


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()
_lowerCAmelCase  :		Dict		     =	logging.get_logger(__name__)

def 			__UpperCamelCase    (    _A    :				int				)    ->      List[str]:




	"""simple docstring"""





	lowerCAmelCase					:    Union[str, Any]      							= """huggingface/label-files"""
	lowerCAmelCase					:    Tuple      							= """imagenet-1k-id2label.json"""
	lowerCAmelCase					:    Tuple      							= json.load(open(hf_hub_download(__lowerCAmelCase					,   __lowerCAmelCase					,   repo_type='dataset'				)					,   'r'				)				)
	lowerCAmelCase					:    str      							= {int(__lowerCAmelCase				): v for k, v in idalabel.items()}
	lowerCAmelCase					:    str      							= {v: k for k, v in idalabel.items()}

	lowerCAmelCase					:    Optional[Any]      							= """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"
	lowerCAmelCase					:    Dict      							= BitConfig(
	    conv_layer=__lowerCAmelCase					,   num_labels=10_00					,   idalabel=__lowerCAmelCase					,   labelaid=__lowerCAmelCase					,   )

	return config

def 			__UpperCamelCase    (    _A    :				List[str]				)    ->      Any:




	"""simple docstring"""





	if "stem.conv" in name:
		lowerCAmelCase					:    str      							= name.replace('stem.conv'					,   'bit.embedder.convolution'				)
	if "blocks" in name:
		lowerCAmelCase					:    str      							= name.replace('blocks'					,   'layers'				)
	if "head.fc" in name:
		lowerCAmelCase					:    int      							= name.replace('head.fc'					,   'classifier.1'				)
	if name.startswith('norm'				):
		lowerCAmelCase					:    str      							= """bit.""" + name
	if "bit" not in name and "classifier" not in name:
		lowerCAmelCase					:    Optional[int]      							= """bit.encoder.""" + name

	return name

def 			__UpperCamelCase    (    )    ->      List[str]:




	"""simple docstring"""





	lowerCAmelCase					:    Union[str, Any]      							= """http://images.cocodataset.org/val2017/000000039769.jpg"""
	lowerCAmelCase					:    Tuple      							= Image.open(requests.get(__lowerCAmelCase					,   stream=__lowerCAmelCase				).raw				)
	return im

@torch.no_grad()
def 			__UpperCamelCase    (    _A    :				Dict					,   _A    :				List[Any]					,   _A    :				List[str]=False				)    ->      Optional[int]:




	"""simple docstring"""





	lowerCAmelCase					:    List[Any]      							= get_config(__lowerCAmelCase				)

	# load original model from timm
	lowerCAmelCase					:    str      							= create_model(__lowerCAmelCase					,   pretrained=__lowerCAmelCase				)
	timm_model.eval()

	# load state_dict of original model
	lowerCAmelCase					:    int      							= timm_model.state_dict()
	for key in state_dict.copy().keys():
		lowerCAmelCase					:    int      							= state_dict.pop(__lowerCAmelCase				)
		lowerCAmelCase					:    Union[str, Any]      							= val.squeeze() if """head""" in key else val

	# load HuggingFace model
	lowerCAmelCase					:    Any      							= BitForImageClassification(__lowerCAmelCase				)
	model.eval()
	model.load_state_dict(__lowerCAmelCase				)

	# create image processor
	lowerCAmelCase					:    Dict      							= create_transform(**resolve_data_config({}					,   model=__lowerCAmelCase				)				)
	lowerCAmelCase					:    Union[str, Any]      							= transform.transforms

	lowerCAmelCase					:    Union[str, Any]      							= {
	    """bilinear""": PILImageResampling.BILINEAR,
	    """bicubic""": PILImageResampling.BICUBIC,
	    """nearest""": PILImageResampling.NEAREST,
	}

	lowerCAmelCase					:    Tuple      							= 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()					,   )

	lowerCAmelCase					:    Union[str, Any]      							= prepare_img()
	lowerCAmelCase					:    Optional[int]      							= transform(__lowerCAmelCase				).unsqueeze(0				)
	lowerCAmelCase					:    Any      							= processor(__lowerCAmelCase					,   return_tensors='pt'				).pixel_values

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

	# verify logits
	with torch.no_grad():
		lowerCAmelCase					:    Optional[int]      							= model(__lowerCAmelCase				)
		lowerCAmelCase					:    List[Any]      							= outputs.logits

	print('Logits:'					,   logits[0, :3]				)
	print('Predicted class:'					,   model.config.idalabel[logits.argmax(-1				).item()]				)
	lowerCAmelCase					:    Optional[int]      							= 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__":
				_lowerCAmelCase  :		str		     =	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.',
				)

