


import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 							a						(_lowerCAmelCase	,							unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				str	    =    DiTPipeline
							__UpperCAmelCase       :				Union[str, Any]	    =    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
							__UpperCAmelCase       :				List[Any]	    =    PipelineTesterMixin.required_optional_params - {
							    "latents",
							    "num_images_per_prompt",
							    "callback",
							    "callback_steps",
							}
							__UpperCAmelCase       :				Optional[int]	    =    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
							__UpperCAmelCase       :				Tuple	    =    False

							def        __snake_case   (				self			:    int							)      ->       Any:
														torch.manual_seed(0							)
														__snake_case						:     Dict									=  TransformeraDModel(
														    sample_size=16       ,      num_layers=2       ,      patch_size=4       ,      attention_head_dim=8       ,      num_attention_heads=2       ,      in_channels=4       ,      out_channels=8       ,      attention_bias=lowerCamelCase       ,      activation_fn="gelu-approximate"       ,      num_embeds_ada_norm=1000       ,      norm_type="ada_norm_zero"       ,      norm_elementwise_affine=lowerCamelCase       ,      )
														__snake_case						:     Optional[int]									=  AutoencoderKL()
														__snake_case						:     str									=  DDIMScheduler()
														__snake_case						:     Union[str, Any]									=  {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
														return components

							def        __snake_case   (				self			:    List[str]       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    List[Any]=0							)      ->       List[Any]:
														if str(lowerCamelCase							).startswith("mps"							):
																					__snake_case						:     Optional[int]									=  torch.manual_seed(lowerCamelCase							)
														else:
																					__snake_case						:     int									=  torch.Generator(device=lowerCamelCase							).manual_seed(lowerCamelCase							)
														__snake_case						:     Tuple									=  {
														    "class_labels": [1],
														    "generator": generator,
														    "num_inference_steps": 2,
														    "output_type": "numpy",
														}
														return inputs

							def        __snake_case   (				self			:    List[Any]							)      ->       List[Any]:
														__snake_case						:     Dict									=  "cpu"

														__snake_case						:     Optional[int]									=  self.get_dummy_components()
														__snake_case						:     Dict									=  self.pipeline_class(**lowerCamelCase							)
														pipe.to(lowerCamelCase							)
														pipe.set_progress_bar_config(disable=lowerCamelCase							)

														__snake_case						:     Optional[Any]									=  self.get_dummy_inputs(lowerCamelCase							)
														__snake_case						:     Tuple									=  pipe(**lowerCamelCase							).images
														__snake_case						:     List[str]									=  image[0, -3:, -3:, -1]

														self.assertEqual(image.shape       ,      (1, 16, 16, 3)							)
														__snake_case						:     int									=  np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57]							)
														__snake_case						:     int									=  np.abs(image_slice.flatten() - expected_slice							).max()
														self.assertLessEqual(lowerCamelCase       ,      1E-3							)

							def        __snake_case   (				self			:    Any							)      ->       Optional[Any]:
														self._test_inference_batch_single_identical(relax_max_difference=lowerCamelCase       ,      expected_max_diff=1E-3							)






							@unittest.skipIf(
							    torch_device != "cuda" or not is_xformers_available()       ,      reason="XFormers attention is only available with CUDA and `xformers` installed"       ,      )
							def        __snake_case   (				self			:    Optional[Any]							)      ->       Optional[int]:
														self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3							)

@require_torch_gpu
@slow
class 							a						(unittest.TestCase ):




							"""simple docstring"""


							def        __snake_case   (				self			:    Optional[Any]							)      ->       Tuple:
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()

							def        __snake_case   (				self			:    List[Any]							)      ->       List[Any]:
														__snake_case						:     Union[str, Any]									=  torch.manual_seed(0							)

														__snake_case						:     Any									=  DiTPipeline.from_pretrained("facebook/DiT-XL-2-256"							)
														pipe.to("cuda"							)

														__snake_case						:     Dict									=  ["vase", "umbrella", "white shark", "white wolf"]
														__snake_case						:     Any									=  pipe.get_label_ids(lowerCamelCase							)

														__snake_case						:     Tuple									=  pipe(lowerCamelCase       ,      generator=lowerCamelCase       ,      num_inference_steps=40       ,      output_type="np"							).images

														for word, image in zip(lowerCamelCase       ,      lowerCamelCase							):
																					__snake_case						:     Union[str, Any]									=  load_numpy(
																					    F'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'							)
																					assert np.abs((expected_image - image).max()							) < 1E-2






							def        __snake_case   (				self			:    Dict							)      ->       Optional[int]:
														__snake_case						:     List[str]									=  DiTPipeline.from_pretrained("facebook/DiT-XL-2-512"							)
														__snake_case						:     Dict									=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config							)
														pipe.to("cuda"							)

														__snake_case						:     List[str]									=  ["vase", "umbrella"]
														__snake_case						:     int									=  pipe.get_label_ids(lowerCamelCase							)

