





import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch
       from torch import nn

       from transformers import MaskFormerSwinBackbone
       from transformers.models.maskformer import MaskFormerSwinModel


class        __SCREAMING_SNAKE_CASE      :







       def __init__(				self					:     Union[str, Any]		, A					:     List[Any]		, A					:     Tuple=1_3		, A					:     Optional[int]=3_2		, A					:     Union[str, Any]=2		, A					:     Union[str, Any]=3		, A					:     Any=1_6		, A					:     int=[1, 2, 1]		, A					:     Dict=[2, 2, 4]		, A					:     Optional[int]=2		, A					:     str=2.0		, A					:     str=True		, A					:     Dict=0.0		, A					:     Optional[Any]=0.0		, A					:     List[str]=0.1		, A					:     int="gelu"		, A					:     Optional[int]=False		, A					:     Union[str, Any]=True		, A					:     Tuple=0.02		, A					:     List[str]=1e-5		, A					:     int=True		, A					:     Dict=None		, A					:     Optional[Any]=True		, A					:     int=1_0		, A					:     int=8		, A					:     str=["stage1", "stage2", "stage3"]		, A					:     List[Any]=[1, 2, 3]		, )       ->Dict:
          lowerCamelCase__       :						List[str]        =							parent
          lowerCamelCase__       :						Optional[int]        =							batch_size
          lowerCamelCase__       :						List[str]        =							image_size
          lowerCamelCase__       :						Any        =							patch_size
          lowerCamelCase__       :						str        =							num_channels
          lowerCamelCase__       :						List[str]        =							embed_dim
          lowerCamelCase__       :						Dict        =							depths
          lowerCamelCase__       :						str        =							num_heads
          lowerCamelCase__       :						str        =							window_size
          lowerCamelCase__       :						List[Any]        =							mlp_ratio
          lowerCamelCase__       :						Optional[int]        =							qkv_bias
          lowerCamelCase__       :						Tuple        =							hidden_dropout_prob
          lowerCamelCase__       :						str        =							attention_probs_dropout_prob
          lowerCamelCase__       :						List[str]        =							drop_path_rate
          lowerCamelCase__       :						Optional[Any]        =							hidden_act
          lowerCamelCase__       :						List[str]        =							use_absolute_embeddings
          lowerCamelCase__       :						List[str]        =							patch_norm
          lowerCamelCase__       :						int        =							layer_norm_eps
          lowerCamelCase__       :						int        =							initializer_range
          lowerCamelCase__       :						int        =							is_training
          lowerCamelCase__       :						Optional[Any]        =							scope
          lowerCamelCase__       :						int        =							use_labels
          lowerCamelCase__       :						Tuple        =							type_sequence_label_size
          lowerCamelCase__       :						Optional[int]        =							encoder_stride
          lowerCamelCase__       :						Union[str, Any]        =							out_features
          lowerCamelCase__       :						int        =							out_indices







       def 						__lowerCamelCase   (				self					:     Any      )       ->List[str]:
          lowerCamelCase__       :						Tuple        =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

          lowerCamelCase__       :						Any        =							None
          if self.use_labels:
             lowerCamelCase__       :						Optional[Any]        =							ids_tensor([self.batch_size]		, self.type_sequence_label_size      )

          lowerCamelCase__       :						int        =							self.get_config()

          return config, pixel_values, labels







       def 						__lowerCamelCase   (				self					:     Any      )       ->List[Any]:
          return MaskFormerSwinConfig(
              image_size=self.image_size		, patch_size=self.patch_size		, num_channels=self.num_channels		, embed_dim=self.embed_dim		, depths=self.depths		, num_heads=self.num_heads		, window_size=self.window_size		, mlp_ratio=self.mlp_ratio		, qkv_bias=self.qkv_bias		, hidden_dropout_prob=self.hidden_dropout_prob		, attention_probs_dropout_prob=self.attention_probs_dropout_prob		, drop_path_rate=self.drop_path_rate		, hidden_act=self.hidden_act		, use_absolute_embeddings=self.use_absolute_embeddings		, path_norm=self.patch_norm		, layer_norm_eps=self.layer_norm_eps		, initializer_range=self.initializer_range		, encoder_stride=self.encoder_stride		, out_features=self.out_features		, out_indices=self.out_indices		, )







