




"""simple docstring"""




import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class       a__	( UpperCamelCase__       , UpperCamelCase__       , unittest.TestCase				):
     a		:      Optional[Any]		 =     StableDiffusionSAGPipeline
     a		:      List[str]		 =     TEXT_TO_IMAGE_PARAMS
     a		:      List[str]		 =     TEXT_TO_IMAGE_BATCH_PARAMS
     a		:      str		 =     TEXT_TO_IMAGE_IMAGE_PARAMS
     a		:      List[str]		 =     TEXT_TO_IMAGE_IMAGE_PARAMS
     a		:      int		 =     False







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          torch.manual_seed(0			)
          a     =				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  ,		)
          a     =				DDIMScheduler(
              beta_start=0.0_0_0_8_5  ,		beta_end=0.0_1_2  ,		beta_schedule="scaled_linear"  ,		clip_sample=A  ,		set_alpha_to_one=A  ,		)
          torch.manual_seed(0			)
          a     =				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  ,		)
          torch.manual_seed(0			)
          a     =				CLIPTextConfig(
              bos_token_id=0  ,		eos_token_id=2  ,		hidden_size=32  ,		intermediate_size=37  ,		layer_norm_eps=1e-05  ,		num_attention_heads=4  ,		num_hidden_layers=5  ,		pad_token_id=1  ,		vocab_size=1000  ,		)
          a     =				CLIPTextModel(A			)
          a     =				CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			)

          a     =				{
              "unet": unet,
              "scheduler": scheduler,
              "vae": vae,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "safety_checker": None,
              "feature_extractor": None,
          }
          return components







     def       lowerCAmelCase_    (     self  ,		A  ,		A=0			)  ->   str:



          '''simple docstring'''



          if str(A			).startswith("mps"			):
               a     =				torch.manual_seed(A			)
          else:
               a     =				torch.Generator(device=A			).manual_seed(A			)
          a     =				{
              "prompt": ".",
              "generator": generator,
              "num_inference_steps": 2,
              "guidance_scale": 1.0,
              "sag_scale": 1.0,
              "output_type": "numpy",
          }
          return inputs







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          super().test_inference_batch_single_identical(expected_max_diff=3e-3			)







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







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



          '''simple docstring'''



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







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          a     =				StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"			)
          a     =				sag_pipe.to(A			)
          sag_pipe.set_progress_bar_config(disable=A			)

          a     =				"."
          a     =				torch.manual_seed(0			)
          a     =				sag_pipe(
              [prompt]  ,		generator=A  ,		guidance_scale=7.5  ,		sag_scale=1.0  ,		num_inference_steps=20  ,		output_type="np"			)

          a     =				output.images

          a     =				image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          a     =				np.array([0.1_5_6_8, 0.1_7_3_8, 0.1_6_9_5, 0.1_6_9_3, 0.1_5_0_7, 0.1_7_0_5, 0.1_5_4_7, 0.1_7_5_1, 0.1_9_4_9]			)

          assert np.abs(image_slice.flatten() - expected_slice			).max() < 5e-2







     def       lowerCAmelCase_    (     self			)  ->   int:



          '''simple docstring'''



          a     =				StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"			)
          a     =				sag_pipe.to(A			)
          sag_pipe.set_progress_bar_config(disable=A			)

          a     =				"."
          a     =				torch.manual_seed(0			)
          a     =				sag_pipe(
              [prompt]  ,		generator=A  ,		guidance_scale=7.5  ,		sag_scale=1.0  ,		num_inference_steps=20  ,		output_type="np"			)

          a     =				output.images

          a     =				image[0, -3:, -3:, -1]

          assert image.shape == (1, 512, 512, 3)
          a     =				np.array([0.3_4_5_9, 0.2_8_7_6, 0.2_5_3_7, 0.3_0_0_2, 0.2_6_7_1, 0.2_1_6_0, 0.3_0_2_6, 0.2_2_6_2, 0.2_3_7_1]			)

          assert np.abs(image_slice.flatten() - expected_slice			).max() < 5e-2







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



          '''simple docstring'''



          a     =				StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"			)
          a     =				sag_pipe.to(A			)
          sag_pipe.set_progress_bar_config(disable=A			)

          a     =				"."
          a     =				torch.manual_seed(0			)
          a     =				sag_pipe(
              [prompt]  ,		width=768  ,		height=512  ,		generator=A  ,		guidance_scale=7.5  ,		sag_scale=1.0  ,		num_inference_steps=20  ,		output_type="np"  ,		)

          a     =				output.images

          assert image.shape == (1, 512, 768, 3)







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)

