





import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()

class  _UpperCAmelCase				(		unittest.TestCase  ):
      def 		_lowerCamelCase   (		self							):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
      @property
      def 		_lowerCamelCase   (		self							):
         A_  :					Optional[int]         =						1
         A_  :					Optional[int]         =						3
         A_  :					List[str]         =						(32, 32)

         A_  :					Optional[Any]         =						floats_tensor((batch_size, num_channels) + sizes   ,							rng=random.Random(0							)							).to(__UpperCAmelCase							)
         return image
      @property
      def 		_lowerCamelCase   (		self							):
         torch.manual_seed(0							)
         A_  :					Union[str, Any]         =						UNetaDConditionModel(
             block_out_channels=(32, 64)   ,							layers_per_block=2   ,							sample_size=32   ,							in_channels=4   ,							out_channels=4   ,							down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")   ,							up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")   ,							cross_attention_dim=32   ,							)
         return model
      @property
      def 		_lowerCamelCase   (		self							):
         torch.manual_seed(0							)
         A_  :					Union[str, Any]         =						AutoencoderKL(
             block_out_channels=[32, 64]   ,							in_channels=3   ,							out_channels=3   ,							down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]   ,							up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]   ,							latent_channels=4   ,							)
         return model
      @property
      def 		_lowerCamelCase   (		self							):
         torch.manual_seed(0							)
         A_  :					Optional[Any]         =						RobertaSeriesConfig(
             hidden_size=32   ,							project_dim=32   ,							intermediate_size=37   ,							layer_norm_eps=1E-05   ,							num_attention_heads=4   ,							num_hidden_layers=5   ,							pad_token_id=1   ,							vocab_size=5006   ,							)
         return RobertaSeriesModelWithTransformation(__UpperCAmelCase							)
      @property
      def 		_lowerCamelCase   (		self							):


         def extract(*a__   ,							**a__							):

            class  _UpperCAmelCase				:
                  def __init__(		self							):
                     A_  :					Tuple         =						torch.ones([0]							)






                  def 		_lowerCamelCase   (		self   ,							a__							):
                     self.pixel_values.to(__UpperCAmelCase							)
                     return self






            return Out()

         return extract
      def 		_lowerCamelCase   (		self							):
         A_  :					Any         =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
         A_  :					Any         =						self.dummy_cond_unet
         A_  :					Union[str, Any]         =						PNDMScheduler(skip_prk_steps=__UpperCAmelCase							)
         A_  :					Optional[Any]         =						self.dummy_vae
         A_  :					Union[str, Any]         =						self.dummy_text_encoder
         A_  :					str         =						XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""							)
         A_  :					Optional[Any]         =						77

         A_  :					Any         =						self.dummy_image.to(__UpperCAmelCase							)
         A_  :					Union[str, Any]         =						init_image / 2 + 0.5

         # make sure here that pndm scheduler skips prk
         A_  :					List[Any]         =						AltDiffusionImgaImgPipeline(
             unet=__UpperCAmelCase   ,							scheduler=__UpperCAmelCase   ,							vae=__UpperCAmelCase   ,							text_encoder=__UpperCAmelCase   ,							tokenizer=__UpperCAmelCase   ,							safety_checker=__UpperCAmelCase   ,							feature_extractor=self.dummy_extractor   ,							)
         A_  :					Any         =						VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,							do_normalize=__UpperCAmelCase							)
         A_  :					Tuple         =						alt_pipe.to(__UpperCAmelCase							)
         alt_pipe.set_progress_bar_config(disable=__UpperCAmelCase							)

         A_  :					Union[str, Any]         =						"""A painting of a squirrel eating a burger"""
         A_  :					List[str]         =						torch.Generator(device=__UpperCAmelCase							).manual_seed(0							)
         A_  :					Optional[Any]         =						alt_pipe(
             [prompt]   ,							generator=__UpperCAmelCase   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type="""np"""   ,							image=__UpperCAmelCase   ,							)

         A_  :					Union[str, Any]         =						output.images

         A_  :					Tuple         =						torch.Generator(device=__UpperCAmelCase							).manual_seed(0							)
         A_  :					Optional[Any]         =						alt_pipe(
             [prompt]   ,							generator=__UpperCAmelCase   ,							guidance_scale=6.0   ,							num_inference_steps=2   ,							output_type="""np"""   ,							image=__UpperCAmelCase   ,							return_dict=__UpperCAmelCase   ,							)[0]

         A_  :					Dict         =						image[0, -3:, -3:, -1]
         A_  :					Union[str, Any]         =						image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         A_  :					Any         =						np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499]							)

         assert np.abs(image_slice.flatten() - expected_slice							).max() < 5E-3
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 5E-3
      @unittest.skipIf(torch_device != """cuda"""   ,							"""This test requires a GPU"""							)
      def 		_lowerCamelCase   (		self							):
         A_  :					Optional[int]         =						self.dummy_cond_unet
         A_  :					Tuple         =						PNDMScheduler(skip_prk_steps=__UpperCAmelCase							)
         A_  :					Optional[Any]         =						self.dummy_vae
         A_  :					int         =						self.dummy_text_encoder
         A_  :					Optional[int]         =						XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""							)
         A_  :					Optional[int]         =						77

         A_  :					Dict         =						self.dummy_image.to(__UpperCAmelCase							)

