"""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


SCREAMING_SNAKE_CASE__:Dict			=	"""base_with_context"""



def 					_lowerCamelCase(					a ,  a							):
							__a     				=  nn.Parameter(torch.FloatTensor(weights["token_embedder"]["embedding"]							)							)
							__a     				=  nn.Parameter(
							    torch.FloatTensor(weights["Embed_0"]["embedding"]							) ,  requires_grad=__UpperCamelCase							)
							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 					_lowerCamelCase(					a ,  a							):
							__a     				=  nn.Parameter(torch.FloatTensor(weights["input_proj"]["kernel"].T							)							)

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

							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 					_lowerCamelCase(					a ,  a							):
							__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=__UpperCamelCase							)

							__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 					_lowerCamelCase(					a							):
							__a     				=  checkpoints.load_tax_checkpoint(args.checkpoint_path							)
							__a     				=  jnp.tree_util.tree_map(onp.array ,  __UpperCamelCase							)

							__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(__UpperCamelCase ,  __UpperCamelCase							)
							__a     				=  inference.InferenceModel(args.checkpoint_path ,  __UpperCamelCase							)

							__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"] ,  __UpperCamelCase							)
							__a     				=  load_continuous_encoder(ta_checkpoint["target"]["continuous_encoder"] ,  __UpperCamelCase							)
							__a     				=  load_decoder(ta_checkpoint["target"]["decoder"] ,  __UpperCamelCase							)

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

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


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE__:List[Any]			=	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__:Dict			=	parser.parse_args()

			main(args)

"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_camembert import CamembertTokenizer
else:
   SCREAMING_SNAKE_CASE__:List[Any]			=	None


SCREAMING_SNAKE_CASE__:Union[str, Any]			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:Tuple			=	{"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_file""": {
        """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
    },
}

SCREAMING_SNAKE_CASE__:List[str]			=	{
    """camembert-base""": 512,
}

SCREAMING_SNAKE_CASE__:str			=	"""▁"""






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			List[Any]           =		VOCAB_FILES_NAMES
 _snake_case   :			List[Any]           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			Optional[int]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case   :			Any           =		["""input_ids""", """attention_mask"""]
 _snake_case   :			str           =		CamembertTokenizer


 def __init__(				self  ,						lowerCamelCase=None  ,						lowerCamelCase=None  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="</s>"  ,						lowerCamelCase="<s>"  ,						lowerCamelCase="<unk>"  ,						lowerCamelCase="<pad>"  ,						lowerCamelCase="<mask>"  ,						lowerCamelCase=["<s>NOTUSED", "</s>NOTUSED"]  ,						**lowerCamelCase  ,						):
        # Mask token behave like a normal word, i.e. include the space before it
        __a     				=  AddedToken(lowerCamelCase  ,						lstrip=lowerCamelCase  ,						rstrip=lowerCamelCase		) if isinstance(lowerCamelCase  ,						lowerCamelCase		) else mask_token

        super().__init__(
            lowerCamelCase  ,						tokenizer_file=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						sep_token=lowerCamelCase  ,						cls_token=lowerCamelCase  ,						unk_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						mask_token=lowerCamelCase  ,						additional_special_tokens=lowerCamelCase  ,						**lowerCamelCase  ,						)

        __a     				=  vocab_file
        __a     				=  False if not self.vocab_file else True


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):

        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __a     				=  [self.cls_token_id]
        __a     				=  [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        __a     				=  [self.sep_token_id]
        __a     				=  [self.cls_token_id]

        if token_ids_a is None:
               return len(cls + token_ids_a + sep		) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if not self.can_save_slow_tokenizer:
               raise ValueError(
                   "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                   "tokenizer."		)

        if not os.path.isdir(lowerCamelCase		):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
               return
        __a     				=  os.path.join(
            lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

        if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCamelCase		):
               copyfile(self.vocab_file  ,						lowerCamelCase		)

        return (out_vocab_file,)
