


'''simple docstring'''



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      a    (		unittest.TestCase	):


     """simple docstring"""






     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							List[str]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()

     @property
     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Dict:
          __UpperCAmelCase     :			Optional[Any]  							=      1
          __UpperCAmelCase     :			Dict  							=      3
          __UpperCAmelCase     :			Dict  							=      (32, 32)

          __UpperCAmelCase     :			str  							=      floats_tensor((batch_size, num_channels) + sizes							,			rng=random.Random(0      )      ).to(snake_case      )
          return image

     @property
     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							Union[str, Any]:
          torch.manual_seed(0      )
          __UpperCAmelCase     :			str  							=      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					:		str      )						->							int:
          torch.manual_seed(0      )
          __UpperCAmelCase     :			int  							=      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					:		Union[str, Any]      )						->							int:
          torch.manual_seed(0      )
          __UpperCAmelCase     :			Union[str, 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(snake_case      )

     @property
     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Optional[int]:
          def extract(*snake_case					:		Any							,			**snake_case					:		List[str]      ):



               class      a    :


                    """simple docstring"""






                    def __init__(    self					:		List[Any]      )						->							str:
                         __UpperCAmelCase     :			Dict  							=      torch.ones([0]      )






                    def 			lowerCamelCase__			(    self					:		Optional[int]							,			snake_case					:		int      )						->							List[Any]:
                         self.pixel_values.to(snake_case      )
                         return self



               return Out()

          return extract

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Dict:
          __UpperCAmelCase     :			Tuple  							=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
          __UpperCAmelCase     :			List[Any]  							=      self.dummy_cond_unet
          __UpperCAmelCase     :			Optional[int]  							=      PNDMScheduler(skip_prk_steps=snake_case      )
          __UpperCAmelCase     :			Optional[int]  							=      self.dummy_vae
          __UpperCAmelCase     :			Optional[Any]  							=      self.dummy_text_encoder
          __UpperCAmelCase     :			str  							=      XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''      )
          __UpperCAmelCase     :			Dict  							=      77

          __UpperCAmelCase     :			Optional[int]  							=      self.dummy_image.to(snake_case      )
          __UpperCAmelCase     :			Any  							=      init_image / 2 + 0.5

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

          __UpperCAmelCase     :			Dict  							=      '''A painting of a squirrel eating a burger'''
          __UpperCAmelCase     :			Any  							=      torch.Generator(device=snake_case      ).manual_seed(0      )
          __UpperCAmelCase     :			Any  							=      alt_pipe(
              [prompt]							,			generator=snake_case							,			guidance_scale=6.0							,			num_inference_steps=2							,			output_type='''np'''							,			image=snake_case							,			)

          __UpperCAmelCase     :			Optional[int]  							=      output.images

          __UpperCAmelCase     :			Tuple  							=      torch.Generator(device=snake_case      ).manual_seed(0      )
          __UpperCAmelCase     :			int  							=      alt_pipe(
              [prompt]							,			generator=snake_case							,			guidance_scale=6.0							,			num_inference_steps=2							,			output_type='''np'''							,			image=snake_case							,			return_dict=snake_case							,			)[0]

          __UpperCAmelCase     :			int  							=      image[0, -3:, -3:, -1]
          __UpperCAmelCase     :			List[Any]  							=      image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          __UpperCAmelCase     :			Optional[int]  							=      np.array([0.4_427, 0.3_731, 0.4_249, 0.4_941, 0.4_546, 0.4_148, 0.4_193, 0.4_666, 0.4_499]      )

          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					:		Union[str, Any]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Optional[int]  							=      self.dummy_cond_unet
          __UpperCAmelCase     :			Optional[Any]  							=      PNDMScheduler(skip_prk_steps=snake_case      )
          __UpperCAmelCase     :			Optional[Any]  							=      self.dummy_vae
          __UpperCAmelCase     :			List[str]  							=      self.dummy_text_encoder
          __UpperCAmelCase     :			Tuple  							=      XLMRobertaTokenizer.from_pretrained('''hf-internal-testing/tiny-xlm-roberta'''      )
          __UpperCAmelCase     :			Any  							=      77

          __UpperCAmelCase     :			Optional[int]  							=      self.dummy_image.to(snake_case      )

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

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

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

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






     @unittest.skipIf(torch_device != '''cuda'''							,			'''This test requires a GPU'''      )
     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Any:
          __UpperCAmelCase     :			Any  							=      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
          __UpperCAmelCase     :			Any  							=      init_image.resize((760, 504)      )