         # put models in fp16
         A_  :					Optional[Any]         =						unet.half()
         A_  :					List[str]         =						vae.half()
         A_  :					Union[str, Any]         =						bert.half()

         # make sure here that pndm scheduler skips prk
         A_  :					Optional[int]         =						AltDiffusionImgaImgPipeline(
             unet=__UpperCAmelCase   ,							scheduler=__UpperCAmelCase   ,							vae=__UpperCAmelCase   ,							text_encoder=__UpperCAmelCase   ,							tokenizer=__UpperCAmelCase   ,							safety_checker=__UpperCAmelCase   ,							feature_extractor=self.dummy_extractor   ,							)
         A_  :					Tuple         =						VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor   ,							do_normalize=__UpperCAmelCase							)
         A_  :					Tuple         =						alt_pipe.to(__UpperCAmelCase							)
         alt_pipe.set_progress_bar_config(disable=__UpperCAmelCase							)

         A_  :					Optional[Any]         =						"""A painting of a squirrel eating a burger"""
         A_  :					Optional[int]         =						torch.manual_seed(0							)
         A_  :					Optional[int]         =						alt_pipe(
             [prompt]   ,							generator=__UpperCAmelCase   ,							num_inference_steps=2   ,							output_type="""np"""   ,							image=__UpperCAmelCase   ,							).images

         assert image.shape == (1, 32, 32, 3)






      @unittest.skipIf(torch_device != """cuda"""   ,							"""This test requires a GPU"""							)
      def 		_lowerCamelCase   (		self							):
         A_  :					Tuple         =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/img2img/sketch-mountains-input.jpg"""							)
         # resize to resolution that is divisible by 8 but not 16 or 32
         A_  :					Optional[int]         =						init_image.resize((760, 504)							)

         A_  :					int         =						"""BAAI/AltDiffusion"""
         A_  :					Union[str, Any]         =						AltDiffusionImgaImgPipeline.from_pretrained(
             __UpperCAmelCase   ,							safety_checker=__UpperCAmelCase   ,							)
         pipe.to(__UpperCAmelCase							)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase							)
         pipe.enable_attention_slicing()

         A_  :					int         =						"""A fantasy landscape, trending on artstation"""

         A_  :					Union[str, Any]         =						torch.manual_seed(0							)
         A_  :					List[Any]         =						pipe(
             prompt=__UpperCAmelCase   ,							image=__UpperCAmelCase   ,							strength=0.75   ,							guidance_scale=7.5   ,							generator=__UpperCAmelCase   ,							output_type="""np"""   ,							)
         A_  :					Optional[Any]         =						output.images[0]

         A_  :					Optional[int]         =						image[255:258, 383:386, -1]

         assert image.shape == (504, 760, 3)
         A_  :					List[Any]         =						np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000]							)

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






@slow
@require_torch_gpu
class  _UpperCAmelCase				(		unittest.TestCase  ):
      def 		_lowerCamelCase   (		self							):
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






      def 		_lowerCamelCase   (		self							):
         A_  :					str         =						load_image(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
             """/img2img/sketch-mountains-input.jpg"""							)
         A_  :					Union[str, Any]         =						init_image.resize((768, 512)							)
         A_  :					Tuple         =						load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy"""							)

         A_  :					Union[str, Any]         =						"""BAAI/AltDiffusion"""
         A_  :					List[Any]         =						AltDiffusionImgaImgPipeline.from_pretrained(
             __UpperCAmelCase   ,							safety_checker=__UpperCAmelCase   ,							)
         pipe.to(__UpperCAmelCase							)
         pipe.set_progress_bar_config(disable=__UpperCAmelCase							)
         pipe.enable_attention_slicing()

         A_  :					List[str]         =						"""A fantasy landscape, trending on artstation"""

         A_  :					Optional[Any]         =						torch.manual_seed(0							)
         A_  :					Optional[Any]         =						pipe(
             prompt=__UpperCAmelCase   ,							image=__UpperCAmelCase   ,							strength=0.75   ,							guidance_scale=7.5   ,							generator=__UpperCAmelCase   ,							output_type="""np"""   ,							)
         A_  :					List[str]         =						output.images[0]

         assert image.shape == (512, 768, 3)
         # img2img is flaky across GPUs even in fp32, so using MAE here
         assert np.abs(expected_image - image							).max() < 1E-2














import argparse

import torch

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


logging.set_verbosity_info()
UpperCAmelCase__							  =						logging.get_logger(__name__)

UpperCAmelCase__							  =						[
    ["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 		_a       ( a					:Optional[Any]					)				->     List[Any]:
 if k == "embeddings.weight":
  return "shared.weight"

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

 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 		_a       ( a					:Dict					)				->     Tuple:
 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(a					)
  a						    = k.replace('''layernorm_embedding'''      ,				'''layer_norm'''					)
  assert new_k not in sd
  a						    = v


UpperCAmelCase__							  =						["START"]
@torch.no_grad()
def 		_a       ( a					:Dict      ,				a					:str      ,				a					:int					)				->     int:
 a						    = torch.load(a      ,				map_location='''cpu'''					)
 a						    = model['''model''']
 a						    = BlenderbotConfig.from_json_file(a					)
 a						    = BlenderbotForConditionalGeneration(a					)
 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(a					)
  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(a					)
 m.model.load_state_dict(a      ,				strict=a					)
 m.half()
 m.save_pretrained(a					)


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


