






'''simple docstring'''





import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
 from transformers import LlamaTokenizerFast
except ImportError as e:
 warnings.warn(e)
 warnings.warn(
     """The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"""
 )
 __A							:							int  							=		None
__A							:							List[str]  							=		{
    """7B""": 1_10_08,
    """13B""": 1_38_24,
    """30B""": 1_79_20,
    """65B""": 2_20_16,
    """70B""": 2_86_72,
}
__A							:							int  							=		{
    """7B""": 1,
    """7Bf""": 1,
    """13B""": 2,
    """13Bf""": 2,
    """30B""": 4,
    """65B""": 8,
    """70B""": 8,
    """70Bf""": 8,
}






def    lowerCamelCase_     (    lowercase__				,     lowercase__=1				,     lowercase__=256):
 return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3)) + multiple_of - 1) // multiple_of)






def    lowerCamelCase_     (    lowercase__):


 with open(lowerCAmelCase_				,     "r") as f:
  return json.load(lowerCAmelCase_)






def    lowerCamelCase_     (    lowercase__				,     lowercase__):


 with open(lowerCAmelCase_				,     "w") as f:
  json.dump(lowerCAmelCase_				,     lowerCAmelCase_)






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__				,     lowercase__=True):
 os.makedirs(lowerCAmelCase_				,     exist_ok=lowerCAmelCase_)
 lowerCamelCase__  =     os.path.join(lowerCAmelCase_				,     "tmp")
 os.makedirs(lowerCAmelCase_				,     exist_ok=lowerCAmelCase_)

 lowerCamelCase__  =     read_json(os.path.join(lowerCAmelCase_				,     "params.json"))
 lowerCamelCase__  =     NUM_SHARDS[model_size]
 lowerCamelCase__  =     params["n_layers"]
 lowerCamelCase__  =     params["n_heads"]
 lowerCamelCase__  =     n_heads // num_shards
 lowerCamelCase__  =     params["dim"]
 lowerCamelCase__  =     dim // n_heads
 lowerCamelCase__  =     10000.0
 lowerCamelCase__  =     1.0 / (base ** (torch.arange(0				,     lowerCAmelCase_				,     2).float() / dims_per_head))

 if "n_kv_heads" in params:
  lowerCamelCase__  =     params["n_kv_heads"]  # for GQA / MQA
  lowerCamelCase__  =     n_heads_per_shard // num_key_value_heads
  lowerCamelCase__  =     dim // num_key_value_heads
 else:  # compatibility with other checkpoints
  lowerCamelCase__  =     n_heads
  lowerCamelCase__  =     n_heads_per_shard
  lowerCamelCase__  =     dim

 # permute for sliced rotary
 def permute(lowercase__				,     lowercase__=n_heads				,     lowercase__=dim				,     lowercase__=dim):
  return w.view(lowerCAmelCase_				,     dima // n_heads // 2				,     2				,     lowerCAmelCase_).transpose(1				,     2).reshape(lowerCAmelCase_				,     lowerCAmelCase_)

 print(F'''Fetching all parameters from the checkpoint at {input_base_path}.''')
 # Load weights
 if model_size == "7B":
  # Not sharded
  # (The sharded implementation would also work, but this is simpler.)
  lowerCamelCase__  =     torch.load(os.path.join(lowerCAmelCase_				,     "consolidated.00.pth")				,     map_location="cpu")
 else:
  # Sharded
  lowerCamelCase__  =     [
      torch.load(os.path.join(lowerCAmelCase_				,     F'''consolidated.{i:02d}.pth''')				,     map_location="cpu")
      for i in range(lowerCAmelCase_)
  ]
 lowerCamelCase__  =     0
 lowerCamelCase__  =     {"weight_map": {}}
 for layer_i in range(lowerCAmelCase_):
  lowerCamelCase__  =     F'''pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin'''
  if model_size == "7B":
   # Unsharded
   lowerCamelCase__  =     {
       F'''model.layers.{layer_i}.self_attn.q_proj.weight''': permute(
           loaded[F'''layers.{layer_i}.attention.wq.weight''']),
       F'''model.layers.{layer_i}.self_attn.k_proj.weight''': permute(
           loaded[F'''layers.{layer_i}.attention.wk.weight''']),
       F'''model.layers.{layer_i}.self_attn.v_proj.weight''': loaded[F'''layers.{layer_i}.attention.wv.weight'''],
       F'''model.layers.{layer_i}.self_attn.o_proj.weight''': loaded[F'''layers.{layer_i}.attention.wo.weight'''],
       F'''model.layers.{layer_i}.mlp.gate_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w1.weight'''],
       F'''model.layers.{layer_i}.mlp.down_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w2.weight'''],
       F'''model.layers.{layer_i}.mlp.up_proj.weight''': loaded[F'''layers.{layer_i}.feed_forward.w3.weight'''],
       F'''model.layers.{layer_i}.input_layernorm.weight''': loaded[F'''layers.{layer_i}.attention_norm.weight'''],
       F'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[F'''layers.{layer_i}.ffn_norm.weight'''],
   }
  else:
   # Sharded
   # Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
   # the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
   # redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

