



'''simple docstring'''





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


__a  			=						logging.get_logger(__name__)

__a  			=						{
    "salesforce/blip2-opt-2.7b": "https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json",
}







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						"blip_2_vision_model"




    def __init__(       self   :	Tuple					,	snake_case_   :	List[Any]=1_408					,	snake_case_   :	Dict=6_144					,	snake_case_   :	Optional[Any]=39					,	snake_case_   :	Dict=16					,	snake_case_   :	Optional[int]=224					,	snake_case_   :	Any=14					,	snake_case_   :	List[Any]="gelu"					,	snake_case_   :	Optional[int]=0.00001					,	snake_case_   :	List[str]=0.0					,	snake_case_   :	Dict=1E-1_0					,	snake_case_   :	List[str]=True					,	**snake_case_   :	Optional[int]					,	):
          super().__init__(**snake_case_		)

          snake_case__    :       Any       		= hidden_size
          snake_case__    :       str       		= intermediate_size
          snake_case__    :       List[Any]       		= num_hidden_layers
          snake_case__    :       int       		= num_attention_heads
          snake_case__    :       int       		= patch_size
          snake_case__    :       int       		= image_size
          snake_case__    :       Any       		= initializer_range
          snake_case__    :       List[Any]       		= attention_dropout
          snake_case__    :       int       		= layer_norm_eps
          snake_case__    :       Optional[int]       		= hidden_act
          snake_case__    :       Any       		= qkv_bias




    @classmethod
    def lowerCamelCase			(       cls   :	Optional[Any]					,	snake_case_   :	Union[str, os.PathLike]					,	**snake_case_   :	int		):
          cls._set_token_in_kwargs(snake_case_		)

          snake_case__		,							snake_case__    :       Dict       		= cls.get_config_dict(snake_case_					,	**snake_case_		)

          # get the vision config dict if we are loading from Blip2Config
          if config_dict.get("""model_type"""		) == "blip-2":
                snake_case__    :       str       		= 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(snake_case_					,	**snake_case_		)







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						"blip_2_qformer"




    def __init__(       self   :	Optional[int]					,	snake_case_   :	Tuple=30_522					,	snake_case_   :	Optional[int]=768					,	snake_case_   :	int=12					,	snake_case_   :	Union[str, Any]=12					,	snake_case_   :	List[Any]=3_072					,	snake_case_   :	Optional[Any]="gelu"					,	snake_case_   :	Union[str, Any]=0.1					,	snake_case_   :	Optional[int]=0.1					,	snake_case_   :	Union[str, Any]=512					,	snake_case_   :	List[str]=0.02					,	snake_case_   :	Dict=1E-1_2					,	snake_case_   :	List[str]=0					,	snake_case_   :	Optional[int]="absolute"					,	snake_case_   :	Optional[Any]=2					,	snake_case_   :	Dict=1_408					,	**snake_case_   :	Union[str, Any]					,	):
          super().__init__(pad_token_id=snake_case_					,	**snake_case_		)

          snake_case__    :       Tuple       		= vocab_size
          snake_case__    :       Tuple       		= hidden_size
          snake_case__    :       List[Any]       		= num_hidden_layers
          snake_case__    :       int       		= num_attention_heads
          snake_case__    :       List[str]       		= hidden_act
          snake_case__    :       Optional[int]       		= intermediate_size
          snake_case__    :       str       		= hidden_dropout_prob
          snake_case__    :       Union[str, Any]       		= attention_probs_dropout_prob
          snake_case__    :       Dict       		= max_position_embeddings
          snake_case__    :       Union[str, Any]       		= initializer_range
          snake_case__    :       str       		= layer_norm_eps
          snake_case__    :       Optional[Any]       		= position_embedding_type
          snake_case__    :       List[str]       		= cross_attention_frequency
          snake_case__    :       Optional[int]       		= encoder_hidden_size




    @classmethod
    def lowerCamelCase			(       cls   :	Optional[Any]					,	snake_case_   :	Union[str, os.PathLike]					,	**snake_case_   :	Tuple		):
          cls._set_token_in_kwargs(snake_case_		)

          snake_case__		,							snake_case__    :       List[Any]       		= cls.get_config_dict(snake_case_					,	**snake_case_		)

          # get the qformer config dict if we are loading from Blip2Config
          if config_dict.get("""model_type"""		) == "blip-2":
                snake_case__    :       Any       		= 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(snake_case_					,	**snake_case_		)







class        UpperCAmelCase_			(			_a      ):
    """simple docstring"""







    lowercase   =						"blip-2"
    lowercase   =						True




    def __init__(       self   :	Optional[int]					,	snake_case_   :	int=None					,	snake_case_   :	Optional[int]=None					,	snake_case_   :	int=None					,	snake_case_   :	Any=32					,	**snake_case_   :	Tuple		):
          super().__init__(**snake_case_		)

          if vision_config is None:
                snake_case__    :       Optional[Any]       		= {}
                logger.info("""vision_config is None. initializing the Blip2VisionConfig with default values."""		)

          if qformer_config is None:
                snake_case__    :       Any       		= {}
                logger.info("""qformer_config is None. Initializing the Blip2QFormerConfig with default values."""		)

