






import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
A__			:    str         =				logging.get_logger()
@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= field(default_factory=A__			)
				lowercase__       							= field(default_factory=A__			)




				def        lowercase__     (  self		:					int,						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Tensor,						lowerCamelCase		:					Tensor						):

								'''simple docstring'''
								lowercase__     				=    len(list(m.modules()						)						) == 1 or isinstance(lowerCamelCase,						nn.Convad						) or isinstance(lowerCamelCase,						nn.BatchNormad						)
								if has_not_submodules:
												self.traced.append(lowerCamelCase						)




				def __call__(  self		:					Dict,						lowerCamelCase		:					Tensor						):

								'''simple docstring'''
								for m in self.module.modules():
												self.handles.append(m.register_forward_hook(self._forward_hook						)						)
								self.module(lowerCamelCase						)
								[x.remove() for x in self.handles]
								return self




				@property
				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								# check the len of the state_dict keys to see if we have learnable params
								return list(filter(lambda lowerCamelCase						: len(list(x.state_dict().keys()						)						) > 0,						self.traced						)						)



@dataclass
class    _UpperCAmelCase      :

				"""simple docstring"""




				lowercase__       							= 42
				lowercase__       							= 42
				lowercase__       							= 0
				lowercase__       							= field(default_factory=A__			)
				lowercase__       							= field(default_factory=A__			)




				def __call__(  self		:					Any,						lowerCamelCase		:					Tensor						):

								'''simple docstring'''
								lowercase__     				=    Tracker(self.dest						)(lowerCamelCase						).parametrized
								lowercase__     				=    Tracker(self.src						)(lowerCamelCase						).parametrized

								lowercase__     				=    list(filter(lambda lowerCamelCase						: type(lowerCamelCase						) not in self.src_skip,						lowerCamelCase						)						)
								lowercase__     				=    list(filter(lambda lowerCamelCase						: type(lowerCamelCase						) not in self.dest_skip,						lowerCamelCase						)						)

								if len(lowerCamelCase						) != len(lowerCamelCase						):
												raise Exception(
												    F"""Numbers of operations are different. Source module has {len(lowerCamelCase						)} operations while"""
												    F""" destination module has {len(lowerCamelCase						)}."""						)

								for dest_m, src_m in zip(lowerCamelCase,						lowerCamelCase						):
												dest_m.load_state_dict(src_m.state_dict()						)
												if self.verbose == 1:
																print(F"""Transfered from={src_m} to={dest_m}"""						)






def 						a    (		lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_ = True       ):






				'''simple docstring'''
				print(F"""Converting {name}..."""       )
				with torch.no_grad():
								lowercase__     				=    timm.create_model(lowerCamelCase_	,     pretrained=lowerCamelCase_       ).eval()
								lowercase__     				=    ResNetForImageClassification(lowerCamelCase_       ).eval()
								lowercase__     				=    ModuleTransfer(src=lowerCamelCase_	,     dest=lowerCamelCase_       )
								lowercase__     				=    torch.randn((1, 3, 224, 224)       )
								module_transfer(lowerCamelCase_       )

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

				lowercase__     				=    F"""resnet{'-'.join(name.split('resnet'       )       )}"""
				print(lowerCamelCase_       )

				if push_to_hub:
								our_model.push_to_hub(
								    repo_path_or_name=save_directory / checkpoint_name	,     commit_message='''Add model'''	,     use_temp_dir=lowerCamelCase_	,     )

								# we can use the convnext one
								lowercase__     				=    AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''       )
								image_processor.push_to_hub(
								    repo_path_or_name=save_directory / checkpoint_name	,     commit_message='''Add image processor'''	,     use_temp_dir=lowerCamelCase_	,     )

								print(F"""Pushed {checkpoint_name}"""       )






def 						a    (		lowerCamelCase_	,     lowerCamelCase_ = None	,     lowerCamelCase_ = True       ):






				'''simple docstring'''
				lowercase__     				=    '''imagenet-1k-id2label.json'''
				lowercase__     				=    1000
				lowercase__     				=    (1, num_labels)

				lowercase__     				=    '''huggingface/label-files'''
				lowercase__     				=    num_labels
				lowercase__     				=    json.load(open(hf_hub_download(lowerCamelCase_	,     lowerCamelCase_	,     repo_type='''dataset'''       )	,     '''r'''       )       )
				lowercase__     				=    {int(lowerCamelCase_       ): v for k, v in idalabel.items()}

				lowercase__     				=    idalabel
				lowercase__     				=    {v: k for k, v in idalabel.items()}

				lowercase__     				=    partial(lowerCamelCase_	,     num_labels=lowerCamelCase_	,     idalabel=lowerCamelCase_	,     labelaid=lowerCamelCase_       )

