"""simple docstring"""






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


__A			   = False


class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    pass



@nightly
@require_torch_gpu
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    def _snake_case			(   self							):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






    def _snake_case			(   self							):
       lowercase__:   Dict					=      VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion'''							)
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   List[str]					=      '''A painting of a squirrel eating a burger '''
       lowercase__:   str					=      torch.manual_seed(0							)
       lowercase__:   Union[str, Any]					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=2    ,			output_type='''numpy'''							).images

       with tempfile.TemporaryDirectory() as tmpdirname:
          pipe.save_pretrained(_UpperCAmelCase							)
          lowercase__:   Optional[Any]					=      VersatileDiffusionTextToImagePipeline.from_pretrained(_UpperCAmelCase							)
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   Optional[int]					=      generator.manual_seed(0							)
       lowercase__:   List[str]					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=2    ,			output_type='''numpy'''							).images

       assert np.abs(image - new_image							).sum() < 1e-5, "Models don't have the same forward pass"






    def _snake_case			(   self							):
       lowercase__:   Dict					=      VersatileDiffusionTextToImagePipeline.from_pretrained(
           '''shi-labs/versatile-diffusion'''    ,			torch_dtype=torch.floataa							)
       pipe.to(_UpperCAmelCase							)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase							)

       lowercase__:   Tuple					=      '''A painting of a squirrel eating a burger '''
       lowercase__:   Optional[Any]					=      torch.manual_seed(0							)
       lowercase__:   Tuple					=      pipe(
           prompt=_UpperCAmelCase    ,			generator=_UpperCAmelCase    ,			guidance_scale=7.5    ,			num_inference_steps=50    ,			output_type='''numpy'''							).images

       lowercase__:   Union[str, Any]					=      image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowercase__:   Any					=      np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]							)

       assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2

"""simple docstring"""






import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[Any]:
   # Initialise PyTorch model
   lowercase__:   Optional[Any]					=      FunnelConfig.from_json_file(__UpperCAmelCase )
   print(F"""Building PyTorch model from configuration: {config}""" )
   lowercase__:   List[Any]					=      FunnelBaseModel(__UpperCAmelCase ) if base_model else FunnelModel(__UpperCAmelCase )

   # Load weights from tf checkpoint
   load_tf_weights_in_funnel(__UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )

   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}""" )
   torch.save(model.state_dict()       ,    __UpperCAmelCase )


if __name__ == "__main__":
       __A			   = argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
       )
       parser.add_argument(
           "--config_file",
           default=None,
           type=str,
           required=True,
           help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
       )
       parser.add_argument(
           "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       parser.add_argument(
           "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
       )
       __A			   = parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
       )
