





"""simple docstring"""
# This model implementation is heavily based on:

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetInpaintPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
    TEXT_TO_IMAGE_IMAGE_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()







class 	a				(					a__					,      a__					,      a__					,      unittest.TestCase   ):
 snake_case__    	=    StableDiffusionControlNetInpaintPipeline
 snake_case__    	=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 snake_case__    	=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 snake_case__    	=    frozenset({'''control_image'''}   )  # skip `image` and `mask` for now, only test for control_image
 snake_case__    	=    TEXT_TO_IMAGE_IMAGE_PARAMS



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  torch.manual_seed(0			)
  lowerCAmelCase  							=      UNetaDConditionModel(
      block_out_channels=(32, 64)							,    layers_per_block=2							,    sample_size=32							,    in_channels=9							,    out_channels=4							,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,    cross_attention_dim=32							,    )
  torch.manual_seed(0			)
  lowerCAmelCase  							=      ControlNetModel(
      block_out_channels=(32, 64)							,    layers_per_block=2							,    in_channels=4							,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,    cross_attention_dim=32							,    conditioning_embedding_out_channels=(16, 32)							,    )
  torch.manual_seed(0			)
  lowerCAmelCase  							=      DDIMScheduler(
      beta_start=0.00_085							,    beta_end=0.012							,    beta_schedule='scaled_linear'							,    clip_sample=_snake_case							,    set_alpha_to_one=_snake_case							,    )
  torch.manual_seed(0			)
  lowerCAmelCase  							=      AutoencoderKL(
      block_out_channels=[32, 64]							,    in_channels=3							,    out_channels=3							,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,    latent_channels=4							,    )
  torch.manual_seed(0			)
  lowerCAmelCase  							=      CLIPTextConfig(
      bos_token_id=0							,    eos_token_id=2							,    hidden_size=32							,    intermediate_size=37							,    layer_norm_eps=1E-05							,    num_attention_heads=4							,    num_hidden_layers=5							,    pad_token_id=1							,    vocab_size=10_00							,    )
  lowerCAmelCase  							=      CLIPTextModel(_snake_case			)
  lowerCAmelCase  							=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

  lowerCAmelCase  							=      {
      'unet': unet,
      'controlnet': controlnet,
      'scheduler': scheduler,
      'vae': vae,
      'text_encoder': text_encoder,
      'tokenizer': tokenizer,
      'safety_checker': None,
      'feature_extractor': None,
  }
  return components



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case=0			):







  """simple docstring"""






  if str(_snake_case			).startswith('mps'			):
   lowerCAmelCase  							=      torch.manual_seed(_snake_case			)
  else:
   lowerCAmelCase  							=      torch.Generator(device=_snake_case			).manual_seed(_snake_case			)

  lowerCAmelCase  							=      2
  lowerCAmelCase  							=      randn_tensor(
      (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)							,    generator=_snake_case							,    device=torch.device(_snake_case			)							,    )
  lowerCAmelCase  							=      floats_tensor((1, 3, 32, 32)							,    rng=random.Random(_snake_case			)			).to(_snake_case			)
  lowerCAmelCase  							=      init_image.cpu().permute(0							,    2							,    3							,    1			)[0]

  lowerCAmelCase  							=      Image.fromarray(np.uinta(_snake_case			)			).convert('RGB'			).resize((64, 64)			)
  lowerCAmelCase  							=      Image.fromarray(np.uinta(init_image + 4			)			).convert('RGB'			).resize((64, 64)			)

  lowerCAmelCase  							=      {
      'prompt': 'A painting of a squirrel eating a burger',
      'generator': generator,
      'num_inference_steps': 2,
      'guidance_scale': 6.0,
      'output_type': 'numpy',
      'image': image,
      'mask_image': mask_image,
      'control_image': control_image,
  }

  return inputs



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3			)



 @unittest.skipIf(
     torch_device != 'cuda' or not is_xformers_available()							,    reason='XFormers attention is only available with CUDA and `xformers` installed'							,    )
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  self._test_inference_batch_single_identical(expected_max_diff=2E-3			)







class 	a				(					a__   ):
 snake_case__    	=    StableDiffusionControlNetInpaintPipeline
 snake_case__    	=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 snake_case__    	=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 snake_case__    	=    frozenset([]   )



