
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        lowerCAmelCase__      (   __lowercase		,	unittest.TestCase	):
						UpperCamelCase_							: Optional[int]	=		DanceDiffusionPipeline
						UpperCamelCase_							: Dict	=		UNCONDITIONAL_AUDIO_GENERATION_PARAMS
						UpperCamelCase_							: str	=		PipelineTesterMixin.required_optional_params - {
						    "callback",
						    "latents",
						    "callback_steps",
						    "output_type",
						    "num_images_per_prompt",
						}
						UpperCamelCase_							: Optional[Any]	=		UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
						UpperCamelCase_							: List[Any]	=		False
						UpperCamelCase_							: Any	=		False

						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								torch.manual_seed(0				)
								_UpperCamelCase						    =	UNetaDModel(
								    block_out_channels=(32, 32, 64)       ,							extra_in_channels=16       ,							sample_size=5_12       ,							sample_rate=1_60_00       ,							in_channels=2       ,							out_channels=2       ,							flip_sin_to_cos=a       ,							use_timestep_embedding=a       ,							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  A_				(			self       ,							a       ,							a=0				)					->      Dict:


								'''simple docstring'''

								if str(a				).startswith("""mps"""				):
										_UpperCamelCase						    =	torch.manual_seed(a				)
								else:
										_UpperCamelCase						    =	torch.Generator(device=a				).manual_seed(a				)
								_UpperCamelCase						    =	{
								    """batch_size""": 1,
								    """generator""": generator,
								    """num_inference_steps""": 4,
								}
								return inputs

						def  A_				(			self				)					->      Union[str, Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
								_UpperCamelCase						    =	self.get_dummy_components()
								_UpperCamelCase						    =	DanceDiffusionPipeline(**a				)
								_UpperCamelCase						    =	pipe.to(a				)
								pipe.set_progress_bar_config(disable=a				)

								_UpperCamelCase						    =	self.get_dummy_inputs(a				)
								_UpperCamelCase						    =	pipe(**a				)
								_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  A_				(			self				)					->      Any:


								'''simple docstring'''

								return super().test_save_load_local()

						@skip_mps
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3				)

						@skip_mps
						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								return super().test_save_load_optional_components()

						@skip_mps
						def  A_				(			self				)					->      int:


								'''simple docstring'''

								return super().test_attention_slicing_forward_pass()







						def  A_				(			self				)					->      Any:


								'''simple docstring'''

								super().test_inference_batch_single_identical(expected_max_diff=3e-3				)







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

						def  A_				(			self				)					->      Union[str, Any]:


								'''simple docstring'''

								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()

						def  A_				(			self				)					->      List[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	torch_device

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

								_UpperCamelCase						    =	torch.manual_seed(0				)
								_UpperCamelCase						    =	pipe(generator=a       ,							num_inference_steps=1_00       ,							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  A_				(			self				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	torch_device

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

								_UpperCamelCase						    =	torch.manual_seed(0				)
								_UpperCamelCase						    =	pipe(generator=a       ,							num_inference_steps=1_00       ,							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





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM


def 							__A(lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		if "model" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""model."""						,  """""" )
		if "norm1" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""norm1"""						,  """attention.output.LayerNorm""" )
		if "norm2" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""norm2"""						,  """output.LayerNorm""" )
		if "norm" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""norm"""						,  """LayerNorm""" )
		if "transformer" in orig_key:
				_UpperCamelCase						    =	orig_key.split(""".""" )[0].split("""_""" )[-1]
				_UpperCamelCase						    =	orig_key.replace(F'transformer_{layer_num}'						,  F'encoder.layer.{layer_num}' )
		if "mha.attn" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mha.attn"""						,  """attention.self""" )
		if "mha" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mha"""						,  """attention""" )
		if "W_q" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""W_q"""						,  """self.query""" )
		if "W_k" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""W_k"""						,  """self.key""" )
		if "W_v" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""W_v"""						,  """self.value""" )
		if "ff1" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""ff1"""						,  """intermediate.dense""" )
		if "ff2" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""ff2"""						,  """output.dense""" )
		if "ff" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""ff"""						,  """output.dense""" )
		if "mlm_class" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mlm.mlm_class"""						,  """cls.predictions.decoder""" )
		if "mlm" in orig_key:
				_UpperCamelCase						    =	orig_key.replace("""mlm"""						,  """cls.predictions.transform""" )
		if "cls" not in orig_key:
				_UpperCamelCase						    =	"""yoso.""" + orig_key

		return orig_key


def 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				List[str]:

		"""simple docstring"""






		for key in orig_state_dict.copy().keys():
				_UpperCamelCase						    =	orig_state_dict.pop(lowerCAmelCase )

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						_UpperCamelCase						    =	val

		_UpperCamelCase						    =	orig_state_dict["""cls.predictions.decoder.bias"""]
		_UpperCamelCase						    =	torch.arange(lowerCAmelCase ).expand((1, -1) ) + 2

		return orig_state_dict


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location="""cpu""" )["""model_state_dict"""]
		_UpperCamelCase						    =	YosoConfig.from_json_file(lowerCAmelCase )
		_UpperCamelCase						    =	YosoForMaskedLM(lowerCAmelCase )

		_UpperCamelCase						    =	convert_checkpoint_helper(config.max_position_embeddings						,  lowerCAmelCase )

		print(model.load_state_dict(lowerCAmelCase ) )
		model.eval()
		model.save_pretrained(lowerCAmelCase )

		print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}' )


if __name__ == "__main__":
		lowerCamelCase__	=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
		)
		parser.add_argument(
		    "--config_file",
		    default=None,
		    type=str,
		    required=True,
		    help="The json file for YOSO model config.",
		)
		parser.add_argument(
		    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		lowerCamelCase__	=							parser.parse_args()
		convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)



