
from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE				:Dict									=							logging.get_logger(__name__)


SCREAMING_SNAKE_CASE				:str									=							{
    'nielsr/canine-s': 2048,
}

# Unicode defines 1,114,112 total “codepoints”
SCREAMING_SNAKE_CASE				:int									=							111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
SCREAMING_SNAKE_CASE				:int									=							0

SCREAMING_SNAKE_CASE				:Optional[int]									=							0xe_000
SCREAMING_SNAKE_CASE				:Dict									=							0xe_001
SCREAMING_SNAKE_CASE				:List[Any]									=							0xe_002
SCREAMING_SNAKE_CASE				:int									=							0xe_003
SCREAMING_SNAKE_CASE				:Any									=							0xe_004

# Maps special codepoints to human-readable names.
SCREAMING_SNAKE_CASE				:Dict[int, str]									=							{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
SCREAMING_SNAKE_CASE				:Dict[str, int]									=							{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}





class 				UpperCAmelCase (    __SCREAMING_SNAKE_CASE    ):




	'''simple docstring'''





	snake_case_          =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES






	def __init__(			self   :					Union[str, Any]				,A   :					Union[str, Any]=chr(A					)				,A   :					Optional[Any]=chr(A					)				,A   :					List[str]=chr(A					)				,A   :					Optional[int]=chr(A					)				,A   :					Any=chr(A					)				,A   :					int=chr(A					)				,A   :					int=False				,A   :					List[str]=20_48				,**A   :					Any				,):
					__A       =					AddedToken(A				,lstrip=A				,rstrip=A					) if isinstance(A				,A					) else bos_token
					__A       =					AddedToken(A				,lstrip=A				,rstrip=A					) if isinstance(A				,A					) else eos_token
					__A       =					AddedToken(A				,lstrip=A				,rstrip=A					) if isinstance(A				,A					) else sep_token
					__A       =					AddedToken(A				,lstrip=A				,rstrip=A					) if isinstance(A				,A					) else cls_token
					__A       =					AddedToken(A				,lstrip=A				,rstrip=A					) if isinstance(A				,A					) else pad_token

					# Mask token behave like a normal word, i.e. include the space before it
					__A       =					AddedToken(A				,lstrip=A				,rstrip=A					) if isinstance(A				,A					) else mask_token

					super().__init__(
					    bos_token=A				,eos_token=A				,sep_token=A				,cls_token=A				,pad_token=A				,mask_token=A				,add_prefix_space=A				,model_max_length=A				,**A				,)

					# Creates a mapping for looking up the IDs of special symbols.
					__A       =					{}
					for codepoint, name in SPECIAL_CODEPOINTS.items():
									__A       =					codepoint

					# Creates a mapping for looking up the string forms of special symbol IDs.
					__A       =					{
					    codepoint: name for name, codepoint in self._special_codepoints.items()
					}

					__A       =					UNICODE_VOCAB_SIZE
					__A       =					len(self._special_codepoints					)






	@property
	def 							UpperCamelCase_    (			self   :					Any					):
					return self._unicode_vocab_size






	def 							UpperCamelCase_    (			self   :					Dict				,A   :					str					):
					return list(A					)






	def 							UpperCamelCase_    (			self   :					Any				,A   :					str					):
					try:
									return ord(A					)
					except TypeError:
									raise ValueError(f'''invalid token: \'{token}\''''					)






	def 							UpperCamelCase_    (			self   :					Union[str, Any]				,A   :					int					):
					try:
									if index in SPECIAL_CODEPOINTS:
													return SPECIAL_CODEPOINTS[index]
									return chr(A					)
					except TypeError:
									raise ValueError(f'''invalid id: {index}'''					)






	def 							UpperCamelCase_    (			self   :					Optional[int]				,A   :					Optional[int]					):
					return "".join(A					)






	def 							UpperCamelCase_    (			self   :					Any				,A   :					List[int]				,A   :					Optional[List[int]] = None					):
					__A       =					[self.sep_token_id]
					__A       =					[self.cls_token_id]

					__A       =					cls + token_ids_a + sep
					if token_ids_a is not None:
									result += token_ids_a + sep
					return result






	def 							UpperCamelCase_    (			self   :					int				,A   :					List[int]				,A   :					Optional[List[int]] = None				,A   :					bool = False					):
					if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=A				,token_ids_a=A				,already_has_special_tokens=A					)

					__A       =					[1] + ([0] * len(A					)) + [1]
					if token_ids_a is not None:
									result += ([0] * len(A					)) + [1]
					return result






	def 							UpperCamelCase_    (			self   :					Tuple				,A   :					List[int]				,A   :					Optional[List[int]] = None					):
					__A       =					[self.sep_token_id]
					__A       =					[self.cls_token_id]

					__A       =					len(cls + token_ids_a + sep					) * [0]
					if token_ids_a is not None:
									result += len(token_ids_a + sep					) * [1]
					return result







	def 							UpperCamelCase_    (			self   :					Dict				,A   :					str				,A   :					Optional[str] = None					):
					return ()





'''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)