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     Any		, A					:     Dict		, A					:     Tuple      )       ->Optional[Any]:
          lowerCamelCase__       :						Tuple        =							MaskFormerSwinModel(config=A      )
          model.to(A      )
          model.eval()
          lowerCamelCase__       :						int        =							model(A      )

          lowerCamelCase__       :						Optional[int]        =							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
          lowerCamelCase__       :						List[Any]        =							int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

          self.parent.assertEqual(result.last_hidden_state.shape		, (self.batch_size, expected_seq_len, expected_dim)      )







       def 						__lowerCamelCase   (				self					:     Optional[int]		, A					:     int		, A					:     Tuple		, A					:     Optional[int]      )       ->List[Any]:
          lowerCamelCase__       :						int        =							MaskFormerSwinBackbone(config=A      )
          model.to(A      )
          model.eval()
          lowerCamelCase__       :						Any        =							model(A      )

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps      )		, len(config.out_features      )      )
          self.parent.assertListEqual(list(result.feature_maps[0].shape      )		, [1_3, 1_6, 1_6, 1_6]      )

          # verify channels
          self.parent.assertEqual(len(model.channels      )		, len(config.out_features      )      )
          self.parent.assertListEqual(model.channels		, [1_6, 3_2, 6_4]      )

          # verify ValueError
          with self.parent.assertRaises(A      ):
             lowerCamelCase__       :						str        =							["""stem"""]
             lowerCamelCase__       :						int        =							MaskFormerSwinBackbone(config=A      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->List[Any]:
          lowerCamelCase__       :						Optional[int]        =							self.prepare_config_and_inputs()
          lowerCamelCase__       :						List[str]        =							config_and_inputs
          lowerCamelCase__       :						Optional[int]        =							{"""pixel_values""": pixel_values}
          return config, inputs_dict


@require_torch
class        __SCREAMING_SNAKE_CASE      (			__lowercase					,__lowercase					,unittest.TestCase ):
       _UpperCAmelCase				:  str			   =   (
           (
               MaskFormerSwinModel,
               MaskFormerSwinBackbone,
           )
           if is_torch_available()
           else ()
       )
       _UpperCAmelCase				:  Union[str, Any]			   =   {'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
       _UpperCAmelCase				:  Any			   =   False
       _UpperCAmelCase				:  Any			   =   False
       _UpperCAmelCase				:  Dict			   =   False
       _UpperCAmelCase				:  int			   =   False
       _UpperCAmelCase				:  Any			   =   False







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->str:
          lowerCamelCase__       :						List[Any]        =							MaskFormerSwinModelTester(self      )
          lowerCamelCase__       :						Dict        =							ConfigTester(self		, config_class=A		, embed_dim=3_7      )







       @require_torch_multi_gpu
       @unittest.skip(
           reason=(
               '''`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'''
               ''' `nn.DataParallel`'''
           )      )
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Optional[Any]:
          pass







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Tuple:
          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()







       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->Optional[Any]:
          return







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Dict:
          lowerCamelCase__       :						Union[str, Any]        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A      )







       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->int:
          lowerCamelCase__       :						List[Any]        =							self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*A      )







       @unittest.skip('''Swin does not use inputs_embeds'''      )
       def 						__lowerCamelCase   (				self					:     Tuple      )       ->List[str]:
          pass







       @unittest.skip('''Swin does not support feedforward chunking'''      )
       def 						__lowerCamelCase   (				self					:     Dict      )       ->Dict:
          pass







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Any:
          lowerCamelCase__       :						int        =							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             lowerCamelCase__       :						Dict        =							model_class(A      )
             self.assertIsInstance(model.get_input_embeddings()		, (nn.Module)      )
             lowerCamelCase__       :						Union[str, Any]        =							model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(A		, nn.Linear      )      )







       def 						__lowerCamelCase   (				self					:     Dict      )       ->List[Any]:
          lowerCamelCase__       :						Any        =							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             lowerCamelCase__       :						Optional[Any]        =							model_class(A      )
             lowerCamelCase__       :						Any        =							inspect.signature(model.forward      )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             lowerCamelCase__       :						List[Any]        =							[*signature.parameters.keys()]

             lowerCamelCase__       :						Optional[Any]        =							["""pixel_values"""]
             self.assertListEqual(arg_names[:1]		, A      )







