

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


_SCREAMING_SNAKE_CASE					:  List[str]             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  Optional[Any]             =      {
    '''salesforce/blip2-opt-2.7b''': '''https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json''',
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "blip_2_vision_model"






  def __init__(							self						:		List[str]     ,       __lowerCamelCase						:		Tuple=1408     ,       __lowerCamelCase						:		Union[str, Any]=6144     ,       __lowerCamelCase						:		Union[str, Any]=39     ,       __lowerCamelCase						:		Any=16     ,       __lowerCamelCase						:		List[Any]=224     ,       __lowerCamelCase						:		str=14     ,       __lowerCamelCase						:		str="gelu"     ,       __lowerCamelCase						:		Dict=0.00001     ,       __lowerCamelCase						:		List[str]=0.0     ,       __lowerCamelCase						:		Tuple=1e-10     ,       __lowerCamelCase						:		str=True     ,       **__lowerCamelCase						:		Dict     ,       )		->							List[str]:
       super().__init__(**__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   intermediate_size
       SCREAMING_SNAKE_CASE__   =   num_hidden_layers
       SCREAMING_SNAKE_CASE__   =   num_attention_heads
       SCREAMING_SNAKE_CASE__   =   patch_size
       SCREAMING_SNAKE_CASE__   =   image_size
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   attention_dropout
       SCREAMING_SNAKE_CASE__   =   layer_norm_eps
       SCREAMING_SNAKE_CASE__   =   hidden_act
       SCREAMING_SNAKE_CASE__   =   qkv_bias







  @classmethod
  def 				lowercase_		(							cls						:		str     ,       __lowerCamelCase						:		Union[str, os.PathLike]     ,       **__lowerCamelCase						:		str )		->							"PretrainedConfig":
       cls._set_token_in_kwargs(__lowerCamelCase )

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   cls.get_config_dict(__lowerCamelCase     ,       **__lowerCamelCase )

       # get the vision config dict if we are loading from Blip2Config
       if config_dict.get('''model_type''' ) == "blip-2":
            SCREAMING_SNAKE_CASE__   =   config_dict['''vision_config''']

       if "model_type" in config_dict and hasattr(cls     ,       '''model_type''' ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
                f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )

       return cls.from_dict(__lowerCamelCase     ,       **__lowerCamelCase )






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "blip_2_qformer"






  def __init__(							self						:		List[Any]     ,       __lowerCamelCase						:		List[str]=3_0522     ,       __lowerCamelCase						:		List[str]=768     ,       __lowerCamelCase						:		Any=12     ,       __lowerCamelCase						:		Tuple=12     ,       __lowerCamelCase						:		Dict=3072     ,       __lowerCamelCase						:		Optional[Any]="gelu"     ,       __lowerCamelCase						:		List[Any]=0.1     ,       __lowerCamelCase						:		List[str]=0.1     ,       __lowerCamelCase						:		Any=512     ,       __lowerCamelCase						:		int=0.02     ,       __lowerCamelCase						:		int=1e-12     ,       __lowerCamelCase						:		Dict=0     ,       __lowerCamelCase						:		Optional[int]="absolute"     ,       __lowerCamelCase						:		List[Any]=2     ,       __lowerCamelCase						:		int=1408     ,       **__lowerCamelCase						:		Dict     ,       )		->							Any:
       super().__init__(pad_token_id=__lowerCamelCase     ,       **__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   num_hidden_layers
       SCREAMING_SNAKE_CASE__   =   num_attention_heads
       SCREAMING_SNAKE_CASE__   =   hidden_act
       SCREAMING_SNAKE_CASE__   =   intermediate_size
       SCREAMING_SNAKE_CASE__   =   hidden_dropout_prob
       SCREAMING_SNAKE_CASE__   =   attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__   =   max_position_embeddings
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   layer_norm_eps
       SCREAMING_SNAKE_CASE__   =   position_embedding_type
       SCREAMING_SNAKE_CASE__   =   cross_attention_frequency
       SCREAMING_SNAKE_CASE__   =   encoder_hidden_size







  @classmethod
  def 				lowercase_		(							cls						:		int     ,       __lowerCamelCase						:		Union[str, os.PathLike]     ,       **__lowerCamelCase						:		Union[str, Any] )		->							"PretrainedConfig":
       cls._set_token_in_kwargs(__lowerCamelCase )

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   cls.get_config_dict(__lowerCamelCase     ,       **__lowerCamelCase )

