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			:							List[Any]     ="base_with_context"







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Optional[Any] 						=						nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]	)	)
    _lowerCamelCase     :		Tuple 						=						nn.Parameter(
        torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]	)   , requires_grad=__A	)
    for lyr_num, lyr in enumerate(model.encoders	):
        _lowerCamelCase     :		List[str] 						=						weights[F"""layers_{lyr_num}"""]
        _lowerCamelCase     :		Any 						=						nn.Parameter(
            torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]	)	)

        _lowerCamelCase     :		List[str] 						=						ly_weight["""attention"""]
        _lowerCamelCase     :		Optional[int] 						=						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		Any 						=						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		Dict 						=						nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		int 						=						nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T	)	)

        _lowerCamelCase     :		Tuple 						=						nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]	)	)

        _lowerCamelCase     :		List[Any] 						=						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		Optional[Any] 						=						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		Optional[int] 						=						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T	)	)

    _lowerCamelCase     :		Union[str, Any] 						=						nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]	)	)
    return model







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		int 						=						nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T	)	)

    _lowerCamelCase     :		List[Any] 						=						nn.Parameter(
        torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]	)   , requires_grad=__A	)

    for lyr_num, lyr in enumerate(model.encoders	):
        _lowerCamelCase     :		Optional[Any] 						=						weights[F"""layers_{lyr_num}"""]
        _lowerCamelCase     :		Tuple 						=						ly_weight["""attention"""]

        _lowerCamelCase     :		List[Any] 						=						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		str 						=						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		List[str] 						=						nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		List[Any] 						=						nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		Optional[Any] 						=						nn.Parameter(
            torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]	)	)

        _lowerCamelCase     :		Optional[Any] 						=						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		str 						=						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		Optional[Any] 						=						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		List[str] 						=						nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]	)	)

    _lowerCamelCase     :		List[Any] 						=						nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]	)	)

    return model







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		int 						=						nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T	)	)
    _lowerCamelCase     :		Dict 						=						nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T	)	)

    _lowerCamelCase     :		List[str] 						=						nn.Parameter(
        torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]	)   , requires_grad=__A	)

    _lowerCamelCase     :		Union[str, Any] 						=						nn.Parameter(
        torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T	)	)

    for lyr_num, lyr in enumerate(model.decoders	):
        _lowerCamelCase     :		int 						=						weights[F"""layers_{lyr_num}"""]
        _lowerCamelCase     :		Tuple 						=						nn.Parameter(
            torch.FloatTensor(ly_weight["""pre_self_attention_layer_norm"""]["""scale"""]	)	)

        _lowerCamelCase     :		List[Any] 						=						nn.Parameter(
            torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T	)	)

        _lowerCamelCase     :		Union[str, Any] 						=						ly_weight["""self_attention"""]
        _lowerCamelCase     :		Dict 						=						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		str 						=						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		Tuple 						=						nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		List[Any] 						=						nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T	)	)

        _lowerCamelCase     :		List[str] 						=						ly_weight["""MultiHeadDotProductAttention_0"""]
        _lowerCamelCase     :		Optional[int] 						=						nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		List[Any] 						=						nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		Union[str, Any] 						=						nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		Tuple 						=						nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		Union[str, Any] 						=						nn.Parameter(
            torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]	)	)

        _lowerCamelCase     :		Dict 						=						nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]	)	)
        _lowerCamelCase     :		List[str] 						=						nn.Parameter(
            torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		Any 						=						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		Union[str, Any] 						=						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T	)	)
        _lowerCamelCase     :		int 						=						nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T	)	)

    _lowerCamelCase     :		Union[str, Any] 						=						nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]	)	)

    _lowerCamelCase     :		Dict 						=						nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T	)	)

    return model







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		List[Any] 						=						checkpoints.load_tax_checkpoint(args.checkpoint_path	)
    _lowerCamelCase     :		Tuple 						=						jnp.tree_util.tree_map(onp.array   , __A	)

    _lowerCamelCase     :		int 						=						[
        """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()""",
    ]

    _lowerCamelCase     :		Tuple 						=						os.path.join(args.checkpoint_path   , """.."""   , """config.gin"""	)
    _lowerCamelCase     :		Any 						=						inference.parse_training_gin_file(__A   , __A	)
    _lowerCamelCase     :		List[Any] 						=						inference.InferenceModel(args.checkpoint_path   , __A	)

    _lowerCamelCase     :		Optional[Any] 						=						DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""   , variance_type="""fixed_large"""	)

    _lowerCamelCase     :		List[str] 						=						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"""   , )

    _lowerCamelCase     :		int 						=						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"""   , )

    _lowerCamelCase     :		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   , )

    _lowerCamelCase     :		str 						=						load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]   , __A	)
    _lowerCamelCase     :		Any 						=						load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]   , __A	)
    _lowerCamelCase     :		str 						=						load_decoder(ta_checkpoint["""target"""]["""decoder"""]   , __A	)

    _lowerCamelCase     :		Optional[int] 						=						OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""	)

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


if __name__ == "__main__":
    lowerCAmelCase			:							str     =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			:							Tuple     =parser.parse_args()

    main(args)





from __future__ import annotations

lowerCAmelCase			:							int     =[]







def 						A__  (					__A   , __A   , __A	):







    '''simple docstring'''
    for i in range(len(__A	)	):
        if board[row][i] == 1:
            return False
    for i in range(len(__A	)	):
        if board[i][column] == 1:
            return False
    for i, j in zip(range(__A   , -1   , -1	)   , range(__A   , -1   , -1	)	):
        if board[i][j] == 1:
            return False
    for i, j in zip(range(__A   , -1   , -1	)   , range(__A   , len(__A	)	)	):
        if board[i][j] == 1:
            return False
    return True







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    if row >= len(__A	):
        solution.append(__A	)
        printboard(__A	)
        print()
        return True
    for i in range(len(__A	)	):
        if is_safe(__A   , __A   , __A	):
            _lowerCamelCase     :		int 						=						1
            solve(__A   , row + 1	)
            _lowerCamelCase     :		List[str] 						=						0
    return False







def 						A__  (					__A	):







    '''simple docstring'''
    for i in range(len(__A	)	):
        for j in range(len(__A	)	):
            if board[i][j] == 1:
                print("""Q"""   , end=""" """	)
            else:
                print("""."""   , end=""" """	)
        print()


# n=int(input("The no. of queens"))
lowerCAmelCase			:							int     =8
lowerCAmelCase			:							Union[str, Any]     =[[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("The total no. of solutions are :", len(solution))




