





'''simple docstring'''







import gc
import unittest

import numpy as np
import torch

from diffusers import DDPMScheduler, MidiProcessor, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
from diffusers.utils import require_torch_gpu, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_note_seq, require_onnxruntime

from ..pipeline_params import TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS, TOKENS_TO_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


snake_case_  :							str            =	"./tests/fixtures/elise_format0.mid"


@unittest.skip("The note-seq package currently throws an error on import"  )
class        __a   (lowerCamelCase							,		unittest.TestCase  ):
							__a       : List[Any]          =							SpectrogramDiffusionPipeline
							__a       : str          =							PipelineTesterMixin.required_optional_params - {
							    "callback",
							    "latents",
							    "callback_steps",
							    "output_type",
							    "num_images_per_prompt",
							}
							__a       : List[str]          =							False
							__a       : Optional[int]          =							False
							__a       : List[str]          =							TOKENS_TO_AUDIO_GENERATION_PARAMS
							__a       : Any          =							TOKENS_TO_AUDIO_GENERATION_BATCH_PARAMS

							def      UpperCAmelCase__						(				self				:  Tuple )       ->							Dict:







													"""simple docstring"""
													torch.manual_seed(0 )
													UpperCAmelCase_      :   List[Any]      =	SpectrogramNotesEncoder(
													    max_length=20_48							,  vocab_size=15_36							,  d_model=7_68							,  dropout_rate=0.1							,  num_layers=1							,  num_heads=1							,  d_kv=4							,  d_ff=20_48							,  feed_forward_proj='''gated-gelu'''							,  )

													UpperCAmelCase_      :   Tuple      =	SpectrogramContEncoder(
													    input_dims=1_28							,  targets_context_length=2_56							,  d_model=7_68							,  dropout_rate=0.1							,  num_layers=1							,  num_heads=1							,  d_kv=4							,  d_ff=20_48							,  feed_forward_proj='''gated-gelu'''							,  )

													UpperCAmelCase_      :   List[Any]      =	TaFilmDecoder(
													    input_dims=1_28							,  targets_length=2_56							,  max_decoder_noise_time=2_0_0_0_0.0							,  d_model=7_68							,  num_layers=1							,  num_heads=1							,  d_kv=4							,  d_ff=20_48							,  dropout_rate=0.1							,  )

													UpperCAmelCase_      :   Optional[int]      =	DDPMScheduler()

													UpperCAmelCase_      :   List[Any]      =	{
													    '''notes_encoder''': notes_encoder.eval(),
													    '''continuous_encoder''': continuous_encoder.eval(),
													    '''decoder''': decoder.eval(),
													    '''scheduler''': scheduler,
													    '''melgan''': None,
													}
													return components

							def      UpperCAmelCase__						(				self				:  int							,  __magic_name__				:  Union[str, Any]							,  __magic_name__				:  Union[str, Any]=0 )       ->							Any:







													"""simple docstring"""
													if str(__magic_name__ ).startswith('''mps''' ):
																			UpperCAmelCase_      :   Dict      =	torch.manual_seed(__magic_name__ )
													else:
																			UpperCAmelCase_      :   Optional[int]      =	torch.Generator(device=__magic_name__ ).manual_seed(__magic_name__ )
													UpperCAmelCase_      :   Optional[int]      =	{
													    '''input_tokens''': [
													        [11_34, 90, 11_35, 11_33, 10_80, 1_12, 11_32, 10_80, 11_33, 10_79, 1_33, 11_32, 10_79, 11_33, 1] + [0] * 20_33
													    ],
													    '''generator''': generator,
													    '''num_inference_steps''': 4,
													    '''output_type''': '''mel''',
													}
													return inputs

							def      UpperCAmelCase__						(				self				:  Optional[int] )       ->							Optional[Any]:







													"""simple docstring"""
													UpperCAmelCase_      :   Tuple      =	'''cpu'''  # ensure determinism for the device-dependent torch.Generator
													UpperCAmelCase_      :   str      =	self.get_dummy_components()
													UpperCAmelCase_      :   Optional[Any]      =	SpectrogramDiffusionPipeline(**__magic_name__ )
													UpperCAmelCase_      :   str      =	pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )

													UpperCAmelCase_      :   Dict      =	self.get_dummy_inputs(__magic_name__ )
													UpperCAmelCase_      :   Any      =	pipe(**__magic_name__ )
													UpperCAmelCase_      :   str      =	output.audios

													UpperCAmelCase_      :   List[Any]      =	mel[0, -3:, -3:]

													assert mel_slice.shape == (3, 3)
													UpperCAmelCase_      :   Union[str, Any]      =	np.array(
													    [-1_1.5_1_2_9_2_5, -4.7_8_8_2_1_5, -0.4_6_1_7_2_9_0_5, -2.0_5_1_7_1_5, -1_0.5_3_9_1_4_7, -1_0.9_7_0_9_6_3, -9.0_9_1_6_3_4, 4.0, 4.0] )
													assert np.abs(mel_slice.flatten() - expected_slice ).max() < 1E-2

