






"""simple docstring"""







from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

lowerCAmelCase_	:    Any            =      '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase=8		):







       '''simple docstring'''
       UpperCAmelCase	    =			height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       UpperCAmelCase	    =			width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase=512	,      lowerCAmelCase=512		):







       '''simple docstring'''
       UpperCAmelCase	    =			pil_image.resize((w, h)	,      resample=Image.BICUBIC	,      reducing_gap=1		)
       UpperCAmelCase	    =			np.array(pil_image.convert("""RGB"""		)		)
       UpperCAmelCase	    =			arr.astype(np.floataa		) / 1_27.5 - 1
       UpperCAmelCase	    =			np.transpose(UpperCAmelCase__	,      [2, 0, 1]		)
       UpperCAmelCase	    =			torch.from_numpy(UpperCAmelCase__		).unsqueeze(0		)
       return image





class   UpperCamelCase_ (       _a  ):



  def __init__(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		)			->			Any:



         """simple docstring"""





         super().__init__()

         self.register_modules(
             unet=_A ,		scheduler=_A ,		movq=_A ,		)
         UpperCAmelCase	    =			2 ** (len(self.movq.config.block_out_channels				) - 1)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__				)			->			str:



         """simple docstring"""





         UpperCAmelCase	    =			min(int(num_inference_steps * strength				) ,		_A				)

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

         return timesteps, num_inference_steps - t_start



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__=None				)			->			Tuple:



         """simple docstring"""





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

         UpperCAmelCase	    =			image.to(device=_A ,		dtype=_A				)

         UpperCAmelCase	    =			batch_size * num_images_per_prompt

         if image.shape[1] == 4:
                UpperCAmelCase	    =			image

         else:
                if isinstance(_A ,		_A				) and len(_A				) != batch_size:
                       raise ValueError(
                           f'''You have passed a list of generators of length {len(_A				)}, but requested an effective batch'''
                           f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''				)

                elif isinstance(_A ,		_A				):
                       UpperCAmelCase	    =			[
                           self.movq.encode(image[i : i + 1]				).latent_dist.sample(generator[i]				) for i in range(_A				)
                       ]
                       UpperCAmelCase	    =			torch.cat(_A ,		dim=0				)
                else:
                       UpperCAmelCase	    =			self.movq.encode(_A				).latent_dist.sample(_A				)

                UpperCAmelCase	    =			self.movq.config.scaling_factor * init_latents

         UpperCAmelCase	    =			torch.cat([init_latents] ,		dim=0				)

         UpperCAmelCase	    =			init_latents.shape
         UpperCAmelCase	    =			randn_tensor(_A ,		generator=_A ,		device=_A ,		dtype=_A				)

         # get latents
         UpperCAmelCase	    =			self.scheduler.add_noise(_A ,		_A ,		_A				)

         UpperCAmelCase	    =			init_latents

         return latents



  def 		UpperCamelCase_				(      self ,		snake_case__=0				)			->			Tuple:



         """simple docstring"""





         if is_accelerate_available():
                from accelerate import cpu_offload
         else:
                raise ImportError("""Please install accelerate via `pip install accelerate`"""				)

         UpperCAmelCase	    =			torch.device(f'''cuda:{gpu_id}'''				)

         UpperCAmelCase	    =			[
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
                if cpu_offloaded_model is not None:
                       cpu_offload(_A ,		_A				)



  def 		UpperCamelCase_				(      self ,		snake_case__=0				)			->			Dict:



         """simple docstring"""





         if is_accelerate_available() and is_accelerate_version(""">=""" ,		"""0.17.0.dev0"""				):
                from accelerate import cpu_offload_with_hook
         else:
                raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""				)

         UpperCAmelCase	    =			torch.device(f'''cuda:{gpu_id}'''				)

         if self.device.type != "cpu":
                self.to("""cpu""" ,		silence_dtype_warnings=_A				)
                torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         UpperCAmelCase	    =			None
         for cpu_offloaded_model in [self.unet, self.movq]:
                UpperCAmelCase    ,       UpperCAmelCase	    =			cpu_offload_with_hook(_A ,		_A ,		prev_module_hook=_A				)

         # We'll offload the last model manually.
         UpperCAmelCase	    =			hook



  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 		UpperCamelCase_				(      self				)			->			Tuple:



