





'''simple docstring'''

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 lowerCamelCase    (lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_    ,   unittest.TestCase       ):
  _lowercase	:						Any							       =					StableDiffusionControlNetInpaintPipeline
  _lowercase	:						List[str]							       =					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  _lowercase	:						Any							       =					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  _lowercase	:						List[str]							       =					frozenset({"""control_image"""}       )  # skip `image` and `mask` for now, only test for control_image
  _lowercase	:						List[Any]							       =					TEXT_TO_IMAGE_IMAGE_PARAMS







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






        """simple docstring"""





        torch.manual_seed(0		)
        _snake_case		:						Tuple         =  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		)
        _snake_case		:						Dict         =  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		)
        _snake_case		:						Optional[Any]         =  DDIMScheduler(
            beta_start=0.00_085  ,  beta_end=0.012  ,  beta_schedule='''scaled_linear'''  ,  clip_sample=__UpperCamelCase  ,  set_alpha_to_one=__UpperCamelCase  ,  )
        torch.manual_seed(0		)
        _snake_case		:						Any         =  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		)
        _snake_case		:						Optional[Any]         =  CLIPTextConfig(
            bos_token_id=0  ,  eos_token_id=2  ,  hidden_size=32  ,  intermediate_size=37  ,  layer_norm_eps=1E-0_5  ,  num_attention_heads=4  ,  num_hidden_layers=5  ,  pad_token_id=1  ,  vocab_size=1_000  ,  )
        _snake_case		:						Optional[int]         =  CLIPTextModel(__UpperCamelCase		)
        _snake_case		:						List[Any]         =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

        _snake_case		:						List[str]         =  {
            "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  ,  lowercase__  ,  lowercase__=0		)		->    Union[str, Any]:






        """simple docstring"""





        if str(__UpperCamelCase		).startswith('''mps'''		):
              _snake_case		:						Optional[int]         =  torch.manual_seed(__UpperCamelCase		)
        else:
              _snake_case		:						Optional[Any]         =  torch.Generator(device=__UpperCamelCase		).manual_seed(__UpperCamelCase		)

        _snake_case		:						Optional[Any]         =  2
        _snake_case		:						int         =  randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)  ,  generator=__UpperCamelCase  ,  device=torch.device(__UpperCamelCase		)  ,  )
        _snake_case		:						Optional[Any]         =  floats_tensor((1, 3, 32, 32)  ,  rng=random.Random(__UpperCamelCase		)		).to(__UpperCamelCase		)
        _snake_case		:						Union[str, Any]         =  init_image.cpu().permute(0  ,  2  ,  3  ,  1		)[0]

        _snake_case		:						Dict         =  Image.fromarray(np.uinta(__UpperCamelCase		)		).convert('''RGB'''		).resize((64, 64)		)
        _snake_case		:						Optional[Any]         =  Image.fromarray(np.uinta(init_image + 4		)		).convert('''RGB'''		).resize((64, 64)		)

        _snake_case		:						Dict         =  {
            "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		)		->    Dict:






        """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		)		->    Dict:






        """simple docstring"""





        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3		)







  def 					UpperCAmelCase_					(		self		)		->    Dict:






        """simple docstring"""





        self._test_inference_batch_single_identical(expected_max_diff=2E-3		)





class lowerCamelCase    (lowerCAmelCase_       ):
  _lowercase	:						Optional[Any]							       =					StableDiffusionControlNetInpaintPipeline
  _lowercase	:						List[Any]							       =					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  _lowercase	:						Tuple							       =					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
  _lowercase	:						Tuple							       =					frozenset([]       )







  def 					UpperCAmelCase_					(		self		)		->    Any:






