


"""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


_snake_case					:		Optional[Any]      =    "base_with_context"







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

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

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

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

							__snake_case		:      Dict										=       nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]     )     )
							return model







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Any										=       nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T     )     )

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

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

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

														__snake_case		:      str										=       nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T     )     )
														__snake_case		:      Optional[int]										=       nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T     )     )
														__snake_case		:      Optional[int]										=       nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T     )     )
														__snake_case		:      Dict										=       nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]     )     )

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

							return model







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Optional[int]										=       nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T     )     )
							__snake_case		:      Dict										=       nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T     )     )

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

							__snake_case		:      Any										=       nn.Parameter(
							    torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T     )     )

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

														__snake_case		:      Tuple										=       nn.Parameter(
														    torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T     )     )

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

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

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

							__snake_case		:      Any										=       nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]     )     )

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

							return model







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      List[str]										=       checkpoints.load_tax_checkpoint(args.checkpoint_path     )
							__snake_case		:      Optional[int]										=       jnp.tree_util.tree_map(onp.array							,     __lowerCamelCase     )

							__snake_case		:      Tuple										=       [
							    "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()",
							]

							__snake_case		:      Optional[int]										=       os.path.join(args.checkpoint_path							,     ".."							,     "config.gin"     )
							__snake_case		:      Tuple										=       inference.parse_training_gin_file(__lowerCamelCase							,     __lowerCamelCase     )
							__snake_case		:      Dict										=       inference.InferenceModel(args.checkpoint_path							,     __lowerCamelCase     )

							__snake_case		:      int										=       DDPMScheduler(beta_schedule="squaredcos_cap_v2"							,     variance_type="fixed_large"     )

							__snake_case		:      int										=       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"							,     )

							__snake_case		:      Union[str, Any]										=       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"							,     )

							__snake_case		:      Dict										=       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							,     )

							__snake_case		:      Dict										=       load_notes_encoder(ta_checkpoint["target"]["token_encoder"]							,     __lowerCamelCase     )
							__snake_case		:      Dict										=       load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"]							,     __lowerCamelCase     )
							__snake_case		:      Tuple										=       load_decoder(ta_checkpoint["target"]["decoder"]							,     __lowerCamelCase     )

							__snake_case		:      List[str]										=       OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"     )

							__snake_case		:      Optional[Any]										=       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__":
			_snake_case					:		Dict      =    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.",
			)
			_snake_case					:		int      =    parser.parse_args()

			main(args)





from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_tf_available():
			import tensorflow as tf

			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
			from ..tf_utils import stable_softmax

if is_torch_available():
			from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

_snake_case					:		Optional[int]      =    logging.get_logger(__name__)

@add_end_docstrings(_lowerCAmelCase				)
class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							def __init__(							self	:  Tuple   ,     *lowerCamelCase	:  Any   ,     **lowerCamelCase	:  Tuple    )     ->   int:
														super().__init__(*lowerCamelCase   ,     **lowerCamelCase    )
														requires_backends(self   ,     "vision"    )
														self.check_model_type(
														    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
														    if self.framework == "tf"
														    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING    )

							def    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  Optional[Any]=None    )     ->   Optional[int]:
														__snake_case		:      Optional[Any]										=       {}
														if top_k is not None:
																					__snake_case		:      List[Any]										=       top_k
														return {}, {}, postprocess_params

							def __call__(							self	:  List[Any]   ,     lowerCamelCase	:  Union[str, List[str], "Image.Image", List["Image.Image"]]   ,     **lowerCamelCase	:  Dict    )     ->   Optional[int]:
														return super().__call__(lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  List[Any]    )     ->   int:
														__snake_case		:      Any										=       load_image(lowerCamelCase    )
														__snake_case		:      str										=       self.image_processor(images=lowerCamelCase   ,     return_tensors=self.framework    )
														return model_inputs

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  List[str]    )     ->   Tuple:
														__snake_case		:      List[Any]										=       self.model(**lowerCamelCase    )
														return model_outputs






							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Optional[Any]=5    )     ->   List[str]:
														if top_k > self.model.config.num_labels:
																					__snake_case		:      int										=       self.model.config.num_labels

														if self.framework == "pt":
																					__snake_case		:      Optional[Any]										=       model_outputs.logits.softmax(-1    )[0]
																					__snake_case			,      __snake_case		:      List[str]										=       probs.topk(lowerCamelCase    )
														elif self.framework == "tf":
																					__snake_case		:      Tuple										=       stable_softmax(model_outputs.logits   ,     axis=-1    )[0]
																					__snake_case		:      Optional[Any]										=       tf.math.top_k(lowerCamelCase   ,     k=lowerCamelCase    )
																					__snake_case			,      __snake_case		:      List[Any]										=       topk.values.numpy(), topk.indices.numpy()
														else:
																					raise ValueError(F'Unsupported framework: {self.framework}'    )

														__snake_case		:      Any										=       scores.tolist()
														__snake_case		:      Optional[int]										=       ids.tolist()
														return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase   ,     lowerCamelCase    )]