							@skip_mps
							def      UpperCAmelCase__						(				self				:  List[str] )       ->							Tuple:







													"""simple docstring"""
													return super().test_save_load_local()

							@skip_mps
							def      UpperCAmelCase__						(				self				:  Any )       ->							str:







													"""simple docstring"""
													return super().test_dict_tuple_outputs_equivalent()

							@skip_mps
							def      UpperCAmelCase__						(				self				:  Dict )       ->							Dict:







													"""simple docstring"""
													return super().test_save_load_optional_components()

							@skip_mps
							def      UpperCAmelCase__						(				self				:  Dict )       ->							List[Any]:







													"""simple docstring"""
													return super().test_attention_slicing_forward_pass()

							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							int:







													"""simple docstring"""
													pass

							def      UpperCAmelCase__						(				self				:  Any )       ->							Dict:







													"""simple docstring"""
													pass



							@skip_mps
							def      UpperCAmelCase__						(				self				:  Tuple )       ->							Any:







													"""simple docstring"""
													return super().test_progress_bar()





@slow
@require_torch_gpu
@require_onnxruntime
@require_note_seq
class        __a   (unittest.TestCase  ):

							def      UpperCAmelCase__						(				self				:  List[Any] )       ->							Tuple:







													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()

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







													"""simple docstring"""
													# TODO - test that pipeline can decode tokens in a callback
													# so that music can be played live
													UpperCAmelCase_      :   Dict      =	torch_device

													UpperCAmelCase_      :   List[str]      =	SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion''' )
													UpperCAmelCase_      :   Union[str, Any]      =	pipe.melgan
													UpperCAmelCase_      :   List[str]      =	None

													UpperCAmelCase_      :   Optional[int]      =	pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )

													def callback(__magic_name__				:  str							,  __magic_name__				:  List[str] ):
																			# decode mel to audio
																			UpperCAmelCase_      :   Tuple      =	melgan(input_features=mel_output.astype(np.floataa ) )[0]
																			assert len(audio[0] ) == 8_19_20 * (step + 1)
																			# simulate that audio is played
																			return audio

													UpperCAmelCase_      :   List[Any]      =	MidiProcessor()
													UpperCAmelCase_      :   List[str]      =	processor(__magic_name__ )

													UpperCAmelCase_      :   Union[str, Any]      =	input_tokens[:3]
													UpperCAmelCase_      :   List[Any]      =	torch.manual_seed(0 )
													pipe(__magic_name__							,  num_inference_steps=5							,  generator=__magic_name__							,  callback=__magic_name__							,  output_type='''mel''' )

							def      UpperCAmelCase__						(				self				:  List[str] )       ->							List[str]:







													"""simple docstring"""
													UpperCAmelCase_      :   Dict      =	torch_device

													UpperCAmelCase_      :   Any      =	SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion''' )
													UpperCAmelCase_      :   Any      =	pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )
													UpperCAmelCase_      :   Union[str, Any]      =	MidiProcessor()

													UpperCAmelCase_      :   List[str]      =	processor(__magic_name__ )
													# just run two denoising loops
													UpperCAmelCase_      :   int      =	input_tokens[:2]

													UpperCAmelCase_      :   List[Any]      =	torch.manual_seed(0 )
													UpperCAmelCase_      :   Optional[Any]      =	pipe(__magic_name__							,  num_inference_steps=2							,  generator=__magic_name__ )

													UpperCAmelCase_      :   Optional[Any]      =	output.audios[0]

													assert abs(np.abs(__magic_name__ ).sum() - 3_6_1_2.8_4_1 ) < 1E-1



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







													"""simple docstring"""
													UpperCAmelCase_      :   str      =	torch_device

													UpperCAmelCase_      :   Optional[int]      =	SpectrogramDiffusionPipeline.from_pretrained('''google/music-spectrogram-diffusion''' )
													UpperCAmelCase_      :   List[Any]      =	pipe.to(__magic_name__ )
													pipe.set_progress_bar_config(disable=__magic_name__ )

													UpperCAmelCase_      :   List[Any]      =	MidiProcessor()

													UpperCAmelCase_      :   List[Any]      =	processor(__magic_name__ )

													# just run 4 denoising loops
													UpperCAmelCase_      :   Dict      =	input_tokens[:4]

													UpperCAmelCase_      :   Tuple      =	torch.manual_seed(0 )
													UpperCAmelCase_      :   Dict      =	pipe(__magic_name__							,  num_inference_steps=1_00							,  generator=__magic_name__ )

