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[str]    		=  'base_with_context'
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']		)		)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							nn.Parameter(
						    torch.FloatTensor(weights['''Embed_0''']['''embedding''']		)	,						requires_grad=_lowercase		)
						for lyr_num, lyr in enumerate(model.encoders		):
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							weights[f"""layers_{lyr_num}"""]
												SCREAMING_SNAKE_CASE							:					List[Any]           =							nn.Parameter(
												    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']		)		)

												SCREAMING_SNAKE_CASE							:					Dict           =							ly_weight['''attention''']
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T		)		)
												SCREAMING_SNAKE_CASE							:					Dict           =							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T		)		)
												SCREAMING_SNAKE_CASE							:					Dict           =							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_mlp_layer_norm''']['''scale''']		)		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T		)		)
												SCREAMING_SNAKE_CASE							:					str           =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T		)		)
												SCREAMING_SNAKE_CASE							:					int           =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T		)		)

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']		)		)
						return model
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					List[str]           =							nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T		)		)

						SCREAMING_SNAKE_CASE							:					Dict           =							nn.Parameter(
						    torch.FloatTensor(weights['''Embed_0''']['''embedding''']		)	,						requires_grad=_lowercase		)

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

												SCREAMING_SNAKE_CASE							:					Any           =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T		)		)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T		)		)
												SCREAMING_SNAKE_CASE							:					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]           =							nn.Parameter(
												    torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']		)		)

												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T		)		)
												SCREAMING_SNAKE_CASE							:					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							:					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    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					List[Any]           =							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T		)		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T		)		)

						SCREAMING_SNAKE_CASE							:					Dict           =							nn.Parameter(
						    torch.FloatTensor(weights['''Embed_0''']['''embedding''']		)	,						requires_grad=_lowercase		)

						SCREAMING_SNAKE_CASE							:					List[str]           =							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							:					List[str]           =							nn.Parameter(
												    torch.FloatTensor(ly_weight['''pre_self_attention_layer_norm''']['''scale''']		)		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							nn.Parameter(
												    torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T		)		)

												SCREAMING_SNAKE_CASE							:					str           =							ly_weight['''self_attention''']
												SCREAMING_SNAKE_CASE							:					str           =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T		)		)
												SCREAMING_SNAKE_CASE							:					int           =							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							:					Optional[int]           =							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T		)		)

												SCREAMING_SNAKE_CASE							:					List[str]           =							ly_weight['''MultiHeadDotProductAttention_0''']
												SCREAMING_SNAKE_CASE							:					Dict           =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T		)		)
												SCREAMING_SNAKE_CASE							:					Dict           =							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							:					Any           =							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T		)		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							nn.Parameter(
												    torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']		)		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']		)		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							nn.Parameter(
												    torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T		)		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T		)		)
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T		)		)
												SCREAMING_SNAKE_CASE							:					Any           =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T		)		)

						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']		)		)

						SCREAMING_SNAKE_CASE							:					List[Any]           =							nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T		)		)

						return model
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Any           =							checkpoints.load_tax_checkpoint(args.checkpoint_path		)
						SCREAMING_SNAKE_CASE							:					Tuple           =							jnp.tree_util.tree_map(onp.array	,						_lowercase		)

						SCREAMING_SNAKE_CASE							:					Optional[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							:					int           =							os.path.join(args.checkpoint_path	,						'''..'''	,						'''config.gin'''		)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							inference.parse_training_gin_file(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							inference.InferenceModel(args.checkpoint_path	,						_lowercase		)

						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''	,						variance_type='''fixed_large'''		)

						SCREAMING_SNAKE_CASE							:					Optional[int]           =							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							:					Optional[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							:					Dict           =							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							:					Any           =							load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							load_decoder(ta_checkpoint['''target''']['''decoder''']	,						_lowercase		)

						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''		)

						SCREAMING_SNAKE_CASE							:					List[Any]           =							SpectrogramDiffusionPipeline(
						    notes_encoder=_lowercase	,						continuous_encoder=_lowercase	,						decoder=_lowercase	,						scheduler=_lowercase	,						melgan=_lowercase	,						)
						if args.save:
												pipe.save_pretrained(args.output_path		)


if __name__ == "__main__":
					__UpperCamelCase	: Dict    		=  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)