   lowerCamelCase__  =     {
       F'''model.layers.{layer_i}.input_layernorm.weight''': loaded[0][
           F'''layers.{layer_i}.attention_norm.weight'''
       ].clone(),
       F'''model.layers.{layer_i}.post_attention_layernorm.weight''': loaded[0][
           F'''layers.{layer_i}.ffn_norm.weight'''
       ].clone(),
   }
   lowerCamelCase__  =     permute(
       torch.cat(
           [
               loaded[i][F'''layers.{layer_i}.attention.wq.weight'''].view(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_)
               for i in range(lowerCAmelCase_)
           ]				,     dim=0				,     ).reshape(lowerCAmelCase_				,     lowerCAmelCase_))
   lowerCamelCase__  =     permute(
       torch.cat(
           [
               loaded[i][F'''layers.{layer_i}.attention.wk.weight'''].view(
                   lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_)
               for i in range(lowerCAmelCase_)
           ]				,     dim=0				,     ).reshape(lowerCAmelCase_				,     lowerCAmelCase_)				,     lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     )
   lowerCamelCase__  =     torch.cat(
       [
           loaded[i][F'''layers.{layer_i}.attention.wv.weight'''].view(
               lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_)
           for i in range(lowerCAmelCase_)
       ]				,     dim=0				,     ).reshape(lowerCAmelCase_				,     lowerCAmelCase_)

   lowerCamelCase__  =     torch.cat(
       [loaded[i][F'''layers.{layer_i}.attention.wo.weight'''] for i in range(lowerCAmelCase_)]				,     dim=1)
   lowerCamelCase__  =     torch.cat(
       [loaded[i][F'''layers.{layer_i}.feed_forward.w1.weight'''] for i in range(lowerCAmelCase_)]				,     dim=0)
   lowerCamelCase__  =     torch.cat(
       [loaded[i][F'''layers.{layer_i}.feed_forward.w2.weight'''] for i in range(lowerCAmelCase_)]				,     dim=1)
   lowerCamelCase__  =     torch.cat(
       [loaded[i][F'''layers.{layer_i}.feed_forward.w3.weight'''] for i in range(lowerCAmelCase_)]				,     dim=0)

  lowerCamelCase__  =     inv_freq
  for k, v in state_dict.items():
   lowerCamelCase__  =     filename
   param_count += v.numel()
  torch.save(lowerCAmelCase_				,     os.path.join(lowerCAmelCase_				,     lowerCAmelCase_))

 lowerCamelCase__  =     F'''pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin'''
 if model_size == "7B":
  # Unsharded
  lowerCamelCase__  =     {
      "model.embed_tokens.weight": loaded["tok_embeddings.weight"],
      "model.norm.weight": loaded["norm.weight"],
      "lm_head.weight": loaded["output.weight"],
  }
 else:
  lowerCamelCase__  =     {
      "model.norm.weight": loaded[0]["norm.weight"],
      "model.embed_tokens.weight": torch.cat(
          [loaded[i]["tok_embeddings.weight"] for i in range(lowerCAmelCase_)]				,     dim=1),
      "lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(lowerCAmelCase_)]				,     dim=0),
  }

 for k, v in state_dict.items():
  lowerCamelCase__  =     filename
  param_count += v.numel()
 torch.save(lowerCAmelCase_				,     os.path.join(lowerCAmelCase_				,     lowerCAmelCase_))

