
"""simple docstring"""





from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)


@flax.struct.dataclass
class A_	(lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						jnp.ndarray
  SCREAMING_SNAKE_CASE__			:						jnp.ndarray




class A_	(nn.Module      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						int
  SCREAMING_SNAKE_CASE__			:						Tuple[int]       =		(16, 32, 96, 256)
  SCREAMING_SNAKE_CASE__			:						jnp.dtype       =		jnp.floataa







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Optional[int]			     =    nn.Conv(
         self.block_out_channels[0]				,   kernel_size=(3, 3)				,   padding=((1, 1), (1, 1))				,   dtype=self.dtype				,   )

     UpperCAmelCase_					:					Union[str, Any]			     =    []
     for i in range(len(self.block_out_channels  ) - 1  ):
        UpperCAmelCase_					:					int			     =    self.block_out_channels[i]
        UpperCAmelCase_					:					Any			     =    self.block_out_channels[i + 1]
        UpperCAmelCase_					:					Union[str, Any]			     =    nn.Conv(
            lowercase_				,   kernel_size=(3, 3)				,   padding=((1, 1), (1, 1))				,   dtype=self.dtype				,   )
        blocks.append(lowercase_  )
        UpperCAmelCase_					:					str			     =    nn.Conv(
            lowercase_				,   kernel_size=(3, 3)				,   strides=(2, 2)				,   padding=((1, 1), (1, 1))				,   dtype=self.dtype				,   )
        blocks.append(lowercase_  )
     UpperCAmelCase_					:					List[Any]			     =    blocks

     UpperCAmelCase_					:					List[Any]			     =    nn.Conv(
         self.conditioning_embedding_channels				,   kernel_size=(3, 3)				,   padding=((1, 1), (1, 1))				,   kernel_init=nn.initializers.zeros_init()				,   bias_init=nn.initializers.zeros_init()				,   dtype=self.dtype				,   )







  def __call__(		self				,   lowercase_  ):

     """simple docstring"""
     UpperCAmelCase_					:					List[Any]			     =    self.conv_in(lowercase_  )
     UpperCAmelCase_					:					List[str]			     =    nn.silu(lowercase_  )

     for block in self.blocks:
        UpperCAmelCase_					:					Optional[Any]			     =    block(lowercase_  )
        UpperCAmelCase_					:					Optional[Any]			     =    nn.silu(lowercase_  )

     UpperCAmelCase_					:					Tuple			     =    self.conv_out(lowercase_  )

     return embedding




@flax_register_to_config
class A_	(nn.Module				,lowercase__				,lowercase__      ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__			:						int       =		32
  SCREAMING_SNAKE_CASE__			:						int       =		4
  SCREAMING_SNAKE_CASE__			:						Tuple[str]       =		(
      "CrossAttnDownBlock2D",
      "CrossAttnDownBlock2D",
      "CrossAttnDownBlock2D",
      "DownBlock2D",
  )
  SCREAMING_SNAKE_CASE__			:						Union[bool, Tuple[bool]]       =		False
  SCREAMING_SNAKE_CASE__			:						Tuple[int]       =		(320, 640, 1280, 1280)
  SCREAMING_SNAKE_CASE__			:						int       =		2
  SCREAMING_SNAKE_CASE__			:						Union[int, Tuple[int]]       =		8
  SCREAMING_SNAKE_CASE__			:						Optional[Union[int, Tuple[int]]]       =		None
  SCREAMING_SNAKE_CASE__			:						int       =		1280
  SCREAMING_SNAKE_CASE__			:						float       =		0.0
  SCREAMING_SNAKE_CASE__			:						bool       =		False
  SCREAMING_SNAKE_CASE__			:						jnp.dtype       =		jnp.floataa
  SCREAMING_SNAKE_CASE__			:						bool       =		True
  SCREAMING_SNAKE_CASE__			:						int       =		0
  SCREAMING_SNAKE_CASE__			:						str       =		"rgb"
  SCREAMING_SNAKE_CASE__			:						Tuple[int]       =		(16, 32, 96, 256)







  def        UpperCamelCase__					(		self				,   lowercase_  ):