       @unittest.skip(reason='''MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'''      )
       def 						__lowerCamelCase   (				self					:     List[str]      )       ->Tuple:
          pass







       @unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone'''      )
       def 						__lowerCamelCase   (				self					:     Optional[Any]      )       ->Any:
          pass







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     List[Any]		, A					:     str		, A					:     List[Any]		, A					:     Any      )       ->Optional[Any]:
          lowerCamelCase__       :						int        =							model_class(A      )
          model.to(A      )
          model.eval()

          with torch.no_grad():
             lowerCamelCase__       :						Any        =							model(**self._prepare_for_class(A		, A      )      )

          lowerCamelCase__       :						Optional[Any]        =							outputs.hidden_states

          lowerCamelCase__       :						Tuple        =							getattr(
              self.model_tester		, '''expected_num_hidden_layers'''		, len(self.model_tester.depths      ) + 1      )
          self.assertEqual(len(A      )		, A      )

          # Swin has a different seq_length
          lowerCamelCase__       :						Optional[Any]        =							(
              config.patch_size
              if isinstance(config.patch_size		, collections.abc.Iterable      )
              else (config.patch_size, config.patch_size)
          )

          lowerCamelCase__       :						Any        =							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

          self.assertListEqual(
              list(hidden_states[0].shape[-2:]      )		, [num_patches, self.model_tester.embed_dim]		, )







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Optional[Any]:
          lowerCamelCase__       :						Tuple        =							self.model_tester.prepare_config_and_inputs_for_common()

          lowerCamelCase__       :						Tuple        =							(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size		, collections.abc.Iterable      )
              else (self.model_tester.image_size, self.model_tester.image_size)
          )

          for model_class in self.all_model_classes:
             lowerCamelCase__       :						Any        =							True
             self.check_hidden_states_output(A		, A		, A		, A      )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             lowerCamelCase__       :						str        =							True

             self.check_hidden_states_output(A		, A		, A		, A      )







       def 						__lowerCamelCase   (				self					:     str      )       ->List[str]:
          lowerCamelCase__       :						Any        =							self.model_tester.prepare_config_and_inputs_for_common()
          lowerCamelCase__       :						Tuple        =							3

          lowerCamelCase__       :						Tuple        =							(
              self.model_tester.image_size
              if isinstance(self.model_tester.image_size		, collections.abc.Iterable      )
              else (self.model_tester.image_size, self.model_tester.image_size)
          )
          lowerCamelCase__       :						str        =							(
              config.patch_size
              if isinstance(config.patch_size		, collections.abc.Iterable      )
              else (config.patch_size, config.patch_size)
          )

          lowerCamelCase__       :						Optional[Any]        =							image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
          lowerCamelCase__       :						Optional[Any]        =							image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

          for model_class in self.all_model_classes:
             lowerCamelCase__       :						str        =							True
             self.check_hidden_states_output(A		, A		, A		, (padded_height, padded_width)      )

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             lowerCamelCase__       :						Tuple        =							True
             self.check_hidden_states_output(A		, A		, A		, (padded_height, padded_width)      )







       @unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints'''      )
       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->int:
          pass







       @unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''      )
       def 						__lowerCamelCase   (				self					:     Dict      )       ->List[str]:
          pass







       @unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''      )
       def 						__lowerCamelCase   (				self					:     int      )       ->Any:
          pass







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Optional[int]:
          lowerCamelCase__       :						Optional[Any]        =							self.model_tester.prepare_config_and_inputs_for_common()

          def set_nan_tensor_to_zero(A					:     Optional[Any]      ):
             lowerCamelCase__       :						Any        =							0
             return t

          def check_equivalence(A					:     Optional[Any]		, A					:     Optional[Any]		, A					:     Optional[Any]		, A					:     int={}      ):
             with torch.no_grad():
                lowerCamelCase__       :						Union[str, Any]        =							model(**A		, return_dict=A		, **A      )
                lowerCamelCase__       :						Tuple        =							model(**A		, return_dict=A		, **A      ).to_tuple()