 # Write configs
 lowerCamelCase__  =     {"total_size": param_count * 2}
 write_json(lowerCAmelCase_				,     os.path.join(lowerCAmelCase_				,     "pytorch_model.bin.index.json"))
 lowerCamelCase__  =     params["ffn_dim_multiplier"] if "ffn_dim_multiplier" in params else 1
 lowerCamelCase__  =     params["multiple_of"] if "multiple_of" in params else 256
 lowerCamelCase__  =     LlamaConfig(
     hidden_size=lowerCAmelCase_				,     intermediate_size=compute_intermediate_size(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_)				,     num_attention_heads=params["n_heads"]				,     num_hidden_layers=params["n_layers"]				,     rms_norm_eps=params["norm_eps"]				,     num_key_value_heads=lowerCAmelCase_				,     )
 config.save_pretrained(lowerCAmelCase_)

 # Make space so we can load the model properly now.
 del state_dict
 del loaded
 gc.collect()

 print("Loading the checkpoint in a Llama model.")
 lowerCamelCase__  =     LlamaForCausalLM.from_pretrained(lowerCAmelCase_				,     torch_dtype=torch.floataa				,     low_cpu_mem_usage=lowerCAmelCase_)
 # Avoid saving this as part of the config.
 del model.config._name_or_path

 print("Saving in the Transformers format.")
 model.save_pretrained(lowerCAmelCase_				,     safe_serialization=lowerCAmelCase_)
 shutil.rmtree(lowerCAmelCase_)






def    lowerCamelCase_     (    lowercase__				,     lowercase__):
 lowerCamelCase__  =     LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
 print(F'''Saving a {tokenizer_class.__name__} to {tokenizer_path}.''')
 lowerCamelCase__  =     tokenizer_class(lowerCAmelCase_)
 tokenizer.save_pretrained(lowerCAmelCase_)






def    lowerCamelCase_     (    ):
 lowerCamelCase__  =     argparse.ArgumentParser()
 parser.add_argument(
     "--input_dir"				,     help="Location of LLaMA weights, which contains tokenizer.model and model folders"				,     )
 parser.add_argument(
     "--model_size"				,     choices=["7B", "7Bf", "13B", "13Bf", "30B", "65B", "70B", "70Bf", "tokenizer_only"]				,     )
 parser.add_argument(
     "--output_dir"				,     help="Location to write HF model and tokenizer"				,     )
 parser.add_argument("--safe_serialization"				,     type=lowerCAmelCase_				,     help="Whether or not to save using `safetensors`.")
 lowerCamelCase__  =     parser.parse_args()
 if args.model_size != "tokenizer_only":
  write_model(
      model_path=args.output_dir				,     input_base_path=os.path.join(args.input_dir				,     args.model_size)				,     model_size=args.model_size				,     safe_serialization=args.safe_serialization				,     )
 lowerCamelCase__  =     os.path.join(args.input_dir				,     "tokenizer.model")
 write_tokenizer(args.output_dir				,     lowerCAmelCase_)


if __name__ == "__main__":
 main()









'''simple docstring'''





import gc
import random
import tempfile
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 				lowercase       (							_lowerCamelCase ,       _lowerCamelCase ,       unittest.TestCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							StableDiffusionDiffEditPipeline
    lowerCAmelCase__      				=							TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
    lowerCAmelCase__      				=							TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
    lowerCAmelCase__      				=							frozenset(
        []      )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    lowerCAmelCase__      				=							frozenset([]      )

    def    a__				(					self     :     int     )							->							Dict:

     '''simple docstring'''




     torch.manual_seed(0     )
     lowerCamelCase__  =     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				,     attention_head_dim=(2, 4)				,     use_linear_projection=__lowerCamelCase				,     )
     lowerCamelCase__  =     DDIMScheduler(
         beta_start=0.0_0_0_8_5				,     beta_end=0.0_1_2				,     beta_schedule="scaled_linear"				,     clip_sample=__lowerCamelCase				,     set_alpha_to_one=__lowerCamelCase				,     )
     lowerCamelCase__  =     DDIMInverseScheduler(
         beta_start=0.0_0_0_8_5				,     beta_end=0.0_1_2				,     beta_schedule="scaled_linear"				,     clip_sample=__lowerCamelCase				,     set_alpha_to_zero=__lowerCamelCase				,     )
     torch.manual_seed(0     )
     lowerCamelCase__  =     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				,     sample_size=128				,     )
     torch.manual_seed(0     )
     lowerCamelCase__  =     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				,     hidden_act="gelu"				,     projection_dim=512				,     )
     lowerCamelCase__  =     CLIPTextModel(__lowerCamelCase     )
     lowerCamelCase__  =     CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"     )