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  torch.manual_seed(0			)
  lowerCAmelCase  							=      UNetaDConditionModel(
      block_out_channels=(32, 64)							,    layers_per_block=2							,    sample_size=32							,    in_channels=4							,    out_channels=4							,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,    cross_attention_dim=32							,    )
  torch.manual_seed(0			)
  lowerCAmelCase  							=      ControlNetModel(
      block_out_channels=(32, 64)							,    layers_per_block=2							,    in_channels=4							,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,    cross_attention_dim=32							,    conditioning_embedding_out_channels=(16, 32)							,    )
  torch.manual_seed(0			)
  lowerCAmelCase  							=      DDIMScheduler(
      beta_start=0.00_085							,    beta_end=0.012							,    beta_schedule='scaled_linear'							,    clip_sample=_snake_case							,    set_alpha_to_one=_snake_case							,    )
  torch.manual_seed(0			)
  lowerCAmelCase  							=      AutoencoderKL(
      block_out_channels=[32, 64]							,    in_channels=3							,    out_channels=3							,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,    latent_channels=4							,    )
  torch.manual_seed(0			)
  lowerCAmelCase  							=      CLIPTextConfig(
      bos_token_id=0							,    eos_token_id=2							,    hidden_size=32							,    intermediate_size=37							,    layer_norm_eps=1E-05							,    num_attention_heads=4							,    num_hidden_layers=5							,    pad_token_id=1							,    vocab_size=10_00							,    )
  lowerCAmelCase  							=      CLIPTextModel(_snake_case			)
  lowerCAmelCase  							=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

  lowerCAmelCase  							=      {
      'unet': unet,
      'controlnet': controlnet,
      'scheduler': scheduler,
      'vae': vae,
      'text_encoder': text_encoder,
      'tokenizer': tokenizer,
      'safety_checker': None,
      'feature_extractor': None,
  }
  return components







class 	a				(					a__					,      a__					,      unittest.TestCase   ):
 snake_case__    	=    StableDiffusionControlNetInpaintPipeline
 snake_case__    	=    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
 snake_case__    	=    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  torch.manual_seed(0			)
  lowerCAmelCase  							=      UNetaDConditionModel(
      block_out_channels=(32, 64)							,    layers_per_block=2							,    sample_size=32							,    in_channels=9							,    out_channels=4							,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,    up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')							,    cross_attention_dim=32							,    )
  torch.manual_seed(0			)

  def init_weights(_snake_case			):
   if isinstance(_snake_case							,    torch.nn.Convad			):
    torch.nn.init.normal(m.weight			)
    m.bias.data.fill_(1.0			)

  lowerCAmelCase  							=      ControlNetModel(
      block_out_channels=(32, 64)							,    layers_per_block=2							,    in_channels=4							,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,    cross_attention_dim=32							,    conditioning_embedding_out_channels=(16, 32)							,    )
  controlneta.controlnet_down_blocks.apply(_snake_case			)

  torch.manual_seed(0			)
  lowerCAmelCase  							=      ControlNetModel(
      block_out_channels=(32, 64)							,    layers_per_block=2							,    in_channels=4							,    down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')							,    cross_attention_dim=32							,    conditioning_embedding_out_channels=(16, 32)							,    )
  controlneta.controlnet_down_blocks.apply(_snake_case			)

  torch.manual_seed(0			)
  lowerCAmelCase  							=      DDIMScheduler(
      beta_start=0.00_085							,    beta_end=0.012							,    beta_schedule='scaled_linear'							,    clip_sample=_snake_case							,    set_alpha_to_one=_snake_case							,    )
  torch.manual_seed(0			)
  lowerCAmelCase  							=      AutoencoderKL(
      block_out_channels=[32, 64]							,    in_channels=3							,    out_channels=3							,    down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']							,    up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']							,    latent_channels=4							,    )
  torch.manual_seed(0			)
  lowerCAmelCase  							=      CLIPTextConfig(
      bos_token_id=0							,    eos_token_id=2							,    hidden_size=32							,    intermediate_size=37							,    layer_norm_eps=1E-05							,    num_attention_heads=4							,    num_hidden_layers=5							,    pad_token_id=1							,    vocab_size=10_00							,    )
  lowerCAmelCase  							=      CLIPTextModel(_snake_case			)
  lowerCAmelCase  							=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