        """simple docstring"""





        torch.manual_seed(0		)
        _snake_case		:						str         =  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		)
        _snake_case		:						List[str]         =  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		)
        _snake_case		:						Optional[Any]         =  DDIMScheduler(
            beta_start=0.00_085  ,  beta_end=0.012  ,  beta_schedule='''scaled_linear'''  ,  clip_sample=__UpperCamelCase  ,  set_alpha_to_one=__UpperCamelCase  ,  )
        torch.manual_seed(0		)
        _snake_case		:						int         =  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		)
        _snake_case		:						List[Any]         =  CLIPTextConfig(
            bos_token_id=0  ,  eos_token_id=2  ,  hidden_size=32  ,  intermediate_size=37  ,  layer_norm_eps=1E-0_5  ,  num_attention_heads=4  ,  num_hidden_layers=5  ,  pad_token_id=1  ,  vocab_size=1_000  ,  )
        _snake_case		:						List[str]         =  CLIPTextModel(__UpperCamelCase		)
        _snake_case		:						str         =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

        _snake_case		:						int         =  {
            "unet": unet,
            "controlnet": controlnet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
            "feature_extractor": None,
        }
        return components





class lowerCamelCase    (lowerCAmelCase_    ,   lowerCAmelCase_    ,   unittest.TestCase       ):
  _lowercase	:						List[Any]							       =					StableDiffusionControlNetInpaintPipeline
  _lowercase	:						Optional[int]							       =					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
  _lowercase	:						List[Any]							       =					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS







  def 					UpperCAmelCase_					(		self		)		->    int:






        """simple docstring"""





        torch.manual_seed(0		)
        _snake_case		:						int         =  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(lowercase__		):
              if isinstance(__UpperCamelCase  ,  torch.nn.Convad		):
                    torch.nn.init.normal(m.weight		)
                    m.bias.data.fill_(1.0		)

        _snake_case		:						Any         =  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(__UpperCamelCase		)

        torch.manual_seed(0		)
        _snake_case		:						List[str]         =  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(__UpperCamelCase		)

        torch.manual_seed(0		)
        _snake_case		:						List[Any]         =  DDIMScheduler(
            beta_start=0.00_085  ,  beta_end=0.012  ,  beta_schedule='''scaled_linear'''  ,  clip_sample=__UpperCamelCase  ,  set_alpha_to_one=__UpperCamelCase  ,  )
        torch.manual_seed(0		)
        _snake_case		:						str         =  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		)
        _snake_case		:						Optional[int]         =  CLIPTextConfig(
            bos_token_id=0  ,  eos_token_id=2  ,  hidden_size=32  ,  intermediate_size=37  ,  layer_norm_eps=1E-0_5  ,  num_attention_heads=4  ,  num_hidden_layers=5  ,  pad_token_id=1  ,  vocab_size=1_000  ,  )
        _snake_case		:						Tuple         =  CLIPTextModel(__UpperCamelCase		)
        _snake_case		:						Union[str, Any]         =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

        _snake_case		:						Dict         =  MultiControlNetModel([controlneta, controlneta]		)

        _snake_case		:						str         =  {
            "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  ,  lowercase__  ,  lowercase__=0		)		->    int:






        """simple docstring"""





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

        _snake_case		:						Dict         =  2

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

        _snake_case		:						str         =  Image.fromarray(np.uinta(__UpperCamelCase		)		).convert('''RGB'''		).resize((64, 64)		)
        _snake_case		:						Any         =  Image.fromarray(np.uinta(init_image + 4		)		).convert('''RGB'''		).resize((64, 64)		)

        _snake_case		:						Optional[int]         =  {
            "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		)		->    List[str]:






        """simple docstring"""





        _snake_case		:						str         =  self.get_dummy_components()
        _snake_case		:						Optional[Any]         =  self.pipeline_class(**__UpperCamelCase		)
        pipe.to(__UpperCamelCase		)

        _snake_case		:						str         =  10.0
        _snake_case		:						Optional[int]         =  4

        _snake_case		:						Optional[Any]         =  self.get_dummy_inputs(__UpperCamelCase		)
        _snake_case		:						Tuple         =  steps
        _snake_case		:						Tuple         =  scale
        _snake_case		:						Union[str, Any]         =  pipe(**__UpperCamelCase		)[0]

