import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__     :   Optional[int]		=   logging.get_logger()
def  UpperCamelCase__						(				A__			, A__			, A__			, A__			, A__ = True    )   ->					Optional[Any]:
				print(F"""Converting {name}..."""    )

				with torch.no_grad():
								if hidden_sizes == 128:
												if name[-1] == "S":
																snake_case__					:	Dict												=      timm.create_model('levit_128s'			, pretrained=A__    )
												else:
																snake_case__					:	List[Any]												=      timm.create_model('levit_128'			, pretrained=A__    )
								if hidden_sizes == 192:
												snake_case__					:	str												=      timm.create_model('levit_192'			, pretrained=A__    )
								if hidden_sizes == 256:
												snake_case__					:	Dict												=      timm.create_model('levit_256'			, pretrained=A__    )
								if hidden_sizes == 384:
												snake_case__					:	Optional[int]												=      timm.create_model('levit_384'			, pretrained=A__    )

								from_model.eval()
								snake_case__					:	int												=      LevitForImageClassificationWithTeacher(A__    ).eval()
								snake_case__					:	int												=      OrderedDict()

								snake_case__					:	Optional[Any]												=      from_model.state_dict()
								snake_case__					:	List[Any]												=      list(from_model.state_dict().keys()    )
								snake_case__					:	int												=      list(our_model.state_dict().keys()    )
								print(len(A__    )			, len(A__    )    )
								for i in range(len(A__    )    ):
												snake_case__					:	Dict												=      weights[og_keys[i]]
								our_model.load_state_dict(A__    )

								snake_case__					:	Optional[Any]												=      torch.randn((2, 3, 224, 224)    )
								snake_case__					:	Tuple												=      from_model(A__    )
								snake_case__					:	str												=      our_model(A__    ).logits

				assert torch.allclose(A__			, A__    ), "The model logits don't match the original one."

				snake_case__					:	Optional[Any]												=      name
				print(A__    )

				if push_to_hub:
								our_model.save_pretrained(save_directory / checkpoint_name    )
								snake_case__					:	Union[str, Any]												=      LevitImageProcessor()
								image_processor.save_pretrained(save_directory / checkpoint_name    )

								print(F"""Pushed {checkpoint_name}"""    )
def  UpperCamelCase__						(				A__			, A__ = None			, A__ = True    )   ->					str:
				snake_case__					:	Union[str, Any]												=      'imagenet-1k-id2label.json'
				snake_case__					:	List[str]												=      1000
				snake_case__					:	Dict												=      (1, num_labels)

				snake_case__					:	Tuple												=      'huggingface/label-files'
				snake_case__					:	int												=      num_labels
				snake_case__					:	Union[str, Any]												=      json.load(open(hf_hub_download(A__			, A__			, repo_type='dataset'    )			, 'r'    )    )
				snake_case__					:	Union[str, Any]												=      {int(A__    ): v for k, v in idalabel.items()}

				snake_case__					:	Any												=      idalabel
				snake_case__					:	List[str]												=      {v: k for k, v in idalabel.items()}

				snake_case__					:	Tuple												=      partial(A__			, num_labels=A__			, idalabel=A__			, labelaid=A__    )

				snake_case__					:	Union[str, Any]												=      {
				    'levit-128S': 128,
				    'levit-128': 128,
				    'levit-192': 192,
				    'levit-256': 256,
				    'levit-384': 384,
				}

				snake_case__					:	Dict												=      {
				    'levit-128S': ImageNetPreTrainedConfig(
				        hidden_sizes=[128, 256, 384]			, num_attention_heads=[4, 6, 8]			, depths=[2, 3, 4]			, key_dim=[16, 16, 16]			, drop_path_rate=0			, ),
				    'levit-128': ImageNetPreTrainedConfig(
				        hidden_sizes=[128, 256, 384]			, num_attention_heads=[4, 8, 12]			, depths=[4, 4, 4]			, key_dim=[16, 16, 16]			, drop_path_rate=0			, ),
				    'levit-192': ImageNetPreTrainedConfig(
				        hidden_sizes=[192, 288, 384]			, num_attention_heads=[3, 5, 6]			, depths=[4, 4, 4]			, key_dim=[32, 32, 32]			, drop_path_rate=0			, ),
				    'levit-256': ImageNetPreTrainedConfig(
				        hidden_sizes=[256, 384, 512]			, num_attention_heads=[4, 6, 8]			, depths=[4, 4, 4]			, key_dim=[32, 32, 32]			, drop_path_rate=0			, ),
				    'levit-384': ImageNetPreTrainedConfig(
				        hidden_sizes=[384, 512, 768]			, num_attention_heads=[6, 9, 12]			, depths=[4, 4, 4]			, key_dim=[32, 32, 32]			, drop_path_rate=0.1			, ),
				}