  lowerCAmelCase  							=      MultiControlNetModel([controlneta, controlneta]			)

  lowerCAmelCase  							=      {
      'unet': unet,
      'controlnet': controlnet,
      'scheduler': scheduler,
      'vae': vae,
      'text_encoder': text_encoder,
      'tokenizer': tokenizer,
      'safety_checker': None,
      'feature_extractor': None,
  }
  return components



 def 					UpperCamelCase__   (			self							,    _snake_case							,    _snake_case=0			):







  """simple docstring"""






  if str(_snake_case			).startswith('mps'			):
   lowerCAmelCase  							=      torch.manual_seed(_snake_case			)
  else:
   lowerCAmelCase  							=      torch.Generator(device=_snake_case			).manual_seed(_snake_case			)

  lowerCAmelCase  							=      2

  lowerCAmelCase  							=      [
      randn_tensor(
          (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)							,    generator=_snake_case							,    device=torch.device(_snake_case			)							,    ),
      randn_tensor(
          (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)							,    generator=_snake_case							,    device=torch.device(_snake_case			)							,    ),
  ]
  lowerCAmelCase  							=      floats_tensor((1, 3, 32, 32)							,    rng=random.Random(_snake_case			)			).to(_snake_case			)
  lowerCAmelCase  							=      init_image.cpu().permute(0							,    2							,    3							,    1			)[0]

  lowerCAmelCase  							=      Image.fromarray(np.uinta(_snake_case			)			).convert('RGB'			).resize((64, 64)			)
  lowerCAmelCase  							=      Image.fromarray(np.uinta(init_image + 4			)			).convert('RGB'			).resize((64, 64)			)

  lowerCAmelCase  							=      {
      'prompt': 'A painting of a squirrel eating a burger',
      'generator': generator,
      'num_inference_steps': 2,
      'guidance_scale': 6.0,
      'output_type': 'numpy',
      'image': image,
      'mask_image': mask_image,
      'control_image': control_image,
  }

  return inputs



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.get_dummy_components()
  lowerCAmelCase  							=      self.pipeline_class(**_snake_case			)
  pipe.to(_snake_case			)

  lowerCAmelCase  							=      10.0
  lowerCAmelCase  							=      4

  lowerCAmelCase  							=      self.get_dummy_inputs(_snake_case			)
  lowerCAmelCase  							=      steps
  lowerCAmelCase  							=      scale
  lowerCAmelCase  							=      pipe(**_snake_case			)[0]

  lowerCAmelCase  							=      self.get_dummy_inputs(_snake_case			)
  lowerCAmelCase  							=      steps
  lowerCAmelCase  							=      scale
  lowerCAmelCase  							=      pipe(**_snake_case							,    control_guidance_start=0.1							,    control_guidance_end=0.2			)[0]

  lowerCAmelCase  							=      self.get_dummy_inputs(_snake_case			)
  lowerCAmelCase  							=      steps
  lowerCAmelCase  							=      scale
  lowerCAmelCase  							=      pipe(**_snake_case							,    control_guidance_start=[0.1, 0.3]							,    control_guidance_end=[0.2, 0.7]			)[0]

  lowerCAmelCase  							=      self.get_dummy_inputs(_snake_case			)
  lowerCAmelCase  							=      steps
  lowerCAmelCase  							=      scale
  lowerCAmelCase  							=      pipe(**_snake_case							,    control_guidance_start=0.4							,    control_guidance_end=[0.5, 0.8]			)[0]

  # make sure that all outputs are different
  assert np.sum(np.abs(output_a - output_a			)			) > 1E-3
  assert np.sum(np.abs(output_a - output_a			)			) > 1E-3
  assert np.sum(np.abs(output_a - output_a			)			) > 1E-3



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3			)



 @unittest.skipIf(
     torch_device != 'cuda' or not is_xformers_available()							,    reason='XFormers attention is only available with CUDA and `xformers` installed'							,    )
 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  self._test_inference_batch_single_identical(expected_max_diff=2E-3			)