     """simple docstring"""
     # init input tensors
     UpperCAmelCase_					:					Tuple			     =    (1, self.in_channels, self.sample_size, self.sample_size)
     UpperCAmelCase_					:					int			     =    jnp.zeros(lowercase_				,   dtype=jnp.floataa  )
     UpperCAmelCase_					:					Any			     =    jnp.ones((1,)				,   dtype=jnp.intaa  )
     UpperCAmelCase_					:					Dict			     =    jnp.zeros((1, 1, self.cross_attention_dim)				,   dtype=jnp.floataa  )
     UpperCAmelCase_					:					Union[str, Any]			     =    (1, 3, self.sample_size * 8, self.sample_size * 8)
     UpperCAmelCase_					:					Tuple			     =    jnp.zeros(lowercase_				,   dtype=jnp.floataa  )

     UpperCAmelCase_     ,   UpperCAmelCase_					:					Dict			     =    jax.random.split(lowercase_  )
     UpperCAmelCase_					:					Any			     =    {"params": params_rng, "dropout": dropout_rng}

     return self.init(lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_  )["params"]







  def        UpperCamelCase__					(		self  ):

     """simple docstring"""
     UpperCAmelCase_					:					Union[str, Any]			     =    self.block_out_channels
     UpperCAmelCase_					:					Any			     =    block_out_channels[0] * 4

     # If `num_attention_heads` is not defined (which is the case for most models)
     # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
     # The reason for this behavior is to correct for incorrectly named variables that were introduced
     # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
     # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
     # which is why we correct for the naming here.
     UpperCAmelCase_					:					Any			     =    self.num_attention_heads or self.attention_head_dim

     # input
     UpperCAmelCase_					:					Tuple			     =    nn.Conv(
         block_out_channels[0]				,   kernel_size=(3, 3)				,   strides=(1, 1)				,   padding=((1, 1), (1, 1))				,   dtype=self.dtype				,   )

     # time
     UpperCAmelCase_					:					str			     =    FlaxTimesteps(
         block_out_channels[0]				,   flip_sin_to_cos=self.flip_sin_to_cos				,   freq_shift=self.config.freq_shift  )
     UpperCAmelCase_					:					int			     =    FlaxTimestepEmbedding(lowercase_				,   dtype=self.dtype  )

     UpperCAmelCase_					:					Tuple			     =    FlaxControlNetConditioningEmbedding(
         conditioning_embedding_channels=block_out_channels[0]				,   block_out_channels=self.conditioning_embedding_out_channels				,   )

     UpperCAmelCase_					:					Optional[Any]			     =    self.only_cross_attention
     if isinstance(lowercase_				,   lowercase_  ):
        UpperCAmelCase_					:					int			     =    (only_cross_attention,) * len(self.down_block_types  )

     if isinstance(lowercase_				,   lowercase_  ):
        UpperCAmelCase_					:					Any			     =    (num_attention_heads,) * len(self.down_block_types  )

     # down
     UpperCAmelCase_					:					Union[str, Any]			     =    []
     UpperCAmelCase_					:					Tuple			     =    []

     UpperCAmelCase_					:					str			     =    block_out_channels[0]