				if model_name:
								convert_weight_and_push(
								    names_to_hidden_sizes[model_name]			, A__			, names_to_config[model_name]			, A__			, A__    )
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(names_to_hidden_sizes[model_name]			, A__			, A__			, A__			, A__    )
				return config, expected_shape


if __name__ == "__main__":
				lowerCAmelCase__     :   Union[str, Any]		=   argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--model_name''',
				    default=None,
				    type=str,
				    help='''The name of the model you wish to convert, it must be one of the supported Levit* architecture,''',
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''',
				    default='''levit-dump-folder/''',
				    type=Path,
				    required=False,
				    help='''Path to the output PyTorch model directory.''',
				)
				parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
				parser.add_argument(
				    '''--no-push_to_hub''',
				    dest='''push_to_hub''',
				    action='''store_false''',
				    help='''Do not push model and image processor to the hub''',
				)

				lowerCAmelCase__     :   int		=   parser.parse_args()
				lowerCAmelCase__     :   str		=   args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)








import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





class     lowerCamelCase__(					__lowerCamelCase ,     __lowerCamelCase ,     __lowerCamelCase ,     unittest.TestCase):
      UpperCAmelCase__      :	Union[str, Any]    		=   StableDiffusionInpaintPipeline
      UpperCAmelCase__      :	Dict    		=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
      UpperCAmelCase__      :	str    		=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
      UpperCAmelCase__      :	int    		=   frozenset(
          [])  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
      UpperCAmelCase__      :	Union[str, Any]    		=   frozenset([])
      def 			lowerCAmelCase__						(				self: str   ):
             torch.manual_seed(0   )
             __lowerCamelCase   =						UNetaDConditionModel(
                 block_out_channels=(32, 64)    ,       layers_per_block=2    ,       sample_size=32    ,       in_channels=9    ,       out_channels=4    ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")    ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")    ,       cross_attention_dim=32    ,       attention_head_dim=(2, 4)    ,       use_linear_projection=UpperCamelCase_    ,       )
             __lowerCamelCase   =						PNDMScheduler(skip_prk_steps=UpperCamelCase_   )
             torch.manual_seed(0   )
             __lowerCamelCase   =						AutoencoderKL(
                 block_out_channels=[32, 64]    ,       in_channels=3    ,       out_channels=3    ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]    ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]    ,       latent_channels=4    ,       sample_size=1_28    ,       )
             torch.manual_seed(0   )
             __lowerCamelCase   =						CLIPTextConfig(
                 bos_token_id=0    ,       eos_token_id=2    ,       hidden_size=32    ,       intermediate_size=37    ,       layer_norm_eps=1E-05    ,       num_attention_heads=4    ,       num_hidden_layers=5    ,       pad_token_id=1    ,       vocab_size=10_00    ,       hidden_act="""gelu"""    ,       projection_dim=5_12    ,       )
             __lowerCamelCase   =						CLIPTextModel(UpperCamelCase_   )
             __lowerCamelCase   =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )

             __lowerCamelCase   =						{
                 """unet""": unet,
                 """scheduler""": scheduler,
                 """vae""": vae,
                 """text_encoder""": text_encoder,
                 """tokenizer""": tokenizer,
                 """safety_checker""": None,
                 """feature_extractor""": None,
             }
             return components
      def 			lowerCAmelCase__						(				self: List[Any]    ,       UpperCamelCase_: Any    ,       UpperCamelCase_: List[Any]=0   ):
             # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
             __lowerCamelCase   =						floats_tensor((1, 3, 32, 32)    ,       rng=random.Random(UpperCamelCase_   )   ).to(UpperCamelCase_   )
             __lowerCamelCase   =						image.cpu().permute(0    ,       2    ,       3    ,       1   )[0]
             __lowerCamelCase   =						Image.fromarray(np.uinta(UpperCamelCase_   )   ).convert("""RGB"""   ).resize((64, 64)   )
             __lowerCamelCase   =						Image.fromarray(np.uinta(image + 4   )   ).convert("""RGB"""   ).resize((64, 64)   )
             if str(UpperCamelCase_   ).startswith("""mps"""   ):
                    __lowerCamelCase   =						torch.manual_seed(UpperCamelCase_   )
             else:
                    __lowerCamelCase   =						torch.Generator(device=UpperCamelCase_   ).manual_seed(UpperCamelCase_   )
             __lowerCamelCase   =						{
                 """prompt""": """A painting of a squirrel eating a burger""",
                 """image""": init_image,
                 """mask_image""": mask_image,
                 """generator""": generator,
                 """num_inference_steps""": 2,
                 """guidance_scale""": 6.0,
                 """output_type""": """numpy""",
             }
             return inputs
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
             __lowerCamelCase   =						self.get_dummy_components()
             __lowerCamelCase   =						StableDiffusionInpaintPipeline(**UpperCamelCase_   )
             __lowerCamelCase   =						sd_pipe.to(UpperCamelCase_   )
             sd_pipe.set_progress_bar_config(disable=UpperCamelCase_   )

