"""simple docstring"""






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class        snake_case (				a__   ,					unittest.TestCase       ):




					"""simple docstring"""

					snake_case__       					=				KandinskyVaaPriorPipeline
					snake_case__       					=				["""prompt"""]
					snake_case__       					=				["""prompt""", """negative_prompt"""]
					snake_case__       					=				[
					    """num_images_per_prompt""",
					    """generator""",
					    """num_inference_steps""",
					    """latents""",
					    """negative_prompt""",
					    """guidance_scale""",
					    """output_type""",
					    """return_dict""",
					]
					snake_case__       					=				False







					@property
					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									return 32







					@property
					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									return 32







					@property
					def 	__lowerCAmelCase		(       self     :			int    ):
									return self.time_input_dim







					@property
					def 	__lowerCAmelCase		(       self     :			int    ):
									return self.time_input_dim * 4







					@property
					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									return 100







					@property
					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )
									return tokenizer







					@property
					def 	__lowerCAmelCase		(       self     :			str    ):
									torch.manual_seed(0    )
									UpperCAmelCase__    =							CLIPTextConfig(
									    bos_token_id=0					,eos_token_id=2					,hidden_size=self.text_embedder_hidden_size					,projection_dim=self.text_embedder_hidden_size					,intermediate_size=37					,layer_norm_eps=1e-05					,num_attention_heads=4					,num_hidden_layers=5					,pad_token_id=1					,vocab_size=1_000					,)
									return CLIPTextModelWithProjection(lowercase__    )







					@property
					def 	__lowerCAmelCase		(       self     :			int    ):
									torch.manual_seed(0    )

									UpperCAmelCase__    =							{
									    '''num_attention_heads''': 2,
									    '''attention_head_dim''': 12,
									    '''embedding_dim''': self.text_embedder_hidden_size,
									    '''num_layers''': 1,
									}

									UpperCAmelCase__    =							PriorTransformer(**lowercase__    )
									# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
									UpperCAmelCase__    =							nn.Parameter(torch.ones(model.clip_std.shape    )    )
									return model







					@property
					def 	__lowerCAmelCase		(       self     :			Tuple    ):
									torch.manual_seed(0    )
									UpperCAmelCase__    =							CLIPVisionConfig(
									    hidden_size=self.text_embedder_hidden_size					,image_size=224					,projection_dim=self.text_embedder_hidden_size					,intermediate_size=37					,num_attention_heads=4					,num_channels=3					,num_hidden_layers=5					,patch_size=14					,)

									UpperCAmelCase__    =							CLIPVisionModelWithProjection(lowercase__    )
									return model







					@property
					def 	__lowerCAmelCase		(       self     :			str    ):
									UpperCAmelCase__    =							CLIPImageProcessor(
									    crop_size=224					,do_center_crop=lowercase__					,do_normalize=lowercase__					,do_resize=lowercase__					,image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]					,image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]					,resample=3					,size=224					,)

									return image_processor







					def 	__lowerCAmelCase		(       self     :			Optional[int]    ):
									UpperCAmelCase__    =							self.dummy_prior
									UpperCAmelCase__    =							self.dummy_image_encoder
									UpperCAmelCase__    =							self.dummy_text_encoder
									UpperCAmelCase__    =							self.dummy_tokenizer
									UpperCAmelCase__    =							self.dummy_image_processor

									UpperCAmelCase__    =							UnCLIPScheduler(
									    variance_type='fixed_small_log'					,prediction_type='sample'					,num_train_timesteps=1_000					,clip_sample=lowercase__					,clip_sample_range=1_0.0					,)

									UpperCAmelCase__    =							{
									    '''prior''': prior,
									    '''image_encoder''': image_encoder,
									    '''text_encoder''': text_encoder,
									    '''tokenizer''': tokenizer,
									    '''scheduler''': scheduler,
									    '''image_processor''': image_processor,
									}

									return components







					def 	__lowerCAmelCase		(       self     :			Optional[Any]					,lowerCamelCase__     :			Tuple					,lowerCamelCase__     :			Optional[Any]=0    ):





									if str(lowercase__    ).startswith('mps'    ):
													UpperCAmelCase__    =							torch.manual_seed(lowercase__    )
									else:
													UpperCAmelCase__    =							torch.Generator(device=lowercase__    ).manual_seed(lowercase__    )
									UpperCAmelCase__    =							{
									    '''prompt''': '''horse''',
									    '''generator''': generator,
									    '''guidance_scale''': 4.0,
									    '''num_inference_steps''': 2,
									    '''output_type''': '''np''',
									}
									return inputs







					def 	__lowerCAmelCase		(       self     :			int    ):
									UpperCAmelCase__    =							'''cpu'''

