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


__lowercase							 =					'''base_with_context'''



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, Any]			  =		nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']	)	)
    __UpperCamelCase     :str			  =		nn.Parameter(
        torch.FloatTensor(weights['''Embed_0''']['''embedding''']	)  ,			requires_grad=__snake_case	)
    for lyr_num, lyr in enumerate(model.encoders	):
        __UpperCamelCase     :str			  =		weights[f"""layers_{lyr_num}"""]
        __UpperCamelCase     :Optional[int]			  =		nn.Parameter(
            torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']	)	)

        __UpperCamelCase     :Tuple			  =		ly_weight['''attention''']
        __UpperCamelCase     :Union[str, Any]			  =		nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T	)	)
        __UpperCamelCase     :List[str]			  =		nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T	)	)
        __UpperCamelCase     :int			  =		nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T	)	)
        __UpperCamelCase     :Optional[int]			  =		nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T	)	)

        __UpperCamelCase     :Union[str, Any]			  =		nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']	)	)

        __UpperCamelCase     :Optional[int]			  =		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T	)	)
        __UpperCamelCase     :Optional[Any]			  =		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T	)	)
        __UpperCamelCase     :List[Any]			  =		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T	)	)

    __UpperCamelCase     :List[str]			  =		nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']	)	)
    return model



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[Any]			  =		nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T	)	)

    __UpperCamelCase     :Dict			  =		nn.Parameter(
        torch.FloatTensor(weights['''Embed_0''']['''embedding''']	)  ,			requires_grad=__snake_case	)

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

        __UpperCamelCase     :str			  =		nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T	)	)
        __UpperCamelCase     :Any			  =		nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T	)	)
        __UpperCamelCase     :Tuple			  =		nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T	)	)
        __UpperCamelCase     :Union[str, Any]			  =		nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T	)	)
        __UpperCamelCase     :int			  =		nn.Parameter(
            torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']	)	)

        __UpperCamelCase     :str			  =		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T	)	)
        __UpperCamelCase     :Optional[int]			  =		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T	)	)
        __UpperCamelCase     :Tuple			  =		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T	)	)
        __UpperCamelCase     :Dict			  =		nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']	)	)

    __UpperCamelCase     :Optional[Any]			  =		nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']	)	)

    return model



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Tuple			  =		nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T	)	)
    __UpperCamelCase     :List[Any]			  =		nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T	)	)

    __UpperCamelCase     :Dict			  =		nn.Parameter(
        torch.FloatTensor(weights['''Embed_0''']['''embedding''']	)  ,			requires_grad=__snake_case	)

    __UpperCamelCase     :Dict			  =		nn.Parameter(
        torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T	)	)

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

        __UpperCamelCase     :Optional[int]			  =		nn.Parameter(
            torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T	)	)

        __UpperCamelCase     :Union[str, Any]			  =		ly_weight['''self_attention''']
        __UpperCamelCase     :List[Any]			  =		nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T	)	)
        __UpperCamelCase     :int			  =		nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T	)	)
        __UpperCamelCase     :int			  =		nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T	)	)
        __UpperCamelCase     :Optional[Any]			  =		nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T	)	)

        __UpperCamelCase     :Any			  =		ly_weight['''MultiHeadDotProductAttention_0''']
        __UpperCamelCase     :Tuple			  =		nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T	)	)
        __UpperCamelCase     :Optional[int]			  =		nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T	)	)
        __UpperCamelCase     :str			  =		nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T	)	)
        __UpperCamelCase     :Union[str, Any]			  =		nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T	)	)
        __UpperCamelCase     :Tuple			  =		nn.Parameter(
            torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']	)	)

        __UpperCamelCase     :Optional[int]			  =		nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']	)	)
        __UpperCamelCase     :int			  =		nn.Parameter(
            torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T	)	)
        __UpperCamelCase     :Tuple			  =		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T	)	)
        __UpperCamelCase     :List[Any]			  =		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T	)	)
        __UpperCamelCase     :Any			  =		nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T	)	)

    __UpperCamelCase     :Optional[Any]			  =		nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']	)	)

    __UpperCamelCase     :List[Any]			  =		nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T	)	)

    return model



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		checkpoints.load_tax_checkpoint(args.checkpoint_path	)
    __UpperCamelCase     :List[str]			  =		jnp.tree_util.tree_map(onp.array  ,			__snake_case	)

    __UpperCamelCase     :str			  =		[
        '''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     :List[Any]			  =		os.path.join(args.checkpoint_path  ,			'''..'''  ,			'''config.gin'''	)
    __UpperCamelCase     :Union[str, Any]			  =		inference.parse_training_gin_file(__snake_case  ,			__snake_case	)
    __UpperCamelCase     :Optional[Any]			  =		inference.InferenceModel(args.checkpoint_path  ,			__snake_case	)

    __UpperCamelCase     :Optional[Any]			  =		DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''  ,			variance_type='''fixed_large'''	)

    __UpperCamelCase     :Tuple			  =		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     :List[str]			  =		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     :List[Any]			  =		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     :List[str]			  =		load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']  ,			__snake_case	)
    __UpperCamelCase     :Optional[Any]			  =		load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']  ,			__snake_case	)
    __UpperCamelCase     :Optional[Any]			  =		load_decoder(ta_checkpoint['''target''']['''decoder''']  ,			__snake_case	)

    __UpperCamelCase     :Union[str, Any]			  =		OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''	)

    __UpperCamelCase     :Union[str, Any]			  =		SpectrogramDiffusionPipeline(
        notes_encoder=__snake_case  ,			continuous_encoder=__snake_case  ,			decoder=__snake_case  ,			scheduler=__snake_case  ,			melgan=__snake_case  ,			)
    if args.save:
        pipe.save_pretrained(args.output_path	)


if __name__ == "__main__":
    __lowercase							 =					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.''',
    )
    __lowercase							 =					parser.parse_args()

    main(args)







"""simple docstring"""





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case,     __snake_case	)							->		np.ndarray:


					"""simple docstring"""





					if (ksize % 2) == 0:
										_UpperCamelCase							 =  ksize + 1
					_UpperCamelCase							 =  np.zeros((ksize, ksize),     dtype=np.floataa	)

					# each value
					for y in range(__snake_case	):
										for x in range(__snake_case	):
															# distance from center
															_UpperCamelCase							 =  x - ksize // 2
															_UpperCamelCase							 =  y - ksize // 2

															# degree to radiant
															_UpperCamelCase							 =  theta / 1_80 * np.pi
															_UpperCamelCase							 =  np.cos(_theta	)
															_UpperCamelCase							 =  np.sin(_theta	)

															# get kernel x
															_UpperCamelCase							 =  cos_theta * px + sin_theta * py

															# get kernel y
															_UpperCamelCase							 =  -sin_theta * px + cos_theta * py

															# fill kernel
															_UpperCamelCase							 =  np.exp(
															    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)	) * np.cos(2 * np.pi * _x / lambd + psi	)

					return gabor


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				# read original image
				_a														=		imread("""../image_data/lena.jpg""")
				# turn image in gray scale value
				_a														=		cvtColor(img, COLOR_BGR2GRAY)

				# Apply multiple Kernel to detect edges
				_a														=		np.zeros(gray.shape[:2])
				for theta in [0, 30, 60, 90, 120, 150]:
								_a														=		gabor_filter_kernel(10, 8, theta, 10, 0, 0)
								out += filteraD(gray, CV_8UC3, kernel_aa)
				_a														=		out / out.max() * 255
				_a														=		out.astype(np.uinta)

				imshow("""Original""", gray)
				imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

				waitKey(0)