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

          snake_case__    :       List[str]       		= BlipaVisionConfig(**snake_case_		)
          snake_case__    :       str       		= BlipaQFormerConfig(**snake_case_		)
          snake_case__    :       int       		= text_config["""model_type"""] if """model_type""" in text_config else """opt"""
          snake_case__    :       Optional[Any]       		= CONFIG_MAPPING[text_model_type](**snake_case_		)

          snake_case__    :       Any       		= self.text_config.tie_word_embeddings
          snake_case__    :       int       		= self.text_config.is_encoder_decoder

          snake_case__    :       Dict       		= num_query_tokens
          snake_case__    :       Dict       		= self.vision_config.hidden_size
          snake_case__    :       List[Any]       		= self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
          snake_case__    :       List[Any]       		= 1.0
          snake_case__    :       Union[str, Any]       		= 0.02




    @classmethod
    def lowerCamelCase			(       cls   :	Optional[int]					,	snake_case_   :	BlipaVisionConfig					,	snake_case_   :	BlipaQFormerConfig					,	snake_case_   :	PretrainedConfig					,	**snake_case_   :	List[Any]					,	):
          return cls(
              vision_config=vision_config.to_dict()					,	qformer_config=qformer_config.to_dict()					,	text_config=text_config.to_dict()					,	**snake_case_					,	)




    def lowerCamelCase			(       self   :	List[Any]		):
          snake_case__    :       Union[str, Any]       		= copy.deepcopy(self.__dict__		)
          snake_case__    :       Dict       		= self.vision_config.to_dict()
          snake_case__    :       Dict       		= self.qformer_config.to_dict()
          snake_case__    :       Optional[int]       		= self.text_config.to_dict()
          snake_case__    :       Optional[Any]       		= self.__class__.model_type
          return output





'''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 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					Union[str, Any]:
      snake_case__    :       Optional[Any]       		= nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]						)						)
      snake_case__    :       Dict       		= 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__    :       Dict       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]						)						)

            snake_case__    :       List[str]       		= ly_weight["""attention"""]
            snake_case__    :       List[str]       		= nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
            snake_case__    :       Optional[Any]       		= nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
            snake_case__    :       List[str]       		= nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
            snake_case__    :       List[str]       		= nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)

            snake_case__    :       int       		= nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]						)						)

            snake_case__    :       List[str]       		= 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__    :       Union[str, Any]       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T						)						)

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





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					List[Any]:
      snake_case__    :       Tuple       		= nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T						)						)

      snake_case__    :       str       		= nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]						)	,  requires_grad=_lowerCAmelCase						)

      for lyr_num, lyr in enumerate(model.encoders						):
            snake_case__    :       List[Any]       		= weights[f"layers_{lyr_num}"]
            snake_case__    :       List[str]       		= ly_weight["""attention"""]

            snake_case__    :       List[str]       		= nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
            snake_case__    :       Dict       		= nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
            snake_case__    :       Tuple       		= nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
            snake_case__    :       Any       		= nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)
            snake_case__    :       Optional[Any]       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]						)						)

            snake_case__    :       Dict       		= 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__    :       Dict       		= 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__    :       str       		= nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]						)						)

      return model





def 		__snake_case(							_lowerCAmelCase	,  _lowerCAmelCase						)						->					List[str]:
      snake_case__    :       int       		= nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T						)						)
      snake_case__    :       List[Any]       		= nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T						)						)

      snake_case__    :       List[Any]       		= nn.Parameter(
          torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]						)	,  requires_grad=_lowerCAmelCase						)

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

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

            snake_case__    :       List[str]       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T						)						)

            snake_case__    :       Any       		= ly_weight["""self_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__    :       Tuple       		= nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
            snake_case__    :       Optional[Any]       		= nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)

            snake_case__    :       int       		= ly_weight["""MultiHeadDotProductAttention_0"""]
            snake_case__    :       int       		= nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T						)						)
            snake_case__    :       List[Any]       		= nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T						)						)
            snake_case__    :       Any       		= nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T						)						)
            snake_case__    :       List[Any]       		= nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T						)						)
            snake_case__    :       List[str]       		= 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__    :       Union[str, Any]       		= nn.Parameter(
                torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T						)						)
            snake_case__    :       str       		= nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T						)						)
            snake_case__    :       Any       		= 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__    :       Tuple       		= nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]						)						)

      snake_case__    :       int       		= nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T						)						)

      return model





def 		__snake_case(							_lowerCAmelCase						)						->					Union[str, Any]:
      snake_case__    :       Tuple       		= checkpoints.load_tax_checkpoint(args.checkpoint_path						)
      snake_case__    :       List[str]       		= jnp.tree_util.tree_map(onp.array	,  _lowerCAmelCase						)

      snake_case__    :       int       		= [
          """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__    :       Any       		= os.path.join(args.checkpoint_path	,  """.."""	,  """config.gin"""						)
      snake_case__    :       Dict       		= inference.parse_training_gin_file(_lowerCAmelCase	,  _lowerCAmelCase						)
      snake_case__    :       int       		= inference.InferenceModel(args.checkpoint_path	,  _lowerCAmelCase						)

      snake_case__    :       Optional[int]       		= DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""	,  variance_type="""fixed_large"""						)

      snake_case__    :       Union[str, 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"""	,  )

      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__    :       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	,  )

      snake_case__    :       Optional[int]       		= load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]	,  _lowerCAmelCase						)
      snake_case__    :       Union[str, Any]       		= load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]	,  _lowerCAmelCase						)
      snake_case__    :       Optional[Any]       		= load_decoder(ta_checkpoint["""target"""]["""decoder"""]	,  _lowerCAmelCase						)

      snake_case__    :       str       		= OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder"""						)

      snake_case__    :       Dict       		= 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__":
       __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)
