




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[str]			 =     """base_with_context"""







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> List[str]:



       """simple docstring"""

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

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

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

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

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







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> Union[str, Any]:



       """simple docstring"""

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

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

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

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

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

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

       return model







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> int:



       """simple docstring"""

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

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

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

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

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

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

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

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

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

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

       return model







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> str:



       """simple docstring"""

       A__   				=					checkpoints.load_tax_checkpoint(args.checkpoint_path		)
       A__   				=					jnp.tree_util.tree_map(onp.array				,       lowercase_		)

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

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

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

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

       A__   				=					SpectrogramDiffusionPipeline(
           notes_encoder=lowercase_				,       continuous_encoder=lowercase_				,       decoder=lowercase_				,       scheduler=lowercase_				,       melgan=lowercase_				,       )
       if args.save:
              pipe.save_pretrained(args.output_path		)


if __name__ == "__main__":
  _lowerCamelCase			:	List[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			:	Optional[Any]			 =     parser.parse_args()

  main(args)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase			:	Tuple							 =     logging.get_logger(__name__)

_lowerCamelCase			:	Tuple							 =     {
    """tiiuae/falcon-40b""": """https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json""",
    """tiiuae/falcon-7b""": """https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json""",
}


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   '''falcon'''
    UpperCAmelCase__													=   ['''past_key_values''']


    def __init__(    self   :			Optional[Any]      ,				UpperCAmelCase__   :			str=65_024      ,				UpperCAmelCase__   :			List[Any]=4_544      ,				UpperCAmelCase__   :			Optional[Any]=32      ,				UpperCAmelCase__   :			Dict=71      ,				UpperCAmelCase__   :			Any=1e-5      ,				UpperCAmelCase__   :			Optional[int]=0.02      ,				UpperCAmelCase__   :			Optional[int]=True      ,				UpperCAmelCase__   :			Union[str, Any]=0.0      ,				UpperCAmelCase__   :			List[str]=0.0      ,				UpperCAmelCase__   :			str=None      ,				UpperCAmelCase__   :			Dict=False      ,				UpperCAmelCase__   :			List[str]=False      ,				UpperCAmelCase__   :			Optional[Any]=True      ,				UpperCAmelCase__   :			Optional[Any]=True      ,				UpperCAmelCase__   :			Optional[Any]=False      ,				UpperCAmelCase__   :			Dict=11      ,				UpperCAmelCase__   :			str=11      ,				**UpperCAmelCase__   :			Dict      ,				)		->Any:





           '''simple docstring'''

           A__   				=					vocab_size
           # Backward compatibility with n_embed kwarg
           A__   				=					kwargs.pop('''n_embed'''      ,				UpperCAmelCase__)
           A__   				=					hidden_size if n_embed is None else n_embed
           A__   				=					num_hidden_layers
           A__   				=					num_attention_heads
           A__   				=					layer_norm_epsilon
           A__   				=					initializer_range
           A__   				=					use_cache
           A__   				=					hidden_dropout
           A__   				=					attention_dropout

           A__   				=					bos_token_id
           A__   				=					eos_token_id
           A__   				=					num_attention_heads if num_kv_heads is None else num_kv_heads
           A__   				=					alibi
           A__   				=					new_decoder_architecture
           A__   				=					multi_query  # Ignored when new_decoder_architecture is True
           A__   				=					parallel_attn
           A__   				=					bias

           super().__init__(bos_token_id=UpperCAmelCase__      ,				eos_token_id=UpperCAmelCase__      ,				**UpperCAmelCase__)


    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->int:





           '''simple docstring'''

           return self.hidden_size // self.num_attention_heads





    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			str)		->Dict:





           '''simple docstring'''

           return not self.alibi


