



'''simple docstring'''




import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
						import tensorflow as tf






class 						_UpperCamelCase  (							enum.Enum    ):


			'''simple docstring'''

			_A      :		Optional[Any]			    =		0
			_A      :		Optional[int]			    =		1
			_A      :		str			    =		2






@add_end_docstrings(lowerCamelCase__    )
class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		str			    =		'''
    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The
    voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western
    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision
    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an
    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,
    begging for his blessing. <eod> </s> <eos>
    '''

			def __init__(							self     : Dict							,     *lowerCAmelCase__     : Any							,     **lowerCAmelCase__     : Union[str, Any]						):





					"""simple docstring"""






					super().__init__(*lowerCAmelCase__							,     **lowerCAmelCase__						)
					self.check_model_type(
					    TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == """tf""" else MODEL_FOR_CAUSAL_LM_MAPPING						)
					if "prefix" not in self._preprocess_params:
							# This is very specific. The logic is quite complex and needs to be done
							# as a "default".
							# It also defines both some preprocess_kwargs and generate_kwargs
							# which is why we cannot put them in their respective methods.
							__SCREAMING_SNAKE_CASE       :		List[Any]           =		None
							if self.model.config.prefix is not None:
									__SCREAMING_SNAKE_CASE       :		Tuple           =		self.model.config.prefix
							if prefix is None and self.model.__class__.__name__ in [
							    "XLNetLMHeadModel",
							    "TransfoXLLMHeadModel",
							    "TFXLNetLMHeadModel",
							    "TFTransfoXLLMHeadModel",
							]:
									# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
									__SCREAMING_SNAKE_CASE       :		List[Any]           =		self.XL_PREFIX
							if prefix is not None:
									# Recalculate some generate_kwargs linked to prefix.
									__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		int           =		self._sanitize_parameters(prefix=lowerCAmelCase__							,     **self._forward_params						)
									__SCREAMING_SNAKE_CASE       :		Tuple           =		{**self._preprocess_params, **preprocess_params}
									__SCREAMING_SNAKE_CASE       :		List[str]           =		{**self._forward_params, **forward_params}

			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : Tuple=None							,     lowerCAmelCase__     : List[str]=None							,     lowerCAmelCase__     : int=None							,     lowerCAmelCase__     : int=None							,     lowerCAmelCase__     : Optional[int]=None							,     lowerCAmelCase__     : str=None							,     lowerCAmelCase__     : List[Any]=None							,     lowerCAmelCase__     : Any=None							,     **lowerCAmelCase__     : Any							,     ):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		{}
					if prefix is not None:
							__SCREAMING_SNAKE_CASE       :		Dict           =		prefix
					if prefix:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.tokenizer(
							    lowerCAmelCase__							,     padding=lowerCAmelCase__							,     add_special_tokens=lowerCAmelCase__							,     return_tensors=self.framework						)
							__SCREAMING_SNAKE_CASE       :		List[str]           =		prefix_inputs["""input_ids"""].shape[-1]

					if handle_long_generation is not None:
							if handle_long_generation not in {"hole"}:
									raise ValueError(
									    F"{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"
									    """ [None, 'hole']"""						)
							__SCREAMING_SNAKE_CASE       :		int           =		handle_long_generation

					preprocess_params.update(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		generate_kwargs

					__SCREAMING_SNAKE_CASE       :		List[Any]           =		{}
					if return_full_text is not None and return_type is None:
							if return_text is not None:
									raise ValueError("""`return_text` is mutually exclusive with `return_full_text`"""						)
							if return_tensors is not None:
									raise ValueError("""`return_full_text` is mutually exclusive with `return_tensors`"""						)
							__SCREAMING_SNAKE_CASE       :		Dict           =		ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
					if return_tensors is not None and return_type is None:
							if return_text is not None:
									raise ValueError("""`return_text` is mutually exclusive with `return_tensors`"""						)
							__SCREAMING_SNAKE_CASE       :		Any           =		ReturnType.TENSORS
					if return_type is not None:
							__SCREAMING_SNAKE_CASE       :		Tuple           =		return_type
					if clean_up_tokenization_spaces is not None:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		clean_up_tokenization_spaces

					if stop_sequence is not None:
							__SCREAMING_SNAKE_CASE       :		str           =		self.tokenizer.encode(lowerCAmelCase__							,     add_special_tokens=lowerCAmelCase__						)
							if len(lowerCAmelCase__						) > 1:
									warnings.warn(
									    """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
									    """ the stop sequence will be used as the stop sequence string in the interim."""						)
							__SCREAMING_SNAKE_CASE       :		int           =		stop_sequence_ids[0]

					return preprocess_params, forward_params, postprocess_params

			def UpperCamelCase__					(							self     : Tuple							,     *lowerCAmelCase__     : Dict							,     **lowerCAmelCase__     : Optional[int]						):





