

"""simple docstring"""

import glob
import os
from typing import Dict, List, Union

import torch

from diffusers.utils import is_safetensors_available


if is_safetensors_available():
							import safetensors.torch

from huggingface_hub import snapshot_download

from diffusers import DiffusionPipeline, __version__
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME


class       _snake_case  (    lowerCamelCase_							):







						def __init__(self				)      ->    int:
											self.register_to_config()
											super().__init__()







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    List[str]:




											if dicta == dicta:
																return True
											else:
																__lowercase        =      self._remove_meta_keys(UpperCAmelCase_				)
																__lowercase        =      self._remove_meta_keys(UpperCAmelCase_				)
																if configa == configa:
																					print(f'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'''				)
																					return True
											return False







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_				)      ->    Any:
											__lowercase        =      []
											__lowercase        =      config_dict.copy()
											for key in config_dict.keys():
																if key.startswith('''_'''				):
																					temp_dict.pop(UpperCAmelCase_				)
																					meta_keys.append(UpperCAmelCase_				)
											return (temp_dict, meta_keys)







						@torch.no_grad()
						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				**UpperCAmelCase_				)      ->    List[Any]:
											__lowercase        =      kwargs.pop('''cache_dir'''  ,				UpperCAmelCase_				)
											__lowercase        =      kwargs.pop('''resume_download'''  ,				UpperCAmelCase_				)
											__lowercase        =      kwargs.pop('''force_download'''  ,				UpperCAmelCase_				)
											__lowercase        =      kwargs.pop('''proxies'''  ,				UpperCAmelCase_				)
											__lowercase        =      kwargs.pop('''local_files_only'''  ,				UpperCAmelCase_				)
											__lowercase        =      kwargs.pop('''use_auth_token'''  ,				UpperCAmelCase_				)
											__lowercase        =      kwargs.pop('''revision'''  ,				UpperCAmelCase_				)
											__lowercase        =      kwargs.pop('''torch_dtype'''  ,				UpperCAmelCase_				)
											__lowercase        =      kwargs.pop('''device_map'''  ,				UpperCAmelCase_				)

											__lowercase        =      kwargs.pop('''alpha'''  ,				0.5				)
											__lowercase        =      kwargs.pop('''interp'''  ,				UpperCAmelCase_				)

											print('''Received list'''  ,				UpperCAmelCase_				)
											print(f'''Combining with alpha={alpha}, interpolation mode={interp}'''				)

											__lowercase        =      len(UpperCAmelCase_				)
											# Ignore result from model_index_json comparision of the two checkpoints
											__lowercase        =      kwargs.pop('''force'''  ,				UpperCAmelCase_				)

											# If less than 2 checkpoints, nothing to merge. If more than 3, not supported for now.
											if checkpoint_count > 3 or checkpoint_count < 2:
																raise ValueError(
																    '''Received incorrect number of checkpoints to merge. Ensure that either 2 or 3 checkpoints are being'''
																    ''' passed.'''				)

											print('''Received the right number of checkpoints'''				)
											# chkpt0, chkpt1 = pretrained_model_name_or_path_list[0:2]
											# chkpt2 = pretrained_model_name_or_path_list[2] if checkpoint_count == 3 else None

											# Validate that the checkpoints can be merged
											# Step 1: Load the model config and compare the checkpoints. We'll compare the model_index.json first while ignoring the keys starting with '_'
											__lowercase        =      []
											for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
																__lowercase        =      DiffusionPipeline.load_config(
																    UpperCAmelCase_  ,				cache_dir=UpperCAmelCase_  ,				resume_download=UpperCAmelCase_  ,				force_download=UpperCAmelCase_  ,				proxies=UpperCAmelCase_  ,				local_files_only=UpperCAmelCase_  ,				use_auth_token=UpperCAmelCase_  ,				revision=UpperCAmelCase_  ,				)
																config_dicts.append(UpperCAmelCase_				)

