


"""simple docstring"""
import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
			import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE :    Optional[int]									=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE :    Optional[int]									=				{"""vocab_file""": """vocab.txt"""}

SCREAMING_SNAKE_CASE :    Optional[Any]									=				{
    """vocab_file""": {
        """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt""",
    },
}

SCREAMING_SNAKE_CASE :    List[Any]									=				{
    """openbmb/cpm-ant-10b""": 1024,
}






def 		lowercase							(     _snake_case						:      List[str]     )     ->Optional[Any]:
	"""simple docstring"""





	__snake_case	:						int						=				collections.OrderedDict()
	with open(_snake_case     ,   '''r'''     ,   encoding='''utf-8'''     ) as reader:
		__snake_case	:						Optional[int]						=				reader.readlines()
	for index, token in enumerate(_snake_case     ):
		__snake_case	:						Optional[Any]						=				token.rstrip('''\n'''     )
		__snake_case	:						int						=				index
	return vocab



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		def __init__(self				,       a_				,       a_="<unk>"				,       a_=2_00					):





			'''simple docstring'''

			__snake_case	:						Any						=				vocab
			__snake_case	:						str						=				unk_token
			__snake_case	:						Tuple						=				max_input_chars_per_word



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			__snake_case	:						str						=				list(a_					)
			if len(a_					) > self.max_input_chars_per_word:
				return [self.unk_token]

			__snake_case	:						List[Any]						=				0
			__snake_case	:						Optional[Any]						=				[]
			while start < len(a_					):
				__snake_case	:						List[str]						=				len(a_					)
				__snake_case	:						List[Any]						=				None
				while start < end:
					__snake_case	:						int						=				''''''.join(chars[start:end]					)
					if substr in self.vocab:
						__snake_case	:						Dict						=				substr
						break
					end -= 1
				if cur_substr is None:
					sub_tokens.append(self.unk_token					)
					start += 1
				else:
					sub_tokens.append(a_					)
					__snake_case	:						List[str]						=				end

			return sub_tokens



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				=VOCAB_FILES_NAMES
		lowerCamelCase__				=PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase__				=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase__				=['input_ids', 'attention_mask']
		lowerCamelCase__				=False



		def __init__(self				,       a_				,       a_="<d>"				,       a_="</d>"				,       a_="<s>"				,       a_="</s>"				,       a_="<pad>"				,       a_="<unk>"				,       a_="</n>"				,       a_="</_>"				,       a_="left"				,       **a_				,       ):





			'''simple docstring'''

			requires_backends(self				,       ['''jieba''']					)
			super().__init__(
			    bod_token=a_				,       eod_token=a_				,       bos_token=a_				,       eos_token=a_				,       pad_token=a_				,       unk_token=a_				,       line_token=a_				,       space_token=a_				,       padding_side=a_				,       **a_				,       )
			__snake_case	:						Union[str, Any]						=				bod_token
			__snake_case	:						List[Any]						=				eod_token
			__snake_case	:						List[Any]						=				load_vocab(a_					)
			__snake_case	:						Dict						=				self.encoder[space_token]
			__snake_case	:						Any						=				self.encoder[line_token]

			del self.encoder[space_token]
			del self.encoder[line_token]

			__snake_case	:						List[str]						=				collections.OrderedDict(sorted(self.encoder.items()				,       key=lambda a_					: x[1]					)					)
			__snake_case	:						str						=				{v: k for k, v in self.encoder.items()}

			__snake_case	:						List[Any]						=				WordpieceTokenizer(vocab=self.encoder				,       unk_token=self.unk_token					)



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return self.encoder[self.bod_token]



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return self.encoder[self.eod_token]



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return self.encoder["\n"]



		@property
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return len(self.encoder					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			return dict(self.encoder				,       **self.added_tokens_encoder					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			__snake_case	:						Any						=				[]
			for x in jieba.cut(a_				,       cut_all=a_					):
				output_tokens.extend(self.wordpiece_tokenizer.tokenize(a_					)					)
			return output_tokens



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       **a_					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				[i for i in token_ids if i >= 0]
			__snake_case	:						Tuple						=				[
			    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
			]
			return super()._decode(a_				,       **a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			return token in self.encoder



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			return "".join(a_					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			return self.encoder.get(a_				,       self.encoder.get(self.unk_token					)					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_					):





			'''simple docstring'''

			return self.decoder.get(a_				,       self.unk_token					)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None					):





			'''simple docstring'''

			if os.path.isdir(a_					):
				__snake_case	:						Optional[Any]						=				os.path.join(
				    a_				,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
			else:
				__snake_case	:						Optional[Any]						=				(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
			__snake_case	:						List[str]						=				0
			if " " in self.encoder:
				__snake_case	:						Optional[Any]						=				self.encoder[''' ''']
				del self.encoder[" "]
			if "\n" in self.encoder:
				__snake_case	:						int						=				self.encoder['''\n''']
				del self.encoder["\n"]
			__snake_case	:						Dict						=				collections.OrderedDict(sorted(self.encoder.items()				,       key=lambda a_					: x[1]					)					)
			with open(a_				,       '''w'''				,       encoding='''utf-8'''					) as writer:
				for token, token_index in self.encoder.items():
					if index != token_index:
						logger.warning(
						    f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
						    ''' Please check that the vocabulary is not corrupted!'''					)
						__snake_case	:						Union[str, Any]						=				token_index
					writer.write(token + '''\n'''					)
					index += 1
			return (vocab_file,)



