   ):
														if base_model:
																					SCREAMING_SNAKE_CASE_				=				''
														else:
																					SCREAMING_SNAKE_CASE_				=				'vit.'
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE_				=				state_dict.pop(F"""blocks.{i}.attn.qkv.weight"""   )
														SCREAMING_SNAKE_CASE_				=				state_dict.pop(F"""blocks.{i}.attn.qkv.bias"""   )
														# next, add query, keys and values (in that order) to the state dict
														SCREAMING_SNAKE_CASE_				=				in_proj_weight[
														    : config.hidden_size, :
														]
														SCREAMING_SNAKE_CASE_				=				in_proj_bias[: config.hidden_size]
														SCREAMING_SNAKE_CASE_				=				in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														SCREAMING_SNAKE_CASE_				=				in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														SCREAMING_SNAKE_CASE_				=				in_proj_weight[
														    -config.hidden_size :, :
														]
														SCREAMING_SNAKE_CASE_				=				in_proj_bias[-config.hidden_size :]




def 							_UpperCamelCase				(   lowerCAmelCase__:					List[str]   )      -> List[str]:
							SCREAMING_SNAKE_CASE_				=				['head.weight', 'head.bias']
							for k in ignore_keys:
														state_dict.pop(_lowerCAmelCase		,_lowerCAmelCase   )




def 							_UpperCamelCase				(   lowerCAmelCase__:					Tuple		,lowerCAmelCase__:					List[str]		,lowerCAmelCase__:					int   )      -> Dict:
							SCREAMING_SNAKE_CASE_				=				dct.pop(_lowerCAmelCase   )
							SCREAMING_SNAKE_CASE_				=				val




def 							_UpperCamelCase				(   )      -> Any:
							SCREAMING_SNAKE_CASE_				=				'http://images.cocodataset.org/val2017/000000039769.jpg'
							SCREAMING_SNAKE_CASE_				=				Image.open(requests.get(_lowerCAmelCase		,stream=_lowerCAmelCase   ).raw   )
							return im




@torch.no_grad()
def 							_UpperCamelCase				(   lowerCAmelCase__:					List[Any]		,lowerCAmelCase__:					Dict		,lowerCAmelCase__:					Any=True   )      -> int:
							SCREAMING_SNAKE_CASE_				=				ViTConfig()
							# patch_size
							if model_name[-1] == "8":
														SCREAMING_SNAKE_CASE_				=				8
							# set labels if required
							if not base_model:
														SCREAMING_SNAKE_CASE_				=				1000
														SCREAMING_SNAKE_CASE_				=				'huggingface/label-files'
														SCREAMING_SNAKE_CASE_				=				'imagenet-1k-id2label.json'
														SCREAMING_SNAKE_CASE_				=				json.load(open(hf_hub_download(_lowerCAmelCase		,_lowerCAmelCase		,repo_type='dataset'   )		,'r'   )   )
														SCREAMING_SNAKE_CASE_				=				{int(_lowerCAmelCase   ): v for k, v in idalabel.items()}
														SCREAMING_SNAKE_CASE_				=				idalabel
														SCREAMING_SNAKE_CASE_				=				{v: k for k, v in idalabel.items()}
							# size of the architecture
							if model_name in ["dino_vits8", "dino_vits16"]:
														SCREAMING_SNAKE_CASE_				=				384
														SCREAMING_SNAKE_CASE_				=				1536
														SCREAMING_SNAKE_CASE_				=				12
														SCREAMING_SNAKE_CASE_				=				6

							# load original model from torch hub
							SCREAMING_SNAKE_CASE_				=				torch.hub.load('facebookresearch/dino:main'		,_lowerCAmelCase   )
							original_model.eval()

							# load state_dict of original model, remove and rename some keys
							SCREAMING_SNAKE_CASE_				=				original_model.state_dict()
							if base_model:
														remove_classification_head_(_lowerCAmelCase   )
							SCREAMING_SNAKE_CASE_				=				create_rename_keys(_lowerCAmelCase		,base_model=_lowerCAmelCase   )
							for src, dest in rename_keys:
														rename_key(_lowerCAmelCase		,_lowerCAmelCase		,_lowerCAmelCase   )
							read_in_q_k_v(_lowerCAmelCase		,_lowerCAmelCase		,_lowerCAmelCase   )

							# load HuggingFace model
							if base_model:
														SCREAMING_SNAKE_CASE_				=				ViTModel(_lowerCAmelCase		,add_pooling_layer=_lowerCAmelCase   ).eval()
							else:
														SCREAMING_SNAKE_CASE_				=				ViTForImageClassification(_lowerCAmelCase   ).eval()
							model.load_state_dict(_lowerCAmelCase   )

							# Check outputs on an image, prepared by ViTImageProcessor
							SCREAMING_SNAKE_CASE_				=				ViTImageProcessor()
							SCREAMING_SNAKE_CASE_				=				image_processor(images=prepare_img()		,return_tensors='pt'   )
							SCREAMING_SNAKE_CASE_				=				encoding['pixel_values']
							SCREAMING_SNAKE_CASE_				=				model(_lowerCAmelCase   )

							if base_model:
														SCREAMING_SNAKE_CASE_				=				original_model(_lowerCAmelCase   )
														assert torch.allclose(_lowerCAmelCase		,outputs.last_hidden_state[:, 0, :]		,atol=1E-1   )
							else:
														SCREAMING_SNAKE_CASE_				=				original_model(_lowerCAmelCase   )
														assert logits.shape == outputs.logits.shape
														assert torch.allclose(_lowerCAmelCase		,outputs.logits		,atol=1E-3   )