         """simple docstring"""





         if not hasattr(self.unet ,		"""_hf_hook"""				):
                return self.device
         for module in self.unet.modules():
                if (
                    hasattr(_A ,		"""_hf_hook"""				)
                    and hasattr(module._hf_hook ,		"""execution_device"""				)
                    and module._hf_hook.execution_device is not None
                ):
                       return torch.device(module._hf_hook.execution_device				)
         return self.device



  @torch.no_grad()
  @replace_example_docstring(_A				)
  def __call__(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ = 5_12 ,		snake_case__ = 5_12 ,		snake_case__ = 1_00 ,		snake_case__ = 4.0 ,		snake_case__ = 0.3 ,		snake_case__ = 1 ,		snake_case__ = None ,		snake_case__ = "pil" ,		snake_case__ = True ,		)			->			Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase	    =			self._execution_device

         UpperCAmelCase	    =			guidance_scale > 1.0

         if isinstance(_A ,		_A				):
                UpperCAmelCase	    =			torch.cat(_A ,		dim=0				)
         UpperCAmelCase	    =			image_embeds.shape[0]
         if isinstance(_A ,		_A				):
                UpperCAmelCase	    =			torch.cat(_A ,		dim=0				)

         if do_classifier_free_guidance:
                UpperCAmelCase	    =			image_embeds.repeat_interleave(_A ,		dim=0				)
                UpperCAmelCase	    =			negative_image_embeds.repeat_interleave(_A ,		dim=0				)

         UpperCAmelCase	    =			torch.cat([negative_image_embeds, image_embeds] ,		dim=0				).to(dtype=self.unet.dtype ,		device=_A				)

         if not isinstance(_A ,		_A				):
                UpperCAmelCase	    =			[image]
         if not all(isinstance(_A ,		(PIL.Image.Image, torch.Tensor)				) for i in image				):
                raise ValueError(
                    f'''Input is in incorrect format: {[type(_A				) for i in image]}. Currently, we only support  PIL image and pytorch tensor'''				)

         UpperCAmelCase	    =			torch.cat([prepare_image(_A ,		_A ,		_A				) for i in image] ,		dim=0				)
         UpperCAmelCase	    =			image.to(dtype=image_embeds.dtype ,		device=_A				)

         UpperCAmelCase	    =			self.movq.encode(_A				)["""latents"""]
         UpperCAmelCase	    =			latents.repeat_interleave(_A ,		dim=0				)
         self.scheduler.set_timesteps(_A ,		device=_A				)
         UpperCAmelCase    ,       UpperCAmelCase	    =			self.get_timesteps(_A ,		_A ,		_A				)
         UpperCAmelCase	    =			timesteps[:1].repeat(batch_size * num_images_per_prompt				)
         UpperCAmelCase    ,       UpperCAmelCase	    =			downscale_height_and_width(_A ,		_A ,		self.movq_scale_factor				)
         UpperCAmelCase	    =			self.prepare_latents(
             _A ,		_A ,		_A ,		_A ,		image_embeds.dtype ,		_A ,		_A				)
         for i, t in enumerate(self.progress_bar(_A				)				):
                # expand the latents if we are doing classifier free guidance
                UpperCAmelCase	    =			torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

                UpperCAmelCase	    =			{"""image_embeds""": image_embeds}
                UpperCAmelCase	    =			self.unet(
                    sample=_A ,		timestep=_A ,		encoder_hidden_states=_A ,		added_cond_kwargs=_A ,		return_dict=_A ,		)[0]

                if do_classifier_free_guidance:
                       UpperCAmelCase    ,       UpperCAmelCase	    =			noise_pred.split(latents.shape[1] ,		dim=1				)
                       UpperCAmelCase    ,       UpperCAmelCase	    =			noise_pred.chunk(2				)
                       UpperCAmelCase    ,       UpperCAmelCase	    =			variance_pred.chunk(2				)
                       UpperCAmelCase	    =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                       UpperCAmelCase	    =			torch.cat([noise_pred, variance_pred_text] ,		dim=1				)

                if not (
                    hasattr(self.scheduler.config ,		"""variance_type"""				)
                    and self.scheduler.config.variance_type in ["learned", "learned_range"]
                ):
                       UpperCAmelCase    ,       UpperCAmelCase	    =			noise_pred.split(latents.shape[1] ,		dim=1				)

