






import argparse

import torch

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


logging.set_verbosity_info()
lowerCamelCase	:							int				=logging.get_logger(__name__)

lowerCamelCase	:							Any				=[
    ['''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							(					__lowerCAmelCase			) ->	List[str]:
     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          UpperCamelCase__			:   Tuple       				=  k.replace(__lowerCAmelCase					,    __lowerCAmelCase			)

     if k.startswith("encoder"			):
          UpperCamelCase__			:   str       				=  k.replace(".attn"					,    ".self_attn"			)
          UpperCamelCase__			:   Optional[int]       				=  k.replace("norm1"					,    "self_attn_layer_norm"			)
          UpperCamelCase__			:   int       				=  k.replace("norm2"					,    "final_layer_norm"			)
     elif k.startswith("decoder"			):
          UpperCamelCase__			:   Any       				=  k.replace("norm1"					,    "self_attn_layer_norm"			)
          UpperCamelCase__			:   Optional[int]       				=  k.replace("norm2"					,    "encoder_attn_layer_norm"			)
          UpperCamelCase__			:   int       				=  k.replace("norm3"					,    "final_layer_norm"			)
     return k







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	str:
     UpperCamelCase__			:   str       				=  [
         "model.encoder.layernorm_embedding.weight",
         "model.encoder.layernorm_embedding.bias",
         "model.decoder.layernorm_embedding.weight",
         "model.decoder.layernorm_embedding.bias",
     ]
     for k in keys:
          UpperCamelCase__			:   Tuple       				=  sd.pop(__lowerCAmelCase			)
          UpperCamelCase__			:   List[Any]       				=  k.replace("layernorm_embedding"					,    "layer_norm"			)
          assert new_k not in sd
          UpperCamelCase__			:   Optional[int]       				=  v


lowerCamelCase	:							Tuple				=['''START''']







@torch.no_grad()
def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase			) ->	str:
     UpperCamelCase__			:   Optional[Any]       				=  torch.load(__lowerCAmelCase					,    map_location="cpu"			)
     UpperCamelCase__			:   Tuple       				=  model["model"]
     UpperCamelCase__			:   Union[str, Any]       				=  BlenderbotConfig.from_json_file(__lowerCAmelCase			)
     UpperCamelCase__			:   Tuple       				=  BlenderbotForConditionalGeneration(__lowerCAmelCase			)
     UpperCamelCase__			:   Tuple       				=  m.model.state_dict().keys()
     UpperCamelCase__			:   List[str]       				=  []
     UpperCamelCase__			:   Optional[Any]       				=  {}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

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


if __name__ == "__main__":
 lowerCamelCase	:							Tuple				=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'''
 )
 lowerCamelCase	:							Tuple				=parser.parse_args()
 convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)







import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


lowerCamelCase	:							Tuple				=False





class 			__a  (    unittest.TestCase       ):
 pass







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







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Union[str, Any]       				=  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)
      UpperCamelCase__			:   List[Any]       				=  torch.manual_seed(0						)
      UpperCamelCase__			:   List[Any]       				=  pipe(
          image=SCREAMING_SNAKE_CASE							,   generator=SCREAMING_SNAKE_CASE							,   guidance_scale=7.5							,   num_inference_steps=50							,   output_type="numpy"							,   ).images

      UpperCamelCase__			:   Union[str, Any]       				=  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__			:   Optional[Any]       				=  np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]						)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2