



import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np
class 			_snake_case     :




				def 							A__ (  self   :  List[Any]  ):
							torch.manual_seed(0  )
							lowercase__   					=			TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"  )

							torch.manual_seed(0  )
							lowercase__   					=			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"  )

							torch.manual_seed(0  )
							lowercase__   					=			UNetaDConditionModel(
							    sample_size=32,   layers_per_block=1,   block_out_channels=[32, 64],   down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ],   mid_block_type="UNetMidBlock2DSimpleCrossAttn",   up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"],   in_channels=3,   out_channels=6,   cross_attention_dim=32,   encoder_hid_dim=32,   attention_head_dim=8,   addition_embed_type="text",   addition_embed_type_num_heads=2,   cross_attention_norm="group_norm",   resnet_time_scale_shift="scale_shift",   act_fn="gelu",   )
							unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

							torch.manual_seed(0  )
							lowercase__   					=			DDPMScheduler(
							    num_train_timesteps=1000,   beta_schedule="squaredcos_cap_v2",   beta_start=0.0001,   beta_end=0.02,   thresholding=__lowercase,   dynamic_thresholding_ratio=0.95,   sample_max_value=1.0,   prediction_type="epsilon",   variance_type="learned_range",   )

							torch.manual_seed(0  )
							lowercase__   					=			IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}




				def 							A__ (  self   :  List[str]  ):
							torch.manual_seed(0  )
							lowercase__   					=			TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"  )

							torch.manual_seed(0  )
							lowercase__   					=			AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"  )

							torch.manual_seed(0  )
							lowercase__   					=			UNetaDConditionModel(
							    sample_size=32,   layers_per_block=[1, 2],   block_out_channels=[32, 64],   down_block_types=[
							        "ResnetDownsampleBlock2D",
							        "SimpleCrossAttnDownBlock2D",
							    ],   mid_block_type="UNetMidBlock2DSimpleCrossAttn",   up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"],   in_channels=6,   out_channels=6,   cross_attention_dim=32,   encoder_hid_dim=32,   attention_head_dim=8,   addition_embed_type="text",   addition_embed_type_num_heads=2,   cross_attention_norm="group_norm",   resnet_time_scale_shift="scale_shift",   act_fn="gelu",   class_embed_type="timestep",   mid_block_scale_factor=1.414,   time_embedding_act_fn="gelu",   time_embedding_dim=32,   )
							unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

							torch.manual_seed(0  )
							lowercase__   					=			DDPMScheduler(
							    num_train_timesteps=1000,   beta_schedule="squaredcos_cap_v2",   beta_start=0.0001,   beta_end=0.02,   thresholding=__lowercase,   dynamic_thresholding_ratio=0.95,   sample_max_value=1.0,   prediction_type="epsilon",   variance_type="learned_range",   )

							torch.manual_seed(0  )
							lowercase__   					=			DDPMScheduler(
							    num_train_timesteps=1000,   beta_schedule="squaredcos_cap_v2",   beta_start=0.0001,   beta_end=0.02,   )

							torch.manual_seed(0  )
							lowercase__   					=			IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "image_noising_scheduler": image_noising_scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}




				def 							A__ (  self   :  str  ):
							lowercase__   					=			self.get_dummy_components()
							lowercase__   					=			self.pipeline_class(**__lowercase  )
							pipe.to(__lowercase  )
							pipe.set_progress_bar_config(disable=__lowercase  )

							lowercase__   					=			self.get_dummy_inputs(__lowercase  )

							lowercase__   					=			inputs["prompt"]
							lowercase__   					=			inputs["generator"]
							lowercase__   					=			inputs["num_inference_steps"]
							lowercase__   					=			inputs["output_type"]

							if "image" in inputs:
										lowercase__   					=			inputs["image"]
							else:
										lowercase__   					=			None

							if "mask_image" in inputs:
										lowercase__   					=			inputs["mask_image"]
							else:
										lowercase__   					=			None

							if "original_image" in inputs:
										lowercase__   					=			inputs["original_image"]
							else:
										lowercase__   					=			None

							lowercase__					,    lowercase__   					=			pipe.encode_prompt(__lowercase  )

							# inputs with prompt converted to embeddings
							lowercase__   					=			{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
										lowercase__   					=			image

							if mask_image is not None:
										lowercase__   					=			mask_image

							if original_image is not None:
										lowercase__   					=			original_image

							# set all optional components to None
							for optional_component in pipe._optional_components:
										setattr(__lowercase,   __lowercase,   __lowercase  )

							lowercase__   					=			pipe(**__lowercase  )[0]

							with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(__lowercase  )
										lowercase__   					=			self.pipeline_class.from_pretrained(__lowercase  )
										pipe_loaded.to(__lowercase  )
										pipe_loaded.set_progress_bar_config(disable=__lowercase  )