     lowerCamelCase__  =     {
         "unet": unet,
         "scheduler": scheduler,
         "inverse_scheduler": inverse_scheduler,
         "vae": vae,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "safety_checker": None,
         "feature_extractor": None,
     }

     return components

    def    a__				(					self     :     List[Any]				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     Any=0     )							->							Tuple:

     '''simple docstring'''




     lowerCamelCase__  =     floats_tensor((1, 16, 16)				,     rng=random.Random(__lowerCamelCase     )     ).to(__lowerCamelCase     )
     lowerCamelCase__  =     floats_tensor((1, 2, 4, 16, 16)				,     rng=random.Random(__lowerCamelCase     )     ).to(__lowerCamelCase     )
     if str(__lowerCamelCase     ).startswith("mps"     ):
      lowerCamelCase__  =     torch.manual_seed(__lowerCamelCase     )
     else:
      lowerCamelCase__  =     torch.Generator(device=__lowerCamelCase     ).manual_seed(__lowerCamelCase     )
     lowerCamelCase__  =     {
         "prompt": "a dog and a newt",
         "mask_image": mask,
         "image_latents": latents,
         "generator": generator,
         "num_inference_steps": 2,
         "inpaint_strength": 1.0,
         "guidance_scale": 6.0,
         "output_type": "numpy",
     }

     return inputs

    def    a__				(					self     :     str				,     __lowerCamelCase     :     Optional[Any]				,     __lowerCamelCase     :     Optional[int]=0     )							->							Optional[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     floats_tensor((1, 3, 32, 32)				,     rng=random.Random(__lowerCamelCase     )     ).to(__lowerCamelCase     )
     lowerCamelCase__  =     image.cpu().permute(0				,     2				,     3				,     1     )[0]
     lowerCamelCase__  =     Image.fromarray(np.uinta(__lowerCamelCase     )     ).convert("RGB"     )
     if str(__lowerCamelCase     ).startswith("mps"     ):
      lowerCamelCase__  =     torch.manual_seed(__lowerCamelCase     )
     else:
      lowerCamelCase__  =     torch.Generator(device=__lowerCamelCase     ).manual_seed(__lowerCamelCase     )
     lowerCamelCase__  =     {
         "image": image,
         "source_prompt": "a cat and a frog",
         "target_prompt": "a dog and a newt",
         "generator": generator,
         "num_inference_steps": 2,
         "num_maps_per_mask": 2,
         "mask_encode_strength": 1.0,
         "guidance_scale": 6.0,
         "output_type": "numpy",
     }

     return inputs

    def    a__				(					self     :     Optional[int]				,     __lowerCamelCase     :     Optional[int]				,     __lowerCamelCase     :     List[str]=0     )							->							List[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     floats_tensor((1, 3, 32, 32)				,     rng=random.Random(__lowerCamelCase     )     ).to(__lowerCamelCase     )
     lowerCamelCase__  =     image.cpu().permute(0				,     2				,     3				,     1     )[0]
     lowerCamelCase__  =     Image.fromarray(np.uinta(__lowerCamelCase     )     ).convert("RGB"     )
     if str(__lowerCamelCase     ).startswith("mps"     ):
      lowerCamelCase__  =     torch.manual_seed(__lowerCamelCase     )
     else:
      lowerCamelCase__  =     torch.Generator(device=__lowerCamelCase     ).manual_seed(__lowerCamelCase     )
     lowerCamelCase__  =     {
         "image": image,
         "prompt": "a cat and a frog",
         "generator": generator,
         "num_inference_steps": 2,
         "inpaint_strength": 1.0,
         "guidance_scale": 6.0,
         "decode_latents": True,
         "output_type": "numpy",
     }
     return inputs

    def    a__				(					self     :     Tuple     )							->							str:

     '''simple docstring'''




     if not hasattr(self.pipeline_class				,     "_optional_components"     ):
      return

     lowerCamelCase__  =     self.get_dummy_components()
     lowerCamelCase__  =     self.pipeline_class(**__lowerCamelCase     )
     pipe.to(__lowerCamelCase     )
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     # set all optional components to None and update pipeline config accordingly
     for optional_component in pipe._optional_components:
      setattr(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase     )
     pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}     )

     lowerCamelCase__  =     self.get_dummy_inputs(__lowerCamelCase     )
     lowerCamelCase__  =     pipe(**__lowerCamelCase     )[0]

     with tempfile.TemporaryDirectory() as tmpdir:
      pipe.save_pretrained(__lowerCamelCase     )
      lowerCamelCase__  =     self.pipeline_class.from_pretrained(__lowerCamelCase     )
      pipe_loaded.to(__lowerCamelCase     )
      pipe_loaded.set_progress_bar_config(disable=__lowerCamelCase     )

     for optional_component in pipe._optional_components:
      self.assertTrue(
          getattr(__lowerCamelCase				,     __lowerCamelCase     ) is None				,     f'''`{optional_component}` did not stay set to None after loading.'''				,     )

     lowerCamelCase__  =     self.get_dummy_inputs(__lowerCamelCase     )
     lowerCamelCase__  =     pipe_loaded(**__lowerCamelCase     )[0]

     lowerCamelCase__  =     np.abs(output - output_loaded     ).max()
     self.assertLess(__lowerCamelCase				,     1E-4     )

    def    a__				(					self     :     List[str]     )							->							List[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     "cpu"

     lowerCamelCase__  =     self.get_dummy_components()
     lowerCamelCase__  =     self.pipeline_class(**__lowerCamelCase     )
     pipe.to(__lowerCamelCase     )
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     self.get_dummy_mask_inputs(__lowerCamelCase     )
     lowerCamelCase__  =     pipe.generate_mask(**__lowerCamelCase     )
     lowerCamelCase__  =     mask[0, -3:, -3:]

     self.assertEqual(mask.shape				,     (1, 16, 16)     )
     lowerCamelCase__  =     np.array([0] * 9     )
     lowerCamelCase__  =     np.abs(mask_slice.flatten() - expected_slice     ).max()
     self.assertLessEqual(__lowerCamelCase				,     1E-3     )
     self.assertEqual(mask[0, -3, -4]				,     0     )

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

     '''simple docstring'''




     lowerCamelCase__  =     "cpu"

     lowerCamelCase__  =     self.get_dummy_components()
     lowerCamelCase__  =     self.pipeline_class(**__lowerCamelCase     )
     pipe.to(__lowerCamelCase     )
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     self.get_dummy_inversion_inputs(__lowerCamelCase     )
     lowerCamelCase__  =     pipe.invert(**__lowerCamelCase     ).images
     lowerCamelCase__  =     image[0, -1, -3:, -3:]

     self.assertEqual(image.shape				,     (2, 32, 32, 3)     )
     lowerCamelCase__  =     np.array(
         [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]				,     )
     lowerCamelCase__  =     np.abs(image_slice.flatten() - expected_slice     ).max()
     self.assertLessEqual(__lowerCamelCase				,     1E-3     )

    def    a__				(					self     :     Tuple     )							->							Tuple:

     '''simple docstring'''




     super().test_inference_batch_single_identical(expected_max_diff=5E-3     )




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

     '''simple docstring'''




     lowerCamelCase__  =     "cpu"

     lowerCamelCase__  =     self.get_dummy_components()

     lowerCamelCase__  =     {"beta_start": 0.0_0_0_8_5, "beta_end": 0.0_1_2, "beta_schedule": "scaled_linear"}
     lowerCamelCase__  =     DPMSolverMultistepScheduler(**__lowerCamelCase     )
     lowerCamelCase__  =     DPMSolverMultistepInverseScheduler(**__lowerCamelCase     )

     lowerCamelCase__  =     self.pipeline_class(**__lowerCamelCase     )
     pipe.to(__lowerCamelCase     )
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     self.get_dummy_inversion_inputs(__lowerCamelCase     )
     lowerCamelCase__  =     pipe.invert(**__lowerCamelCase     ).images
     lowerCamelCase__  =     image[0, -1, -3:, -3:]

     self.assertEqual(image.shape				,     (2, 32, 32, 3)     )
     lowerCamelCase__  =     np.array(
         [0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9]				,     )
     lowerCamelCase__  =     np.abs(image_slice.flatten() - expected_slice     ).max()
     self.assertLessEqual(__lowerCamelCase				,     1E-3     )
@require_torch_gpu
@slow
class 				lowercase       (							unittest.TestCase      ):