       # get the qformer config dict if we are loading from Blip2Config
       if config_dict.get('''model_type''' ) == "blip-2":
            SCREAMING_SNAKE_CASE__   =   config_dict['''qformer_config''']

       if "model_type" in config_dict and hasattr(cls     ,       '''model_type''' ) and config_dict["model_type"] != cls.model_type:
            logger.warning(
                f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
                f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )

       return cls.from_dict(__lowerCamelCase     ,       **__lowerCamelCase )






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "blip-2"
  a									= True






  def __init__(							self						:		List[Any]     ,       __lowerCamelCase						:		Dict=None     ,       __lowerCamelCase						:		Tuple=None     ,       __lowerCamelCase						:		Any=None     ,       __lowerCamelCase						:		Optional[int]=32     ,       **__lowerCamelCase						:		Union[str, Any] )		->							Tuple:
       super().__init__(**__lowerCamelCase )

       if vision_config is None:
            SCREAMING_SNAKE_CASE__   =   {}
            logger.info('''vision_config is None. initializing the Blip2VisionConfig with default values.''' )

       if qformer_config is None:
            SCREAMING_SNAKE_CASE__   =   {}
            logger.info('''qformer_config is None. Initializing the Blip2QFormerConfig with default values.''' )

       if text_config is None:
            SCREAMING_SNAKE_CASE__   =   {}
            logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).''' )

       SCREAMING_SNAKE_CASE__   =   BlipaVisionConfig(**__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   BlipaQFormerConfig(**__lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
       SCREAMING_SNAKE_CASE__   =   CONFIG_MAPPING[text_model_type](**__lowerCamelCase )

       SCREAMING_SNAKE_CASE__   =   self.text_config.tie_word_embeddings
       SCREAMING_SNAKE_CASE__   =   self.text_config.is_encoder_decoder

       SCREAMING_SNAKE_CASE__   =   num_query_tokens
       SCREAMING_SNAKE_CASE__   =   self.vision_config.hidden_size
       SCREAMING_SNAKE_CASE__   =   self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
       SCREAMING_SNAKE_CASE__   =   1.0
       SCREAMING_SNAKE_CASE__   =   0.02






  @classmethod
  def 				lowercase_		(							cls						:		Tuple     ,       __lowerCamelCase						:		BlipaVisionConfig     ,       __lowerCamelCase						:		BlipaQFormerConfig     ,       __lowerCamelCase						:		PretrainedConfig     ,       **__lowerCamelCase						:		int     ,       )		->							List[str]:
       return cls(
           vision_config=vision_config.to_dict()     ,       qformer_config=qformer_config.to_dict()     ,       text_config=text_config.to_dict()     ,       **__lowerCamelCase     ,       )







  def 				lowercase_		(							self						:		Optional[Any] )		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__   =   copy.deepcopy(self.__dict__ )
       SCREAMING_SNAKE_CASE__   =   self.vision_config.to_dict()
       SCREAMING_SNAKE_CASE__   =   self.qformer_config.to_dict()
       SCREAMING_SNAKE_CASE__   =   self.text_config.to_dict()
       SCREAMING_SNAKE_CASE__   =   self.__class__.model_type
       return output





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


_SCREAMING_SNAKE_CASE					:  Dict             =      '''base_with_context'''
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




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

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

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

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

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']    )    )
     return model
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




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

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

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

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

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

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

     return model
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




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

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

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

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

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

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

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

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

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

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

     return model
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   checkpoints.load_tax_checkpoint(args.checkpoint_path    )
     SCREAMING_SNAKE_CASE__   =   jnp.tree_util.tree_map(onp.array				,		_A    )

     SCREAMING_SNAKE_CASE__   =   [
         '''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()''',
     ]

     SCREAMING_SNAKE_CASE__   =   os.path.join(args.checkpoint_path				,		'''..'''				,		'''config.gin'''    )
     SCREAMING_SNAKE_CASE__   =   inference.parse_training_gin_file(_A				,		_A    )
     SCREAMING_SNAKE_CASE__   =   inference.InferenceModel(args.checkpoint_path				,		_A    )

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

     SCREAMING_SNAKE_CASE__   =   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'''				,		)

     SCREAMING_SNAKE_CASE__   =   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'''				,		)

     SCREAMING_SNAKE_CASE__   =   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				,		)

     SCREAMING_SNAKE_CASE__   =   load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']				,		_A    )
     SCREAMING_SNAKE_CASE__   =   load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']				,		_A    )
     SCREAMING_SNAKE_CASE__   =   load_decoder(ta_checkpoint['''target''']['''decoder''']				,		_A    )

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

     SCREAMING_SNAKE_CASE__   =   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__":
       _SCREAMING_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.''',
       )
       _SCREAMING_SNAKE_CASE					:  Tuple             =      parser.parse_args()

       main(args)


