


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     __lowerCamelCase     (  __a     ):


							"""simple docstring"""

							def __init__(  self  )     ->       Dict:






								'''simple docstring'''






								self.register_to_config()
								super().__init__()




							def 				A__      (  self						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       List[Any]:






								'''simple docstring'''






								if dicta == dicta:
									return True
								else:
									lowercase_ ,	lowercase_			 = self._remove_meta_keys(lowerCAmelCase_  )
									lowercase_ ,	lowercase_			 = self._remove_meta_keys(lowerCAmelCase_  )
									if configa == configa:
										print(F'Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'  )
										return True
								return False




							def 				A__      (  self						,       UpperCAmelCase  )     ->       List[Any]:






								'''simple docstring'''






								lowercase_			 = []
								lowercase_			 = config_dict.copy()
								for key in config_dict.keys():
									if key.startswith("_"  ):
										temp_dict.pop(lowerCAmelCase_  )
										meta_keys.append(lowerCAmelCase_  )
								return (temp_dict, meta_keys)




							@torch.no_grad()
							def 				A__      (  self						,       UpperCAmelCase						,       **UpperCAmelCase  )     ->       Union[str, Any]:






								'''simple docstring'''






								lowercase_			 = kwargs.pop("cache_dir"						,       lowerCAmelCase_  )
								lowercase_			 = kwargs.pop("resume_download"						,       lowerCAmelCase_  )
								lowercase_			 = kwargs.pop("force_download"						,       lowerCAmelCase_  )
								lowercase_			 = kwargs.pop("proxies"						,       lowerCAmelCase_  )
								lowercase_			 = kwargs.pop("local_files_only"						,       lowerCAmelCase_  )
								lowercase_			 = kwargs.pop("use_auth_token"						,       lowerCAmelCase_  )
								lowercase_			 = kwargs.pop("revision"						,       lowerCAmelCase_  )
								lowercase_			 = kwargs.pop("torch_dtype"						,       lowerCAmelCase_  )
								lowercase_			 = kwargs.pop("device_map"						,       lowerCAmelCase_  )

								lowercase_			 = kwargs.pop("alpha"						,       0.5  )
								lowercase_			 = kwargs.pop("interp"						,       lowerCAmelCase_  )

								print("Received list"						,       lowerCAmelCase_  )
								print(F'Combining with alpha={alpha}, interpolation mode={interp}'  )

								lowercase_			 = len(lowerCAmelCase_  )
								# Ignore result from model_index_json comparision of the two checkpoints
								lowercase_			 = kwargs.pop("force"						,       lowerCAmelCase_  )

								# 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(
									    lowerCAmelCase_						,       cache_dir=lowerCAmelCase_						,       resume_download=lowerCAmelCase_						,       force_download=lowerCAmelCase_						,       proxies=lowerCAmelCase_						,       local_files_only=lowerCAmelCase_						,       use_auth_token=lowerCAmelCase_						,       revision=lowerCAmelCase_						,       )
									config_dicts.append(lowerCAmelCase_  )

								lowercase_			 = True
								for idx in range(1						,       len(lowerCAmelCase_  )  ):
									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(lowerCAmelCase_						,       lowerCAmelCase_  ):
									lowercase_			 = [k for k in config_dict.keys() if not k.startswith("_"  )]
									lowercase_			 = [os.path.join(lowerCAmelCase_						,       "*"  ) 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(lowerCAmelCase_  )
									    else snapshot_download(
									        lowerCAmelCase_						,       cache_dir=lowerCAmelCase_						,       resume_download=lowerCAmelCase_						,       proxies=lowerCAmelCase_						,       local_files_only=lowerCAmelCase_						,       use_auth_token=lowerCAmelCase_						,       revision=lowerCAmelCase_						,       allow_patterns=lowerCAmelCase_						,       user_agent=lowerCAmelCase_						,       )
									)
									print("Cached Folder"						,       lowerCAmelCase_  )
									cached_folders.append(lowerCAmelCase_  )

								# 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=lowerCAmelCase_						,       device_map=lowerCAmelCase_  )
								final_pipe.to(self.device  )

								lowercase_			 = None
								if len(lowerCAmelCase_  ) > 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]						,       lowerCAmelCase_  )
										if os.path.exists(lowerCAmelCase_  ):
											lowercase_			 = [
											    *glob.glob(os.path.join(lowerCAmelCase_						,       "*.safetensors"  )  ),
											    *glob.glob(os.path.join(lowerCAmelCase_						,       "*.bin"  )  ),
											]
											lowercase_			 = files[0] if len(lowerCAmelCase_  ) > 0 else None
										if len(lowerCAmelCase_  ) < 3:
											lowercase_			 = None
										else:
											lowercase_			 = os.path.join(cached_folders[2]						,       lowerCAmelCase_  )
											if os.path.exists(lowerCAmelCase_  ):
												lowercase_			 = [
												    *glob.glob(os.path.join(lowerCAmelCase_						,       "*.safetensors"  )  ),
												    *glob.glob(os.path.join(lowerCAmelCase_						,       "*.bin"  )  ),
												]
												lowercase_			 = files[0] if len(lowerCAmelCase_  ) > 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(lowerCAmelCase_						,       lowerCAmelCase_  )
											if isinstance(lowerCAmelCase_						,       lowerCAmelCase_  ):  # ignore requires_safety_checker boolean
												continue
											lowercase_			 = getattr(lowerCAmelCase_						,       "state_dict"  )
											lowercase_			 = theta_a()

											lowercase_			 = getattr(lowerCAmelCase_						,       "load_state_dict"  )
											lowercase_			 = (
											    safetensors.torch.load_file(lowerCAmelCase_  )
											    if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"  ))
											    else torch.load(lowerCAmelCase_						,       map_location="cpu"  )
											)
											lowercase_			 = None
											if checkpoint_path_a:
												lowercase_			 = (
												    safetensors.torch.load_file(lowerCAmelCase_  )
												    if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"  ))
												    else torch.load(lowerCAmelCase_						,       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(lowerCAmelCase_  )}'  )
											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]						,       lowerCAmelCase_  )
											else:
												lowercase_			 = theta_func(theta_a[key]						,       theta_a[key]						,       lowerCAmelCase_						,       lowerCAmelCase_  )

										del theta_a
										del theta_a
										update_theta_a(lowerCAmelCase_  )

										del theta_a
								return final_pipe




							@staticmethod
							def 				A__      (  UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       str:






								'''simple docstring'''






								return ((1 - alpha) * thetaa) + (alpha * thetaa)




							@staticmethod
							def 				A__      (  UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       str:






								'''simple docstring'''






								lowercase_			 = alpha * alpha * (3 - (2 * alpha))
								return thetaa + ((thetaa - thetaa) * alpha)




							@staticmethod
							def 				A__      (  UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       str:






								'''simple docstring'''






								import math

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




							@staticmethod
							def 				A__      (  UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase						,       UpperCAmelCase  )     ->       Optional[int]:






								'''simple docstring'''






								return thetaa + (thetaa - thetaa) * (1.0 - alpha)






'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


lowercase											=      {}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase											=      ['''BarthezTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase											=      ['''BarthezTokenizerFast''']


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_barthez import BarthezTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_barthez_fast import BarthezTokenizerFast

else:
  import sys

  lowercase											=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


