
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__		:str   =    logging.get_logger(__name__)



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase							=						'''huggingface/label-files'''
					lowercase							=						'''imagenet-1k-id2label.json'''
					lowercase							=						json.load(open(hf_hub_download(lowerCAmelCase__ ,			lowerCAmelCase__ ,			repo_type='''dataset'''		) ,			'''r'''		)		)
					lowercase							=						{int(lowerCAmelCase__		): v for k, v in idalabel.items()}
					lowercase							=						{v: k for k, v in idalabel.items()}

					lowercase							=						'''std_conv''' if '''bit''' in model_name else False

					# note that when using BiT as backbone for ViT-hybrid checkpoints,
					# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
					# config.conv_layer = "std_conv_same"
					lowercase							=						BitConfig(
					    conv_layer=lowerCAmelCase__ ,			num_labels=1000 ,			idalabel=lowerCAmelCase__ ,			labelaid=lowerCAmelCase__ ,			)

					return config



def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					if "stem.conv" in name:
										lowercase							=						name.replace('''stem.conv''' ,			'''bit.embedder.convolution'''		)
					if "blocks" in name:
										lowercase							=						name.replace('''blocks''' ,			'''layers'''		)
					if "head.fc" in name:
										lowercase							=						name.replace('''head.fc''' ,			'''classifier.1'''		)
					if name.startswith('''norm'''		):
										lowercase							=						'''bit.''' + name
					if "bit" not in name and "classifier" not in name:
										lowercase							=						'''bit.encoder.''' + name

					return name



def        UpperCamelCase    (							):
					'''simple docstring'''
					lowercase							=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					lowercase							=						Image.open(requests.get(lowerCAmelCase__ ,			stream=lowerCAmelCase__		).raw		)
					return im



@torch.no_grad()
def        UpperCamelCase    (							lowerCAmelCase__ ,			lowerCAmelCase__ ,			lowerCAmelCase__=False		):
					'''simple docstring'''
					lowercase							=						get_config(lowerCAmelCase__		)

					# load original model from timm
					lowercase							=						create_model(lowerCAmelCase__ ,			pretrained=lowerCAmelCase__		)
					timm_model.eval()

					# load state_dict of original model
					lowercase							=						timm_model.state_dict()
					for key in state_dict.copy().keys():
										lowercase							=						state_dict.pop(lowerCAmelCase__		)
										lowercase							=						val.squeeze() if '''head''' in key else val

					# load HuggingFace model
					lowercase							=						BitForImageClassification(lowerCAmelCase__		)
					model.eval()
					model.load_state_dict(lowerCAmelCase__		)

					# create image processor
					lowercase							=						create_transform(**resolve_data_config({} ,			model=lowerCAmelCase__		)		)
					lowercase							=						transform.transforms

					lowercase							=						{
					    '''bilinear''': PILImageResampling.BILINEAR,
					    '''bicubic''': PILImageResampling.BICUBIC,
					    '''nearest''': PILImageResampling.NEAREST,
					}

					lowercase							=						BitImageProcessor(
					    do_resize=lowerCAmelCase__ ,			size={'''shortest_edge''': timm_transforms[0].size} ,			resample=pillow_resamplings[timm_transforms[0].interpolation.value] ,			do_center_crop=lowerCAmelCase__ ,			crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]} ,			do_normalize=lowerCAmelCase__ ,			image_mean=timm_transforms[-1].mean.tolist() ,			image_std=timm_transforms[-1].std.tolist() ,			)

					lowercase							=						prepare_img()
					lowercase							=						transform(lowerCAmelCase__		).unsqueeze(0		)
					lowercase							=						processor(lowerCAmelCase__ ,			return_tensors='''pt'''		).pixel_values

					# verify pixel values
					assert torch.allclose(lowerCAmelCase__ ,			lowerCAmelCase__		)

					# verify logits
					with torch.no_grad():
										lowercase							=						model(lowerCAmelCase__		)
										lowercase							=						outputs.logits

					print('''Logits:''' ,			logits[0, :3]		)
					print('''Predicted class:''' ,			model.config.idalabel[logits.argmax(-1		).item()]		)
					lowercase							=						timm_model(lowerCAmelCase__		)
					assert timm_logits.shape == outputs.logits.shape
					assert torch.allclose(lowerCAmelCase__ ,			outputs.logits ,			atol=1E-3		)
					print('''Looks ok!'''		)

					if pytorch_dump_folder_path is not None:
										Path(lowerCAmelCase__		).mkdir(exist_ok=lowerCAmelCase__		)
										print(f'Saving model {model_name} and processor to {pytorch_dump_folder_path}'		)
										model.save_pretrained(lowerCAmelCase__		)
										processor.save_pretrained(lowerCAmelCase__		)

					if push_to_hub:
										print(f'Pushing model {model_name} and processor to the hub'		)
										model.push_to_hub(f'ybelkada/{model_name}'		)
										processor.push_to_hub(f'ybelkada/{model_name}'		)


if __name__ == "__main__":
							lowercase__		:List[Any]   =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--model_name",
							    default="resnetv2_50x1_bitm",
							    type=str,
							    help="Name of the BiT timm model 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(
							    "--push_to_hub",
							    action="store_true",
							    help="Whether to push the model to the hub.",
							)

							lowercase__		:List[str]   =    parser.parse_args()
							convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin







@skip_mps
class 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Optional[Any]       					=IFPipeline
							lowercase_       :       List[str]       					=TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
							lowercase_       :       List[str]       					=TEXT_TO_IMAGE_BATCH_PARAMS
							lowercase_       :       int       					=PipelineTesterMixin.required_optional_params - {'''latents'''}




							def  A__      (				self):
												return self._get_dummy_components()




							def  A__      (				self   ,A__   ,A__=0):
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)

												lowercase							=						{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''generator''': generator,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''numpy''',
												}

												return inputs




							def  A__      (				self):
												self._test_save_load_optional_components()




							@unittest.skipIf(torch_device != '''cuda'''   ,reason='''float16 requires CUDA''')
							def  A__      (				self):
												# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
												super().test_save_load_floataa(expected_max_diff=1E-1)




							def  A__      (				self):
												self._test_attention_slicing_forward_pass(expected_max_diff=1E-2)




							def  A__      (				self):
												self._test_save_load_local()




							def  A__      (				self):
												self._test_inference_batch_single_identical(
												    expected_max_diff=1E-2   ,)





							@unittest.skipIf(
							    torch_device != '''cuda''' or not is_xformers_available()   ,reason='''XFormers attention is only available with CUDA and `xformers` installed'''   ,)
							def  A__      (				self):
												self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3)







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




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




							def  A__      (				self):
												# if

												lowercase							=						IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0'''   ,variant='''fp16'''   ,torch_dtype=torch.floataa)

												lowercase							=						IFSuperResolutionPipeline.from_pretrained(
												    '''DeepFloyd/IF-II-L-v1.0'''   ,variant='''fp16'''   ,torch_dtype=torch.floataa   ,text_encoder=A__   ,tokenizer=A__)

												# pre compute text embeddings and remove T5 to save memory

												pipe_a.text_encoder.to('''cuda''')

												lowercase	,						lowercase							=						pipe_a.encode_prompt('''anime turtle'''   ,device='''cuda''')

												del pipe_a.tokenizer
												del pipe_a.text_encoder
												gc.collect()

												lowercase							=						None
												lowercase							=						None

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

												self._test_if(A__   ,A__   ,A__   ,A__)

												pipe_a.remove_all_hooks()
												pipe_a.remove_all_hooks()

												# img2img

												lowercase							=						IFImgaImgPipeline(**pipe_a.components)
												lowercase							=						IFImgaImgSuperResolutionPipeline(**pipe_a.components)

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

												self._test_if_imgaimg(A__   ,A__   ,A__   ,A__)

												pipe_a.remove_all_hooks()
												pipe_a.remove_all_hooks()

												# inpainting

												lowercase							=						IFInpaintingPipeline(**pipe_a.components)
												lowercase							=						IFInpaintingSuperResolutionPipeline(**pipe_a.components)

												pipe_a.enable_model_cpu_offload()
												pipe_a.enable_model_cpu_offload()

												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())
												pipe_a.unet.set_attn_processor(AttnAddedKVProcessor())

												self._test_if_inpainting(A__   ,A__   ,A__   ,A__)




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__):
												# pipeline 1

												_start_torch_memory_measurement()

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)
												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,num_inference_steps=2   ,generator=A__   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (6_4, 6_4, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 1_3 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy''')
												assert_mean_pixel_difference(A__   ,A__)

												# pipeline 2

												_start_torch_memory_measurement()

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)

												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)

												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,generator=A__   ,num_inference_steps=2   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (2_5_6, 2_5_6, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy''')
												assert_mean_pixel_difference(A__   ,A__)




							def  A__      (				self   ,A__   ,A__   ,A__   ,A__):
												# pipeline 1

												_start_torch_memory_measurement()

												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)

												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,num_inference_steps=2   ,generator=A__   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (6_4, 6_4, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 1_0 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy''')
												assert_mean_pixel_difference(A__   ,A__)

												# pipeline 2

												_start_torch_memory_measurement()

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)

												lowercase							=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,rng=random.Random(0)).to(A__)
												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)

												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,original_image=A__   ,generator=A__   ,num_inference_steps=2   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (2_5_6, 2_5_6, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy''')
												assert_mean_pixel_difference(A__   ,A__)





							def  A__      (				self   ,A__   ,A__   ,A__   ,A__):
												# pipeline 1

												_start_torch_memory_measurement()

												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)
												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(1)).to(A__)

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)
												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,mask_image=A__   ,num_inference_steps=2   ,generator=A__   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (6_4, 6_4, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 1_0 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy''')
												assert_mean_pixel_difference(A__   ,A__)

												# pipeline 2

												_start_torch_memory_measurement()

												lowercase							=						torch.Generator(device='''cpu''').manual_seed(0)

												lowercase							=						floats_tensor((1, 3, 6_4, 6_4)   ,rng=random.Random(0)).to(A__)
												lowercase							=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,rng=random.Random(0)).to(A__)
												lowercase							=						floats_tensor((1, 3, 2_5_6, 2_5_6)   ,rng=random.Random(1)).to(A__)

												lowercase							=						pipe_a(
												    prompt_embeds=A__   ,negative_prompt_embeds=A__   ,image=A__   ,mask_image=A__   ,original_image=A__   ,generator=A__   ,num_inference_steps=2   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (2_5_6, 2_5_6, 3)

												lowercase							=						torch.cuda.max_memory_allocated()
												assert mem_bytes < 4 * 1_0**9

												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy''')
												assert_mean_pixel_difference(A__   ,A__)



def        UpperCamelCase    (							):
					'''simple docstring'''
					torch.cuda.empty_cache()
					torch.cuda.reset_max_memory_allocated()
					torch.cuda.reset_peak_memory_stats()