                # compute the previous noisy sample x_t -> x_t-1
                UpperCAmelCase	    =			self.scheduler.step(
                    _A ,		_A ,		_A ,		generator=_A ,		)[0]

         # post-processing
         UpperCAmelCase	    =			self.movq.decode(_A ,		force_not_quantize=_A				)["""sample"""]

         if output_type not in ["pt", "np", "pil"]:
                raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''				)

         if output_type in ["np", "pil"]:
                UpperCAmelCase	    =			image * 0.5 + 0.5
                UpperCAmelCase	    =			image.clamp(0 ,		1				)
                UpperCAmelCase	    =			image.cpu().permute(0 ,		2 ,		3 ,		1				).float().numpy()

         if output_type == "pil":
                UpperCAmelCase	    =			self.numpy_to_pil(_A				)

         if not return_dict:
                return (image,)

         return ImagePipelineOutput(images=_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()
lowerCAmelCase_	:    List[Any]            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Tuple            =      {
    '''b0''': efficientnet.EfficientNetBa,
    '''b1''': efficientnet.EfficientNetBa,
    '''b2''': efficientnet.EfficientNetBa,
    '''b3''': efficientnet.EfficientNetBa,
    '''b4''': efficientnet.EfficientNetBa,
    '''b5''': efficientnet.EfficientNetBa,
    '''b6''': efficientnet.EfficientNetBa,
    '''b7''': efficientnet.EfficientNetBa,
}

lowerCAmelCase_	:    int            =      {
    '''b0''': {
        '''hidden_dim''': 1_2_8_0,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 2_2_4,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 1_2_8_0,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 2_4_0,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [1_6],
    },
    '''b2''': {
        '''hidden_dim''': 1_4_0_8,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 2_6_0,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 1_6],
    },
    '''b3''': {
        '''hidden_dim''': 1_5_3_6,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 3_0_0,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 1_8],
    },
    '''b4''': {
        '''hidden_dim''': 1_7_9_2,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 3_8_0,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 2_0_4_8,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 4_5_6,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [1_3, 2_7],
    },
    '''b6''': {
        '''hidden_dim''': 2_3_0_4,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 5_2_8,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [3_1],
    },
    '''b7''': {
        '''hidden_dim''': 2_5_6_0,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 6_0_0,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [1_8],
    },
}






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			EfficientNetConfig()
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""hidden_dim"""]
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""width_coef"""]
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""depth_coef"""]
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""image_size"""]
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""dropout_rate"""]
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""dw_padding"""]

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

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






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       UpperCAmelCase	    =			Image.open(requests.get(lowerCAmelCase	,      stream=lowerCAmelCase		).raw		)
       return im






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""image_size"""]
       UpperCAmelCase	    =			EfficientNetImageProcessor(
           size={"""height""": size, """width""": size}	,      image_mean=[0.4_85, 0.4_56, 0.4_06]	,      image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63]	,      do_center_crop=lowerCAmelCase	,      )
       return preprocessor






def       _lowerCAmelCase       (      lowerCAmelCase		):







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

       UpperCAmelCase	    =			[]
       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:
              UpperCAmelCase	    =			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""")		)

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

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






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







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

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

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






@torch.no_grad()
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			model_classes[model_name](
           include_top=lowerCAmelCase	,      weights="""imagenet"""	,      input_tensor=lowerCAmelCase	,      input_shape=lowerCAmelCase	,      pooling=lowerCAmelCase	,      classes=1000	,      classifier_activation="""softmax"""	,      )

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

       # Load HuggingFace model
       UpperCAmelCase	    =			get_efficientnet_config(lowerCAmelCase		)
       UpperCAmelCase	    =			EfficientNetForImageClassification(lowerCAmelCase		).eval()
       UpperCAmelCase	    =			hf_model.state_dict()

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

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

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

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

       # Check whether original and HF model outputs match  -> np.allclose
       assert np.allclose(lowerCAmelCase	,      lowerCAmelCase	,      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(lowerCAmelCase		):
                     os.mkdir(lowerCAmelCase		)
              # Save converted model and image processor
              hf_model.save_pretrained(lowerCAmelCase		)
              preprocessor.save_pretrained(lowerCAmelCase		)

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


if __name__ == "__main__":
 lowerCAmelCase_	:    int            =      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''')

 lowerCAmelCase_	:    Optional[Any]            =      parser.parse_args()
 convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
