





import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()
def      A__       (    SCREAMING_SNAKE_CASE_   :      Union[str, Any]				,	SCREAMING_SNAKE_CASE_   :      Dict				,	SCREAMING_SNAKE_CASE_   :      List[str]						)  ->  int:






   """simple docstring"""







   _UpperCAmelCase           =   MobileBertConfig.from_json_file(SCREAMING_SNAKE_CASE_						)
   print(F'''Building PyTorch model from configuration: {config}'''						)
   _UpperCAmelCase           =   MobileBertForPreTraining(SCREAMING_SNAKE_CASE_						)
   # Load weights from tf checkpoint
   _UpperCAmelCase           =   load_tf_weights_in_mobilebert(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)
   # Save pytorch-model
   print(F'''Save PyTorch model to {pytorch_dump_path}'''						)
   torch.save(model.state_dict()				,	SCREAMING_SNAKE_CASE_						)


if __name__ == "__main__":
   UpperCAmelCase_           =	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(
       "--mobilebert_config_file",
       default=None,
       type=str,
       required=True,
       help=(
           "The config json file corresponding to the pre-trained MobileBERT 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."
   )
   UpperCAmelCase_           =	parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)






import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 			__UpperCamelCase    (							A__	, unittest.TestCase    ):
     __A	:  Any							  =   DanceDiffusionPipeline
     __A	:  Any							  =   UNCONDITIONAL_AUDIO_GENERATION_PARAMS
     __A	:  Tuple							  =   PipelineTesterMixin.required_optional_params - {
         """callback""",
         """latents""",
         """callback_steps""",
         """output_type""",
         """num_images_per_prompt""",
     }
     __A	:  Tuple							  =   UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
     __A	:  List[str]							  =   False
     __A	:  str							  =   False





     def 		UpperCamelCase(     self					):
        torch.manual_seed(0					)
        _UpperCAmelCase           =   UNetaDModel(
            block_out_channels=(32, 32, 64)				,							extra_in_channels=16				,							sample_size=512				,							sample_rate=16000				,							in_channels=2				,							out_channels=2				,							flip_sin_to_cos=_UpperCamelCase				,							use_timestep_embedding=_UpperCamelCase				,							time_embedding_type='''fourier'''				,							mid_block_type='''UNetMidBlock1D'''				,							down_block_types=('''DownBlock1DNoSkip''', '''DownBlock1D''', '''AttnDownBlock1D''')				,							up_block_types=('''AttnUpBlock1D''', '''UpBlock1D''', '''UpBlock1DNoSkip''')				,							)
        _UpperCAmelCase           =   IPNDMScheduler()

        _UpperCAmelCase           =   {
            '''unet''': unet,
            '''scheduler''': scheduler,
        }
        return components





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase=0					):
        if str(_UpperCamelCase					).startswith('''mps'''					):
           _UpperCAmelCase           =   torch.manual_seed(_UpperCamelCase					)
        else:
           _UpperCAmelCase           =   torch.Generator(device=_UpperCamelCase					).manual_seed(_UpperCamelCase					)
        _UpperCAmelCase           =   {
            '''batch_size''': 1,
            '''generator''': generator,
            '''num_inference_steps''': 4,
        }
        return inputs





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
        _UpperCAmelCase           =   self.get_dummy_components()
        _UpperCAmelCase           =   DanceDiffusionPipeline(**_UpperCamelCase					)
        _UpperCAmelCase           =   pipe.to(_UpperCamelCase					)
        pipe.set_progress_bar_config(disable=_UpperCamelCase					)

        _UpperCAmelCase           =   self.get_dummy_inputs(_UpperCamelCase					)
        _UpperCAmelCase           =   pipe(**_UpperCamelCase					)
        _UpperCAmelCase           =   output.audios

        _UpperCAmelCase           =   audio[0, -3:, -3:]

        assert audio.shape == (1, 2, components["unet"].sample_size)
        _UpperCAmelCase           =   np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000]					)
        assert np.abs(audio_slice.flatten() - expected_slice					).max() < 1e-2





     @skip_mps
     def 		UpperCamelCase(     self					):
        return super().test_save_load_local()





     @skip_mps
     def 		UpperCamelCase(     self					):
        return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)





     @skip_mps
     def 		UpperCamelCase(     self					):
        return super().test_save_load_optional_components()





     @skip_mps
     def 		UpperCamelCase(     self					):
        return super().test_attention_slicing_forward_pass()





     def 		UpperCamelCase(     self					):
        super().test_inference_batch_single_identical(expected_max_diff=3e-3					)




@slow
@require_torch_gpu
class 			__UpperCamelCase    (							unittest.TestCase    ):





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





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   torch_device

        _UpperCAmelCase           =   DanceDiffusionPipeline.from_pretrained('''harmonai/maestro-150k'''					)
        _UpperCAmelCase           =   pipe.to(_UpperCamelCase					)
        pipe.set_progress_bar_config(disable=_UpperCamelCase					)

        _UpperCAmelCase           =   torch.manual_seed(0					)
        _UpperCAmelCase           =   pipe(generator=_UpperCamelCase				,							num_inference_steps=100				,							audio_length_in_s=4.096					)
        _UpperCAmelCase           =   output.audios

        _UpperCAmelCase           =   audio[0, -3:, -3:]

        assert audio.shape == (1, 2, pipe.unet.sample_size)
        _UpperCAmelCase           =   np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020]					)

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





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   torch_device

        _UpperCAmelCase           =   DanceDiffusionPipeline.from_pretrained('''harmonai/maestro-150k'''				,							torch_dtype=torch.floataa					)
        _UpperCAmelCase           =   pipe.to(_UpperCamelCase					)
        pipe.set_progress_bar_config(disable=_UpperCamelCase					)

        _UpperCAmelCase           =   torch.manual_seed(0					)
        _UpperCAmelCase           =   pipe(generator=_UpperCamelCase				,							num_inference_steps=100				,							audio_length_in_s=4.096					)
        _UpperCAmelCase           =   output.audios

        _UpperCAmelCase           =   audio[0, -3:, -3:]

        assert audio.shape == (1, 2, pipe.unet.sample_size)
        _UpperCAmelCase           =   np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341]					)

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