






import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)






def        lowerCAmelCase (      UpperCamelCase_:							Optional[Any]     , UpperCamelCase_:							Optional[Any]     , UpperCamelCase_:							List[str]					)       ->							Union[str, Any]:







  '''simple docstring'''







  if isinstance(UpperCamelCase_     , torch.Tensor					):
    return image
  elif isinstance(UpperCamelCase_     , PIL.Image.Image					):
    _a							=	[image]

  if isinstance(image[0]     , PIL.Image.Image					):
    _a							=	[np.array(i.resize((w, h)     , resample=PIL_INTERPOLATION["lanczos"]					)					)[None, :] for i in image]
    _a							=	np.concatenate(UpperCamelCase_     , axis=0					)
    _a							=	np.array(UpperCamelCase_					).astype(np.floataa					) / 255.0
    _a							=	image.transpose(0     , 3     , 1     , 2					)
    _a							=	2.0 * image - 1.0
    _a							=	torch.from_numpy(UpperCamelCase_					)
  elif isinstance(image[0]     , torch.Tensor					):
    _a							=	torch.cat(UpperCamelCase_     , dim=0					)
  return image






def        lowerCAmelCase (      UpperCamelCase_:							Dict     , UpperCamelCase_:							Optional[int]     , UpperCamelCase_:							int     , UpperCamelCase_:							List[Any]=0.9995					)       ->							int:







  '''simple docstring'''







  if not isinstance(UpperCamelCase_     , np.ndarray					):
    _a							=	True
    _a							=	va.device
    _a							=	va.cpu().numpy()
    _a							=	va.cpu().numpy()

  _a							=	np.sum(va * va / (np.linalg.norm(UpperCamelCase_					) * np.linalg.norm(UpperCamelCase_					))					)
  if np.abs(UpperCamelCase_					) > DOT_THRESHOLD:
    _a							=	(1 - t) * va + t * va
  else:
    _a							=	np.arccos(UpperCamelCase_					)
    _a							=	np.sin(UpperCamelCase_					)
    _a							=	theta_a * t
    _a							=	np.sin(UpperCamelCase_					)
    _a							=	np.sin(theta_a - theta_t					) / sin_theta_a
    _a							=	sin_theta_t / sin_theta_a
    _a							=	sa * va + sa * va

  if inputs_are_torch:
    _a							=	torch.from_numpy(UpperCamelCase_					).to(UpperCamelCase_					)

  return va






def        lowerCAmelCase (      UpperCamelCase_:							Optional[Any]     , UpperCamelCase_:							List[str]					)       ->							int:







  '''simple docstring'''







  _a							=	F.normalize(UpperCamelCase_     , dim=-1					)
  _a							=	F.normalize(UpperCamelCase_     , dim=-1					)
  return (x - y).norm(dim=-1					).div(2					).arcsin().pow(2					).mul(2					)






def        lowerCAmelCase (      UpperCamelCase_:							Tuple     , UpperCamelCase_:							Tuple					)       ->							Any:







  '''simple docstring'''







  for param in model.parameters():
    _a							=	value




class    lowercase_    (_UpperCAmelCase     ):






      def __init__(    self     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_=None     ,     a_=None     ,     a_=None     ,     )	->int:







        '''simple docstring'''


        super().__init__()
        self.register_modules(
            vae=a_     ,     text_encoder=a_     ,     clip_model=a_     ,     tokenizer=a_     ,     unet=a_     ,     scheduler=a_     ,     feature_extractor=a_     ,     coca_model=a_     ,     coca_tokenizer=a_     ,     coca_transform=a_     ,     )
        _a							=	(
            feature_extractor.size
            if isinstance(feature_extractor.size     ,     a_	)
            else feature_extractor.size["shortest_edge"]
        )
        _a							=	transforms.Normalize(mean=feature_extractor.image_mean     ,     std=feature_extractor.image_std	)
        set_requires_grad(self.text_encoder     ,     a_	)
        set_requires_grad(self.clip_model     ,     a_	)






      def 			lowerCamelCase__    (    self     ,     a_ = "auto"	)	->str:







        '''simple docstring'''


        if slice_size == "auto":
          # half the attention head size is usually a good trade-off between
          # speed and memory
          _a							=	self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(a_	)