											__lowercase        =      True
											for idx in range(1  ,				len(UpperCAmelCase_				)				):
																comparison_result &= self._compare_model_configs(config_dicts[idx - 1]  ,				config_dicts[idx]				)
																if not force and comparison_result is False:
																					raise ValueError('''Incompatible checkpoints. Please check model_index.json for the models.'''				)
																					print(config_dicts[0]  ,				config_dicts[1]				)
											print('''Compatible model_index.json files found'''				)
											# Step 2: Basic Validation has succeeded. Let's download the models and save them into our local files.
											__lowercase        =      []
											for pretrained_model_name_or_path, config_dict in zip(UpperCAmelCase_  ,				UpperCAmelCase_				):
																__lowercase        =      [k for k in config_dict.keys() if not k.startswith('''_'''				)]
																__lowercase        =      [os.path.join(UpperCAmelCase_  ,				'''*'''				) for k in folder_names]
																allow_patterns += [
																    WEIGHTS_NAME,
																    SCHEDULER_CONFIG_NAME,
																    CONFIG_NAME,
																    ONNX_WEIGHTS_NAME,
																    DiffusionPipeline.config_name,
																]
																__lowercase        =      config_dict.get('''_class_name'''				)
																__lowercase        =      {'''diffusers''': __version__, '''pipeline_class''': requested_pipeline_class}

																__lowercase        =      (
																    pretrained_model_name_or_path
																    if os.path.isdir(UpperCAmelCase_				)
																    else snapshot_download(
																        UpperCAmelCase_  ,				cache_dir=UpperCAmelCase_  ,				resume_download=UpperCAmelCase_  ,				proxies=UpperCAmelCase_  ,				local_files_only=UpperCAmelCase_  ,				use_auth_token=UpperCAmelCase_  ,				revision=UpperCAmelCase_  ,				allow_patterns=UpperCAmelCase_  ,				user_agent=UpperCAmelCase_  ,				)
																)
																print('''Cached Folder'''  ,				UpperCAmelCase_				)
																cached_folders.append(UpperCAmelCase_				)

											# Step 3:-
											# Load the first checkpoint as a diffusion pipeline and modify its module state_dict in place
											__lowercase        =      DiffusionPipeline.from_pretrained(
											    cached_folders[0]  ,				torch_dtype=UpperCAmelCase_  ,				device_map=UpperCAmelCase_				)
											final_pipe.to(self.device				)

											__lowercase        =      None
											if len(UpperCAmelCase_				) > 2:
																__lowercase        =      os.path.join(cached_folders[2]				)

											if interp == "sigmoid":
																__lowercase        =      CheckpointMergerPipeline.sigmoid
											elif interp == "inv_sigmoid":
																__lowercase        =      CheckpointMergerPipeline.inv_sigmoid
											elif interp == "add_diff":
																__lowercase        =      CheckpointMergerPipeline.add_difference
											else:
																__lowercase        =      CheckpointMergerPipeline.weighted_sum

											# Find each module's state dict.
											for attr in final_pipe.config.keys():
																if not attr.startswith('''_'''				):
																					__lowercase        =      os.path.join(cached_folders[1]  ,				UpperCAmelCase_				)
																					if os.path.exists(UpperCAmelCase_				):
																										__lowercase        =      [
																										    *glob.glob(os.path.join(UpperCAmelCase_  ,				'''*.safetensors'''				)				),
																										    *glob.glob(os.path.join(UpperCAmelCase_  ,				'''*.bin'''				)				),
																										]
																										__lowercase        =      files[0] if len(UpperCAmelCase_				) > 0 else None
																					if len(UpperCAmelCase_				) < 3:
																										__lowercase        =      None
																					else:
																										__lowercase        =      os.path.join(cached_folders[2]  ,				UpperCAmelCase_				)
																										if os.path.exists(UpperCAmelCase_				):
																															__lowercase        =      [
																															    *glob.glob(os.path.join(UpperCAmelCase_  ,				'''*.safetensors'''				)				),
																															    *glob.glob(os.path.join(UpperCAmelCase_  ,				'''*.bin'''				)				),
																															]
																															__lowercase        =      files[0] if len(UpperCAmelCase_				) > 0 else None
                # For an attr if both checkpoint_path_1 and 2 are None, ignore.
                # If atleast one is present, deal with it according to interp method, of course only if the state_dict keys match.
																					if checkpoint_path_a is None and checkpoint_path_a is None:
																										print(f'''Skipping {attr}: not present in 2nd or 3d model'''				)
																										continue
																					try:
																										__lowercase        =      getattr(UpperCAmelCase_  ,				UpperCAmelCase_				)
																										if isinstance(UpperCAmelCase_  ,				UpperCAmelCase_				):  # ignore requires_safety_checker boolean
																															continue
																										__lowercase        =      getattr(UpperCAmelCase_  ,				'''state_dict'''				)
																										__lowercase        =      theta_a()