        _snake_case		:						Optional[Any]         =  self.get_dummy_inputs(__UpperCamelCase		)
        _snake_case		:						List[str]         =  steps
        _snake_case		:						Optional[Any]         =  scale
        _snake_case		:						Tuple         =  pipe(**__UpperCamelCase  ,  control_guidance_start=0.1  ,  control_guidance_end=0.2		)[0]

        _snake_case		:						List[Any]         =  self.get_dummy_inputs(__UpperCamelCase		)
        _snake_case		:						Union[str, Any]         =  steps
        _snake_case		:						str         =  scale
        _snake_case		:						Union[str, Any]         =  pipe(**__UpperCamelCase  ,  control_guidance_start=[0.1, 0.3]  ,  control_guidance_end=[0.2, 0.7]		)[0]

        _snake_case		:						List[str]         =  self.get_dummy_inputs(__UpperCamelCase		)
        _snake_case		:						List[Any]         =  steps
        _snake_case		:						Union[str, Any]         =  scale
        _snake_case		:						str         =  pipe(**__UpperCamelCase  ,  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		)		->    List[str]:






        """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		)		->    str:






        """simple docstring"""





        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3		)







  def 					UpperCAmelCase_					(		self		)		->    Dict:






        """simple docstring"""





        self._test_inference_batch_single_identical(expected_max_diff=2E-3		)







  def 					UpperCAmelCase_					(		self		)		->    Dict:






        """simple docstring"""





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





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







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






        """simple docstring"""





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







  def 					UpperCAmelCase_					(		self		)		->    Any:






        """simple docstring"""





        _snake_case		:						Optional[int]         =  ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''		)

        _snake_case		:						List[Any]         =  StableDiffusionControlNetInpaintPipeline.from_pretrained(
            '''runwayml/stable-diffusion-inpainting'''  ,  safety_checker=__UpperCamelCase  ,  controlnet=__UpperCamelCase		)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=__UpperCamelCase		)

        _snake_case		:						Optional[int]         =  torch.Generator(device='''cpu'''		).manual_seed(0		)
        _snake_case		:						Dict         =  load_image(
            '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''		).resize((512, 512)		)

        _snake_case		:						str         =  load_image(
            '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main'''
            '''/stable_diffusion_inpaint/input_bench_mask.png'''		).resize((512, 512)		)

        _snake_case		:						Union[str, Any]         =  "pitch black hole"

        _snake_case		:						Union[str, Any]         =  load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png'''		).resize((512, 512)		)

        _snake_case		:						Dict         =  pipe(
            __UpperCamelCase  ,  image=__UpperCamelCase  ,  mask_image=__UpperCamelCase  ,  control_image=__UpperCamelCase  ,  generator=__UpperCamelCase  ,  output_type='''np'''  ,  num_inference_steps=3  ,  )

        _snake_case		:						Optional[int]         =  output.images[0]

        assert image.shape == (512, 512, 3)

        _snake_case		:						Tuple         =  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		)		->    str:






        """simple docstring"""





        _snake_case		:						Any         =  ControlNetModel.from_pretrained('''lllyasviel/control_v11p_sd15_inpaint'''		)

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

        _snake_case		:						List[str]         =  torch.Generator(device='''cpu'''		).manual_seed(33		)

        _snake_case		:						int         =  load_image(
            '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy.png'''		)
        _snake_case		:						List[str]         =  init_image.resize((512, 512)		)

        _snake_case		:						Any         =  load_image(
            '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_inpaint/boy_mask.png'''		)
        _snake_case		:						Dict         =  mask_image.resize((512, 512)		)

        _snake_case		:						Any         =  "a handsome man with ray-ban sunglasses"

        def make_inpaint_condition(lowercase__  ,  lowercase__		):
              _snake_case		:						Any         =  np.array(image.convert('''RGB'''		)		).astype(np.floataa		) / 255.0
              _snake_case		:						Tuple         =  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"
              _snake_case		:						str         =  -1.0  # set as masked pixel
              _snake_case		:						Dict         =  np.expand_dims(__UpperCamelCase  ,  0		).transpose(0  ,  3  ,  1  ,  2		)
              _snake_case		:						int         =  torch.from_numpy(__UpperCamelCase		)
              return image