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      self.get_dummy_components()
  lowerCAmelCase  							=      self.pipeline_class(**_snake_case			)
  pipe.to(_snake_case			)
  pipe.set_progress_bar_config(disable=_snake_case			)
  with tempfile.TemporaryDirectory() as tmpdir:
   try:
    # save_pretrained is not implemented for Multi-ControlNet
    pipe.save_pretrained(_snake_case			)
   except NotImplementedError:
    pass







@slow
@require_torch_gpu
class 	a				(					unittest.TestCase   ):



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      ControlNetModel.from_pretrained('lllyasviel/sd-controlnet-canny'			)

  lowerCAmelCase  							=      StableDiffusionControlNetInpaintPipeline.from_pretrained(
      'runwayml/stable-diffusion-inpainting'							,    safety_checker=_snake_case							,    controlnet=_snake_case			)
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=_snake_case			)

  lowerCAmelCase  							=      torch.Generator(device='cpu'			).manual_seed(0			)
  lowerCAmelCase  							=      load_image(
      'https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'			).resize((5_12, 5_12)			)

  lowerCAmelCase  							=      load_image(
      'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'
      '/stable_diffusion_inpaint/input_bench_mask.png'			).resize((5_12, 5_12)			)

  lowerCAmelCase  							=      'pitch black hole'

  lowerCAmelCase  							=      load_image(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'			).resize((5_12, 5_12)			)

  lowerCAmelCase  							=      pipe(
      _snake_case							,    image=_snake_case							,    mask_image=_snake_case							,    control_image=_snake_case							,    generator=_snake_case							,    output_type='np'							,    num_inference_steps=3							,    )

  lowerCAmelCase  							=      output.images[0]

  assert image.shape == (5_12, 5_12, 3)

  lowerCAmelCase  							=      load_numpy(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/inpaint.npy'			)

  assert np.abs(expected_image - image			).max() < 9E-2



 def 					UpperCamelCase__   (			self			):







  """simple docstring"""






  lowerCAmelCase  							=      ControlNetModel.from_pretrained('lllyasviel/control_v11p_sd15_inpaint'			)

  lowerCAmelCase  							=      StableDiffusionControlNetInpaintPipeline.from_pretrained(
      'runwayml/stable-diffusion-v1-5'							,    safety_checker=_snake_case							,    controlnet=_snake_case			)
  lowerCAmelCase  							=      DDIMScheduler.from_config(pipe.scheduler.config			)
  pipe.enable_model_cpu_offload()
  pipe.set_progress_bar_config(disable=_snake_case			)

  lowerCAmelCase  							=      torch.Generator(device='cpu'			).manual_seed(33			)

  lowerCAmelCase  							=      load_image(
      'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'			)
  lowerCAmelCase  							=      init_image.resize((5_12, 5_12)			)

  lowerCAmelCase  							=      load_image(
      'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'			)
  lowerCAmelCase  							=      mask_image.resize((5_12, 5_12)			)

  lowerCAmelCase  							=      'a handsome man with ray-ban sunglasses'

  def make_inpaint_condition(_snake_case							,    _snake_case			):
   lowerCAmelCase  							=      np.array(image.convert('RGB'			)			).astype(np.floataa			) / 255.0
   lowerCAmelCase  							=      np.array(image_mask.convert('L'			)			).astype(np.floataa			) / 255.0

   assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
   lowerCAmelCase  							=      -1.0  # set as masked pixel
   lowerCAmelCase  							=      np.expand_dims(_snake_case							,    0			).transpose(0							,    3							,    1							,    2			)
   lowerCAmelCase  							=      torch.from_numpy(_snake_case			)
   return image

  lowerCAmelCase  							=      make_inpaint_condition(_snake_case							,    _snake_case			)

  lowerCAmelCase  							=      pipe(
      _snake_case							,    image=_snake_case							,    mask_image=_snake_case							,    control_image=_snake_case							,    guidance_scale=9.0							,    eta=1.0							,    generator=_snake_case							,    num_inference_steps=20							,    output_type='np'							,    )
  lowerCAmelCase  							=      output.images[0]

  assert image.shape == (5_12, 5_12, 3)

  lowerCAmelCase  							=      load_numpy(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/boy_ray_ban.npy'			)

  assert np.abs(expected_image - image			).max() < 9E-2












"""simple docstring"""
import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase			:		Union[str, Any]		    =   logging.get_logger(__name__)