      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        self.enable_attention_slicing(a_	)






      def 			lowerCamelCase__    (    self	)	->Optional[int]:







        '''simple docstring'''


        set_requires_grad(self.vae     ,     a_	)






      def 			lowerCamelCase__    (    self	)	->Any:







        '''simple docstring'''


        set_requires_grad(self.vae     ,     a_	)






      def 			lowerCamelCase__    (    self	)	->Tuple:







        '''simple docstring'''


        set_requires_grad(self.unet     ,     a_	)






      def 			lowerCamelCase__    (    self	)	->Union[str, Any]:







        '''simple docstring'''


        set_requires_grad(self.unet     ,     a_	)






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     a_	)	->Any:







        '''simple docstring'''


        _a							=	min(int(num_inference_steps * strength	)     ,     a_	)

        _a							=	max(num_inference_steps - init_timestep     ,     0	)
        _a							=	self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_=None	)	->Optional[int]:







        '''simple docstring'''


        if not isinstance(a_     ,     torch.Tensor	):
          raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(a_	)}'''	)

        _a							=	image.to(device=a_     ,     dtype=a_	)

        if isinstance(a_     ,     a_	):
          _a							=	[
              self.vae.encode(image[i : i + 1]	).latent_dist.sample(generator[i]	) for i in range(a_	)
          ]
          _a							=	torch.cat(a_     ,     dim=0	)
        else:
          _a							=	self.vae.encode(a_	).latent_dist.sample(a_	)

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
        _a							=	0.18_215 * init_latents
        _a							=	init_latents.repeat_interleave(a_     ,     dim=0	)

        _a							=	randn_tensor(init_latents.shape     ,     generator=a_     ,     device=a_     ,     dtype=a_	)

        # get latents
        _a							=	self.scheduler.add_noise(a_     ,     a_     ,     a_	)
        _a							=	init_latents

        return latents






      def 			lowerCamelCase__    (    self     ,     a_	)	->int:







        '''simple docstring'''


        _a							=	self.coca_transform(a_	).unsqueeze(0	)
        with torch.no_grad(), torch.cuda.amp.autocast():
          _a							=	self.coca_model.generate(transformed_image.to(device=self.device     ,     dtype=self.coca_model.dtype	)	)
        _a							=	self.coca_tokenizer.decode(generated[0].cpu().numpy()	)
        return generated.split("<end_of_text>"	)[0].replace("<start_of_text>"     ,     ""	).rstrip(" .,"	)






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_	)	->str:







        '''simple docstring'''


        _a							=	self.feature_extractor.preprocess(a_	)
        _a							=	torch.from_numpy(clip_image_input["pixel_values"][0]	).unsqueeze(0	).to(self.device	).half()
        _a							=	self.clip_model.get_image_features(a_	)
        _a							=	image_embeddings_clip / image_embeddings_clip.norm(p=2     ,     dim=-1     ,     keepdim=a_	)
        _a							=	image_embeddings_clip.repeat_interleave(a_     ,     dim=0	)
        return image_embeddings_clip






      @torch.enable_grad()
      def 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_     ,     )	->int:







        '''simple docstring'''


        _a							=	latents.detach().requires_grad_()

        _a							=	self.scheduler.scale_model_input(a_     ,     a_	)

        # predict the noise residual
        _a							=	self.unet(a_     ,     a_     ,     encoder_hidden_states=a_	).sample

        if isinstance(self.scheduler     ,     (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)	):
          _a							=	self.scheduler.alphas_cumprod[timestep]
          _a							=	1 - alpha_prod_t
          # compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          _a							=	(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

          _a							=	torch.sqrt(a_	)
          _a							=	pred_original_sample * (fac) + latents * (1 - fac)
        elif isinstance(self.scheduler     ,     a_	):
          _a							=	self.scheduler.sigmas[index]
          _a							=	latents - sigma * noise_pred
        else:
          raise ValueError(f'''scheduler type {type(self.scheduler	)} not supported'''	)

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
        _a							=	1 / 0.18_215 * sample
        _a							=	self.vae.decode(a_	).sample
        _a							=	(image / 2 + 0.5).clamp(0     ,     1	)

        _a							=	transforms.Resize(self.feature_extractor_size	)(a_	)
        _a							=	self.normalize(a_	).to(latents.dtype	)

