
'''simple docstring'''

import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor







class 	__lowerCAmelCase    (  unittest.TestCase				):



     @property
     def     snake_case_ (self	):
          _UpperCAmelCase					:  List[Any]         =    1
          _UpperCAmelCase					:  Union[str, Any]         =    3
          _UpperCAmelCase					:  Tuple         =    8
          _UpperCAmelCase					:  int         =    8

          _UpperCAmelCase					:  str         =    torch.rand((batch_size, num_channels, height, width)	)

          return sample



     def     snake_case_ (self						,	lowerCAmelCase__	):
          if isinstance(image[0]						,	PIL.Image.Image	):
               return np.stack([np.array(lowerCAmelCase__	) for i in image]						,	axis=0	)
          elif isinstance(lowerCAmelCase__						,	torch.Tensor	):
               return image.cpu().numpy().transpose(0						,	2						,	3						,	1	)
          return image



     def     snake_case_ (self	):
          _UpperCAmelCase					:  List[Any]         =    VaeImageProcessor(do_resize=lowerCAmelCase__						,	do_normalize=lowerCAmelCase__	)

          _UpperCAmelCase					:  List[str]         =    self.dummy_sample
          _UpperCAmelCase					:  Optional[Any]         =    self.to_np(lowerCAmelCase__	)

          for output_type in ["pt", "np", "pil"]:
               _UpperCAmelCase					:  int         =    image_processor.postprocess(
                   image_processor.preprocess(lowerCAmelCase__	)						,	output_type=lowerCAmelCase__						,	)
               _UpperCAmelCase					:  Dict         =    self.to_np(lowerCAmelCase__	)
               _UpperCAmelCase					:  int         =    (input_np * 2_5_5).round() if output_type == """pil""" else input_np
               assert (
                   np.abs(in_np - out_np	).max() < 1e-6
               ), F"decoded output does not match input for output_type {output_type}"



     def     snake_case_ (self	):
          _UpperCAmelCase					:  List[str]         =    VaeImageProcessor(do_resize=lowerCAmelCase__						,	do_normalize=lowerCAmelCase__	)
          _UpperCAmelCase					:  List[Any]         =    self.dummy_sample.cpu().numpy().transpose(0						,	2						,	3						,	1	)

          for output_type in ["pt", "np", "pil"]:
               _UpperCAmelCase					:  Tuple         =    image_processor.postprocess(image_processor.preprocess(lowerCAmelCase__	)						,	output_type=lowerCAmelCase__	)

               _UpperCAmelCase					:  Union[str, Any]         =    self.to_np(lowerCAmelCase__	)
               _UpperCAmelCase					:  Union[str, Any]         =    (input_np * 2_5_5).round() if output_type == """pil""" else input_np
               assert (
                   np.abs(in_np - out_np	).max() < 1e-6
               ), F"decoded output does not match input for output_type {output_type}"



     def     snake_case_ (self	):
          _UpperCAmelCase					:  str         =    VaeImageProcessor(do_resize=lowerCAmelCase__						,	do_normalize=lowerCAmelCase__	)

          _UpperCAmelCase					:  List[str]         =    self.dummy_sample.cpu().numpy().transpose(0						,	2						,	3						,	1	)
          _UpperCAmelCase					:  List[Any]         =    image_processor.numpy_to_pil(lowerCAmelCase__	)

          for output_type in ["pt", "np", "pil"]:
               _UpperCAmelCase					:  Optional[Any]         =    image_processor.postprocess(image_processor.preprocess(lowerCAmelCase__	)						,	output_type=lowerCAmelCase__	)
               for i, o in zip(lowerCAmelCase__						,	lowerCAmelCase__	):
                    _UpperCAmelCase					:  int         =    np.array(lowerCAmelCase__	)
                    _UpperCAmelCase					:  Tuple         =    self.to_np(lowerCAmelCase__	) if output_type == """pil""" else (self.to_np(lowerCAmelCase__	) * 2_5_5).round()
                    assert (
                        np.abs(in_np - out_np	).max() < 1e-6
                    ), F"decoded output does not match input for output_type {output_type}"



     def     snake_case_ (self	):
          _UpperCAmelCase					:  str         =    VaeImageProcessor(do_resize=lowerCAmelCase__						,	do_normalize=lowerCAmelCase__	)