    '''simple docstring'''

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

     '''simple docstring'''




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

    @classmethod
    def    a__				(					cls     :     int     )							->							List[Any]:

     '''simple docstring'''




     lowerCamelCase__  =     load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"     )

     lowerCamelCase__  =     raw_image.convert("RGB"     ).resize((768, 768)     )

     lowerCamelCase__  =     raw_image

    def    a__				(					self     :     Optional[Any]     )							->							str:

     '''simple docstring'''




     lowerCamelCase__  =     torch.manual_seed(0     )

     lowerCamelCase__  =     StableDiffusionDiffEditPipeline.from_pretrained(
         "stabilityai/stable-diffusion-2-1"				,     safety_checker=__lowerCamelCase				,     torch_dtype=torch.floataa     )
     lowerCamelCase__  =     DDIMScheduler.from_config(pipe.scheduler.config     )
     lowerCamelCase__  =     DDIMInverseScheduler.from_config(pipe.scheduler.config     )
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     "a bowl of fruit"
     lowerCamelCase__  =     "a bowl of pears"

     lowerCamelCase__  =     pipe.generate_mask(
         image=self.raw_image				,     source_prompt=__lowerCamelCase				,     target_prompt=__lowerCamelCase				,     generator=__lowerCamelCase				,     )

     lowerCamelCase__  =     pipe.invert(
         prompt=__lowerCamelCase				,     image=self.raw_image				,     inpaint_strength=0.7				,     generator=__lowerCamelCase     ).latents

     lowerCamelCase__  =     pipe(
         prompt=__lowerCamelCase				,     mask_image=__lowerCamelCase				,     image_latents=__lowerCamelCase				,     generator=__lowerCamelCase				,     negative_prompt=__lowerCamelCase				,     inpaint_strength=0.7				,     output_type="numpy"				,     ).images[0]

     lowerCamelCase__  =     (
         np.array(
             load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/diffedit/pears.png"     ).resize((768, 768)     )     )
         / 255
     )
     assert np.abs((expected_image - image).max()     ) < 5E-1




    def    a__				(					self     :     List[str]     )							->							Dict:

     '''simple docstring'''




     lowerCamelCase__  =     torch.manual_seed(0     )

     lowerCamelCase__  =     StableDiffusionDiffEditPipeline.from_pretrained(
         "stabilityai/stable-diffusion-2-1"				,     safety_checker=__lowerCamelCase				,     torch_dtype=torch.floataa     )
     lowerCamelCase__  =     DPMSolverMultistepScheduler.from_config(pipe.scheduler.config     )
     lowerCamelCase__  =     DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config     )
     pipe.enable_model_cpu_offload()
     pipe.set_progress_bar_config(disable=__lowerCamelCase     )

     lowerCamelCase__  =     "a bowl of fruit"
     lowerCamelCase__  =     "a bowl of pears"

     lowerCamelCase__  =     pipe.generate_mask(
         image=self.raw_image				,     source_prompt=__lowerCamelCase				,     target_prompt=__lowerCamelCase				,     generator=__lowerCamelCase				,     )

     lowerCamelCase__  =     pipe.invert(
         prompt=__lowerCamelCase				,     image=self.raw_image				,     inpaint_strength=0.7				,     generator=__lowerCamelCase				,     num_inference_steps=25				,     ).latents

     lowerCamelCase__  =     pipe(
         prompt=__lowerCamelCase				,     mask_image=__lowerCamelCase				,     image_latents=__lowerCamelCase				,     generator=__lowerCamelCase				,     negative_prompt=__lowerCamelCase				,     inpaint_strength=0.7				,     num_inference_steps=25				,     output_type="numpy"				,     ).images[0]

     lowerCamelCase__  =     (
         np.array(
             load_image(
                 "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
                 "/diffedit/pears.png"     ).resize((768, 768)     )     )
         / 255
     )
     assert np.abs((expected_image - image).max()     ) < 5E-1

