
"""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_       :    Dict						      =			'''base_with_context'''
def 				A_  (__a					, __a					):

					'''simple docstring'''





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

					'''simple docstring'''





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

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

					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 				A_  (__a					, __a					):

					'''simple docstring'''





					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=__a					)

					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 				A_  (__a					):

					'''simple docstring'''





					A_								=    checkpoints.load_tax_checkpoint(args.checkpoint_path					)
					A_								=    jnp.tree_util.tree_map(onp.array					, __a					)

					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(__a					, __a					)
					A_								=    inference.InferenceModel(args.checkpoint_path					, __a					)

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

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

					A_								=    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_       :    str						      =			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)











'''simple docstring'''






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

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


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, TensorType

A__							:  Tuple    =logging.get_logger(__name__)

A__							:  Any    ={
    '''openai/imagegpt-small''': '''''',
    '''openai/imagegpt-medium''': '''''',
    '''openai/imagegpt-large''': '''''',
}




class 					UpperCAmelCase	(     snake_case_  ):
      _lowercase:     Tuple			=  '''imagegpt'''
      _lowercase:     Optional[int]			=  ['''past_key_values''']
      _lowercase:     str			=  {
          '''hidden_size''': '''n_embd''',
          '''max_position_embeddings''': '''n_positions''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }




      def __init__(     self     :					Optional[int]     ,     __snake_case     :					Optional[Any]=5_12 + 1     ,     __snake_case     :					Union[str, Any]=32 * 32     ,     __snake_case     :					List[Any]=5_12     ,     __snake_case     :					Any=24     ,     __snake_case     :					Optional[Any]=8     ,     __snake_case     :					List[Any]=None     ,     __snake_case     :					str="quick_gelu"     ,     __snake_case     :					List[Any]=0.1     ,     __snake_case     :					str=0.1     ,     __snake_case     :					Optional[int]=0.1     ,     __snake_case     :					Tuple=1E-5     ,     __snake_case     :					Optional[int]=0.02     ,     __snake_case     :					str=True     ,     __snake_case     :					Dict=True     ,     __snake_case     :					Union[str, Any]=False     ,     __snake_case     :					Dict=False     ,     __snake_case     :					Union[str, Any]=False     ,     **__snake_case     :					Union[str, Any]     ,     )							-> List[Any]:
            _lowerCAmelCase					=				vocab_size
            _lowerCAmelCase					=				n_positions
            _lowerCAmelCase					=				n_embd
            _lowerCAmelCase					=				n_layer
            _lowerCAmelCase					=				n_head
            _lowerCAmelCase					=				n_inner
            _lowerCAmelCase					=				activation_function
            _lowerCAmelCase					=				resid_pdrop
            _lowerCAmelCase					=				embd_pdrop
            _lowerCAmelCase					=				attn_pdrop
            _lowerCAmelCase					=				layer_norm_epsilon
            _lowerCAmelCase					=				initializer_range
            _lowerCAmelCase					=				scale_attn_weights
            _lowerCAmelCase					=				use_cache
            _lowerCAmelCase					=				scale_attn_by_inverse_layer_idx
            _lowerCAmelCase					=				reorder_and_upcast_attn
            _lowerCAmelCase					=				tie_word_embeddings

            super().__init__(tie_word_embeddings=__snake_case     ,     **__snake_case    )




class 					UpperCAmelCase	(     snake_case_  ):




      @property
      def 							lowercase__       (     self     :					Optional[Any]    )							-> Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ]    )




      def 							lowercase__       (     self     :					Union[str, Any]     ,     __snake_case     :					"FeatureExtractionMixin"     ,     __snake_case     :					int = 1     ,     __snake_case     :					int = -1     ,     __snake_case     :					bool = False     ,     __snake_case     :					Optional["TensorType"] = None     ,     __snake_case     :					int = 3     ,     __snake_case     :					int = 32     ,     __snake_case     :					int = 32     ,     )							-> Mapping[str, Any]:
            _lowerCAmelCase					=				self._generate_dummy_images(__snake_case     ,     __snake_case     ,     __snake_case     ,     __snake_case    )
            _lowerCAmelCase					=				dict(preprocessor(images=__snake_case     ,     return_tensors=__snake_case    )    )

            return inputs