                def recursive_check(A					:     Dict		, A					:     Tuple      ):
                   if isinstance(A		, (List, Tuple)      ):
                      for tuple_iterable_value, dict_iterable_value in zip(A		, A      ):
                         recursive_check(A		, A      )
                   elif isinstance(A		, A      ):
                      for tuple_iterable_value, dict_iterable_value in zip(
                          tuple_object.values()		, dict_object.values()      ):
                         recursive_check(A		, A      )
                   elif tuple_object is None:
                      return
                   else:
                      self.assertTrue(
                          torch.allclose(
                              set_nan_tensor_to_zero(A      )		, set_nan_tensor_to_zero(A      )		, atol=1e-5      )		, msg=(
                              '''Tuple and dict output are not equal. Difference:'''
                              F" {torch.max(torch.abs(tuple_object - dict_object      )      )}. Tuple has `nan`:"
                              F" {torch.isnan(A      ).any()} and `inf`: {torch.isinf(A      )}. Dict has"
                              F" `nan`: {torch.isnan(A      ).any()} and `inf`: {torch.isinf(A      )}."
                          )		, )

                recursive_check(A		, A      )

          for model_class in self.all_model_classes:
             lowerCamelCase__       :						Union[str, Any]        =							model_class(A      )
             model.to(A      )
             model.eval()

             lowerCamelCase__       :						Union[str, Any]        =							self._prepare_for_class(A		, A      )
             lowerCamelCase__       :						Tuple        =							self._prepare_for_class(A		, A      )
             check_equivalence(A		, A		, A      )

             lowerCamelCase__       :						int        =							self._prepare_for_class(A		, A		, return_labels=A      )
             lowerCamelCase__       :						Tuple        =							self._prepare_for_class(A		, A		, return_labels=A      )
             check_equivalence(A		, A		, A      )

             lowerCamelCase__       :						Tuple        =							self._prepare_for_class(A		, A      )
             lowerCamelCase__       :						Optional[Any]        =							self._prepare_for_class(A		, A      )
             check_equivalence(A		, A		, A		, {'''output_hidden_states''': True}      )

             lowerCamelCase__       :						Union[str, Any]        =							self._prepare_for_class(A		, A		, return_labels=A      )
             lowerCamelCase__       :						Optional[Any]        =							self._prepare_for_class(A		, A		, return_labels=A      )
             check_equivalence(A		, A		, A		, {'''output_hidden_states''': True}      )


@require_torch
class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase					,__lowercase ):
       _UpperCAmelCase				:  Optional[int]			   =   (MaskFormerSwinBackbone,) if is_torch_available() else ()
       _UpperCAmelCase				:  Optional[Any]			   =   MaskFormerSwinConfig







       def 						__lowerCamelCase   (				self					:     List[str]      )       ->List[Any]:
          lowerCamelCase__       :						List[Any]        =							MaskFormerSwinModelTester(self      )







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Any:
          lowerCamelCase__       :						Optional[int]        =							self.model_tester.prepare_config_and_inputs_for_common()
          lowerCamelCase__       :						int        =							inputs_dict["""pixel_values"""].shape[0]

          for backbone_class in self.all_model_classes:
             lowerCamelCase__       :						Union[str, Any]        =							backbone_class(A      )
             backbone.to(A      )
             backbone.eval()

             lowerCamelCase__       :						int        =							backbone(**A      )

             # Test default outputs and verify feature maps
             self.assertIsInstance(outputs.feature_maps		, A      )
             self.assertTrue(len(outputs.feature_maps      ) == len(backbone.channels      )      )
             for feature_map, n_channels in zip(outputs.feature_maps		, backbone.channels      ):
                self.assertTrue(feature_map.shape[:2]		, (batch_size, n_channels)      )
             self.assertIsNone(outputs.hidden_states      )
             self.assertIsNone(outputs.attentions      )

             # Test output_hidden_states=True
             lowerCamelCase__       :						List[Any]        =							backbone(**A		, output_hidden_states=A      )
             self.assertIsNotNone(outputs.hidden_states      )
             self.assertTrue(len(outputs.hidden_states      )		, len(backbone.stage_names      )      )
             # We skip the stem layer
             for hidden_states, n_channels in zip(outputs.hidden_states[1:]		, backbone.channels      ):
                for hidden_state in hidden_states:
                   # Hidden states are in the format (batch_size, (height * width), n_channels)
                   lowerCamelCase__       :						str        =							hidden_state.shape
                   self.assertTrue((h_batch_size, h_n_channels)		, (batch_size, n_channels)      )