													UpperCAmelCase_      :   Optional[int]      =	output.audios[0]
													assert abs(np.abs(__magic_name__ ).sum() - 9_3_8_9.1_1_1_1 ) < 5E-2









"""simple docstring"""
import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling






def 						lowercase				(	_snake_case		: Optional[Any]	)	->Union[str, Any]:
	"""simple docstring"""





	__snake_case      :		str         =							'''https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'''

	with open(_snake_case	,  '''wb'''	) as f:
		with requests.get(_snake_case	,  stream=_snake_case	) as req:
			for chunk in req.iter_content(chunk_size=2_048	):
				f.write(_snake_case	)






def 						lowercase				(	)	->VivitConfig:
	"""simple docstring"""





	__snake_case      :		int         =							VivitConfig()

	__snake_case      :		Tuple         =							400
	__snake_case      :		Tuple         =							'''huggingface/label-files'''
	__snake_case      :		List[str]         =							'''kinetics400-id2label.json'''

	__snake_case      :		Dict         =							json.load(open(hf_hub_download(_snake_case	,  _snake_case	,  repo_type='''dataset'''	)	,  '''r'''	)	)
	__snake_case      :		List[str]         =							{int(_snake_case	): v for k, v in idalabel.items()}
	__snake_case      :		Tuple         =							idalabel
	__snake_case      :		Optional[int]         =							{v: k for k, v in idalabel.items()}
	return config






def 						lowercase				(	)	->Optional[int]:
	"""simple docstring"""





	__snake_case      :		List[Any]         =							hf_hub_download(
	    repo_id='''hf-internal-testing/spaghetti-video'''	,  filename='''eating_spaghetti_32_frames.npy'''	,  repo_type='''dataset'''	)
	__snake_case      :		str         =							np.load(_snake_case	)
	return list(_snake_case	)






def 						lowercase				(	_snake_case		: np.ndarray	)	->str:
	"""simple docstring"""





	if np.ndim(_snake_case	) == 2:
		return transform_attention_bias(_snake_case	)

	elif np.ndim(_snake_case	) == 3:
		return transform_attention_kernel(_snake_case	)

	else:
		raise Exception(f"""Invalid number of dimesions: {np.ndim(_snake_case	)}"""	)






def 						lowercase				(	_snake_case		: np.ndarray	)	->Tuple:
	"""simple docstring"""





	return current.flatten()






def 						lowercase				(	_snake_case		: np.ndarray	)	->List[str]:
	"""simple docstring"""





	return np.reshape(_snake_case	,  (current.shape[0], current.shape[1] * current.shape[2])	).T






def 						lowercase				(	_snake_case		: np.ndarray	)	->Optional[Any]:
	"""simple docstring"""





	return np.reshape(_snake_case	,  (current.shape[0] * current.shape[1], current.shape[2])	).T






def 						lowercase				(	_snake_case		: Dict	,  _snake_case		: Tuple	)	->Tuple:
	"""simple docstring"""





	__snake_case      :		int         =							state_dict['''optimizer''']['''target''']['''Transformer'''][f"""encoderblock_{i}"""]

	__snake_case      :		Optional[int]         =							f"""encoder.layer.{i}."""
	__snake_case      :		Dict         =							{
	    prefix + '''intermediate.dense.bias''': state['''MlpBlock_0''']['''Dense_0''']['''bias'''],
	    prefix + '''intermediate.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_0''']['''kernel''']	),
	    prefix + '''output.dense.bias''': state['''MlpBlock_0''']['''Dense_1''']['''bias'''],
	    prefix + '''output.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_1''']['''kernel''']	),
	    prefix + '''layernorm_before.bias''': state['''LayerNorm_0''']['''bias'''],
	    prefix + '''layernorm_before.weight''': state['''LayerNorm_0''']['''scale'''],
	    prefix + '''layernorm_after.bias''': state['''LayerNorm_1''']['''bias'''],
	    prefix + '''layernorm_after.weight''': state['''LayerNorm_1''']['''scale'''],
	    prefix
	    + '''attention.attention.query.bias''': transform_attention(
	        state['''MultiHeadDotProductAttention_0''']['''query''']['''bias''']	),
	    prefix
	    + '''attention.attention.query.weight''': transform_attention(
	        state['''MultiHeadDotProductAttention_0''']['''query''']['''kernel''']	),
	    prefix
	    + '''attention.attention.key.bias''': transform_attention(state['''MultiHeadDotProductAttention_0''']['''key''']['''bias''']	),
	    prefix
	    + '''attention.attention.key.weight''': transform_attention(
	        state['''MultiHeadDotProductAttention_0''']['''key''']['''kernel''']	),
	    prefix
	    + '''attention.attention.value.bias''': transform_attention(
	        state['''MultiHeadDotProductAttention_0''']['''value''']['''bias''']	),
	    prefix
	    + '''attention.attention.value.weight''': transform_attention(
	        state['''MultiHeadDotProductAttention_0''']['''value''']['''kernel''']	),
	    prefix + '''attention.output.dense.bias''': state['''MultiHeadDotProductAttention_0''']['''out''']['''bias'''],
	    prefix
	    + '''attention.output.dense.weight''': transform_attention_output_weight(
	        state['''MultiHeadDotProductAttention_0''']['''out''']['''kernel''']	),
	}