																										__lowercase        =      getattr(UpperCAmelCase_  ,				'''load_state_dict'''				)
																										__lowercase        =      (
																										    safetensors.torch.load_file(UpperCAmelCase_				)
																										    if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors'''				))
																										    else torch.load(UpperCAmelCase_  ,				map_location='''cpu'''				)
																										)
																										__lowercase        =      None
																										if checkpoint_path_a:
																															__lowercase        =      (
																															    safetensors.torch.load_file(UpperCAmelCase_				)
																															    if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors'''				))
																															    else torch.load(UpperCAmelCase_  ,				map_location='''cpu'''				)
																															)

																										if not theta_a.keys() == theta_a.keys():
																															print(f'''Skipping {attr}: key mismatch'''				)
																															continue
																										if theta_a and not theta_a.keys() == theta_a.keys():
																															print(f'''Skipping {attr}:y mismatch'''				)
																					except Exception as e:
																										print(f'''Skipping {attr} do to an unexpected error: {str(UpperCAmelCase_				)}'''				)
																										continue
																					print(f'''MERGING {attr}'''				)

																					for key in theta_a.keys():
																										if theta_a:
																															__lowercase        =      theta_func(theta_a[key]  ,				theta_a[key]  ,				theta_a[key]  ,				UpperCAmelCase_				)
																										else:
																															__lowercase        =      theta_func(theta_a[key]  ,				theta_a[key]  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)

																					del theta_a
																					del theta_a
																					update_theta_a(UpperCAmelCase_				)

																					del theta_a
											return final_pipe







						@staticmethod
						def 							_UpperCamelCase     (UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    Any:
											return ((1 - alpha) * thetaa) + (alpha * thetaa)







						@staticmethod
						def 							_UpperCamelCase     (UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    Dict:
											__lowercase        =      alpha * alpha * (3 - (2 * alpha))
											return thetaa + ((thetaa - thetaa) * alpha)







						@staticmethod
						def 							_UpperCamelCase     (UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    Dict:
											import math

											__lowercase        =      0.5 - math.sin(math.asin(1.0 - 2.0 * alpha				) / 3.0				)
											return thetaa + ((thetaa - thetaa) * alpha)







						@staticmethod
						def 							_UpperCamelCase     (UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    str:
											return thetaa + (thetaa - thetaa) * (1.0 - alpha)








'''simple docstring'''






import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
      from .testing_utils import (
          floats_tensor,
          load_hf_numpy,
          load_image,
          load_numpy,
          load_pt,
          nightly,
          parse_flag_from_env,
          print_tensor_test,
          require_torch_a,
          require_torch_gpu,
          skip_mps,
          slow,
          torch_all_close,
          torch_device,
      )
      from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


lowercase						:   Tuple      						=							get_logger(__name__)




def 		a_(			lowerCamelCase   )					->Any:






       '''simple docstring'''




       if version.parse(lowerCamelCase   ) < version.parse(lowerCamelCase   ):
              if "dev" in min_version:
                     lowercase	:		str    =   (
                         '''This example requires a source install from HuggingFace diffusers (see '''
                         '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
                     )
              else:
                     lowercase	:		List[str]    =   f"""This example requires a minimum version of {min_version},"""
              error_message += f""" but the version found is {__version__}.\n"""
              raise ImportError(lowerCamelCase   )




