





'''simple docstring'''






import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()
class 					__snake_case  (					unittest.TestCase							):






   '''simple docstring'''





   @property
   def 		__UpperCamelCase						(							self       ):
    torch.manual_seed(0       )
    snake_case__     :					Dict    =  UNetaDModel(
        block_out_channels=(3_2, 6_4)	,		layers_per_block=2	,		sample_size=3_2	,		in_channels=3	,		out_channels=3	,		down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")	,		up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")	,		)
    return model


   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Tuple    =  self.dummy_uncond_unet
    snake_case__     :					int    =  PNDMScheduler()

    snake_case__     :					int    =  PNDMPipeline(unet=__SCREAMING_SNAKE_CASE	,		scheduler=__SCREAMING_SNAKE_CASE       )
    pndm.to(__SCREAMING_SNAKE_CASE       )
    pndm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )

    snake_case__     :					Optional[int]    =  torch.manual_seed(0       )
    snake_case__     :					Union[str, Any]    =  pndm(generator=__SCREAMING_SNAKE_CASE	,		num_inference_steps=2_0	,		output_type="""numpy"""       ).images

    snake_case__     :					Tuple    =  torch.manual_seed(0       )
    snake_case__     :					Union[str, Any]    =  pndm(generator=__SCREAMING_SNAKE_CASE	,		num_inference_steps=2_0	,		output_type="""numpy"""	,		return_dict=__SCREAMING_SNAKE_CASE       )[0]

    snake_case__     :					Dict    =  image[0, -3:, -3:, -1]
    snake_case__     :					List[str]    =  image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 3_2, 3_2, 3)
    snake_case__     :					Dict    =  np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

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



@slow
@require_torch
class 					__snake_case  (					unittest.TestCase							):






   '''simple docstring'''





   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					Tuple    =  """google/ddpm-cifar10-32"""

    snake_case__     :					Dict    =  UNetaDModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
    snake_case__     :					str    =  PNDMScheduler()

    snake_case__     :					Optional[int]    =  PNDMPipeline(unet=__SCREAMING_SNAKE_CASE	,		scheduler=__SCREAMING_SNAKE_CASE       )
    pndm.to(__SCREAMING_SNAKE_CASE       )
    pndm.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE       )
    snake_case__     :					Optional[Any]    =  torch.manual_seed(0       )
    snake_case__     :					str    =  pndm(generator=__SCREAMING_SNAKE_CASE	,		output_type="""numpy"""       ).images

    snake_case__     :					str    =  image[0, -3:, -3:, -1]

    assert image.shape == (1, 3_2, 3_2, 3)
    snake_case__     :					Optional[Any]    =  np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

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






"""simple docstring"""




import argparse

from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging


logging.set_verbosity_info()




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  _lowercase:      int   			=  BigBirdConfig.from_json_file(_UpperCamelCase    )
  print(f'''Building PyTorch model from configuration: {config}'''    )

  if is_trivia_qa:
    _lowercase:      Union[str, Any]   			=  BigBirdForQuestionAnswering(_UpperCamelCase    )
  else:
    _lowercase:      Dict   			=  BigBirdForPreTraining(_UpperCamelCase    )

  # Load weights from tf checkpoint
  load_tf_weights_in_big_bird(_UpperCamelCase			,					_UpperCamelCase			,					is_trivia_qa=_UpperCamelCase    )

  # Save pytorch-model
  print(f'''Save PyTorch model to {pytorch_dump_path}'''    )
  model.save_pretrained(_UpperCamelCase    )


if __name__ == "__main__":
  A__ :       List[Any]				= 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(
      '--big_bird_config_file',
      default=None,
      type=str,
      required=True,
      help=(
          'The config json file corresponding to the pre-trained BERT model. \n'
          'This 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(
      '--is_trivia_qa', action='store_true', help='Whether to convert a model with a trivia_qa head.'
  )
  A__ :       Optional[Any]				= parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
  )