	return new_state






def 						lowercase				(	_snake_case		: int	)	->List[str]:
	"""simple docstring"""





	return sum([1 if '''encoderblock_''' in k else 0 for k in state_dict['''optimizer''']['''target''']['''Transformer'''].keys()]	)






def 						lowercase				(	_snake_case		: Optional[Any]	,  _snake_case		: Optional[int]=False	)	->Union[str, Any]:
	"""simple docstring"""





	__snake_case      :		Tuple         =							get_n_layers(_snake_case	)

	__snake_case      :		str         =							OrderedDict()

	__snake_case      :		List[Any]         =							state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
	__snake_case      :		List[str]         =							state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

	__snake_case      :		Optional[Any]         =							np.transpose(
	    state_dict['''optimizer''']['''target''']['''embedding''']['''kernel''']	,  (4, 3, 0, 1, 2)	)
	__snake_case      :		Optional[Any]         =							state_dict['''optimizer''']['''target''']['''embedding''']['''bias''']

	__snake_case      :		Union[str, Any]         =							state_dict['''optimizer''']['''target''']['''cls''']
	__snake_case      :		List[str]         =							state_dict['''optimizer''']['''target''']['''Transformer''']['''posembed_input'''][
	    '''pos_embedding'''
	]

	for i in range(_snake_case	):
		new_state.update(transform_state_encoder_block(_snake_case	,  _snake_case	)	)

	if classification_head:
		__snake_case      :		List[Any]         =							{'''vivit.''' + k: v for k, v in new_state.items()}
		__snake_case      :		str         =							np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''kernel''']	)
		__snake_case      :		int         =							np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''bias''']	)

	return {k: torch.tensor(_snake_case	) for k, v in new_state.items()}






def 						lowercase				(	)	->VivitImageProcessor:
	"""simple docstring"""





	__snake_case      :		List[str]         =							VivitImageProcessor()

	assert extractor.do_resize is True
	assert extractor.size == {"shortest_edge": 256}
	assert extractor.do_center_crop is True
	assert extractor.crop_size == {"width": 224, "height": 224}
	assert extractor.resample == PILImageResampling.BILINEAR

	# here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
	# one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
	# which effectively means no normalization (and ViViT does not overwrite those when calling this func)
	assert extractor.do_normalize is False
	assert extractor.do_rescale is True
	assert extractor.rescale_factor == 1 / 255

	# zero-centering = True in original implementation
	assert extractor.do_zero_centering is True

	return extractor






def 						lowercase				(	_snake_case		: str	)	->Optional[Any]:
	"""simple docstring"""





	__snake_case      :		Tuple         =							'''checkpoint'''

	if not os.path.exists(_snake_case	):
		download_checkpoint(_snake_case	)

	__snake_case      :		List[Any]         =							restore_checkpoint(_snake_case	,  _snake_case	)
	__snake_case      :		Union[str, Any]         =							transform_state(_snake_case	,  classification_head=_snake_case	)

	__snake_case      :		Optional[Any]         =							get_vivit_config()

	assert config.image_size == 224
	assert config.num_frames == 32

	__snake_case      :		List[str]         =							VivitForVideoClassification(_snake_case	)
	model.load_state_dict(_snake_case	)
	model.eval()

	__snake_case      :		Tuple         =							get_processor()

	__snake_case      :		Any         =							prepare_video()
	__snake_case      :		List[str]         =							extractor(_snake_case	,  return_tensors='''pt'''	)

	__snake_case      :		Union[str, Any]         =							model(**_snake_case	)

	__snake_case      :		List[str]         =							torch.Size([1, 400]	)
	__snake_case      :		Optional[int]         =							torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]	)

	assert outputs.logits.shape == expected_shape
	assert torch.allclose(outputs.logits[0, :5]	,  _snake_case	,  atol=1e-4	), outputs.logits[0, :5]

	model.save_pretrained(_snake_case	)
	extractor.save_pretrained(_snake_case	)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							argparse.ArgumentParser()

			parser.add_argument("""--output_model_name""", """-o""", type=str, help="""Output path for the converted HuggingFace model""")

			SCREAMING_SNAKE_CASE					:       List[str]							   =							parser.parse_args()
			convert(args.output_model_name)