        _a							=	self.clip_model.get_image_features(a_	)
        _a							=	image_embeddings_clip / image_embeddings_clip.norm(p=2     ,     dim=-1     ,     keepdim=a_	)

        _a							=	spherical_dist_loss(a_     ,     a_	).mean() * clip_guidance_scale

        _a							=	-torch.autograd.grad(a_     ,     a_	)[0]

        if isinstance(self.scheduler     ,     a_	):
          _a							=	latents.detach() + grads * (sigma**2)
          _a							=	noise_pred_original
        else:
          _a							=	noise_pred_original - torch.sqrt(a_	) * grads
        return noise_pred, latents






      @torch.no_grad()
      def __call__(    self     ,     a_     ,     a_     ,     a_ = None     ,     a_ = None     ,     a_ = 5_1_2     ,     a_ = 5_1_2     ,     a_ = 0.6     ,     a_ = 5_0     ,     a_ = 7.5     ,     a_ = 1     ,     a_ = 0.0     ,     a_ = 1_0_0     ,     a_ = None     ,     a_ = "pil"     ,     a_ = True     ,     a_ = 0.8     ,     a_ = 0.1     ,     a_ = 0.1     ,     )	->Tuple:







        '''simple docstring'''


        if isinstance(a_     ,     a_	) and len(a_	) != batch_size:
          raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(a_	)} generators.'''	)

        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 isinstance(a_     ,     torch.Generator	) and batch_size > 1:
          _a							=	[generator] + [None] * (batch_size - 1)

        _a							=	[
            ("model", self.coca_model is None),
            ("tokenizer", self.coca_tokenizer is None),
            ("transform", self.coca_transform is None),
        ]
        _a							=	[x[0] for x in coca_is_none if x[1]]
        _a							=	", ".join(a_	)
        # generate prompts with coca model if prompt is None
        if content_prompt is None:
          if len(a_	):
            raise ValueError(
                f'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
                f'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''	)
          _a							=	self.get_image_description(a_	)
        if style_prompt is None:
          if len(a_	):
            raise ValueError(
                f'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
                f''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'''	)
          _a							=	self.get_image_description(a_	)

        # get prompt text embeddings for content and style
        _a							=	self.tokenizer(
            a_     ,     padding="max_length"     ,     max_length=self.tokenizer.model_max_length     ,     truncation=a_     ,     return_tensors="pt"     ,     )
        _a							=	self.text_encoder(content_text_input.input_ids.to(self.device	)	)[0]

        _a							=	self.tokenizer(
            a_     ,     padding="max_length"     ,     max_length=self.tokenizer.model_max_length     ,     truncation=a_     ,     return_tensors="pt"     ,     )
        _a							=	self.text_encoder(style_text_input.input_ids.to(self.device	)	)[0]

        _a							=	slerp(a_     ,     a_     ,     a_	)

        # duplicate text embeddings for each generation per prompt
        _a							=	text_embeddings.repeat_interleave(a_     ,     dim=0	)

        # set timesteps
        _a							=	"offset" in set(inspect.signature(self.scheduler.set_timesteps	).parameters.keys()	)
        _a							=	{}
        if accepts_offset:
          _a							=	1

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

        _a			,	_a							=	self.get_timesteps(a_     ,     a_     ,     self.device	)
        _a							=	timesteps[:1].repeat(a_	)

        # Preprocess image
        _a							=	preprocess(a_     ,     a_     ,     a_	)
        _a							=	self.prepare_latents(
            a_     ,     a_     ,     a_     ,     text_embeddings.dtype     ,     self.device     ,     a_	)

        _a							=	preprocess(a_     ,     a_     ,     a_	)
        _a							=	self.prepare_latents(
            a_     ,     a_     ,     a_     ,     text_embeddings.dtype     ,     self.device     ,     a_	)

        _a							=	slerp(a_     ,     a_     ,     a_	)

        if clip_guidance_scale > 0:
          _a							=	self.get_clip_image_embeddings(a_     ,     a_	)
          _a							=	self.get_clip_image_embeddings(a_     ,     a_	)
          _a							=	slerp(
              a_     ,     a_     ,     a_	)

        # 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.
        _a							=	guidance_scale > 1.0
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
          _a							=	content_text_input.input_ids.shape[-1]
          _a							=	self.tokenizer([""]     ,     padding="max_length"     ,     max_length=a_     ,     return_tensors="pt"	)
          _a							=	self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]
          # duplicate unconditional embeddings for each generation per prompt
          _a							=	uncond_embeddings.repeat_interleave(a_     ,     dim=0	)

