



'''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


lowerCAmelCase	:		str				=   '''base_with_context'''



def   __lowerCAmelCase					(							lowerCamelCase   :						Union[str, Any]				,		lowerCamelCase   :						Dict					):






	'''simple docstring'''




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

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

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

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

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



def   __lowerCAmelCase					(							lowerCamelCase   :						int				,		lowerCamelCase   :						Dict					):






	'''simple docstring'''




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

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

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

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

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

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

	return model



def   __lowerCAmelCase					(							lowerCamelCase   :						Tuple				,		lowerCamelCase   :						Union[str, Any]					):






	'''simple docstring'''




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

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

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

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

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

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

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

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

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

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

	return model



def   __lowerCAmelCase					(							lowerCamelCase   :						Any					):






	'''simple docstring'''




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

	__lowerCAmelCase												=  [
	    "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()",
	]

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

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

	__lowerCAmelCase												=  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"				,		)

	__lowerCAmelCase												=  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"				,		)

	__lowerCAmelCase												=  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				,		)

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

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

	__lowerCAmelCase												=  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__":
	lowerCAmelCase	:		Optional[int]				=   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.''',
	)
	lowerCAmelCase	:		Optional[int]				=   parser.parse_args()

	main(args)




'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


lowerCAmelCase	:		Optional[Any]				=   logging.get_logger(__name__)

lowerCAmelCase	:		Optional[Any]				=   {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCAmelCase	:		str				=   {
    '''vocab_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'''
        ),
        '''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt''',
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''squeezebert/squeezebert-uncased''': (
            '''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'''
        ),
        '''squeezebert/squeezebert-mnli-headless''': (
            '''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCAmelCase	:		Optional[Any]				=   {
    '''squeezebert/squeezebert-uncased''': 5_1_2,
    '''squeezebert/squeezebert-mnli''': 5_1_2,
    '''squeezebert/squeezebert-mnli-headless''': 5_1_2,
}


lowerCAmelCase	:		Tuple				=   {
    '''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
    '''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     Dict        =							VOCAB_FILES_NAMES
					a      :     Any        =							PRETRAINED_VOCAB_FILES_MAP
					a      :     Dict        =							PRETRAINED_INIT_CONFIGURATION
					a      :     str        =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a      :     Optional[Any]        =							SqueezeBertTokenizer



					def __init__(    self			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=True			,				UpperCamelCase="[UNK]"			,				UpperCamelCase="[SEP]"			,				UpperCamelCase="[PAD]"			,				UpperCamelCase="[CLS]"			,				UpperCamelCase="[MASK]"			,				UpperCamelCase=True			,				UpperCamelCase=None			,				**UpperCamelCase			,				)     ->			List[Any]:
						super().__init__(
						    UpperCamelCase			,				tokenizer_file=UpperCamelCase			,				do_lower_case=UpperCamelCase			,				unk_token=UpperCamelCase			,				sep_token=UpperCamelCase			,				pad_token=UpperCamelCase			,				cls_token=UpperCamelCase			,				mask_token=UpperCamelCase			,				tokenize_chinese_chars=UpperCamelCase			,				strip_accents=UpperCamelCase			,				**UpperCamelCase			,				)

						__lowerCAmelCase												=  json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
						if (
						    normalizer_state.get("lowercase"			,				UpperCamelCase     ) != do_lower_case
						    or normalizer_state.get("strip_accents"			,				UpperCamelCase     ) != strip_accents
						    or normalizer_state.get("handle_chinese_chars"			,				UpperCamelCase     ) != tokenize_chinese_chars
						):
							__lowerCAmelCase												=  getattr(UpperCamelCase			,				normalizer_state.pop("type"     )     )
							__lowerCAmelCase												=  do_lower_case
							__lowerCAmelCase												=  strip_accents
							__lowerCAmelCase												=  tokenize_chinese_chars
							__lowerCAmelCase												=  normalizer_class(**UpperCamelCase     )

						__lowerCAmelCase												=  do_lower_case



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase=None     )     ->			str:
						__lowerCAmelCase												=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
							output += token_ids_a + [self.sep_token_id]

						return output



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = None     )     ->			List[int]:
						__lowerCAmelCase												=  [self.sep_token_id]
						__lowerCAmelCase												=  [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     ) * [0] + len(token_ids_a + sep     ) * [1]



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = None     )     ->			Tuple[str]:
						__lowerCAmelCase												=  self._tokenizer.model.save(UpperCamelCase			,				name=UpperCamelCase     )
						return tuple(UpperCamelCase     )