									UpperCAmelCase__    =							self.get_dummy_components()

									UpperCAmelCase__    =							self.pipeline_class(**lowercase__    )
									UpperCAmelCase__    =							pipe.to(lowercase__    )

									pipe.set_progress_bar_config(disable=lowercase__    )

									UpperCAmelCase__    =							pipe(**self.get_dummy_inputs(lowercase__    )    )
									UpperCAmelCase__    =							output.image_embeds

									UpperCAmelCase__    =							pipe(
									    **self.get_dummy_inputs(lowercase__    )					,return_dict=lowercase__					,)[0]

									UpperCAmelCase__    =							image[0, -10:]
									UpperCAmelCase__    =							image_from_tuple[0, -10:]

									assert image.shape == (1, 32)

									UpperCAmelCase__    =							np.array(
									    [-0.0_5_3_2, 1.7_1_2_0, 0.3_6_5_6, -1.0_8_5_2, -0.8_9_4_6, -1.1_7_5_6, 0.4_3_4_8, 0.2_4_8_2, 0.5_1_4_6, -0.1_1_5_6]    )

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







					@skip_mps
					def 	__lowerCAmelCase		(       self     :			str    ):
									UpperCAmelCase__    =							torch_device == '''cpu'''
									UpperCAmelCase__    =							True
									UpperCAmelCase__    =							False

									self._test_inference_batch_single_identical(
									    test_max_difference=lowercase__					,relax_max_difference=lowercase__					,test_mean_pixel_difference=lowercase__					,)







					@skip_mps
					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									UpperCAmelCase__    =							torch_device == '''cpu'''
									UpperCAmelCase__    =							False

									self._test_attention_slicing_forward_pass(
									    test_max_difference=lowercase__					,test_mean_pixel_difference=lowercase__					,)




"""simple docstring"""






import argparse
import os

import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints

from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder


lowerCAmelCase__						:						str		  =			'base_with_context'



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding'] ) )
				UpperCAmelCase__    =							nn.Parameter(
				    torch.FloatTensor(weights['Embed_0']['embedding'] )      ,    requires_grad=lowerCamelCase )
				for lyr_num, lyr in enumerate(model.encoders ):
								UpperCAmelCase__    =							weights[f'''layers_{lyr_num}''']
								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale'] ) )

								UpperCAmelCase__    =							ly_weight['attention']
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )

								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale'] ) )

								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T ) )

				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale'] ) )
				return model



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T ) )

				UpperCAmelCase__    =							nn.Parameter(
				    torch.FloatTensor(weights['Embed_0']['embedding'] )      ,    requires_grad=lowerCamelCase )

				for lyr_num, lyr in enumerate(model.encoders ):
								UpperCAmelCase__    =							weights[f'''layers_{lyr_num}''']
								UpperCAmelCase__    =							ly_weight['attention']

								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale'] ) )

								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale'] ) )

				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['encoder_norm']['scale'] ) )

				return model



def 						a_		(      lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T ) )
				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T ) )

				UpperCAmelCase__    =							nn.Parameter(
				    torch.FloatTensor(weights['Embed_0']['embedding'] )      ,    requires_grad=lowerCamelCase )

				UpperCAmelCase__    =							nn.Parameter(
				    torch.FloatTensor(weights['continuous_inputs_projection']['kernel'].T ) )

				for lyr_num, lyr in enumerate(model.decoders ):
								UpperCAmelCase__    =							weights[f'''layers_{lyr_num}''']
								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['pre_self_attention_layer_norm']['scale'] ) )

								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['FiLMLayer_0']['DenseGeneral_0']['kernel'].T ) )

								UpperCAmelCase__    =							ly_weight['self_attention']
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )

								UpperCAmelCase__    =							ly_weight['MultiHeadDotProductAttention_0']
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['query']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['key']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['value']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(attention_weights['out']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['pre_cross_attention_layer_norm']['scale'] ) )

								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['pre_mlp_layer_norm']['scale'] ) )
								UpperCAmelCase__    =							nn.Parameter(
								    torch.FloatTensor(ly_weight['FiLMLayer_1']['DenseGeneral_0']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_0']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wi_1']['kernel'].T ) )
								UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(ly_weight['mlp']['wo']['kernel'].T ) )

