
'''simple docstring'''





import gc
import unittest

import numpy as np
import torch

from diffusers import (
    AudioDiffusionPipeline,
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    DiffusionPipeline,
    Mel,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Union[str, Any] ):



   '''simple docstring'''




   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







 @property
 def 							UpperCamelCase  (					self		:  Any ):



   '''simple docstring'''




   torch.manual_seed(0 )
   UpperCAmelCase__      :					int				       =							UNetaDModel(
       sample_size=(32, 64)			,				in_channels=1			,				out_channels=1			,				layers_per_block=2			,				block_out_channels=(1_28, 1_28)			,				down_block_types=("AttnDownBlock2D", "DownBlock2D")			,				up_block_types=("UpBlock2D", "AttnUpBlock2D")			,				)
   return model







 @property
 def 							UpperCamelCase  (					self		:  List[str] ):



   '''simple docstring'''




   torch.manual_seed(0 )
   UpperCAmelCase__      :					List[str]				       =							UNetaDConditionModel(
       sample_size=(64, 32)			,				in_channels=1			,				out_channels=1			,				layers_per_block=2			,				block_out_channels=(1_28, 1_28)			,				down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")			,				up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")			,				cross_attention_dim=10			,				)
   return model







 @property
 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   torch.manual_seed(0 )
   UpperCAmelCase__      :					int				       =							AutoencoderKL(
       sample_size=(1_28, 64)			,				in_channels=1			,				out_channels=1			,				latent_channels=1			,				layers_per_block=2			,				block_out_channels=(1_28, 1_28)			,				down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D")			,				up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D")			,				)
   UpperCAmelCase__      :					Union[str, Any]				       =							UNetaDModel(
       sample_size=(64, 32)			,				in_channels=1			,				out_channels=1			,				layers_per_block=2			,				block_out_channels=(1_28, 1_28)			,				down_block_types=("AttnDownBlock2D", "DownBlock2D")			,				up_block_types=("UpBlock2D", "AttnUpBlock2D")			,				)
   return vqvae, unet







 @slow
 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   UpperCAmelCase__      :					Optional[int]				       =							"cpu"  # ensure determinism for the device-dependent torch.Generator
   UpperCAmelCase__      :					Any				       =							Mel(
       x_res=self.dummy_unet.config.sample_size[1]			,				y_res=self.dummy_unet.config.sample_size[0]			,				)

   UpperCAmelCase__      :					List[Any]				       =							DDPMScheduler()
   UpperCAmelCase__      :					Optional[Any]				       =							AudioDiffusionPipeline(vqvae=lowercase__			,				unet=self.dummy_unet			,				mel=lowercase__			,				scheduler=lowercase__ )
   UpperCAmelCase__      :					List[str]				       =							pipe.to(lowercase__ )
   pipe.set_progress_bar_config(disable=lowercase__ )

   UpperCAmelCase__      :					Union[str, Any]				       =							torch.Generator(device=lowercase__ ).manual_seed(42 )
   UpperCAmelCase__      :					Any				       =							pipe(generator=lowercase__			,				steps=4 )
   UpperCAmelCase__      :					Union[str, Any]				       =							output.audios[0]
   UpperCAmelCase__      :					List[str]				       =							output.images[0]

   UpperCAmelCase__      :					Optional[Any]				       =							torch.Generator(device=lowercase__ ).manual_seed(42 )
   UpperCAmelCase__      :					str				       =							pipe(generator=lowercase__			,				steps=4			,				return_dict=lowercase__ )
   UpperCAmelCase__      :					List[str]				       =							output[0][0]

   assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
   assert (
       image.height == self.dummy_unet.config.sample_size[0]
       and image.width == self.dummy_unet.config.sample_size[1]
   )
   UpperCAmelCase__      :					List[str]				       =							np.frombuffer(image.tobytes()			,				dtype="uint8" )[:10]
   UpperCAmelCase__      :					List[str]				       =							np.frombuffer(image_from_tuple.tobytes()			,				dtype="uint8" )[:10]
   UpperCAmelCase__      :					Any				       =							np.array([69, 2_55, 2_55, 2_55, 0, 0, 77, 1_81, 12, 1_27] )

   assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
   assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0

   UpperCAmelCase__      :					Any				       =							Mel(
       x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1]			,				y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0]			,				)

   UpperCAmelCase__      :					Any				       =							DDIMScheduler()
   UpperCAmelCase__      :					Any				       =							self.dummy_vqvae_and_unet
   UpperCAmelCase__      :					Union[str, Any]				       =							AudioDiffusionPipeline(
       vqvae=self.dummy_vqvae_and_unet[0]			,				unet=dummy_vqvae_and_unet[1]			,				mel=lowercase__			,				scheduler=lowercase__ )
   UpperCAmelCase__      :					int				       =							pipe.to(lowercase__ )
   pipe.set_progress_bar_config(disable=lowercase__ )

   np.random.seed(0 )
   UpperCAmelCase__      :					List[Any]				       =							np.random.uniform(-1			,				1			,				((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
   UpperCAmelCase__      :					Any				       =							torch.Generator(device=lowercase__ ).manual_seed(42 )
   UpperCAmelCase__      :					str				       =							pipe(raw_audio=lowercase__			,				generator=lowercase__			,				start_step=5			,				steps=10 )
   UpperCAmelCase__      :					List[Any]				       =							output.images[0]

