

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import VivitImageProcessor






class 	UpperCAmelCase__		(	unittest.TestCase	):






  """simple docstring"""


  def __init__(							self						:		Optional[Any]     ,       __lowerCamelCase						:		Optional[int]     ,       __lowerCamelCase						:		Dict=7     ,       __lowerCamelCase						:		List[str]=3     ,       __lowerCamelCase						:		Tuple=10     ,       __lowerCamelCase						:		Optional[Any]=18     ,       __lowerCamelCase						:		List[str]=30     ,       __lowerCamelCase						:		Union[str, Any]=400     ,       __lowerCamelCase						:		int=True     ,       __lowerCamelCase						:		Tuple=None     ,       __lowerCamelCase						:		Dict=True     ,       __lowerCamelCase						:		Any=[0.5, 0.5, 0.5]     ,       __lowerCamelCase						:		List[Any]=[0.5, 0.5, 0.5]     ,       __lowerCamelCase						:		List[Any]=None     ,       )		->							Tuple:
       SCREAMING_SNAKE_CASE__   =   size if size is not None else {'''shortest_edge''': 18}
       SCREAMING_SNAKE_CASE__   =   crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

       SCREAMING_SNAKE_CASE__   =   parent
       SCREAMING_SNAKE_CASE__   =   batch_size
       SCREAMING_SNAKE_CASE__   =   num_channels
       SCREAMING_SNAKE_CASE__   =   num_frames
       SCREAMING_SNAKE_CASE__   =   image_size
       SCREAMING_SNAKE_CASE__   =   min_resolution
       SCREAMING_SNAKE_CASE__   =   max_resolution
       SCREAMING_SNAKE_CASE__   =   do_resize
       SCREAMING_SNAKE_CASE__   =   size
       SCREAMING_SNAKE_CASE__   =   do_normalize
       SCREAMING_SNAKE_CASE__   =   image_mean
       SCREAMING_SNAKE_CASE__   =   image_std
       SCREAMING_SNAKE_CASE__   =   crop_size







  def 				lowercase_		(							self						:		Optional[int] )		->							List[Any]:
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_size": self.crop_size,
       }






@require_torch
@require_vision
class 	UpperCAmelCase__		(	A__						,					unittest.TestCase	):






  """simple docstring"""


  a									= VivitImageProcessor if is_vision_available() else None






  def 				lowercase_		(							self						:		List[str] )		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__   =   VivitImageProcessingTester(self )






  @property
  def 				lowercase_		(							self						:		List[str] )		->							int:
       return self.image_processor_tester.prepare_image_processor_dict()






  def 				lowercase_		(							self						:		List[Any] )		->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__   =   self.image_processing_class(**self.image_processor_dict )
       self.assertTrue(hasattr(__lowerCamelCase     ,       '''image_mean''' ) )
       self.assertTrue(hasattr(__lowerCamelCase     ,       '''image_std''' ) )
       self.assertTrue(hasattr(__lowerCamelCase     ,       '''do_normalize''' ) )
       self.assertTrue(hasattr(__lowerCamelCase     ,       '''do_resize''' ) )
       self.assertTrue(hasattr(__lowerCamelCase     ,       '''do_center_crop''' ) )
       self.assertTrue(hasattr(__lowerCamelCase     ,       '''size''' ) )






  def 				lowercase_		(							self						:		int )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   self.image_processing_class.from_dict(self.image_processor_dict )
       self.assertEqual(image_processor.size     ,       {'''shortest_edge''': 18} )
       self.assertEqual(image_processor.crop_size     ,       {'''height''': 18, '''width''': 18} )

       SCREAMING_SNAKE_CASE__   =   self.image_processing_class.from_dict(self.image_processor_dict     ,       size=42     ,       crop_size=84 )
       self.assertEqual(image_processor.size     ,       {'''shortest_edge''': 42} )
       self.assertEqual(image_processor.crop_size     ,       {'''height''': 84, '''width''': 84} )






  def 				lowercase_		(							self						:		List[str] )		->							Dict:
       # Initialize image_processing
       SCREAMING_SNAKE_CASE__   =   self.image_processing_class(**self.image_processor_dict )
       # create random PIL videos
       SCREAMING_SNAKE_CASE__   =   prepare_video_inputs(self.image_processor_tester     ,       equal_resolution=__lowerCamelCase )
       for video in video_inputs:
            self.assertIsInstance(__lowerCamelCase     ,       __lowerCamelCase )
            self.assertIsInstance(video[0]     ,       Image.Image )

       # Test not batched input
       SCREAMING_SNAKE_CASE__   =   image_processing(video_inputs[0]     ,       return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_videos.shape     ,       (
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )     ,       )

       # Test batched
       SCREAMING_SNAKE_CASE__   =   image_processing(__lowerCamelCase     ,       return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_videos.shape     ,       (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )     ,       )






  def 				lowercase_		(							self						:		Optional[int] )		->							Tuple:
       # Initialize image_processing
       SCREAMING_SNAKE_CASE__   =   self.image_processing_class(**self.image_processor_dict )
       # create random numpy tensors
       SCREAMING_SNAKE_CASE__   =   prepare_video_inputs(self.image_processor_tester     ,       equal_resolution=__lowerCamelCase     ,       numpify=__lowerCamelCase )
       for video in video_inputs:
            self.assertIsInstance(__lowerCamelCase     ,       __lowerCamelCase )
            self.assertIsInstance(video[0]     ,       np.ndarray )