          # 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
          _a							=	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`.
        _a							=	(batch_size, self.unet.config.in_channels, height // 8, width // 8)
        _a							=	text_embeddings.dtype
        if latents is None:
          if self.device.type == "mps":
            # randn does not work reproducibly on mps
            _a							=	torch.randn(a_     ,     generator=a_     ,     device="cpu"     ,     dtype=a_	).to(
                self.device	)
          else:
            _a							=	torch.randn(a_     ,     generator=a_     ,     device=self.device     ,     dtype=a_	)
        else:
          if latents.shape != latents_shape:
            raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''	)
          _a							=	latents.to(self.device	)

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

        # check if the scheduler accepts generator
        _a							=	"generator" in set(inspect.signature(self.scheduler.step	).parameters.keys()	)
        if accepts_generator:
          _a							=	generator

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

            # predict the noise residual
            _a							=	self.unet(a_     ,     a_     ,     encoder_hidden_states=a_	).sample

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

            # perform clip guidance
            if clip_guidance_scale > 0:
              _a							=	(
                  text_embeddings.chunk(2	)[1] if do_classifier_free_guidance else text_embeddings
              )
              _a			,	_a							=	self.cond_fn(
                  a_     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_     ,     a_     ,     )

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
        _a							=	1 / 0.18_215 * latents
        _a							=	self.vae.decode(a_	).sample

        _a							=	(image / 2 + 0.5).clamp(0     ,     1	)
        _a							=	image.cpu().permute(0     ,     2     ,     3     ,     1	).numpy()

        if output_type == "pil":
          _a							=	self.numpy_to_pil(a_	)

        if not return_dict:
          return (image, None)

        return StableDiffusionPipelineOutput(images=a_     ,     nsfw_content_detected=a_	)










"""simple docstring"""




import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase							       =    logging.get_logger(__name__)

UpperCamelCase							       =    {
    """b0""": efficientnet.EfficientNetBa,
    """b1""": efficientnet.EfficientNetBa,
    """b2""": efficientnet.EfficientNetBa,
    """b3""": efficientnet.EfficientNetBa,
    """b4""": efficientnet.EfficientNetBa,
    """b5""": efficientnet.EfficientNetBa,
    """b6""": efficientnet.EfficientNetBa,
    """b7""": efficientnet.EfficientNetBa,
}

UpperCamelCase							       =    {
    """b0""": {
        """hidden_dim""": 1_280,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 224,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 1_280,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 240,
        """dropout_rate""": 0.2,
        """dw_padding""": [16],
    },
    """b2""": {
        """hidden_dim""": 1_408,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 260,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 16],
    },
    """b3""": {
        """hidden_dim""": 1_536,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 300,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 18],
    },
    """b4""": {
        """hidden_dim""": 1_792,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 380,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 2_048,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 456,
        """dropout_rate""": 0.4,
        """dw_padding""": [13, 27],
    },
    """b6""": {
        """hidden_dim""": 2_304,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 528,
        """dropout_rate""": 0.5,
        """dw_padding""": [31],
    },
    """b7""": {
        """hidden_dim""": 2_560,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 600,
        """dropout_rate""": 0.5,
        """dw_padding""": [18],
    },
}






def        lowerCAmelCase (      UpperCamelCase_:							Union[str, Any]					)       ->							List[Any]:







  '''simple docstring'''







  _a							=	EfficientNetConfig()
  _a							=	CONFIG_MAP[model_name]["hidden_dim"]
  _a							=	CONFIG_MAP[model_name]["width_coef"]
  _a							=	CONFIG_MAP[model_name]["depth_coef"]
  _a							=	CONFIG_MAP[model_name]["image_size"]
  _a							=	CONFIG_MAP[model_name]["dropout_rate"]
  _a							=	CONFIG_MAP[model_name]["dw_padding"]

  _a							=	"huggingface/label-files"
  _a							=	"imagenet-1k-id2label.json"
  _a							=	1000
  _a							=	json.load(open(hf_hub_download(UpperCamelCase_     , UpperCamelCase_     , repo_type="dataset"					)     , "r"					)					)
  _a							=	{int(UpperCamelCase_					): v for k, v in idalabel.items()}