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


__UpperCamelCase	: str    		=  logging.get_logger(__name__)

__UpperCamelCase	: str    		=  {
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""layoutlmv3"""


						def __init__( self			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]=5_0265					,	UpperCamelCase__			:       List[Any]=768					,	UpperCamelCase__			:       Union[str, Any]=12					,	UpperCamelCase__			:       Union[str, Any]=12					,	UpperCamelCase__			:       Tuple=3072					,	UpperCamelCase__			:       List[Any]="gelu"					,	UpperCamelCase__			:       List[Any]=0.1					,	UpperCamelCase__			:       List[str]=0.1					,	UpperCamelCase__			:       Any=512					,	UpperCamelCase__			:       List[Any]=2					,	UpperCamelCase__			:       List[Any]=0.02					,	UpperCamelCase__			:       Optional[Any]=1E-5					,	UpperCamelCase__			:       List[str]=1					,	UpperCamelCase__			:       int=0					,	UpperCamelCase__			:       str=2					,	UpperCamelCase__			:       List[str]=1024					,	UpperCamelCase__			:       str=128					,	UpperCamelCase__			:       str=128					,	UpperCamelCase__			:       Tuple=True					,	UpperCamelCase__			:       Optional[int]=32					,	UpperCamelCase__			:       Any=128					,	UpperCamelCase__			:       Optional[Any]=64					,	UpperCamelCase__			:       Dict=256					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       str=True					,	UpperCamelCase__			:       Dict=True					,	UpperCamelCase__			:       Dict=224					,	UpperCamelCase__			:       Optional[Any]=3					,	UpperCamelCase__			:       Any=16					,	UpperCamelCase__			:       Any=None					,	**UpperCamelCase__			:       Optional[Any]					,	):





												'''simple docstring'''





												super().__init__(
												    vocab_size=UpperCamelCase__					,	hidden_size=UpperCamelCase__					,	num_hidden_layers=UpperCamelCase__					,	num_attention_heads=UpperCamelCase__					,	intermediate_size=UpperCamelCase__					,	hidden_act=UpperCamelCase__					,	hidden_dropout_prob=UpperCamelCase__					,	attention_probs_dropout_prob=UpperCamelCase__					,	max_position_embeddings=UpperCamelCase__					,	type_vocab_size=UpperCamelCase__					,	initializer_range=UpperCamelCase__					,	layer_norm_eps=UpperCamelCase__					,	pad_token_id=UpperCamelCase__					,	bos_token_id=UpperCamelCase__					,	eos_token_id=UpperCamelCase__					,	**UpperCamelCase__					,	)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							max_ad_position_embeddings
												SCREAMING_SNAKE_CASE							:					List[Any]           =							coordinate_size
												SCREAMING_SNAKE_CASE							:					List[str]           =							shape_size
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							has_relative_attention_bias
												SCREAMING_SNAKE_CASE							:					List[Any]           =							rel_pos_bins
												SCREAMING_SNAKE_CASE							:					str           =							max_rel_pos
												SCREAMING_SNAKE_CASE							:					Any           =							has_spatial_attention_bias
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							rel_ad_pos_bins
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							max_rel_ad_pos
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							text_embed
												SCREAMING_SNAKE_CASE							:					List[str]           =							visual_embed
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input_size
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							num_channels
												SCREAMING_SNAKE_CASE							:					List[Any]           =							patch_size
												SCREAMING_SNAKE_CASE							:					List[Any]           =							classifier_dropout






class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	version.parse("""1.12""")


						@property
						def  __A ( self			:       str		):





												'''simple docstring'''





												if self.task in ["question-answering", "sequence-classification"]:
																		return OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
																		    ]		)
												else:
																		return OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
																		    ]		)


						@property
						def  __A ( self			:       int		):





												'''simple docstring'''





												return 1E-5


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return 12



						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       "ProcessorMixin"					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       Optional["TensorType"] = None					,	UpperCamelCase__			:       int = 3					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       int = 40					,	):





												'''simple docstring'''





												setattr(processor.image_processor					,	'''apply_ocr'''					,	UpperCamelCase__		)

												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												SCREAMING_SNAKE_CASE							:					Any           =							compute_effective_axis_dimension(
												    UpperCamelCase__					,	fixed_dimension=OnnxConfig.default_fixed_batch					,	num_token_to_add=0		)
												# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
												SCREAMING_SNAKE_CASE							:					List[Any]           =							processor.tokenizer.num_special_tokens_to_add(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							compute_effective_axis_dimension(
												    UpperCamelCase__					,	fixed_dimension=OnnxConfig.default_fixed_sequence					,	num_token_to_add=UpperCamelCase__		)
												# Generate dummy inputs according to compute batch and sequence
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[[''' '''.join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

												# Generate dummy bounding boxes
												SCREAMING_SNAKE_CASE							:					Any           =							[[[48, 84, 73, 128]]] * batch_size

												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self._generate_dummy_images(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							dict(
												    processor(
												        UpperCamelCase__					,	text=UpperCamelCase__					,	boxes=UpperCamelCase__					,	return_tensors=UpperCamelCase__					,	)		)

												return inputs