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  Optional[int]       ):
 lowerCAmelCase  							=      tf.image.resize(_UpperCAmelCase   ,			(346, 346)       )
 lowerCAmelCase  							=      tf.image.crop_to_bounding_box(_UpperCAmelCase   ,			(346 - 289) // 2   ,			(346 - 289) // 2   ,			289   ,			289       )
 return image

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      EfficientNetConfig.from_pretrained('google/efficientnet-b7'       )
 lowerCAmelCase  							=      289
 lowerCAmelCase  							=      640
 lowerCAmelCase  							=      {'0': 'LABEL_0', '1': 'LABEL_1'}
 lowerCAmelCase  							=      {'LABEL_0': 0, 'LABEL_1': 1}
 lowerCAmelCase  							=      []

 lowerCAmelCase  							=      BertConfig()
 lowerCAmelCase  							=      AlignConfig.from_text_vision_configs(
     text_config=_UpperCAmelCase   ,			vision_config=_UpperCAmelCase   ,			projection_dim=640       )
 return config

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

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      EfficientNetImageProcessor(
     do_center_crop=_UpperCAmelCase   ,			rescale_factor=1 / 127.5   ,			rescale_offset=_UpperCAmelCase   ,			do_normalize=_UpperCAmelCase   ,			include_top=_UpperCAmelCase   ,			resample=Image.BILINEAR   ,			)
 lowerCAmelCase  							=      BertTokenizer.from_pretrained('bert-base-uncased'       )
 lowerCAmelCase  							=      64
 lowerCAmelCase  							=      AlignProcessor(image_processor=_UpperCAmelCase   ,			tokenizer=_UpperCAmelCase       )
 return processor

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  List[str]       ):
 # EfficientNet image encoder
 lowerCAmelCase  							=      [v.split('_'       )[0].split('block'       )[1] for v in original_param_names if v.startswith('block'       )]
 lowerCAmelCase  							=      list(set(_UpperCAmelCase       )       )
 lowerCAmelCase  							=      sorted(_UpperCAmelCase       )
 lowerCAmelCase  							=      len(_UpperCAmelCase       )
 lowerCAmelCase  							=      {b: str(_UpperCAmelCase       ) for b, i in zip(_UpperCAmelCase   ,			range(_UpperCAmelCase       )       )}

 lowerCAmelCase  							=      []
 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:
  lowerCAmelCase  							=      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')       )

 lowerCAmelCase  							=      {}
 for item in rename_keys:
  if item[0] in original_param_names:
   lowerCAmelCase  							=      'vision_model.' + item[1]