				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['decoder_norm']['scale'] ) )

				UpperCAmelCase__    =							nn.Parameter(torch.FloatTensor(weights['spec_out_dense']['kernel'].T ) )

				return model



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							checkpoints.load_tax_checkpoint(args.checkpoint_path )
				UpperCAmelCase__    =							jnp.tree_util.tree_map(onp.array      ,    lowerCamelCase )

				UpperCAmelCase__    =							[
				    'from __gin__ import dynamic_registration',
				    'from music_spectrogram_diffusion.models.diffusion import diffusion_utils',
				    'diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0',
				    'diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()',
				]

				UpperCAmelCase__    =							os.path.join(args.checkpoint_path      ,    '..'      ,    'config.gin' )
				UpperCAmelCase__    =							inference.parse_training_gin_file(lowerCamelCase      ,    lowerCamelCase )
				UpperCAmelCase__    =							inference.InferenceModel(args.checkpoint_path      ,    lowerCamelCase )

				UpperCAmelCase__    =							DDPMScheduler(beta_schedule='squaredcos_cap_v2'      ,    variance_type='fixed_large' )

				UpperCAmelCase__    =							SpectrogramNotesEncoder(
				    max_length=synth_model.sequence_length['inputs']      ,    vocab_size=synth_model.model.module.config.vocab_size      ,    d_model=synth_model.model.module.config.emb_dim      ,    dropout_rate=synth_model.model.module.config.dropout_rate      ,    num_layers=synth_model.model.module.config.num_encoder_layers      ,    num_heads=synth_model.model.module.config.num_heads      ,    d_kv=synth_model.model.module.config.head_dim      ,    d_ff=synth_model.model.module.config.mlp_dim      ,    feed_forward_proj='gated-gelu'      ,    )

				UpperCAmelCase__    =							SpectrogramContEncoder(
				    input_dims=synth_model.audio_codec.n_dims      ,    targets_context_length=synth_model.sequence_length['targets_context']      ,    d_model=synth_model.model.module.config.emb_dim      ,    dropout_rate=synth_model.model.module.config.dropout_rate      ,    num_layers=synth_model.model.module.config.num_encoder_layers      ,    num_heads=synth_model.model.module.config.num_heads      ,    d_kv=synth_model.model.module.config.head_dim      ,    d_ff=synth_model.model.module.config.mlp_dim      ,    feed_forward_proj='gated-gelu'      ,    )

				UpperCAmelCase__    =							TaFilmDecoder(
				    input_dims=synth_model.audio_codec.n_dims      ,    targets_length=synth_model.sequence_length['targets_context']      ,    max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time      ,    d_model=synth_model.model.module.config.emb_dim      ,    num_layers=synth_model.model.module.config.num_decoder_layers      ,    num_heads=synth_model.model.module.config.num_heads      ,    d_kv=synth_model.model.module.config.head_dim      ,    d_ff=synth_model.model.module.config.mlp_dim      ,    dropout_rate=synth_model.model.module.config.dropout_rate      ,    )

				UpperCAmelCase__    =							load_notes_encoder(ta_checkpoint['target']['token_encoder']      ,    lowerCamelCase )
				UpperCAmelCase__    =							load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']      ,    lowerCamelCase )
				UpperCAmelCase__    =							load_decoder(ta_checkpoint['target']['decoder']      ,    lowerCamelCase )

				UpperCAmelCase__    =							OnnxRuntimeModel.from_pretrained('kashif/soundstream_mel_decoder' )

				UpperCAmelCase__    =							SpectrogramDiffusionPipeline(
				    notes_encoder=lowerCamelCase      ,    continuous_encoder=lowerCamelCase      ,    decoder=lowerCamelCase      ,    scheduler=lowerCamelCase      ,    melgan=lowerCamelCase      ,    )
				if args.save:
								pipe.save_pretrained(args.output_path )


if __name__ == "__main__":
						lowerCAmelCase__						:						Tuple		  =			argparse.ArgumentParser()

						parser.add_argument('--output_path', default=None, type=str, required=True, help='Path to the converted model.')
						parser.add_argument(
						    '--save', default=True, type=bool, required=False, help='Whether to save the converted model or not.'
						)
						parser.add_argument(
						    '--checkpoint_path',
						    default=F"""{MODEL}/checkpoint_500000""",
						    type=str,
						    required=False,
						    help='Path to the original jax model checkpoint.',
						)
						lowerCAmelCase__						:						List[str]		  =			parser.parse_args()

						main(args)