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




'''simple docstring'''




import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


_lowerCAmelCase  :		Dict		     =	logging.get_logger(__name__)  # pylint: disable=invalid-name



class  lowerCAmelCase				(				a							):



	def __init__(					self ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					snake_case__ ,					):
		super().__init__()

		if safety_checker is None:
			logger.warning(
			    f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
			    ' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'
			    ' results in services or applications open to the public. Both the diffusers team and Hugging Face'
			    ' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'
			    ' it only for use-cases that involve analyzing network behavior or auditing its results. For more'
			    ' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'    )

		self.register_modules(
		    speech_model=snake_case__ ,					speech_processor=snake_case__ ,					vae=snake_case__ ,					text_encoder=snake_case__ ,					tokenizer=snake_case__ ,					unet=snake_case__ ,					scheduler=snake_case__ ,					feature_extractor=snake_case__ ,					)



	def 				lowercase     (					self ,					snake_case__ = "auto"    ):
		if slice_size == "auto":
			lowerCAmelCase					:    Union[str, Any]      							= self.unet.config.attention_head_dim // 2
		self.unet.set_attention_slice(snake_case__    )



	def 				lowercase     (					self    ):
		self.enable_attention_slicing(snake_case__    )



	@torch.no_grad()
	def __call__(					self ,					snake_case__ ,					snake_case__=1_6000 ,					snake_case__ = 512 ,					snake_case__ = 512 ,					snake_case__ = 50 ,					snake_case__ = 7.5 ,					snake_case__ = None ,					snake_case__ = 1 ,					snake_case__ = 0.0 ,					snake_case__ = None ,					snake_case__ = None ,					snake_case__ = "pil" ,					snake_case__ = True ,					snake_case__ = None ,					snake_case__ = 1 ,					**snake_case__ ,					):
		lowerCAmelCase					:    List[str]      							= self.speech_processor.feature_extractor(
		    snake_case__ ,					return_tensors='pt' ,					sampling_rate=snake_case__    ).input_features.to(self.device    )
		lowerCAmelCase					:    Optional[Any]      							= self.speech_model.generate(snake_case__ ,					max_length=48_0000    )

		lowerCAmelCase					:    str      							= self.speech_processor.tokenizer.batch_decode(snake_case__ ,					skip_special_tokens=snake_case__ ,					normalize=snake_case__    )[
		    0
		]

		if isinstance(snake_case__ ,					snake_case__    ):
			lowerCAmelCase					:    Optional[int]      							= 1
		elif isinstance(snake_case__ ,					snake_case__    ):
			lowerCAmelCase					:    Optional[int]      							= len(snake_case__    )
		else:
			raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(snake_case__    )}"    )

		if height % 8 != 0 or width % 8 != 0:
			raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}."    )

		if (callback_steps is None) or (
		    callback_steps is not None and (not isinstance(snake_case__ ,					snake_case__    ) or callback_steps <= 0)
		):
			raise ValueError(
			    f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
			    f" {type(snake_case__    )}."    )

		# get prompt text embeddings
		lowerCAmelCase					:    str      							= self.tokenizer(
		    snake_case__ ,					padding='max_length' ,					max_length=self.tokenizer.model_max_length ,					return_tensors='pt' ,					)
		lowerCAmelCase					:    Tuple      							= text_inputs.input_ids

		if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
			lowerCAmelCase					:    str      							= self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]    )
			logger.warning(
			    'The following part of your input was truncated because CLIP can only handle sequences up to'
			    f" {self.tokenizer.model_max_length} tokens: {removed_text}"    )
			lowerCAmelCase					:    Union[str, Any]      							= text_input_ids[:, : self.tokenizer.model_max_length]
		lowerCAmelCase					:    Union[str, Any]      							= self.text_encoder(text_input_ids.to(self.device    )    )[0]

		# duplicate text embeddings for each generation per prompt, using mps friendly method
		lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					:    int      							= text_embeddings.shape
		lowerCAmelCase					:    Any      							= text_embeddings.repeat(1 ,					snake_case__ ,					1    )
		lowerCAmelCase					:    Optional[int]      							= text_embeddings.view(bs_embed * num_images_per_prompt ,					snake_case__ ,					-1    )

		# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
		# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
		# corresponds to doing no classifier free guidance.
		lowerCAmelCase					:    List[str]      							= guidance_scale > 1.0
		# get unconditional embeddings for classifier free guidance
		if do_classifier_free_guidance:
			lowerCAmelCase					:    List[str]
			if negative_prompt is None:
				lowerCAmelCase					:    Any      							= [''] * batch_size
			elif type(snake_case__    ) is not type(snake_case__    ):
				raise TypeError(
				    f"`negative_prompt` should be the same type to `prompt`, but got {type(snake_case__    )} !="
				    f" {type(snake_case__    )}."    )
			elif isinstance(snake_case__ ,					snake_case__    ):
				lowerCAmelCase					:    Union[str, Any]      							= [negative_prompt]
			elif batch_size != len(snake_case__    ):
				raise ValueError(
				    f"`negative_prompt`: {negative_prompt} has batch size {len(snake_case__    )}, but `prompt`:"
				    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
				    ' the batch size of `prompt`.'    )
			else:
				lowerCAmelCase					:    Dict      							= negative_prompt

			lowerCAmelCase					:    Optional[int]      							= text_input_ids.shape[-1]
			lowerCAmelCase					:    int      							= self.tokenizer(
			    snake_case__ ,					padding='max_length' ,					max_length=snake_case__ ,					truncation=snake_case__ ,					return_tensors='pt' ,					)
			lowerCAmelCase					:    Union[str, Any]      							= self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

			# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
			lowerCAmelCase					:    List[Any]      							= uncond_embeddings.shape[1]
			lowerCAmelCase					:    List[str]      							= uncond_embeddings.repeat(1 ,					snake_case__ ,					1    )
			lowerCAmelCase					:    Optional[Any]      							= uncond_embeddings.view(batch_size * num_images_per_prompt ,					snake_case__ ,					-1    )

			# For classifier free guidance, we need to do two forward passes.
			# Here we concatenate the unconditional and text embeddings into a single batch
			# to avoid doing two forward passes
			lowerCAmelCase					:    List[str]      							= torch.cat([uncond_embeddings, text_embeddings]    )

		# get the initial random noise unless the user supplied it

		# Unlike in other pipelines, latents need to be generated in the target device
		# for 1-to-1 results reproducibility with the CompVis implementation.
		# However this currently doesn't work in `mps`.
		lowerCAmelCase					:    Union[str, Any]      							= (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
		lowerCAmelCase					:    Dict      							= text_embeddings.dtype
		if latents is None:
			if self.device.type == "mps":
				# randn does not exist on mps
				lowerCAmelCase					:    str      							= torch.randn(snake_case__ ,					generator=snake_case__ ,					device='cpu' ,					dtype=snake_case__    ).to(
				    self.device    )
			else:
				lowerCAmelCase					:    Tuple      							= torch.randn(snake_case__ ,					generator=snake_case__ ,					device=self.device ,					dtype=snake_case__    )
		else:
			if latents.shape != latents_shape:
				raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"    )
			lowerCAmelCase					:    str      							= latents.to(self.device    )

		# set timesteps
		self.scheduler.set_timesteps(snake_case__    )

		# Some schedulers like PNDM have timesteps as arrays
		# It's more optimized to move all timesteps to correct device beforehand
		lowerCAmelCase					:    Union[str, Any]      							= self.scheduler.timesteps.to(self.device    )

		# scale the initial noise by the standard deviation required by the scheduler
		lowerCAmelCase					:    Any      							= latents * self.scheduler.init_noise_sigma

		# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
		# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
		# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
		# and should be between [0, 1]
		lowerCAmelCase					:    Tuple      							= 'eta' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
		lowerCAmelCase					:    Union[str, Any]      							= {}
		if accepts_eta:
			lowerCAmelCase					:    int      							= eta

		for i, t in enumerate(self.progress_bar(snake_case__    )    ):
			# expand the latents if we are doing classifier free guidance
			lowerCAmelCase					:    Dict      							= torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents
			lowerCAmelCase					:    Tuple      							= self.scheduler.scale_model_input(snake_case__ ,					snake_case__    )

			# predict the noise residual
			lowerCAmelCase					:    List[str]      							= self.unet(snake_case__ ,					snake_case__ ,					encoder_hidden_states=snake_case__    ).sample

			# perform guidance
			if do_classifier_free_guidance:
				lowerCAmelCase							,   lowerCAmelCase					:    Dict      							= noise_pred.chunk(2    )
				lowerCAmelCase					:    Tuple      							= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

			# compute the previous noisy sample x_t -> x_t-1
			lowerCAmelCase					:    int      							= self.scheduler.step(snake_case__ ,					snake_case__ ,					snake_case__ ,					**snake_case__    ).prev_sample

			# call the callback, if provided
			if callback is not None and i % callback_steps == 0:
				callback(snake_case__ ,					snake_case__ ,					snake_case__    )

		lowerCAmelCase					:    List[Any]      							= 1 / 0.1_8_2_1_5 * latents
		lowerCAmelCase					:    Dict      							= self.vae.decode(snake_case__    ).sample

		lowerCAmelCase					:    List[Any]      							= (image / 2 + 0.5).clamp(0 ,					1    )

		# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
		lowerCAmelCase					:    Union[str, Any]      							= image.cpu().permute(0 ,					2 ,					3 ,					1    ).float().numpy()

		if output_type == "pil":
			lowerCAmelCase					:    Dict      							= self.numpy_to_pil(snake_case__    )

		if not return_dict:
			return image

		return StableDiffusionPipelineOutput(images=snake_case__ ,					nsfw_content_detected=snake_case__    )