     UpperCAmelCase_					:					Optional[int]			     =    nn.Conv(
         lowercase_				,   kernel_size=(1, 1)				,   padding="VALID"				,   kernel_init=nn.initializers.zeros_init()				,   bias_init=nn.initializers.zeros_init()				,   dtype=self.dtype				,   )
     controlnet_down_blocks.append(lowercase_  )

     for i, down_block_type in enumerate(self.down_block_types  ):
        UpperCAmelCase_					:					Tuple			     =    output_channel
        UpperCAmelCase_					:					Tuple			     =    block_out_channels[i]
        UpperCAmelCase_					:					str			     =    i == len(lowercase_  ) - 1

        if down_block_type == "CrossAttnDownBlock2D":
           UpperCAmelCase_					:					Any			     =    FlaxCrossAttnDownBlockaD(
               in_channels=lowercase_				,   out_channels=lowercase_				,   dropout=self.dropout				,   num_layers=self.layers_per_block				,   num_attention_heads=num_attention_heads[i]				,   add_downsample=not is_final_block				,   use_linear_projection=self.use_linear_projection				,   only_cross_attention=only_cross_attention[i]				,   dtype=self.dtype				,   )
        else:
           UpperCAmelCase_					:					str			     =    FlaxDownBlockaD(
               in_channels=lowercase_				,   out_channels=lowercase_				,   dropout=self.dropout				,   num_layers=self.layers_per_block				,   add_downsample=not is_final_block				,   dtype=self.dtype				,   )

        down_blocks.append(lowercase_  )

        for _ in range(self.layers_per_block  ):
           UpperCAmelCase_					:					Tuple			     =    nn.Conv(
               lowercase_				,   kernel_size=(1, 1)				,   padding="VALID"				,   kernel_init=nn.initializers.zeros_init()				,   bias_init=nn.initializers.zeros_init()				,   dtype=self.dtype				,   )
           controlnet_down_blocks.append(lowercase_  )

        if not is_final_block:
           UpperCAmelCase_					:					str			     =    nn.Conv(
               lowercase_				,   kernel_size=(1, 1)				,   padding="VALID"				,   kernel_init=nn.initializers.zeros_init()				,   bias_init=nn.initializers.zeros_init()				,   dtype=self.dtype				,   )
           controlnet_down_blocks.append(lowercase_  )

     UpperCAmelCase_					:					List[Any]			     =    down_blocks
     UpperCAmelCase_					:					Tuple			     =    controlnet_down_blocks

     # mid
     UpperCAmelCase_					:					Any			     =    block_out_channels[-1]
     UpperCAmelCase_					:					Union[str, Any]			     =    FlaxUNetMidBlockaDCrossAttn(
         in_channels=lowercase_				,   dropout=self.dropout				,   num_attention_heads=num_attention_heads[-1]				,   use_linear_projection=self.use_linear_projection				,   dtype=self.dtype				,   )

     UpperCAmelCase_					:					List[str]			     =    nn.Conv(
         lowercase_				,   kernel_size=(1, 1)				,   padding="VALID"				,   kernel_init=nn.initializers.zeros_init()				,   bias_init=nn.initializers.zeros_init()				,   dtype=self.dtype				,   )







  def __call__(		self				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_				,   lowercase_ = 1.0				,   lowercase_ = True				,   lowercase_ = False				,   ):

     """simple docstring"""
     UpperCAmelCase_					:					Any			     =    self.controlnet_conditioning_channel_order
     if channel_order == "bgr":
        UpperCAmelCase_					:					List[str]			     =    jnp.flip(lowercase_				,   axis=1  )

     # 1. time
     if not isinstance(lowercase_				,   jnp.ndarray  ):
        UpperCAmelCase_					:					Any			     =    jnp.array([timesteps]				,   dtype=jnp.intaa  )
     elif isinstance(lowercase_				,   jnp.ndarray  ) and len(timesteps.shape  ) == 0:
        UpperCAmelCase_					:					str			     =    timesteps.astype(dtype=jnp.floataa  )
        UpperCAmelCase_					:					Optional[int]			     =    jnp.expand_dims(lowercase_				,   0  )

     UpperCAmelCase_					:					Optional[int]			     =    self.time_proj(lowercase_  )
     UpperCAmelCase_					:					str			     =    self.time_embedding(lowercase_  )