				lowercase__     				=    {
				    '''resnet18''': ImageNetPreTrainedConfig(
				        depths=[2, 2, 2, 2]	,     hidden_sizes=[64, 128, 256, 512]	,     layer_type='''basic'''       ),
				    '''resnet26''': ImageNetPreTrainedConfig(
				        depths=[2, 2, 2, 2]	,     hidden_sizes=[256, 512, 1024, 2048]	,     layer_type='''bottleneck'''       ),
				    '''resnet34''': ImageNetPreTrainedConfig(
				        depths=[3, 4, 6, 3]	,     hidden_sizes=[64, 128, 256, 512]	,     layer_type='''basic'''       ),
				    '''resnet50''': ImageNetPreTrainedConfig(
				        depths=[3, 4, 6, 3]	,     hidden_sizes=[256, 512, 1024, 2048]	,     layer_type='''bottleneck'''       ),
				    '''resnet101''': ImageNetPreTrainedConfig(
				        depths=[3, 4, 23, 3]	,     hidden_sizes=[256, 512, 1024, 2048]	,     layer_type='''bottleneck'''       ),
				    '''resnet152''': ImageNetPreTrainedConfig(
				        depths=[3, 8, 36, 3]	,     hidden_sizes=[256, 512, 1024, 2048]	,     layer_type='''bottleneck'''       ),
				}

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


if __name__ == "__main__":
							A__			:    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 resnet* architecture,'
							        ' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'
							    ),
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path',
							    default=None,
							    type=Path,
							    required=True,
							    help='Path to the output PyTorch model directory.',
							)
							parser.add_argument(
							    '--push_to_hub',
							    default=True,
							    type=bool,
							    required=False,
							    help='If True, push model and image processor to the hub.',
							)

							A__			:    Optional[int]         =				parser.parse_args()
							A__			:    Path         =				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 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


A__			:    Any         =				logging.get_logger(__name__)  # pylint: disable=invalid-name
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				def __init__(  self		:					Optional[int],						lowerCamelCase		:					WhisperForConditionalGeneration,						lowerCamelCase		:					WhisperProcessor,						lowerCamelCase		:					AutoencoderKL,						lowerCamelCase		:					CLIPTextModel,						lowerCamelCase		:					CLIPTokenizer,						lowerCamelCase		:					UNetaDConditionModel,						lowerCamelCase		:					Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],						lowerCamelCase		:					StableDiffusionSafetyChecker,						lowerCamelCase		:					CLIPImageProcessor,						):

								'''simple docstring'''
								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=lowerCamelCase,						speech_processor=lowerCamelCase,						vae=lowerCamelCase,						text_encoder=lowerCamelCase,						tokenizer=lowerCamelCase,						unet=lowerCamelCase,						scheduler=lowerCamelCase,						feature_extractor=lowerCamelCase,						)




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					Optional[Union[str, int]] = "auto"						):

								'''simple docstring'''
								if slice_size == "auto":
												lowercase__     				=    self.unet.config.attention_head_dim // 2
								self.unet.set_attention_slice(lowerCamelCase						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								self.enable_attention_slicing(lowerCamelCase						)




				@torch.no_grad()
				def __call__(  self		:					Any,						lowerCamelCase		:					Optional[Any],						lowerCamelCase		:					Optional[Any]=16_000,						lowerCamelCase		:					int = 512,						lowerCamelCase		:					int = 512,						lowerCamelCase		:					int = 50,						lowerCamelCase		:					float = 7.5,						lowerCamelCase		:					Optional[Union[str, List[str]]] = None,						lowerCamelCase		:					Optional[int] = 1,						lowerCamelCase		:					float = 0.0,						lowerCamelCase		:					Optional[torch.Generator] = None,						lowerCamelCase		:					Optional[torch.FloatTensor] = None,						lowerCamelCase		:					Optional[str] = "pil",						lowerCamelCase		:					bool = True,						lowerCamelCase		:					Optional[Callable[[int, int, torch.FloatTensor], None]] = None,						lowerCamelCase		:					int = 1,						**lowerCamelCase		:					Optional[Any],						):

								'''simple docstring'''
								lowercase__     				=    self.speech_processor.feature_extractor(
								    lowerCamelCase,						return_tensors='''pt''',						sampling_rate=lowerCamelCase						).input_features.to(self.device						)
								lowercase__     				=    self.speech_model.generate(lowerCamelCase,						max_length=480_000						)

								lowercase__     				=    self.speech_processor.tokenizer.batch_decode(lowerCamelCase,						skip_special_tokens=lowerCamelCase,						normalize=lowerCamelCase						)[
								    0
								]

								if isinstance(lowerCamelCase,						lowerCamelCase						):
												lowercase__     				=    1
								elif isinstance(lowerCamelCase,						lowerCamelCase						):
												lowercase__     				=    len(lowerCamelCase						)
								else:
												raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase						)}"""						)

