from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available



@dataclass
# Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     A_					    =	42
     A_					    =	42


if is_transformers_available() and is_torch_available():
     from .modeling_roberta_series import RobertaSeriesModelWithTransformation
     from .pipeline_alt_diffusion import AltDiffusionPipeline
     from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
import argparse

from transformers import CanineConfig, CanineModel, CanineTokenizer, load_tf_weights_in_canine
from transformers.utils import logging


logging.set_verbosity_info()
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     Tuple:



      '''simple docstring'''

      _lowerCamelCase           =					CanineConfig()
      _lowerCamelCase           =					CanineModel(__UpperCAmelCase		)
      model.eval()

      print(F'Building PyTorch model from configuration: {config}'		)

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

      # Save pytorch-model (weights and configuration)
      print(F'Save PyTorch model to {pytorch_dump_path}'		)
      model.save_pretrained(__UpperCAmelCase		)

      # Save tokenizer files
      _lowerCamelCase           =					CanineTokenizer()
      print(F'Save tokenizer files to {pytorch_dump_path}'		)
      tokenizer.save_pretrained(__UpperCAmelCase		)


if __name__ == "__main__":
     snake_case__						      =			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--tf_checkpoint_path',
         default=None,
         type=str,
         required=True,
         help='Path to the TensorFlow checkpoint. Should end with model.ckpt',
     )
     parser.add_argument(
         '--pytorch_dump_path',
         default=None,
         type=str,
         required=True,
         help='Path to a folder where the PyTorch model will be placed.',
     )
     snake_case__						      =			parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)