  _a							=	idalabel
  _a							=	{v: k for k, v in idalabel.items()}
  return config






def        lowerCAmelCase (      )       ->							List[str]:







  '''simple docstring'''







  _a							=	"http://images.cocodataset.org/val2017/000000039769.jpg"
  _a							=	Image.open(requests.get(UpperCamelCase_     , stream=UpperCamelCase_					).raw					)
  return im






def        lowerCAmelCase (      UpperCamelCase_:							int					)       ->							List[str]:







  '''simple docstring'''







  _a							=	CONFIG_MAP[model_name]["image_size"]
  _a							=	EfficientNetImageProcessor(
      size={"height": size, "width": size}     , image_mean=[0.485, 0.456, 0.406]     , image_std=[0.4785_3944, 0.473_2864, 0.4743_4163]     , do_center_crop=UpperCamelCase_     , )
  return preprocessor






def        lowerCAmelCase (      UpperCamelCase_:							Dict					)       ->							Dict:







  '''simple docstring'''







  _a							=	[v.split("_"					)[0].split("block"					)[1] for v in original_param_names if v.startswith("block"					)]
  _a							=	sorted(set(UpperCamelCase_					)					)
  _a							=	len(UpperCamelCase_					)
  _a							=	{b: str(UpperCamelCase_					) for b, i in zip(UpperCamelCase_     , range(UpperCamelCase_					)					)}

  _a							=	[]
  rename_keys.append(("stem_conv/kernel:0", "embeddings.convolution.weight")					)
  rename_keys.append(("stem_bn/gamma:0", "embeddings.batchnorm.weight")					)
  rename_keys.append(("stem_bn/beta:0", "embeddings.batchnorm.bias")					)
  rename_keys.append(("stem_bn/moving_mean:0", "embeddings.batchnorm.running_mean")					)
  rename_keys.append(("stem_bn/moving_variance:0", "embeddings.batchnorm.running_var")					)

  for b in block_names:
    _a							=	block_name_mapping[b]
    rename_keys.append((f'''block{b}_expand_conv/kernel:0''', f'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''')					)
    rename_keys.append((f'''block{b}_expand_bn/gamma:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''')					)
    rename_keys.append((f'''block{b}_expand_bn/beta:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''')					)
    rename_keys.append(
        (f'''block{b}_expand_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''')					)
    rename_keys.append(
        (f'''block{b}_expand_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''')					)
    rename_keys.append(
        (f'''block{b}_dwconv/depthwise_kernel:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''')					)
    rename_keys.append((f'''block{b}_bn/gamma:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''')					)
    rename_keys.append((f'''block{b}_bn/beta:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''')					)
    rename_keys.append(
        (f'''block{b}_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''')					)
    rename_keys.append(
        (f'''block{b}_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''')					)

    rename_keys.append((f'''block{b}_se_reduce/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''')					)
    rename_keys.append((f'''block{b}_se_reduce/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''')					)
    rename_keys.append((f'''block{b}_se_expand/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''')					)
    rename_keys.append((f'''block{b}_se_expand/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''')					)
    rename_keys.append(
        (f'''block{b}_project_conv/kernel:0''', f'''encoder.blocks.{hf_b}.projection.project_conv.weight''')					)
    rename_keys.append((f'''block{b}_project_bn/gamma:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.weight''')					)
    rename_keys.append((f'''block{b}_project_bn/beta:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.bias''')					)
    rename_keys.append(
        (f'''block{b}_project_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''')					)
    rename_keys.append(
        (f'''block{b}_project_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_var''')					)

  rename_keys.append(("top_conv/kernel:0", "encoder.top_conv.weight")					)
  rename_keys.append(("top_bn/gamma:0", "encoder.top_bn.weight")					)
  rename_keys.append(("top_bn/beta:0", "encoder.top_bn.bias")					)
  rename_keys.append(("top_bn/moving_mean:0", "encoder.top_bn.running_mean")					)
  rename_keys.append(("top_bn/moving_variance:0", "encoder.top_bn.running_var")					)

  _a							=	{}
  for item in rename_keys:
    if item[0] in original_param_names:
      _a							=	"efficientnet." + item[1]