								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(lowerCamelCase,						lowerCamelCase						) or callback_steps <= 0)
								):
												raise ValueError(
												    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
												    F""" {type(lowerCamelCase						)}."""						)

								# get prompt text embeddings
								lowercase__     				=    self.tokenizer(
								    lowerCamelCase,						padding='''max_length''',						max_length=self.tokenizer.model_max_length,						return_tensors='''pt''',						)
								lowercase__     				=    text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
												lowercase__     				=    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}"""						)
												lowercase__     				=    text_input_ids[:, : self.tokenizer.model_max_length]
								lowercase__     				=    self.text_encoder(text_input_ids.to(self.device						)						)[0]

								# duplicate text embeddings for each generation per prompt, using mps friendly method
								lowercase__				,		lowercase__				,		lowercase__     				=    text_embeddings.shape
								lowercase__     				=    text_embeddings.repeat(1,						lowerCamelCase,						1						)
								lowercase__     				=    text_embeddings.view(bs_embed * num_images_per_prompt,						lowerCamelCase,						-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.
								lowercase__     				=    guidance_scale > 1.0
								# get unconditional embeddings for classifier free guidance
								if do_classifier_free_guidance:
												lowercase__     				=    42
												if negative_prompt is None:
																lowercase__     				=    [''''''] * batch_size
												elif type(lowerCamelCase						) is not type(lowerCamelCase						):
																raise TypeError(
																    F"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCamelCase						)} !="""
																    F""" {type(lowerCamelCase						)}."""						)
												elif isinstance(lowerCamelCase,						lowerCamelCase						):
																lowercase__     				=    [negative_prompt]
												elif batch_size != len(lowerCamelCase						):
																raise ValueError(
																    F"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCamelCase						)}, but `prompt`:"""
																    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																    ''' the batch size of `prompt`.'''						)
												else:
																lowercase__     				=    negative_prompt

												lowercase__     				=    text_input_ids.shape[-1]
												lowercase__     				=    self.tokenizer(
												    lowerCamelCase,						padding='''max_length''',						max_length=lowerCamelCase,						truncation=lowerCamelCase,						return_tensors='''pt''',						)
												lowercase__     				=    self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												lowercase__     				=    uncond_embeddings.shape[1]
												lowercase__     				=    uncond_embeddings.repeat(1,						lowerCamelCase,						1						)
												lowercase__     				=    uncond_embeddings.view(batch_size * num_images_per_prompt,						lowerCamelCase,						-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
												lowercase__     				=    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`.
								lowercase__     				=    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
								lowercase__     				=    text_embeddings.dtype
								if latents is None:
												if self.device.type == "mps":
																# randn does not exist on mps
																lowercase__     				=    torch.randn(lowerCamelCase,						generator=lowerCamelCase,						device='''cpu''',						dtype=lowerCamelCase						).to(
																    self.device						)
												else:
																lowercase__     				=    torch.randn(lowerCamelCase,						generator=lowerCamelCase,						device=self.device,						dtype=lowerCamelCase						)
								else:
												if latents.shape != latents_shape:
																raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""						)
												lowercase__     				=    latents.to(self.device						)

								# set timesteps
								self.scheduler.set_timesteps(lowerCamelCase						)

								# Some schedulers like PNDM have timesteps as arrays
								# It's more optimized to move all timesteps to correct device beforehand
								lowercase__     				=    self.scheduler.timesteps.to(self.device						)

								# scale the initial noise by the standard deviation required by the scheduler
								lowercase__     				=    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]
								lowercase__     				=    '''eta''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
								lowercase__     				=    {}
								if accepts_eta:
												lowercase__     				=    eta

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

												# predict the noise residual
												lowercase__     				=    self.unet(lowerCamelCase,						lowerCamelCase,						encoder_hidden_states=lowerCamelCase						).sample

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

												# compute the previous noisy sample x_t -> x_t-1
												lowercase__     				=    self.scheduler.step(lowerCamelCase,						lowerCamelCase,						lowerCamelCase,						**lowerCamelCase						).prev_sample

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

								lowercase__     				=    1 / 0.18215 * latents
								lowercase__     				=    self.vae.decode(lowerCamelCase						).sample

								lowercase__     				=    (image / 2 + 0.5).clamp(0,						1						)

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

								if output_type == "pil":
												lowercase__     				=    self.numpy_to_pil(lowerCamelCase						)

								if not return_dict:
												return image

								return StableDiffusionPipelineOutput(images=lowerCamelCase,						nsfw_content_detected=lowerCamelCase						)