     # 2. pre-process
     UpperCAmelCase_					:					str			     =    jnp.transpose(lowercase_				,   (0, 2, 3, 1)  )
     UpperCAmelCase_					:					Tuple			     =    self.conv_in(lowercase_  )

     UpperCAmelCase_					:					int			     =    jnp.transpose(lowercase_				,   (0, 2, 3, 1)  )
     UpperCAmelCase_					:					int			     =    self.controlnet_cond_embedding(lowercase_  )
     sample += controlnet_cond

     # 3. down
     UpperCAmelCase_					:					Optional[int]			     =    (sample,)
     for down_block in self.down_blocks:
        if isinstance(lowercase_				,   lowercase_  ):
           UpperCAmelCase_     ,   UpperCAmelCase_					:					Dict			     =    down_block(lowercase_				,   lowercase_				,   lowercase_				,   deterministic=not train  )
        else:
           UpperCAmelCase_     ,   UpperCAmelCase_					:					str			     =    down_block(lowercase_				,   lowercase_				,   deterministic=not train  )
        down_block_res_samples += res_samples

     # 4. mid
     UpperCAmelCase_					:					Union[str, Any]			     =    self.mid_block(lowercase_				,   lowercase_				,   lowercase_				,   deterministic=not train  )

     # 5. contronet blocks
     UpperCAmelCase_					:					Tuple			     =    ()
     for down_block_res_sample, controlnet_block in zip(lowercase_				,   self.controlnet_down_blocks  ):
        UpperCAmelCase_					:					List[Any]			     =    controlnet_block(lowercase_  )
        controlnet_down_block_res_samples += (down_block_res_sample,)

     UpperCAmelCase_					:					List[str]			     =    controlnet_down_block_res_samples

     UpperCAmelCase_					:					List[str]			     =    self.controlnet_mid_block(lowercase_  )

     # 6. scaling
     UpperCAmelCase_					:					Optional[int]			     =    [sample * conditioning_scale for sample in down_block_res_samples]
     mid_block_res_sample *= conditioning_scale

     if not return_dict:
        return (down_block_res_samples, mid_block_res_sample)

     return FlaxControlNetOutput(
         down_block_res_samples=lowercase_				,   mid_block_res_sample=lowercase_  )







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()
__snake_case						:Any      		=     logging.get_logger(__name__)

__snake_case						:Optional[Any]      		=     {
    '''b0''': efficientnet.EfficientNetBa,
    '''b1''': efficientnet.EfficientNetBa,
    '''b2''': efficientnet.EfficientNetBa,
    '''b3''': efficientnet.EfficientNetBa,
    '''b4''': efficientnet.EfficientNetBa,
    '''b5''': efficientnet.EfficientNetBa,
    '''b6''': efficientnet.EfficientNetBa,
    '''b7''': efficientnet.EfficientNetBa,
}

__snake_case						:List[Any]      		=     {
    '''b0''': {
        '''hidden_dim''': 1280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 224,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 1280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 240,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [16],
    },
    '''b2''': {
        '''hidden_dim''': 1408,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 260,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 16],
    },
    '''b3''': {
        '''hidden_dim''': 1536,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 300,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 18],
    },
    '''b4''': {
        '''hidden_dim''': 1792,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 380,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 2048,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 456,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [13, 27],
    },
    '''b6''': {
        '''hidden_dim''': 2304,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 528,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [31],
    },
    '''b7''': {
        '''hidden_dim''': 2560,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 600,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [18],
    },
}





def      __snake_case	(			_UpperCAmelCase				):
    __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      __snake_case	(			):
    __a						       =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __a						       =				Image.open(requests.get(_UpperCAmelCase   , stream=_UpperCAmelCase				).raw				)
    return im





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				CONFIG_MAP[model_name]['''image_size''']
    __a						       =				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=_UpperCAmelCase   , )
    return preprocessor





def      __snake_case	(			_UpperCAmelCase				):
    __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      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    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      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    __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__":
 __snake_case						: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''')

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