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

          __UpperCAmelCase     :			int  							=      '''A fantasy landscape, trending on artstation'''

          __UpperCAmelCase     :			Optional[int]  							=      torch.manual_seed(0      )
          __UpperCAmelCase     :			Any  							=      pipe(
              prompt=snake_case							,			image=snake_case							,			strength=0.75							,			guidance_scale=7.5							,			generator=snake_case							,			output_type='''np'''							,			)
          __UpperCAmelCase     :			Tuple  							=      output.images[0]

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

          assert image.shape == (504, 760, 3)
          __UpperCAmelCase     :			List[Any]  							=      np.array([0.9_358, 0.9_397, 0.9_599, 0.9_901, 1.0_000, 1.0_000, 0.9_882, 1.0_000, 1.0_000]      )

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



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


     """simple docstring"""






     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Any:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






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

          __UpperCAmelCase     :			Optional[Any]  							=      '''BAAI/AltDiffusion'''
          __UpperCAmelCase     :			Union[str, Any]  							=      AltDiffusionImgaImgPipeline.from_pretrained(
              snake_case							,			safety_checker=snake_case							,			)
          pipe.to(snake_case      )
          pipe.set_progress_bar_config(disable=snake_case      )
          pipe.enable_attention_slicing()

          __UpperCAmelCase     :			List[Any]  							=      '''A fantasy landscape, trending on artstation'''

          __UpperCAmelCase     :			Tuple  							=      torch.manual_seed(0      )
          __UpperCAmelCase     :			Dict  							=      pipe(
              prompt=snake_case							,			image=snake_case							,			strength=0.75							,			guidance_scale=7.5							,			generator=snake_case							,			output_type='''np'''							,			)
          __UpperCAmelCase     :			Any  							=      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



'''simple docstring'''



import argparse

import torch

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


logging.set_verbosity_info()
__UpperCAmelCase      :Any			    =       logging.get_logger(__name__)

__UpperCAmelCase      :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      _a     (						_lowercase :     Tuple  ):





     '''simple docstring'''






     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          __UpperCAmelCase     :			Any  							=      k.replace(_lowercase      , _lowercase  )

     if k.startswith('''encoder'''  ):
          __UpperCAmelCase     :			str  							=      k.replace('''.attn'''      , '''.self_attn'''  )
          __UpperCAmelCase     :			Any  							=      k.replace('''norm1'''      , '''self_attn_layer_norm'''  )
          __UpperCAmelCase     :			List[str]  							=      k.replace('''norm2'''      , '''final_layer_norm'''  )
     elif k.startswith('''decoder'''  ):
          __UpperCAmelCase     :			int  							=      k.replace('''norm1'''      , '''self_attn_layer_norm'''  )
          __UpperCAmelCase     :			Union[str, Any]  							=      k.replace('''norm2'''      , '''encoder_attn_layer_norm'''  )
          __UpperCAmelCase     :			List[Any]  							=      k.replace('''norm3'''      , '''final_layer_norm'''  )
     return k




def      _a     (						_lowercase :     Union[str, Any]  ):





     '''simple docstring'''






     __UpperCAmelCase     :			int  							=      [
         '''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     :			Any  							=      sd.pop(_lowercase  )
          __UpperCAmelCase     :			Optional[int]  							=      k.replace('''layernorm_embedding'''      , '''layer_norm'''  )
          assert new_k not in sd
          __UpperCAmelCase     :			List[str]  							=      v


__UpperCAmelCase      :str			    =       ["START"]




@torch.no_grad()
def      _a     (						_lowercase :     Optional[int]      , _lowercase :     Optional[int]      , _lowercase :     str  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Any  							=      torch.load(_lowercase      , map_location='''cpu'''  )
     __UpperCAmelCase     :			List[str]  							=      model['''model''']
     __UpperCAmelCase     :			Optional[Any]  							=      BlenderbotConfig.from_json_file(_lowercase  )
     __UpperCAmelCase     :			Optional[Any]  							=      BlenderbotForConditionalGeneration(_lowercase  )
     __UpperCAmelCase     :			Optional[Any]  							=      m.model.state_dict().keys()
     __UpperCAmelCase     :			int  							=      []
     __UpperCAmelCase     :			List[str]  							=      {}
     for k, v in sd.items():
          if k in IGNORE_KEYS:
               continue

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


if __name__ == "__main__":
      __UpperCAmelCase      :Optional[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"
      )
      __UpperCAmelCase      :Tuple			    =       parser.parse_args()
      convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)