





"""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


a_					=     """base_with_context"""







def 						__lowercase	(  snake_case_    :    Tuple							,snake_case_    :    List[Any]		)  ->Optional[int]:



    '''simple docstring'''







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

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

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

        __A       :		Optional[Any]							=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T		)		)
        __A       :		Tuple							=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T		)		)
        __A       :		Union[str, Any]							=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T		)		)

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







def 						__lowercase	(  snake_case_    :    Optional[Any]							,snake_case_    :    Dict		)  ->List[str]:



    '''simple docstring'''







    __A       :		int							=	nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T		)		)

    __A       :		str							=	nn.Parameter(
        torch.FloatTensor(weights['''Embed_0''']['''embedding''']		)							,requires_grad=snake_case_		)

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

        __A       :		Tuple							=	nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T		)		)
        __A       :		str							=	nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T		)		)
        __A       :		str							=	nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T		)		)
        __A       :		List[str]							=	nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T		)		)
        __A       :		Optional[int]							=	nn.Parameter(
            torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']		)		)

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

    __A       :		int							=	nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']		)		)

    return model







def 						__lowercase	(  snake_case_    :    Union[str, Any]							,snake_case_    :    str		)  ->Union[str, Any]:



    '''simple docstring'''







    __A       :		Union[str, Any]							=	nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T		)		)
    __A       :		int							=	nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T		)		)

    __A       :		int							=	nn.Parameter(
        torch.FloatTensor(weights['''Embed_0''']['''embedding''']		)							,requires_grad=snake_case_		)

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

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

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

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

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

        __A       :		str							=	nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']		)		)
        __A       :		str							=	nn.Parameter(
            torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T		)		)
        __A       :		Union[str, Any]							=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T		)		)
        __A       :		Any							=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T		)		)
        __A       :		Union[str, Any]							=	nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T		)		)

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

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

    return model







def 						__lowercase	(  snake_case_    :    List[Any]		)  ->int:



    '''simple docstring'''







    __A       :		Optional[Any]							=	checkpoints.load_tax_checkpoint(args.checkpoint_path		)
    __A       :		List[Any]							=	jnp.tree_util.tree_map(onp.array							,snake_case_		)

    __A       :		Optional[Any]							=	[
        '''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()''',
    ]

    __A       :		Union[str, Any]							=	os.path.join(args.checkpoint_path							,'''..'''							,'''config.gin'''		)
    __A       :		Optional[int]							=	inference.parse_training_gin_file(snake_case_							,snake_case_		)
    __A       :		Tuple							=	inference.InferenceModel(args.checkpoint_path							,snake_case_		)

    __A       :		List[str]							=	DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''							,variance_type='''fixed_large'''		)

    __A       :		Any							=	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'''							,)

    __A       :		Optional[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'''							,)

    __A       :		Union[str, 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							,)

    __A       :		Dict							=	load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']							,snake_case_		)
    __A       :		str							=	load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']							,snake_case_		)
    __A       :		List[Any]							=	load_decoder(ta_checkpoint['''target''']['''decoder''']							,snake_case_		)

    __A       :		int							=	OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''		)

    __A       :		Tuple							=	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__":
       a_					=     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.""",
       )
       a_					=     parser.parse_args()

       main(args)











"""simple docstring"""



import requests

a_					=     """https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""







def 						__lowercase	(  snake_case_    :    str		)  ->None:



    '''simple docstring'''







    __A       :		str							=	requests.get(_NEWS_API + bbc_news_api_key		).json()
    # each article in the list is a dict
    for i, article in enumerate(bbc_news_page['''articles''']							,1		):
        print(F"""{i}.) {article['title']}"""		)


if __name__ == "__main__":
       fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")




