


"""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          =							"base_with_context"



def   snake_case						(				_a:     Optional[int]		,   _a:     List[Any]							)->		Optional[int]:
      '''simple docstring'''


      lowerCamelCase__           =		nn.Parameter(torch.FloatTensor(weights['token_embedder']['embedding']							)							)
      lowerCamelCase__           =		nn.Parameter(
          torch.FloatTensor(weights['Embed_0']['embedding']							)		,   requires_grad=_a							)
      for lyr_num, lyr in enumerate(model.encoders							):
            lowerCamelCase__           =		weights[F'layers_{lyr_num}']
            lowerCamelCase__           =		nn.Parameter(
                torch.FloatTensor(ly_weight['pre_attention_layer_norm']['scale']							)							)

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

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

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

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



def   snake_case						(				_a:     List[str]		,   _a:     Dict							)->		Tuple:
      '''simple docstring'''


      lowerCamelCase__           =		nn.Parameter(torch.FloatTensor(weights['input_proj']['kernel'].T							)							)

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

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

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

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

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

      return model



def   snake_case						(				_a:     str		,   _a:     Optional[Any]							)->		int:
      '''simple docstring'''


      lowerCamelCase__           =		nn.Parameter(torch.FloatTensor(weights['time_emb_dense0']['kernel'].T							)							)
      lowerCamelCase__           =		nn.Parameter(torch.FloatTensor(weights['time_emb_dense1']['kernel'].T							)							)

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

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

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

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

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

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

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

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

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

      return model



def   snake_case						(				_a:     str							)->		Union[str, Any]:
      '''simple docstring'''


      lowerCamelCase__           =		checkpoints.load_tax_checkpoint(args.checkpoint_path							)
      lowerCamelCase__           =		jnp.tree_util.tree_map(onp.array		,   _a							)

      lowerCamelCase__           =		[
          '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__           =		os.path.join(args.checkpoint_path		,   '..'		,   'config.gin'							)
      lowerCamelCase__           =		inference.parse_training_gin_file(_a		,   _a							)
      lowerCamelCase__           =		inference.InferenceModel(args.checkpoint_path		,   _a							)

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

      lowerCamelCase__           =		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__           =		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__           =		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__           =		load_notes_encoder(ta_checkpoint['target']['token_encoder']		,   _a							)
      lowerCamelCase__           =		load_continuous_encoder(ta_checkpoint['target']['continuous_encoder']		,   _a							)
      lowerCamelCase__           =		load_decoder(ta_checkpoint['target']['decoder']		,   _a							)

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

      lowerCamelCase__           =		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__":
   _snake_case          =							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          =							parser.parse_args()

   main(args)









"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_snake_case          =							{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							["BlenderbotTokenizerFast"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							[
       "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "BlenderbotForCausalLM",
       "BlenderbotForConditionalGeneration",
       "BlenderbotModel",
       "BlenderbotPreTrainedModel",
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							[
       "TFBlenderbotForConditionalGeneration",
       "TFBlenderbotModel",
       "TFBlenderbotPreTrainedModel",
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case          =							[
       "FlaxBlenderbotForConditionalGeneration",
       "FlaxBlenderbotModel",
       "FlaxBlenderbotPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_blenderbot import (
       BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BlenderbotConfig,
       BlenderbotOnnxConfig,
   )
   from .tokenization_blenderbot import BlenderbotTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_blenderbot import (
          BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
          BlenderbotForCausalLM,
          BlenderbotForConditionalGeneration,
          BlenderbotModel,
          BlenderbotPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_blenderbot import (
          TFBlenderbotForConditionalGeneration,
          TFBlenderbotModel,
          TFBlenderbotPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_blenderbot import (
          FlaxBlenderbotForConditionalGeneration,
          FlaxBlenderbotModel,
          FlaxBlenderbotPreTrainedModel,
      )

else:
   import sys

   _snake_case          =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