					"""simple docstring"""






					if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
							kwargs.update({"""add_space_before_punct_symbol""": True}						)

					return super()._parse_and_tokenize(*lowerCAmelCase__							,     **lowerCAmelCase__						)

			def __call__(							self     : str							,     lowerCAmelCase__     : int							,     **lowerCAmelCase__     : str						):





					"""simple docstring"""






					return super().__call__(lowerCAmelCase__							,     **lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Tuple							,     lowerCAmelCase__     : List[Any]							,     lowerCAmelCase__     : Dict=""							,     lowerCAmelCase__     : List[str]=None							,     **lowerCAmelCase__     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.tokenizer(
					    prefix + prompt_text							,     padding=lowerCAmelCase__							,     add_special_tokens=lowerCAmelCase__							,     return_tensors=self.framework						)
					__SCREAMING_SNAKE_CASE       :		Any           =		prompt_text

					if handle_long_generation == "hole":
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		inputs["""input_ids"""].shape[-1]
							if "max_new_tokens" in generate_kwargs:
									__SCREAMING_SNAKE_CASE       :		List[str]           =		generate_kwargs["""max_new_tokens"""]
							else:
									__SCREAMING_SNAKE_CASE       :		str           =		generate_kwargs.get("""max_length"""							,     self.model.config.max_length						) - cur_len
									if new_tokens < 0:
											raise ValueError("""We cannot infer how many new tokens are expected"""						)
							if cur_len + new_tokens > self.tokenizer.model_max_length:
									__SCREAMING_SNAKE_CASE       :		Optional[int]           =		self.tokenizer.model_max_length - new_tokens
									if keep_length <= 0:
											raise ValueError(
											    """We cannot use `hole` to handle this generation the number of desired tokens exceeds the"""
											    """ models max length"""						)

									__SCREAMING_SNAKE_CASE       :		Optional[int]           =		inputs["""input_ids"""][:, -keep_length:]
									if "attention_mask" in inputs:
											__SCREAMING_SNAKE_CASE       :		Optional[int]           =		inputs["""attention_mask"""][:, -keep_length:]

					return inputs

			def UpperCamelCase__					(							self     : List[str]							,     lowerCAmelCase__     : str							,     **lowerCAmelCase__     : Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		model_inputs["""input_ids"""]
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		model_inputs.get("""attention_mask"""							,     lowerCAmelCase__						)
					# Allow empty prompts
					if input_ids.shape[1] == 0:
							__SCREAMING_SNAKE_CASE       :		Tuple           =		None
							__SCREAMING_SNAKE_CASE       :		List[str]           =		None
							__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		1
					else:
							__SCREAMING_SNAKE_CASE       :		Any           =		input_ids.shape[0]
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		model_inputs.pop("""prompt_text"""						)

					# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
					# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		generate_kwargs.pop("""prefix_length"""							,     0						)
					if prefix_length > 0:
							__SCREAMING_SNAKE_CASE       :		str           =		"""max_new_tokens""" in generate_kwargs or (
							    """generation_config""" in generate_kwargs
							    and generate_kwargs["""generation_config"""].max_new_tokens is not None
							)
							if not has_max_new_tokens:
									__SCREAMING_SNAKE_CASE       :		Tuple           =		generate_kwargs.get("""max_length"""						) or self.model.config.max_length
									generate_kwargs["max_length"] += prefix_length
							__SCREAMING_SNAKE_CASE       :		str           =		"""min_new_tokens""" in generate_kwargs or (
							    """generation_config""" in generate_kwargs
							    and generate_kwargs["""generation_config"""].min_new_tokens is not None
							)
							if not has_min_new_tokens and "min_length" in generate_kwargs:
									generate_kwargs["min_length"] += prefix_length

        # BS x SL
					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		self.model.generate(input_ids=lowerCAmelCase__							,     attention_mask=lowerCAmelCase__							,     **lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Tuple           =		generated_sequence.shape[0]
					if self.framework == "pt":
							__SCREAMING_SNAKE_CASE       :		List[str]           =		generated_sequence.reshape(lowerCAmelCase__							,     out_b // in_b							,     *generated_sequence.shape[1:]						)
					elif self.framework == "tf":
							__SCREAMING_SNAKE_CASE       :		Dict           =		tf.reshape(lowerCAmelCase__							,     (in_b, out_b // in_b, *generated_sequence.shape[1:])						)
					return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}



			def UpperCamelCase__					(							self     : List[str]							,     lowerCAmelCase__     : Tuple							,     lowerCAmelCase__     : Dict=ReturnType.FULL_TEXT							,     lowerCAmelCase__     : str=True						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		str           =		model_outputs["""generated_sequence"""][0]
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		model_outputs["""input_ids"""]
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		model_outputs["""prompt_text"""]
					__SCREAMING_SNAKE_CASE       :		str           =		generated_sequence.numpy().tolist()
					__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		[]
					for sequence in generated_sequence:
							if return_type == ReturnType.TENSORS:
									__SCREAMING_SNAKE_CASE       :		Tuple           =		{"""generated_token_ids""": sequence}
							elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
									# Decode text
									__SCREAMING_SNAKE_CASE       :		Any           =		self.tokenizer.decode(
									    lowerCAmelCase__							,     skip_special_tokens=lowerCAmelCase__							,     clean_up_tokenization_spaces=lowerCAmelCase__							,     )

									# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
									if input_ids is None:
											__SCREAMING_SNAKE_CASE       :		List[str]           =		0
									else:
											__SCREAMING_SNAKE_CASE       :		Any           =		len(
											    self.tokenizer.decode(
											        input_ids[0]							,     skip_special_tokens=lowerCAmelCase__							,     clean_up_tokenization_spaces=lowerCAmelCase__							,     )						)

									if return_type == ReturnType.FULL_TEXT:
											__SCREAMING_SNAKE_CASE       :		int           =		prompt_text + text[prompt_length:]
									else:
											__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		text[prompt_length:]

									__SCREAMING_SNAKE_CASE       :		str           =		{"""generated_text""": all_text}
							records.append(lowerCAmelCase__						)

					return records




'''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


UpperCamelCase__       :	List[Any]    = '''base_with_context'''


def 				lowerCAmelCase_   (	_lowerCamelCase:   Tuple       ,      _lowerCamelCase:   Dict					):
		__SCREAMING_SNAKE_CASE       :		str           =		nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""]					)					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]					)       ,      requires_grad=_lowerCamelCase					)
		for lyr_num, lyr in enumerate(model.encoders					):
				__SCREAMING_SNAKE_CASE       :		Tuple           =		weights[F"layers_{lyr_num}"]
				__SCREAMING_SNAKE_CASE       :		str           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		ly_weight["""attention"""]
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T					)					)

				__SCREAMING_SNAKE_CASE       :		Dict           =		nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T					)					)

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


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[str]       ,      _lowerCamelCase:   List[str]					):
		__SCREAMING_SNAKE_CASE       :		List[Any]           =		nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T					)					)

		__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]					)       ,      requires_grad=_lowerCamelCase					)

		for lyr_num, lyr in enumerate(model.encoders					):
				__SCREAMING_SNAKE_CASE       :		Tuple           =		weights[F"layers_{lyr_num}"]
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		ly_weight["""attention"""]

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

				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		List[str]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]					)					)

		__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""]					)					)

		return model


def 				lowerCAmelCase_   (	_lowerCamelCase:   List[str]       ,      _lowerCamelCase:   Any					):
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T					)					)
		__SCREAMING_SNAKE_CASE       :		Any           =		nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T					)					)

		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		nn.Parameter(
		    torch.FloatTensor(weights["""Embed_0"""]["""embedding"""]					)       ,      requires_grad=_lowerCamelCase					)

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

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

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

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

				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		ly_weight["""MultiHeadDotProductAttention_0"""]
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Any           =		nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		int           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""]					)					)

				__SCREAMING_SNAKE_CASE       :		Dict           =		nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""]					)					)
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		nn.Parameter(
				    torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Tuple           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T					)					)
				__SCREAMING_SNAKE_CASE       :		Dict           =		nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T					)					)

		__SCREAMING_SNAKE_CASE       :		Any           =		nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""]					)					)

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

		return model


def 				lowerCAmelCase_   (	_lowerCamelCase:   Any					):
		__SCREAMING_SNAKE_CASE       :		int           =		checkpoints.load_tax_checkpoint(args.checkpoint_path					)
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		jnp.tree_util.tree_map(onp.array       ,      _lowerCamelCase					)

		__SCREAMING_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()""",
		]

		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		os.path.join(args.checkpoint_path       ,      """.."""       ,      """config.gin"""					)
		__SCREAMING_SNAKE_CASE       :		Any           =		inference.parse_training_gin_file(_lowerCamelCase       ,      _lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Tuple           =		inference.InferenceModel(args.checkpoint_path       ,      _lowerCamelCase					)

		__SCREAMING_SNAKE_CASE       :		str           =		DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""       ,      variance_type="""fixed_large"""					)

		__SCREAMING_SNAKE_CASE       :		List[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"""       ,      )

		__SCREAMING_SNAKE_CASE       :		int           =		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"""       ,      )

		__SCREAMING_SNAKE_CASE       :		Tuple           =		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       ,      )

		__SCREAMING_SNAKE_CASE       :		int           =		load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""]       ,      _lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Dict           =		load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""]       ,      _lowerCamelCase					)
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		load_decoder(ta_checkpoint["""target"""]["""decoder"""]       ,      _lowerCamelCase					)

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

		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		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__":
						UpperCamelCase__       :	Tuple    = 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__       :	List[str]    = parser.parse_args()

						main(args)