             __lowerCamelCase   =						self.get_dummy_inputs(UpperCamelCase_   )
             __lowerCamelCase   =						sd_pipe(**UpperCamelCase_   ).images
             __lowerCamelCase   =						image[0, -3:, -3:, -1]

             assert image.shape == (1, 64, 64, 3)
             __lowerCamelCase   =						np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476]   )

             assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2




      def 			lowerCAmelCase__						(				self: int   ):
             super().test_inference_batch_single_identical(expected_max_diff=3E-3   )





@slow
@require_torch_gpu
class     lowerCamelCase__(					unittest.TestCase):
      def 			lowerCAmelCase__						(				self: str   ):
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()
      def 			lowerCAmelCase__						(				self: List[Any]   ):
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                 """/sd2-inpaint/init_image.png"""   )
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""   )
             __lowerCamelCase   =						load_numpy(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
                 """/yellow_cat_sitting_on_a_park_bench.npy"""   )

             __lowerCamelCase   =						"""stabilityai/stable-diffusion-2-inpainting"""
             __lowerCamelCase   =						StableDiffusionInpaintPipeline.from_pretrained(UpperCamelCase_    ,       safety_checker=UpperCamelCase_   )
             pipe.to(UpperCamelCase_   )
             pipe.set_progress_bar_config(disable=UpperCamelCase_   )
             pipe.enable_attention_slicing()

             __lowerCamelCase   =						"""Face of a yellow cat, high resolution, sitting on a park bench"""

             __lowerCamelCase   =						torch.manual_seed(0   )
             __lowerCamelCase   =						pipe(
                 prompt=UpperCamelCase_    ,       image=UpperCamelCase_    ,       mask_image=UpperCamelCase_    ,       generator=UpperCamelCase_    ,       output_type="""np"""    ,       )
             __lowerCamelCase   =						output.images[0]

             assert image.shape == (5_12, 5_12, 3)
             assert np.abs(expected_image - image   ).max() < 9E-3
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                 """/sd2-inpaint/init_image.png"""   )
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""   )
             __lowerCamelCase   =						load_numpy(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
                 """/yellow_cat_sitting_on_a_park_bench_fp16.npy"""   )

             __lowerCamelCase   =						"""stabilityai/stable-diffusion-2-inpainting"""
             __lowerCamelCase   =						StableDiffusionInpaintPipeline.from_pretrained(
                 UpperCamelCase_    ,       torch_dtype=torch.floataa    ,       safety_checker=UpperCamelCase_    ,       )
             pipe.to(UpperCamelCase_   )
             pipe.set_progress_bar_config(disable=UpperCamelCase_   )
             pipe.enable_attention_slicing()

             __lowerCamelCase   =						"""Face of a yellow cat, high resolution, sitting on a park bench"""

             __lowerCamelCase   =						torch.manual_seed(0   )
             __lowerCamelCase   =						pipe(
                 prompt=UpperCamelCase_    ,       image=UpperCamelCase_    ,       mask_image=UpperCamelCase_    ,       generator=UpperCamelCase_    ,       output_type="""np"""    ,       )
             __lowerCamelCase   =						output.images[0]

             assert image.shape == (5_12, 5_12, 3)
             assert np.abs(expected_image - image   ).max() < 5E-1




      def 			lowerCAmelCase__						(				self: int   ):
             torch.cuda.empty_cache()
             torch.cuda.reset_max_memory_allocated()
             torch.cuda.reset_peak_memory_stats()

             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
                 """/sd2-inpaint/init_image.png"""   )
             __lowerCamelCase   =						load_image(
                 """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png"""   )

             __lowerCamelCase   =						"""stabilityai/stable-diffusion-2-inpainting"""
             __lowerCamelCase   =						PNDMScheduler.from_pretrained(UpperCamelCase_    ,       subfolder="""scheduler"""   )
             __lowerCamelCase   =						StableDiffusionInpaintPipeline.from_pretrained(
                 UpperCamelCase_    ,       safety_checker=UpperCamelCase_    ,       scheduler=UpperCamelCase_    ,       torch_dtype=torch.floataa    ,       )
             pipe.to(UpperCamelCase_   )
             pipe.set_progress_bar_config(disable=UpperCamelCase_   )
             pipe.enable_attention_slicing(1   )
             pipe.enable_sequential_cpu_offload()

             __lowerCamelCase   =						"""Face of a yellow cat, high resolution, sitting on a park bench"""

             __lowerCamelCase   =						torch.manual_seed(0   )
             __lowerCamelCase   =						pipe(
                 prompt=UpperCamelCase_    ,       image=UpperCamelCase_    ,       mask_image=UpperCamelCase_    ,       generator=UpperCamelCase_    ,       num_inference_steps=2    ,       output_type="""np"""    ,       )

             __lowerCamelCase   =						torch.cuda.max_memory_allocated()
             # make sure that less than 2.65 GB is allocated
             assert mem_bytes < 2.65 * 10**9