        _snake_case		:						Optional[int]         =  make_inpaint_condition(__UpperCamelCase  ,  __UpperCamelCase		)

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

        assert image.shape == (512, 512, 3)

        _snake_case		:						Optional[int]         =  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 torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()



def 				a__			(					lowerCAmelCase						:							Optional[Any]							,     lowerCAmelCase						:							Dict							,     lowerCAmelCase						:							Tuple		):






     '''simple docstring'''
     # Initialise PyTorch model
     UpperCAmelCase__				:			int							=    FNetConfig.from_json_file(lowerCAmelCase		)
     print(F"Building PyTorch model from configuration: {config}"		)
     UpperCAmelCase__				:			Union[str, Any]							=    FNetForPreTraining(lowerCAmelCase		)

     UpperCAmelCase__				:			int							=    restore_checkpoint(lowerCAmelCase							,     lowerCAmelCase		)
     UpperCAmelCase__				:			List[str]							=    checkpoint_dict["target"]

     # Embeddings
     # Position IDs
     UpperCAmelCase__				:			Union[str, Any]							=    fnet_pretraining_model.state_dict()

     UpperCAmelCase__				:			Optional[Any]							=    state_dict["fnet.embeddings.position_ids"]
     UpperCAmelCase__				:			Dict							=    {"fnet.embeddings.position_ids": position_ids}
     # Embedding Layers
     UpperCAmelCase__				:			Dict							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]		)
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]		)
     UpperCAmelCase__				:			Tuple							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]		)
     UpperCAmelCase__				:			int							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]		).T
     UpperCAmelCase__				:			List[Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]		)
     UpperCAmelCase__				:			Optional[Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]		)
     UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]		)

     # Encoder Layers
     for layer in range(config.num_hidden_layers		):
          UpperCAmelCase__				:			Any							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["mixing_layer_norm"]["scale"]		)
          UpperCAmelCase__				:			List[str]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["mixing_layer_norm"]["bias"]		)

          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["intermediate"]["kernel"]		).T
          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["intermediate"]["bias"]		)

          UpperCAmelCase__				:			Dict							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["output"]["kernel"]		).T
          UpperCAmelCase__				:			List[str]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["output"]["bias"]		)

          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["output_layer_norm"]["scale"]		)
          UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["output_layer_norm"]["bias"]		)

     # Pooler Layers
     UpperCAmelCase__				:			Optional[Any]							=    torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]		).T
     UpperCAmelCase__				:			Optional[int]							=    torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]		)

     # Masked LM Layers
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["predictions_dense"]["kernel"]		).T
     UpperCAmelCase__				:			int							=    torch.tensor(
         pretrained_model_params["predictions_dense"]["bias"]		)
     UpperCAmelCase__				:			Tuple							=    torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["scale"]		)
     UpperCAmelCase__				:			List[str]							=    torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["bias"]		)
     UpperCAmelCase__				:			List[Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]		)
     UpperCAmelCase__				:			Optional[int]							=    torch.tensor(
         pretrained_model_params["predictions_output"]["output_bias"]		)
     UpperCAmelCase__				:			Any							=    torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]		)

     # Seq Relationship Layers
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["classification"]["output_kernel"]		)
     UpperCAmelCase__				:			str							=    torch.tensor(
         pretrained_model_params["classification"]["output_bias"]		)

     # Load State Dict
     fnet_pretraining_model.load_state_dict(lowerCAmelCase		)

     # Save PreTrained
     print(F"Saving pretrained model to {save_path}"		)
     fnet_pretraining_model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
    A__   :						Union[str, Any] 							=     argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--flax_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--fnet_config_file""",
        default=None,
        type=str,
        required=True,
        help=(
            """The config json file corresponding to the pre-trained FNet model. \n"""
            """This specifies the model architecture."""
        ),
    )
    parser.add_argument("""--save_path""", default=None, type=str, required=True, help="""Path to the output model.""")
    A__   :						Dict 							=     parser.parse_args()
    convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)