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

							for optional_component in pipe._optional_components:
										self.assertTrue(
										    getattr(__lowercase,   __lowercase  ) is None,   F'''`{optional_component}` did not stay set to None after loading.''',   )

							lowercase__   					=			self.get_dummy_inputs(__lowercase  )

							lowercase__   					=			inputs["generator"]
							lowercase__   					=			inputs["num_inference_steps"]
							lowercase__   					=			inputs["output_type"]

							# inputs with prompt converted to embeddings
							lowercase__   					=			{
							    "prompt_embeds": prompt_embeds,
							    "negative_prompt_embeds": negative_prompt_embeds,
							    "generator": generator,
							    "num_inference_steps": num_inference_steps,
							    "output_type": output_type,
							}

							if image is not None:
										lowercase__   					=			image

							if mask_image is not None:
										lowercase__   					=			mask_image

							if original_image is not None:
										lowercase__   					=			original_image

							lowercase__   					=			pipe_loaded(**__lowercase  )[0]

							lowercase__   					=			np.abs(to_np(__lowercase  ) - to_np(__lowercase  )  ).max()
							self.assertLess(__lowercase,   1e-4  )




				def 							A__ (  self   :  Optional[int]  ):
							lowercase__   					=			self.get_dummy_components()
							lowercase__   					=			self.pipeline_class(**__lowercase  )
							pipe.to(__lowercase  )
							pipe.set_progress_bar_config(disable=__lowercase  )

							lowercase__   					=			self.get_dummy_inputs(__lowercase  )
							lowercase__   					=			pipe(**__lowercase  )[0]

							with tempfile.TemporaryDirectory() as tmpdir:
										pipe.save_pretrained(__lowercase  )
										lowercase__   					=			self.pipeline_class.from_pretrained(__lowercase  )
										pipe_loaded.to(__lowercase  )
										pipe_loaded.set_progress_bar_config(disable=__lowercase  )

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()  )  # For reproducibility tests

							lowercase__   					=			self.get_dummy_inputs(__lowercase  )
							lowercase__   					=			pipe_loaded(**__lowercase  )[0]

							lowercase__   					=			np.abs(to_np(__lowercase  ) - to_np(__lowercase  )  ).max()
							self.assertLess(__lowercase,   1e-4  )









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_						=       logging.get_logger(__name__)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			"huggingface/label-files"
			lowercase__   					=			"imagenet-1k-id2label.json"
			lowercase__   					=			json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					repo_type="dataset"					)  ,					"r"					)					)
			lowercase__   					=			{int(SCREAMING_SNAKE_CASE_					): 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=SCREAMING_SNAKE_CASE_  ,					num_labels=1000  ,					idalabel=SCREAMING_SNAKE_CASE_  ,					labelaid=SCREAMING_SNAKE_CASE_  ,					)

			return config



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_					):
			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 __lowerCAmelCase	(       ):
			lowercase__   					=			"http://images.cocodataset.org/val2017/000000039769.jpg"
			lowercase__   					=			Image.open(requests.get(SCREAMING_SNAKE_CASE_  ,					stream=SCREAMING_SNAKE_CASE_					).raw					)
			return im



@torch.no_grad()
def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=False					):
			lowercase__   					=			get_config(SCREAMING_SNAKE_CASE_					)

			# load original model from timm
			lowercase__   					=			create_model(SCREAMING_SNAKE_CASE_  ,					pretrained=SCREAMING_SNAKE_CASE_					)
			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(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			val.squeeze() if "head" in key else val

			# load HuggingFace model
			lowercase__   					=			BitForImageClassification(SCREAMING_SNAKE_CASE_					)
			model.eval()
			model.load_state_dict(SCREAMING_SNAKE_CASE_					)

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

			lowercase__   					=			{
			    "bilinear": PILImageResampling.BILINEAR,
			    "bicubic": PILImageResampling.BICUBIC,
			    "nearest": PILImageResampling.NEAREST,
			}

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

			lowercase__   					=			prepare_img()
			lowercase__   					=			transform(SCREAMING_SNAKE_CASE_					).unsqueeze(0					)
			lowercase__   					=			processor(SCREAMING_SNAKE_CASE_  ,					return_tensors="pt"					).pixel_values

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

			# verify logits
			with torch.no_grad():
						lowercase__   					=			model(SCREAMING_SNAKE_CASE_					)
						lowercase__   					=			outputs.logits

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

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

			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_						=       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_						=       parser.parse_args()
							convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