							Path(_lowerCAmelCase   ).mkdir(exist_ok=_lowerCAmelCase   )
							print(F"""Saving model {model_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__":
							SCREAMING_SNAKE_CASE							:				str     	=			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--model_name",
							    default="dino_vitb16",
							    type=str,
							    help="Name of the model trained with DINO you\'d like to convert.",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
							)
							parser.add_argument(
							    "--base_model",
							    action="store_true",
							    help="Whether to only convert the base model (no projection head weights).",
							)

							parser.set_defaults(base_model=True)
							SCREAMING_SNAKE_CASE							:				Any     	=			parser.parse_args()
							convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model)









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      UpperCamelCase(	_a  ,				unittest.TestCase ):
					snake_case_		:					Any    					=						PriorTransformer
					snake_case_		:					List[str]    					=						"""hidden_states"""


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						str:




											'''simple docstring'''

											__snake_case				=		4
											__snake_case				=		8
											__snake_case				=		7

											__snake_case				=		floats_tensor((batch_size, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )

											__snake_case				=		floats_tensor((batch_size, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )
											__snake_case				=		floats_tensor((batch_size, num_embeddings, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=0    )      ->						Any:




											'''simple docstring'''

											torch.manual_seed(SCREAMING_SNAKE_CASE    )
											__snake_case				=		4
											__snake_case				=		8
											__snake_case				=		7

											__snake_case				=		torch.randn((batch_size, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )

											__snake_case				=		torch.randn((batch_size, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )
											__snake_case				=		torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )

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


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						str:




											'''simple docstring'''

											return (4, 8)


					@property
					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[str]    )      ->						Any:




											'''simple docstring'''

											return (4, 8)


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		{
											    "num_attention_heads": 2,
											    "attention_head_dim": 4,
											    "num_layers": 2,
											    "embedding_dim": 8,
											    "num_embeddings": 7,
											    "additional_embeddings": 4,
											}
											__snake_case				=		self.dummy_input
											return init_dict, inputs_dict


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						Dict:




											'''simple docstring'''

											__snake_case		,       __snake_case				=		PriorTransformer.from_pretrained(
											    "hf-internal-testing/prior-dummy"  ,     output_loading_info=SCREAMING_SNAKE_CASE    )
											self.assertIsNotNone(SCREAMING_SNAKE_CASE    )
											self.assertEqual(len(loading_info["missing_keys"]    )  ,     0    )

											model.to(SCREAMING_SNAKE_CASE    )
											__snake_case				=		model(**self.dummy_input    )[0]

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case		,       __snake_case				=		self.prepare_init_args_and_inputs_for_common()

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

											__snake_case				=		["hidden_states", "timestep"]
											self.assertListEqual(arg_names[:2]  ,     SCREAMING_SNAKE_CASE    )






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						List[str]:




											'''simple docstring'''

											__snake_case				=		PriorTransformer.from_pretrained("hf-internal-testing/prior-dummy"    )
											__snake_case				=		model.to(SCREAMING_SNAKE_CASE    )

											if hasattr(SCREAMING_SNAKE_CASE  ,     "set_default_attn_processor"    ):
																	model.set_default_attn_processor()

											__snake_case				=		self.get_dummy_seed_input()

											with torch.no_grad():
																	__snake_case				=		model(**SCREAMING_SNAKE_CASE    )[0]

											__snake_case				=		output[0, :5].flatten().cpu()
											print(SCREAMING_SNAKE_CASE    )

											# 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.
											__snake_case				=		torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]    )
											self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     rtol=1e-2    )    )






@slow
class      UpperCamelCase(	unittest.TestCase ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]  ,     SCREAMING_SNAKE_CASE						:		int=1  ,     SCREAMING_SNAKE_CASE						:		str=7_6_8  ,     SCREAMING_SNAKE_CASE						:		Dict=7_7  ,     SCREAMING_SNAKE_CASE						:		int=0    )      ->						List[str]:




											'''simple docstring'''

											torch.manual_seed(SCREAMING_SNAKE_CASE    )
											__snake_case				=		batch_size
											__snake_case				=		embedding_dim
											__snake_case				=		num_embeddings

											__snake_case				=		torch.randn((batch_size, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )

											__snake_case				=		torch.randn((batch_size, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )
											__snake_case				=		torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(SCREAMING_SNAKE_CASE    )

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Union[str, Any]:




											'''simple docstring'''

											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()






					@parameterized.expand(
					    [
					        # fmt: off
					        [1_3, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
					        [3_7, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
					        # fmt: on
					    ]    )
					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]  ,     SCREAMING_SNAKE_CASE						:		int  ,     SCREAMING_SNAKE_CASE						:		Tuple    )      ->						List[str]:




											'''simple docstring'''

											__snake_case				=		PriorTransformer.from_pretrained("kandinsky-community/kandinsky-2-1-prior"  ,     subfolder="prior"    )
											model.to(SCREAMING_SNAKE_CASE    )
											__snake_case				=		self.get_dummy_seed_input(seed=SCREAMING_SNAKE_CASE    )

											with torch.no_grad():
																	__snake_case				=		model(**SCREAMING_SNAKE_CASE    )[0]

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

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

											assert torch_all_close(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE  ,     atol=1e-3    )