  _a							=	"classifier.weight"
  _a							=	"classifier.bias"
  return key_mapping






def        lowerCAmelCase (      UpperCamelCase_:							int     , UpperCamelCase_:							Tuple     , UpperCamelCase_:							Any					)       ->							int:







  '''simple docstring'''







  for key, value in tf_params.items():
    if "normalization" in key:
      continue

    _a							=	key_mapping[key]
    if "_conv" in key and "kernel" in key:
      _a							=	torch.from_numpy(UpperCamelCase_					).permute(3     , 2     , 0     , 1					)
    elif "depthwise_kernel" in key:
      _a							=	torch.from_numpy(UpperCamelCase_					).permute(2     , 3     , 0     , 1					)
    elif "kernel" in key:
      _a							=	torch.from_numpy(np.transpose(UpperCamelCase_					)					)
    else:
      _a							=	torch.from_numpy(UpperCamelCase_					)

    # Replace HF parameters with original TF model parameters
    assert hf_params[hf_key].shape == new_hf_value.shape
    hf_params[hf_key].copy_(UpperCamelCase_					)






@torch.no_grad()
def        lowerCAmelCase (      UpperCamelCase_:							int     , UpperCamelCase_:							Tuple     , UpperCamelCase_:							str     , UpperCamelCase_:							str					)       ->							List[Any]:







  '''simple docstring'''







  _a							=	model_classes[model_name](
      include_top=UpperCamelCase_     , weights="imagenet"     , input_tensor=UpperCamelCase_     , input_shape=UpperCamelCase_     , pooling=UpperCamelCase_     , classes=1000     , classifier_activation="softmax"     , )

  _a							=	original_model.trainable_variables
  _a							=	original_model.non_trainable_variables
  _a							=	{param.name: param.numpy() for param in tf_params}
  for param in tf_non_train_params:
    _a							=	param.numpy()
  _a							=	list(tf_params.keys()					)

  # Load HuggingFace model
  _a							=	get_efficientnet_config(UpperCamelCase_					)
  _a							=	EfficientNetForImageClassification(UpperCamelCase_					).eval()
  _a							=	hf_model.state_dict()

  # Create src-to-dst parameter name mapping dictionary
  print("Converting parameters..."					)
  _a							=	rename_keys(UpperCamelCase_					)
  replace_params(UpperCamelCase_     , UpperCamelCase_     , UpperCamelCase_					)

  # Initialize preprocessor and preprocess input image
  _a							=	convert_image_processor(UpperCamelCase_					)
  _a							=	preprocessor(images=prepare_img()     , return_tensors="pt"					)

  # HF model inference
  hf_model.eval()
  with torch.no_grad():
    _a							=	hf_model(**UpperCamelCase_					)
  _a							=	outputs.logits.detach().numpy()

  # Original model inference
  _a							=	False
  _a							=	CONFIG_MAP[model_name]["image_size"]
  _a							=	prepare_img().resize((image_size, image_size)     , resample=PIL.Image.NEAREST					)
  _a							=	image.img_to_array(UpperCamelCase_					)
  _a							=	np.expand_dims(UpperCamelCase_     , axis=0					)
  _a							=	original_model.predict(UpperCamelCase_					)

  # Check whether original and HF model outputs match  -> np.allclose
  assert np.allclose(UpperCamelCase_     , UpperCamelCase_     , atol=1E-3					), "The predicted logits are not the same."
  print("Model outputs match!"					)

  if save_model:
    # Create folder to save model
    if not os.path.isdir(UpperCamelCase_					):
      os.mkdir(UpperCamelCase_					)
    # Save converted model and image processor
    hf_model.save_pretrained(UpperCamelCase_					)
    preprocessor.save_pretrained(UpperCamelCase_					)

  if push_to_hub:
    # Push model and image processor to hub
    print(f'''Pushing converted {model_name} to the hub...'''					)
    _a							=	f'''efficientnet-{model_name}'''
    preprocessor.push_to_hub(UpperCamelCase_					)
    hf_model.push_to_hub(UpperCamelCase_					)


if __name__ == "__main__":
     UpperCamelCase							       =    argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--model_name""",
         default="""b0""",
         type=str,
         help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default="""hf_model""",
         type=str,
         help="""Path to the output PyTorch model directory.""",
     )
     parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
     parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")

     UpperCamelCase							       =    parser.parse_args()
     convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)


