




"""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       a__	( unittest.TestCase				):







     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''simple docstring'''



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







     @property
     def       lowerCAmelCase_    (     self			)  ->   Dict:



          '''simple docstring'''



          torch.manual_seed(0			)
          a     =				UNetaDModel(
              sample_size=(32, 64)  ,		in_channels=1  ,		out_channels=1  ,		layers_per_block=2  ,		block_out_channels=(128, 128)  ,		down_block_types=("AttnDownBlock2D", "DownBlock2D")  ,		up_block_types=("UpBlock2D", "AttnUpBlock2D")  ,		)
          return model







     @property
     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          torch.manual_seed(0			)
          a     =				UNetaDConditionModel(
              sample_size=(64, 32)  ,		in_channels=1  ,		out_channels=1  ,		layers_per_block=2  ,		block_out_channels=(128, 128)  ,		down_block_types=("CrossAttnDownBlock2D", "DownBlock2D")  ,		up_block_types=("UpBlock2D", "CrossAttnUpBlock2D")  ,		cross_attention_dim=10  ,		)
          return model







     @property
     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          torch.manual_seed(0			)
          a     =				AutoencoderKL(
              sample_size=(128, 64)  ,		in_channels=1  ,		out_channels=1  ,		latent_channels=1  ,		layers_per_block=2  ,		block_out_channels=(128, 128)  ,		down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D")  ,		up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D")  ,		)
          a     =				UNetaDModel(
              sample_size=(64, 32)  ,		in_channels=1  ,		out_channels=1  ,		layers_per_block=2  ,		block_out_channels=(128, 128)  ,		down_block_types=("AttnDownBlock2D", "DownBlock2D")  ,		up_block_types=("UpBlock2D", "AttnUpBlock2D")  ,		)
          return vqvae, unet







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



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

          a     =				DDPMScheduler()
          a     =				AudioDiffusionPipeline(vqvae=A  ,		unet=self.dummy_unet  ,		mel=A  ,		scheduler=A			)
          a     =				pipe.to(A			)
          pipe.set_progress_bar_config(disable=A			)

          a     =				torch.Generator(device=A			).manual_seed(42			)
          a     =				pipe(generator=A  ,		steps=4			)
          a     =				output.audios[0]
          a     =				output.images[0]

          a     =				torch.Generator(device=A			).manual_seed(42			)
          a     =				pipe(generator=A  ,		steps=4  ,		return_dict=A			)
          a     =				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]
          )
          a     =				np.frombuffer(image.tobytes()  ,		dtype="uint8"			)[:10]
          a     =				np.frombuffer(image_from_tuple.tobytes()  ,		dtype="uint8"			)[:10]
          a     =				np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127]			)

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

          a     =				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]  ,		)

          a     =				DDIMScheduler()
          a     =				self.dummy_vqvae_and_unet
          a     =				AudioDiffusionPipeline(
              vqvae=self.dummy_vqvae_and_unet[0]  ,		unet=dummy_vqvae_and_unet[1]  ,		mel=A  ,		scheduler=A			)
          a     =				pipe.to(A			)
          pipe.set_progress_bar_config(disable=A			)

          np.random.seed(0			)
          a     =				np.random.uniform(-1  ,		1  ,		((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,)			)
          a     =				torch.Generator(device=A			).manual_seed(42			)
          a     =				pipe(raw_audio=A  ,		generator=A  ,		start_step=5  ,		steps=10			)
          a     =				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]
          )
          a     =				np.frombuffer(image.tobytes()  ,		dtype="uint8"			)[:10]
          a     =				np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121]			)

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

          a     =				self.dummy_unet_condition
          a     =				AudioDiffusionPipeline(
              vqvae=self.dummy_vqvae_and_unet[0]  ,		unet=A  ,		mel=A  ,		scheduler=A			)
          a     =				pipe.to(A			)
          pipe.set_progress_bar_config(disable=A			)

          np.random.seed(0			)
          a     =				torch.rand((1, 1, 10)			)
          a     =				pipe(generator=A  ,		encoding=A			)
          a     =				output.images[0]
          a     =				np.frombuffer(image.tobytes()  ,		dtype="uint8"			)[:10]
          a     =				np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111]			)

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







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







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				torch_device

          a     =				DiffusionPipeline.from_pretrained("teticio/audio-diffusion-ddim-256"			)
          a     =				pipe.to(A			)
          pipe.set_progress_bar_config(disable=A			)

          a     =				torch.Generator(device=A			).manual_seed(42			)
          a     =				pipe(generator=A			)
          a     =				output.audios[0]
          a     =				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]
          a     =				np.frombuffer(image.tobytes()  ,		dtype="uint8"			)[:10]
          a     =				np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26]			)

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







import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__				:      Union[str, Any]	   = logging.get_logger(__name__)

lowercase__				:      Dict	   = [
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Tuple:
     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          a     =				k.replace(__UpperCamelCase		, __UpperCamelCase)

     if k.startswith("encoder"):
          a     =				k.replace(".attn"		, ".self_attn")
          a     =				k.replace("norm1"		, "self_attn_layer_norm")
          a     =				k.replace("norm2"		, "final_layer_norm")
     elif k.startswith("decoder"):
          a     =				k.replace("norm1"		, "self_attn_layer_norm")
          a     =				k.replace("norm2"		, "encoder_attn_layer_norm")
          a     =				k.replace("norm3"		, "final_layer_norm")
     return k

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					str:
     a     =				[
         "model.encoder.layernorm_embedding.weight",
         "model.encoder.layernorm_embedding.bias",
         "model.decoder.layernorm_embedding.weight",
         "model.decoder.layernorm_embedding.bias",
     ]
     for k in keys:
          a     =				sd.pop(__UpperCamelCase)
          a     =				k.replace("layernorm_embedding"		, "layer_norm")
          assert new_k not in sd
          a     =				v


lowercase__				:      Optional[Any]	   = ["START"]

@torch.no_grad()
def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					int:
     a     =				torch.load(__UpperCamelCase		, map_location="cpu")
     a     =				model["model"]
     a     =				BlenderbotConfig.from_json_file(__UpperCamelCase)
     a     =				BlenderbotForConditionalGeneration(__UpperCamelCase)
     a     =				m.model.state_dict().keys()
     a     =				[]
     a     =				{}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

          a     =				rename_state_dict_key(__UpperCamelCase)
          if new_k not in valid_keys:
               failures.append([k, new_k])
          else:
               a     =				v
     if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
          rename_layernorm_keys(__UpperCamelCase)
     m.model.load_state_dict(__UpperCamelCase		, strict=__UpperCamelCase)
     m.half()
     m.save_pretrained(__UpperCamelCase)


if __name__ == "__main__":
   lowercase__				:      int	   = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
   parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
   parser.add_argument(
       "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
   )
   lowercase__				:      str	   = parser.parse_args()
   convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)