       # Test not batched input
       SCREAMING_SNAKE_CASE__   =   image_processing(video_inputs[0]     ,       return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_videos.shape     ,       (
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )     ,       )

       # Test batched
       SCREAMING_SNAKE_CASE__   =   image_processing(__lowerCamelCase     ,       return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_videos.shape     ,       (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )     ,       )







  def 				lowercase_		(							self						:		Tuple )		->							Tuple:
       # Initialize image_processing
       SCREAMING_SNAKE_CASE__   =   self.image_processing_class(**self.image_processor_dict )
       # create random PyTorch tensors
       SCREAMING_SNAKE_CASE__   =   prepare_video_inputs(self.image_processor_tester     ,       equal_resolution=__lowerCamelCase     ,       torchify=__lowerCamelCase )
       for video in video_inputs:
            self.assertIsInstance(__lowerCamelCase     ,       __lowerCamelCase )
            self.assertIsInstance(video[0]     ,       torch.Tensor )

       # Test not batched input
       SCREAMING_SNAKE_CASE__   =   image_processing(video_inputs[0]     ,       return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_videos.shape     ,       (
               1,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )     ,       )

       # Test batched
       SCREAMING_SNAKE_CASE__   =   image_processing(__lowerCamelCase     ,       return_tensors='''pt''' ).pixel_values
       self.assertEqual(
           encoded_videos.shape     ,       (
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_frames,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           )     ,       )





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


_SCREAMING_SNAKE_CASE					:  Dict             =      '''base_with_context'''
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




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

          SCREAMING_SNAKE_CASE__   =   ly_weight['''attention''']
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']    )    )

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']    )    )
     return model
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''input_proj''']['''kernel'''].T    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(
         torch.FloatTensor(weights['''Embed_0''']['''embedding''']    )				,		requires_grad=_A    )

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

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(
              torch.FloatTensor(ly_weight['''pre_attention_layer_norm''']['''scale''']    )    )

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''encoder_norm''']['''scale''']    )    )

     return model
def     UpperCAmelCase_       (						_A				,		_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''time_emb_dense0''']['''kernel'''].T    )    )
     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''time_emb_dense1''']['''kernel'''].T    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(
         torch.FloatTensor(weights['''Embed_0''']['''embedding''']    )				,		requires_grad=_A    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(
         torch.FloatTensor(weights['''continuous_inputs_projection''']['''kernel'''].T    )    )

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

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

          SCREAMING_SNAKE_CASE__   =   ly_weight['''self_attention''']
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )

          SCREAMING_SNAKE_CASE__   =   ly_weight['''MultiHeadDotProductAttention_0''']
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''query''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''key''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''value''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(attention_weights['''out''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(
              torch.FloatTensor(ly_weight['''pre_cross_attention_layer_norm''']['''scale''']    )    )

          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''pre_mlp_layer_norm''']['''scale''']    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(
              torch.FloatTensor(ly_weight['''FiLMLayer_1''']['''DenseGeneral_0''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_0''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wi_1''']['''kernel'''].T    )    )
          SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(ly_weight['''mlp''']['''wo''']['''kernel'''].T    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''decoder_norm''']['''scale''']    )    )

     SCREAMING_SNAKE_CASE__   =   nn.Parameter(torch.FloatTensor(weights['''spec_out_dense''']['''kernel'''].T    )    )

     return model
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   checkpoints.load_tax_checkpoint(args.checkpoint_path    )
     SCREAMING_SNAKE_CASE__   =   jnp.tree_util.tree_map(onp.array				,		_A    )

     SCREAMING_SNAKE_CASE__   =   [
         '''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__   =   os.path.join(args.checkpoint_path				,		'''..'''				,		'''config.gin'''    )
     SCREAMING_SNAKE_CASE__   =   inference.parse_training_gin_file(_A				,		_A    )
     SCREAMING_SNAKE_CASE__   =   inference.InferenceModel(args.checkpoint_path				,		_A    )

     SCREAMING_SNAKE_CASE__   =   DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''				,		variance_type='''fixed_large'''    )

     SCREAMING_SNAKE_CASE__   =   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__   =   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__   =   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__   =   load_notes_encoder(ta_checkpoint['''target''']['''token_encoder''']				,		_A    )
     SCREAMING_SNAKE_CASE__   =   load_continuous_encoder(ta_checkpoint['''target''']['''continuous_encoder''']				,		_A    )
     SCREAMING_SNAKE_CASE__   =   load_decoder(ta_checkpoint['''target''']['''decoder''']				,		_A    )

     SCREAMING_SNAKE_CASE__   =   OnnxRuntimeModel.from_pretrained('''kashif/soundstream_mel_decoder'''    )

     SCREAMING_SNAKE_CASE__   =   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__":
       _SCREAMING_SNAKE_CASE					:  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.''',
       )
       _SCREAMING_SNAKE_CASE					:  Tuple             =      parser.parse_args()

       main(args)