   assert (
       image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
       and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
   )
   UpperCAmelCase__      :					Any				       =							np.frombuffer(image.tobytes()			,				dtype="uint8" )[:10]
   UpperCAmelCase__      :					List[str]				       =							np.array([1_20, 1_17, 1_10, 1_09, 1_38, 1_67, 1_38, 1_48, 1_32, 1_21] )

   assert np.abs(image_slice.flatten() - expected_slice ).max() == 0

   UpperCAmelCase__      :					Dict				       =							self.dummy_unet_condition
   UpperCAmelCase__      :					Optional[Any]				       =							AudioDiffusionPipeline(
       vqvae=self.dummy_vqvae_and_unet[0]			,				unet=lowercase__			,				mel=lowercase__			,				scheduler=lowercase__ )
   UpperCAmelCase__      :					Union[str, Any]				       =							pipe.to(lowercase__ )
   pipe.set_progress_bar_config(disable=lowercase__ )

   np.random.seed(0 )
   UpperCAmelCase__      :					List[Any]				       =							torch.rand((1, 1, 10) )
   UpperCAmelCase__      :					List[str]				       =							pipe(generator=lowercase__			,				encoding=lowercase__ )
   UpperCAmelCase__      :					Optional[int]				       =							output.images[0]
   UpperCAmelCase__      :					Dict				       =							np.frombuffer(image.tobytes()			,				dtype="uint8" )[:10]
   UpperCAmelCase__      :					str				       =							np.array([1_07, 1_03, 1_20, 1_27, 1_42, 1_22, 1_13, 1_22, 97, 1_11] )

   assert np.abs(image_slice.flatten() - expected_slice ).max() == 0






@slow
@require_torch_gpu
class 							UpperCAmelCase_							(    unittest.TestCase				):

 '''simple docstring'''







 def 							UpperCamelCase  (					self		:  Optional[int] ):



   '''simple docstring'''




   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







 def 							UpperCamelCase  (					self		:  str ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							torch_device

   UpperCAmelCase__      :					List[Any]				       =							DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256" )
   UpperCAmelCase__      :					List[Any]				       =							pipe.to(lowercase__ )
   pipe.set_progress_bar_config(disable=lowercase__ )

   UpperCAmelCase__      :					Optional[int]				       =							torch.Generator(device=lowercase__ ).manual_seed(42 )
   UpperCAmelCase__      :					Optional[int]				       =							pipe(generator=lowercase__ )
   UpperCAmelCase__      :					List[Any]				       =							output.audios[0]
   UpperCAmelCase__      :					Dict				       =							output.images[0]

   assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
   assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
   UpperCAmelCase__      :					List[Any]				       =							np.frombuffer(image.tobytes()			,				dtype="uint8" )[:10]
   UpperCAmelCase__      :					Optional[int]				       =							np.array([1_51, 1_67, 1_54, 1_44, 1_22, 1_34, 1_21, 1_05, 70, 26] )

   assert np.abs(image_slice.flatten() - expected_slice ).max() == 0






import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration





def      A__					(			__A  :						Any     ,				__A  :						str     ,				__A  :						str     ,				__A  :						Path     ,				__A  :						str = None     ,				__A  :						str = None     ,				__A  :						str = None     ,				)					->Optional[Any]:
      if config_name_or_path is None:
            __A    ='''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''

      if generator_tokenizer_name_or_path is None:
            __A    =generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            __A    =question_encoder_name_or_path

      __A    =RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration

      # Save model.
      __A    =RagConfig.from_pretrained(__A			)
      __A    =AutoConfig.from_pretrained(__A			)
      __A    =AutoConfig.from_pretrained(__A			)

      __A    =gen_config
      __A    =question_encoder_config

      __A    =model_class.from_pretrained_question_encoder_generator(
          __A     ,				__A     ,				config=__A			)
      rag_model.save_pretrained(__A			)

      # Sanity check.
      model_class.from_pretrained(__A			)

      # Save tokenizers.
      __A    =AutoTokenizer.from_pretrained(__A			)
      gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/'''			)
      __A    =AutoTokenizer.from_pretrained(__A			)
      question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/'''			)


if __name__ == "__main__":
   _lowerCamelCase      :					List[str]					    =      argparse.ArgumentParser()
   parser.add_argument(
       '''--model_type''',
       choices=['''rag_sequence''', '''rag_token'''],
       required=True,
       type=str,
       help='''RAG model type: rag_sequence, rag_token''',
   )
   parser.add_argument('''--dest''', type=str, required=True, help='''Path to the output checkpoint directory.''')
   parser.add_argument('''--generator_name_or_path''', type=str, required=True, help='''Generator model identifier''')
   parser.add_argument(
       '''--question_encoder_name_or_path''', type=str, required=True, help='''Question encoder model identifier'''
   )

   parser.add_argument(
       '''--generator_tokenizer_name_or_path''',
       type=str,
       help='''Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``''',
   )
   parser.add_argument(
       '''--question_encoder_tokenizer_name_or_path''',
       type=str,
       help='''Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``''',
   )
   parser.add_argument(
       '''--config_name_or_path''',
       type=str,
       help=(
           '''Identifier of the model config to use, if not provided, resolves to a base config for a given'''
           ''' ``model_type``'''
       ),
   )

   _lowerCamelCase      :					str					    =      parser.parse_args()

   _lowerCamelCase      :					Optional[int]					    =      Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )