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None					):





			'''simple docstring'''

			if token_ids_a is None:
				return [self.bos_token_id] + token_ids_a
			return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_ = None				,       a_ = False					):





			'''simple docstring'''

			if already_has_special_tokens:
				return super().get_special_tokens_mask(
				    token_ids_a=a_				,       token_ids_a=a_				,       already_has_special_tokens=a_					)

			if token_ids_a is not None:
				return [1] + ([0] * len(a_					)) + [1] + ([0] * len(a_					))
			return [1] + ([0] * len(a_					))






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


UpperCamelCase__											=					'base_with_context'




def    lowerCAmelCase_ (     __A,  __A							)     ->				int:







      '''simple docstring'''


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

            UpperCAmelCase__								=  ly_weight["attention"]
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T							)							)

            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]							)							)

            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T							)							)

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




def    lowerCAmelCase_ (     __A,  __A							)     ->				Tuple:







      '''simple docstring'''


      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T							)							)

      UpperCAmelCase__								=  nn.Parameter(
          torch.FloatTensor(weights["Embed_0"]["embedding"]							),  requires_grad=__A							)

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

            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(
                torch.FloatTensor(ly_weight["pre_attention_layer_norm"]["scale"]							)							)

            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]							)							)

      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["encoder_norm"]["scale"]							)							)

      return model




def    lowerCAmelCase_ (     __A,  __A							)     ->				List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["time_emb_dense0"]["kernel"].T							)							)
      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["time_emb_dense1"]["kernel"].T							)							)

      UpperCAmelCase__								=  nn.Parameter(
          torch.FloatTensor(weights["Embed_0"]["embedding"]							),  requires_grad=__A							)

      UpperCAmelCase__								=  nn.Parameter(
          torch.FloatTensor(weights["continuous_inputs_projection"]["kernel"].T							)							)

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

            UpperCAmelCase__								=  nn.Parameter(
                torch.FloatTensor(ly_weight["FiLMLayer_0"]["DenseGeneral_0"]["kernel"].T							)							)

            UpperCAmelCase__								=  ly_weight["self_attention"]
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T							)							)

            UpperCAmelCase__								=  ly_weight["MultiHeadDotProductAttention_0"]
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["query"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["key"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["value"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(attention_weights["out"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(
                torch.FloatTensor(ly_weight["pre_cross_attention_layer_norm"]["scale"]							)							)

            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["pre_mlp_layer_norm"]["scale"]							)							)
            UpperCAmelCase__								=  nn.Parameter(
                torch.FloatTensor(ly_weight["FiLMLayer_1"]["DenseGeneral_0"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_0"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wi_1"]["kernel"].T							)							)
            UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(ly_weight["mlp"]["wo"]["kernel"].T							)							)

      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["decoder_norm"]["scale"]							)							)

      UpperCAmelCase__								=  nn.Parameter(torch.FloatTensor(weights["spec_out_dense"]["kernel"].T							)							)

      return model




def    lowerCAmelCase_ (     __A							)     ->				int:







      '''simple docstring'''


      UpperCAmelCase__								=  checkpoints.load_tax_checkpoint(args.checkpoint_path							)
      UpperCAmelCase__								=  jnp.tree_util.tree_map(onp.array,  __A							)

      UpperCAmelCase__								=  [
          "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()",
      ]

      UpperCAmelCase__								=  os.path.join(args.checkpoint_path,  "..",  "config.gin"							)
      UpperCAmelCase__								=  inference.parse_training_gin_file(__A,  __A							)
      UpperCAmelCase__								=  inference.InferenceModel(args.checkpoint_path,  __A							)

      UpperCAmelCase__								=  DDPMScheduler(beta_schedule="squaredcos_cap_v2",  variance_type="fixed_large"							)

      UpperCAmelCase__								=  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",  )

      UpperCAmelCase__								=  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",  )

      UpperCAmelCase__								=  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,  )

      UpperCAmelCase__								=  load_notes_encoder(ta_checkpoint["target"]["token_encoder"],  __A							)
      UpperCAmelCase__								=  load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"],  __A							)
      UpperCAmelCase__								=  load_decoder(ta_checkpoint["target"]["decoder"],  __A							)

      UpperCAmelCase__								=  OnnxRuntimeModel.from_pretrained("kashif/soundstream_mel_decoder"							)

      UpperCAmelCase__								=  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__":
      UpperCamelCase__											=					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.',
      )
      UpperCamelCase__											=					parser.parse_args()

      main(args)



