'''simple docstring'''

from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION







class     _lowercase					(    UpperCAmelCase__  ,							UpperCAmelCase__      ):


 '''simple docstring'''






 def __init__(				self       :     List[Any]							)  ->   List[Any]:
  super().__init__()

  self.register_buffer("""watermark_image"""				,	torch.zeros((62, 62, 4)							)							)
  __lowerCAmelCase    							=						None





 def a (				self       :     int				,	SCREAMING_SNAKE_CASE__       :     List[PIL.Image.Image]				,	SCREAMING_SNAKE_CASE__       :     List[str]=None							)  ->   Any:
  # copied from https://github.com/deep-floyd/IF/blob/b77482e36ca2031cb94dbca1001fc1e6400bf4ab/deepfloyd_if/modules/base.py#L287

  __lowerCAmelCase    							=						images[0].height
  __lowerCAmelCase    							=						images[0].width

  __lowerCAmelCase    							=						sample_size or h

  __lowerCAmelCase    							=						min(h / sample_size				,	w / sample_size							)
  __lowerCAmelCase							,	__lowerCAmelCase    							=						(int(h / coef							), int(w / coef							)) if coef < 1 else (h, w)

  __lowerCAmelCase							,	__lowerCAmelCase    							=						10_24**2, img_w * img_h
  __lowerCAmelCase    							=						(Sa / Sa) ** 0.5
  __lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase    							=						int(K * 62							), img_w - int(14 * K							), img_h - int(14 * K							)

  if self.watermark_image_as_pil is None:
   __lowerCAmelCase    							=						self.watermark_image.to(torch.uinta							).cpu().numpy()
   __lowerCAmelCase    							=						Image.fromarray(SCREAMING_SNAKE_CASE__				,	mode="""RGBA"""							)
   __lowerCAmelCase    							=						watermark_image

  __lowerCAmelCase    							=						self.watermark_image_as_pil.resize(
      (wm_size, wm_size)				,	PIL_INTERPOLATION["""bicubic"""]				,	reducing_gap=SCREAMING_SNAKE_CASE__							)

  for pil_img in images:
   pil_img.paste(SCREAMING_SNAKE_CASE__				,	box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)				,	mask=wm_img.split()[-1]							)

  return images







'''simple docstring'''

import argparse

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


logging.set_verbosity_info()







def 					UpperCamelCase_						( snake_case_	:	List[str]						,		snake_case_	:	Optional[Any]				) ->					Tuple:






 '''simple docstring'''

 __lowerCAmelCase    							=						CanineConfig()
 __lowerCAmelCase    							=						CanineModel(snake_case_				)
 model.eval()

 print(f"""Building PyTorch model from configuration: {config}"""				)

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

 # Save pytorch-model (weights and configuration)
 print(f"""Save PyTorch model to {pytorch_dump_path}"""				)
 model.save_pretrained(snake_case_				)

 # Save tokenizer files
 __lowerCAmelCase    							=						CanineTokenizer()
 print(f"""Save tokenizer files to {pytorch_dump_path}"""				)
 tokenizer.save_pretrained(snake_case_				)


if __name__ == "__main__":
 _A      :				int 				=    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.''',
 )
 _A      :				Dict 				=    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.pytorch_dump_path)