														__snake_case						:     Optional[Any]									=  torch.manual_seed(0							)
														__snake_case						:     Dict									=  pipe(lowerCamelCase       ,      generator=lowerCamelCase       ,      num_inference_steps=25       ,      output_type="np"							).images

														for word, image in zip(lowerCamelCase       ,      lowerCamelCase							):
																					__snake_case						:     Tuple									=  load_numpy(
																					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
																					    F'/dit/{word}_512.npy'							)

																					assert np.abs((expected_image - image).max()							) < 1E-1





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 (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase:						List[str]   				=       logging.get_logger(__name__)

_lowerCAmelCase:						Tuple   				=       torch.device('cpu')






def   _lowercase(					):
							a__      ='http://images.cocodataset.org/val2017/000000039769.jpg'
							a__      =Image.open(requests.get(__a    ,     stream=__a     ).raw     )
							return im






def   _lowercase(					__a				:						Optional[Any]     ):
							if swiftformer_name == "swiftformer_xs":
														return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01]     )

							elif swiftformer_name == "swiftformer_s":
														return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01]     )

							elif swiftformer_name == "swiftformer_l1":
														return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02]     )

							elif swiftformer_name == "swiftformer_l3":
														return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02]     )






def   _lowercase(					__a				:						int    ,     __a				:						int    ,     __a				:						Optional[Any]     ):
							a__      =dct.pop(__a     )
							a__      =val






def   _lowercase(					__a				:						Optional[Any]     ):
							a__      =[]
							for k in state_dict.keys():
														a__      =k
														if ".pwconv" in k:
																					a__      =k_new.replace('.pwconv'    ,     '.point_wise_conv'     )
														if ".dwconv" in k:
																					a__      =k_new.replace('.dwconv'    ,     '.depth_wise_conv'     )
														if ".Proj." in k:
																					a__      =k_new.replace('.Proj.'    ,     '.proj.'     )
														if "patch_embed" in k_new:
																					a__      =k_new.replace('patch_embed'    ,     'swiftformer.patch_embed.patch_embedding'     )
														if "network" in k_new:
																					a__      =k_new.split('.'     )
																					if ls[2].isdigit():
																												a__      ='swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:]     )
																					else:
																												a__      =k_new.replace('network'    ,     'swiftformer.encoder.network'     )
														rename_keys.append((k, k_new)     )
							return rename_keys






@torch.no_grad()
def   _lowercase(					__a				:						Union[str, Any]    ,     __a				:						int    ,     __a				:						str     ):
							a__      =SwiftFormerConfig()

							# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
							a__      =1000
							a__      ='huggingface/label-files'
							a__      ='imagenet-1k-id2label.json'
							a__      =json.load(open(hf_hub_download(__a    ,     __a    ,     repo_type='dataset'     )    ,     'r'     )     )
							a__      ={int(__a     ): v for k, v in idalabel.items()}
							a__      =idalabel
							a__      ={v: k for k, v in idalabel.items()}

							# size of the architecture
							if swiftformer_name == "swiftformer_xs":
														a__      =[3, 3, 6, 4]
														a__      =[48, 56, 112, 220]

							elif swiftformer_name == "swiftformer_s":
														a__      =[3, 3, 9, 6]
														a__      =[48, 64, 168, 224]

							elif swiftformer_name == "swiftformer_l1":
														a__      =[4, 3, 10, 5]
														a__      =[48, 96, 192, 384]

							elif swiftformer_name == "swiftformer_l3":
														a__      =[4, 4, 12, 6]
														a__      =[64, 128, 320, 512]

							# load state_dict of original model, remove and rename some keys
							if original_ckpt:
														if original_ckpt.startswith('https'     ):
																					a__      =torch.hub.load_state_dict_from_url(__a    ,     map_location='cpu'    ,     check_hash=__a     )
														else:
																					a__      =torch.load(__a    ,     map_location='cpu'     )
							a__      =checkpoint

							a__      =create_rename_keys(__a     )
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(__a    ,     __a    ,     __a     )

							# load HuggingFace model
							a__      =SwiftFormerForImageClassification(__a     ).eval()
							hf_model.load_state_dict(__a     )

							# prepare test inputs
							a__      =prepare_img()
							a__      =ViTImageProcessor.from_pretrained('preprocessor_config'     )
							a__      =processor(images=__a    ,     return_tensors='pt'     )

							# compare outputs from both models
							a__      =get_expected_output(__a     )
							a__      =hf_model(inputs['pixel_values']     ).logits

							assert hf_logits.shape == torch.Size([1, 1000]     )
							assert torch.allclose(hf_logits[0, 0:5]    ,     __a    ,     atol=1e-3     )

							Path(__a     ).mkdir(exist_ok=__a     )
							print(f"""Saving model {swiftformer_name} to {pytorch_dump_folder_path}"""     )
							hf_model.save_pretrained(__a     )


if __name__ == "__main__":
	_lowerCAmelCase:						Optional[Any]   				=       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--swiftformer_name',
	    default='swiftformer_xs',
	    choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
	    type=str,
	    help='Name of the SwiftFormer model you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path',
	    default='./converted_outputs/',
	    type=str,
	    help='Path to the output PyTorch model directory.',
	)
	parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')

	_lowerCAmelCase:						Optional[int]   				=       parser.parse_args()
	convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)