            # Test output_attentions=True
             if self.has_attentions:
                lowerCamelCase__       :						Optional[int]        =							backbone(**A		, output_attentions=A      )
                self.assertIsNotNone(outputs.attentions      )









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


_A :   List[Any]  			=  'base_with_context'



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Tuple:







   """simple docstring"""
   lowerCamelCase__       :						Dict        =							nn.Parameter(torch.FloatTensor(weights['''token_embedder''']['''embedding''']					)					)
   lowerCamelCase__       :						List[str]        =							nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)      ,  requires_grad=UpperCAmelCase					)
   for lyr_num, lyr in enumerate(model.encoders					):
      lowerCamelCase__       :						Any        =							weights[f"layers_{lyr_num}"]
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']					)					)

      lowerCamelCase__       :						int        =							ly_weight['''attention''']
      lowerCamelCase__       :						List[str]        =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T					)					)
      lowerCamelCase__       :						str        =							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T					)					)
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T					)					)

      lowerCamelCase__       :						str        =							nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']					)					)

      lowerCamelCase__       :						List[Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T					)					)

   lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']					)					)
   return model



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T					)					)

   lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)      ,  requires_grad=UpperCAmelCase					)

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

      lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T					)					)
      lowerCamelCase__       :						List[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T					)					)
      lowerCamelCase__       :						int        =							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Tuple        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']					)					)

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

   lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']					)					)

   return model



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T					)					)
   lowerCamelCase__       :						int        =							nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T					)					)

   lowerCamelCase__       :						str        =							nn.Parameter(
       torch.FloatTensor(weights['''Embed_0''']['''embedding''']					)      ,  requires_grad=UpperCAmelCase					)

   lowerCamelCase__       :						Tuple        =							nn.Parameter(
       torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T					)					)

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

      lowerCamelCase__       :						Any        =							nn.Parameter(
          torch.FloatTensor(ly_weight['''FiLMLayer_0''']['''DenseGeneral_0''']['''kernel'''].T					)					)

      lowerCamelCase__       :						Optional[Any]        =							ly_weight['''self_attention''']
      lowerCamelCase__       :						Dict        =							nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Union[str, Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T					)					)
      lowerCamelCase__       :						Optional[Any]        =							nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T					)					)

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

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

   lowerCamelCase__       :						Any        =							nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']					)					)

   lowerCamelCase__       :						Tuple        =							nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T					)					)

   return model



def 	_a						(   UpperCAmelCase					)    ->    Optional[Any]:







   """simple docstring"""
   lowerCamelCase__       :						Tuple        =							checkpoints.load_tax_checkpoint(args.checkpoint_path					)
   lowerCamelCase__       :						Optional[int]        =							jnp.tree_util.tree_map(onp.array      ,  UpperCAmelCase					)

   lowerCamelCase__       :						List[str]        =							[
       '''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__       :						List[Any]        =							os.path.join(args.checkpoint_path      ,  '''..'''      ,  '''config.gin'''					)
   lowerCamelCase__       :						Optional[Any]        =							inference.parse_training_gin_file(UpperCAmelCase      ,  UpperCAmelCase					)
   lowerCamelCase__       :						Tuple        =							inference.InferenceModel(args.checkpoint_path      ,  UpperCAmelCase					)

   lowerCamelCase__       :						int        =							DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''      ,  variance_type='''fixed_large'''					)

   lowerCamelCase__       :						str        =							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__       :						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'''      ,  )

   lowerCamelCase__       :						Optional[int]        =							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__       :						Optional[int]        =							load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']      ,  UpperCAmelCase					)
   lowerCamelCase__       :						int        =							load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']      ,  UpperCAmelCase					)
   lowerCamelCase__       :						List[str]        =							load_decoder(ta_checkpoint['''target''']['''decoder''']      ,  UpperCAmelCase					)

   lowerCamelCase__       :						List[str]        =							OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''					)

   lowerCamelCase__       :						List[Any]        =							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__":
       _A :   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.',
       )
       _A :   Tuple  			=  parser.parse_args()

       main(args)