          _UpperCAmelCase					:  List[str]         =    self.dummy_sample
          _UpperCAmelCase					:  int         =    input_pt_ad.squeeze(0	)

          _UpperCAmelCase					:  Tuple         =    image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__	)						,	output_type="""np"""						,	)
          _UpperCAmelCase					:  str         =    image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__	)						,	output_type="""np"""						,	)

          _UpperCAmelCase					:  Union[str, Any]         =    self.to_np(self.dummy_sample	)
          _UpperCAmelCase					:  Tuple         =    input_np_ad.squeeze(0	)

          _UpperCAmelCase					:  List[str]         =    image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__	)						,	output_type="""np"""						,	)
          _UpperCAmelCase					:  str         =    image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__	)						,	output_type="""np"""						,	)

          assert np.abs(out_pt_ad - out_pt_ad	).max() < 1e-6
          assert np.abs(out_np_ad - out_np_ad	).max() < 1e-6



     def     snake_case_ (self	):
          _UpperCAmelCase					:  Union[str, Any]         =    VaeImageProcessor(do_resize=lowerCAmelCase__						,	do_normalize=lowerCAmelCase__	)

          _UpperCAmelCase					:  List[Any]         =    self.dummy_sample
          _UpperCAmelCase					:  Dict         =    list(lowerCAmelCase__	)

          _UpperCAmelCase					:  str         =    image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__	)						,	output_type="""np"""						,	)

          _UpperCAmelCase					:  str         =    image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__	)						,	output_type="""np"""						,	)

          _UpperCAmelCase					:  Tuple         =    self.to_np(self.dummy_sample	)
          list(lowerCAmelCase__	)

          _UpperCAmelCase					:  Any         =    image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__	)						,	output_type="""np"""						,	)

          _UpperCAmelCase					:  Optional[Any]         =    image_processor.postprocess(
              image_processor.preprocess(lowerCAmelCase__	)						,	output_type="""np"""						,	)

          assert np.abs(out_pt_ad - out_pt_list	).max() < 1e-6
          assert np.abs(out_np_ad - out_np_list	).max() < 1e-6




'''simple docstring'''



import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def       lowerCAmelCase_		( SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_  )   ->   Any:




   """simple docstring"""
   # Initialise PyTorch model
   _SCREAMING_SNAKE_CASE          = BertConfig.from_pretrained(
       """bert-large-cased"""			,					vocab_size=SCREAMING_SNAKE_CASE_			,					max_position_embeddings=5_12			,					is_decoder=SCREAMING_SNAKE_CASE_			,					add_cross_attention=SCREAMING_SNAKE_CASE_			,					)
   _SCREAMING_SNAKE_CASE          = bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   _SCREAMING_SNAKE_CASE          = BertGenerationConfig(**SCREAMING_SNAKE_CASE_  )
   if is_encoder:
      _SCREAMING_SNAKE_CASE          = BertGenerationEncoder(SCREAMING_SNAKE_CASE_  )
   else:
      _SCREAMING_SNAKE_CASE          = BertGenerationDecoder(SCREAMING_SNAKE_CASE_  )
   print(F"Building PyTorch model from configuration: {config}"  )

   # Load weights from tf checkpoint
   load_tf_weights_in_bert_generation(
       SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					model_class="""bert"""			,					is_encoder_named_decoder=SCREAMING_SNAKE_CASE_			,					is_encoder=SCREAMING_SNAKE_CASE_			,					)

   # Save pytorch-model
   print(F"Save PyTorch model and config to {pytorch_dump_path}"  )
   model.save_pretrained(SCREAMING_SNAKE_CASE_  )


if __name__ == "__main__":
     UpperCamelCase__					:						int											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
     )
     parser.add_argument(
         "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--is_encoder_named_decoder",
         action="store_true",
         help="If decoder has to be renamed to encoder in PyTorch model.",
     )
     parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
     parser.add_argument("--vocab_size", default=50_358, type=int, help="Vocab size of model")
     UpperCamelCase__					:						int											=     parser.parse_args()
     convert_tf_checkpoint_to_pytorch(
         args.tf_hub_path,
         args.pytorch_dump_path,
         args.is_encoder_named_decoder,
         args.vocab_size,
         is_encoder=args.is_encoder,
     )