    # BERT text encoder
 lowerCAmelCase  							=      []
 lowerCAmelCase  							=      'tf_bert_model/bert'
 lowerCAmelCase  							=      'text_model'
 for i in range(12       ):
  rename_keys.append(
      (
          F'{old}/encoder/layer_._{i}/attention/self/query/kernel:0',
          F'{new}.encoder.layer.{i}.attention.self.query.weight',
      )       )
  rename_keys.append(
      (
          F'{old}/encoder/layer_._{i}/attention/self/query/bias:0',
          F'{new}.encoder.layer.{i}.attention.self.query.bias',
      )       )
  rename_keys.append(
      (
          F'{old}/encoder/layer_._{i}/attention/self/key/kernel:0',
          F'{new}.encoder.layer.{i}.attention.self.key.weight',
      )       )
  rename_keys.append(
      (
          F'{old}/encoder/layer_._{i}/attention/self/key/bias:0',
          F'{new}.encoder.layer.{i}.attention.self.key.bias',
      )       )
  rename_keys.append(
      (
          F'{old}/encoder/layer_._{i}/attention/self/value/kernel:0',
          F'{new}.encoder.layer.{i}.attention.self.value.weight',
      )       )
  rename_keys.append(
      (
          F'{old}/encoder/layer_._{i}/attention/self/value/bias:0',
          F'{new}.encoder.layer.{i}.attention.self.value.bias',
      )       )
  rename_keys.append(
      (
          F'{old}/encoder/layer_._{i}/attention/output/dense/kernel:0',
          F'{new}.encoder.layer.{i}.attention.output.dense.weight',
      )       )
  rename_keys.append(
      (
          F'{old}/encoder/layer_._{i}/attention/output/dense/bias:0',
          F'{new}.encoder.layer.{i}.attention.output.dense.bias',
      )       )
  rename_keys.append(
      (
          F'{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0',
          F'{new}.encoder.layer.{i}.attention.output.LayerNorm.weight',
      )       )
  rename_keys.append(
      (
          F'{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0',
          F'{new}.encoder.layer.{i}.attention.output.LayerNorm.bias',
      )       )
  rename_keys.append(
      (
          F'{old}/encoder/layer_._{i}/intermediate/dense/kernel:0',
          F'{new}.encoder.layer.{i}.intermediate.dense.weight',
      )       )
  rename_keys.append(
      (
          F'{old}/encoder/layer_._{i}/intermediate/dense/bias:0',
          F'{new}.encoder.layer.{i}.intermediate.dense.bias',
      )       )
  rename_keys.append(
      (F'{old}/encoder/layer_._{i}/output/dense/kernel:0', F'{new}.encoder.layer.{i}.output.dense.weight')       )
  rename_keys.append(
      (F'{old}/encoder/layer_._{i}/output/dense/bias:0', F'{new}.encoder.layer.{i}.output.dense.bias')       )
  rename_keys.append(
      (F'{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0', F'{new}.encoder.layer.{i}.output.LayerNorm.weight')       )
  rename_keys.append(
      (F'{old}/encoder/layer_._{i}/output/LayerNorm/beta:0', F'{new}.encoder.layer.{i}.output.LayerNorm.bias')       )

 rename_keys.append((F'{old}/embeddings/word_embeddings/weight:0', F'{new}.embeddings.word_embeddings.weight')       )
 rename_keys.append(
     (F'{old}/embeddings/position_embeddings/embeddings:0', F'{new}.embeddings.position_embeddings.weight')       )
 rename_keys.append(
     (F'{old}/embeddings/token_type_embeddings/embeddings:0', F'{new}.embeddings.token_type_embeddings.weight')       )
 rename_keys.append((F'{old}/embeddings/LayerNorm/gamma:0', F'{new}.embeddings.LayerNorm.weight')       )
 rename_keys.append((F'{old}/embeddings/LayerNorm/beta:0', F'{new}.embeddings.LayerNorm.bias')       )

 rename_keys.append((F'{old}/pooler/dense/kernel:0', F'{new}.pooler.dense.weight')       )
 rename_keys.append((F'{old}/pooler/dense/bias:0', F'{new}.pooler.dense.bias')       )
 rename_keys.append(('dense/kernel:0', 'text_projection.weight')       )
 rename_keys.append(('dense/bias:0', 'text_projection.bias')       )
 rename_keys.append(('dense/bias:0', 'text_projection.bias')       )
 rename_keys.append(('temperature:0', 'temperature')       )

 for item in rename_keys:
  if item[0] in original_param_names:
   lowerCAmelCase  							=      item[1]
 return key_mapping

def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  List[Any]   ,			_UpperCAmelCase :  List[str]   ,			_UpperCAmelCase :  Any       ):
 list(hf_params.keys()       )

 for key, value in tf_params.items():
  if key not in key_mapping:
   continue

  lowerCAmelCase  							=      key_mapping[key]
  if "_conv" in key and "kernel" in key:
   lowerCAmelCase  							=      torch.from_numpy(_UpperCAmelCase       ).permute(3   ,			2   ,			0   ,			1       )
  elif "embeddings" in key:
   lowerCAmelCase  							=      torch.from_numpy(_UpperCAmelCase       )
  elif "depthwise_kernel" in key:
   lowerCAmelCase  							=      torch.from_numpy(_UpperCAmelCase       ).permute(2   ,			3   ,			0   ,			1       )
  elif "kernel" in key:
   lowerCAmelCase  							=      torch.from_numpy(np.transpose(_UpperCAmelCase       )       )
  elif "temperature" in key:
   lowerCAmelCase  							=      value
  elif "bn/gamma" or "bn/beta" in key:
   lowerCAmelCase  							=      torch.from_numpy(np.transpose(_UpperCAmelCase       )       ).squeeze()
  else:
   lowerCAmelCase  							=      torch.from_numpy(_UpperCAmelCase       )

  # Replace HF parameters with original TF model parameters
  hf_params[hf_key].copy_(_UpperCAmelCase       )

@torch.no_grad()
def 						_SCREAMING_SNAKE_CASE       (_UpperCAmelCase :  str   ,			_UpperCAmelCase :  Tuple   ,			_UpperCAmelCase :  Any   ,			_UpperCAmelCase :  Any       ):
 lowerCAmelCase  							=      64
 lowerCAmelCase  							=      Tokenizer(_UpperCAmelCase       )
 lowerCAmelCase  							=      align.Align('efficientnet-b7'   ,			'bert-base'   ,			640   ,			_UpperCAmelCase   ,			tok.get_vocab_size()       )
 original_model.compile()
 original_model.load_weights(_UpperCAmelCase       )

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

 # Load HuggingFace model
 lowerCAmelCase  							=      get_align_config()
 lowerCAmelCase  							=      AlignModel(_UpperCAmelCase       ).eval()
 lowerCAmelCase  							=      hf_model.state_dict()

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

 # Initialize processor
 lowerCAmelCase  							=      get_processor()
 lowerCAmelCase  							=      processor(
     images=prepare_img()   ,			text='A picture of a cat'   ,			padding='max_length'   ,			max_length=64   ,			return_tensors='pt'       )

 # HF model inference
 hf_model.eval()
 with torch.no_grad():
  lowerCAmelCase  							=      hf_model(**_UpperCAmelCase       )

 lowerCAmelCase  							=      outputs.image_embeds.detach().numpy()
 lowerCAmelCase  							=      outputs.text_embeds.detach().numpy()

 # Original model inference
 lowerCAmelCase  							=      False
 lowerCAmelCase  							=      EfficientNetImageProcessor(
     do_center_crop=_UpperCAmelCase   ,			do_rescale=_UpperCAmelCase   ,			do_normalize=_UpperCAmelCase   ,			include_top=_UpperCAmelCase   ,			resample=Image.BILINEAR   ,			)
 lowerCAmelCase  							=      tf_image_processor(images=prepare_img()   ,			return_tensors='tf'   ,			data_format='channels_last'       )['pixel_values']
 lowerCAmelCase  							=      tok(tf.constant(['A picture of a cat']       )       )

 lowerCAmelCase  							=      original_model.image_encoder(_UpperCAmelCase   ,			training=_UpperCAmelCase       )
 lowerCAmelCase  							=      original_model.text_encoder(_UpperCAmelCase   ,			training=_UpperCAmelCase       )

 lowerCAmelCase  							=      tf.nn.la_normalize(_UpperCAmelCase   ,			axis=-1       )
 lowerCAmelCase  							=      tf.nn.la_normalize(_UpperCAmelCase   ,			axis=-1       )

 # Check whether original and HF model outputs match  -> np.allclose
 if not np.allclose(_UpperCAmelCase   ,			_UpperCAmelCase   ,			atol=1e-3       ):
  raise ValueError('The predicted image features are not the same.'       )
 if not np.allclose(_UpperCAmelCase   ,			_UpperCAmelCase   ,			atol=1e-3       ):
  raise ValueError('The predicted text features 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       )
  processor.save_pretrained(_UpperCAmelCase       )

 if push_to_hub:
  # Push model and image processor to hub
  print('Pushing converted ALIGN to the hub...'       )
  processor.push_to_hub('align-base'       )
  hf_model.push_to_hub('align-base'       )


if __name__ == "__main__":
       __UpperCamelCase			:		str		    =   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '''--checkpoint_path''',
           default='''./weights/model-weights''',
           type=str,
           help='''Path to the pretrained TF ALIGN checkpoint.''',
       )
       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			:		int		    =   parser.parse_args()
       convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





