"""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      _lowercase						(				__lowerCamelCase					):

				def __init__(       self    :      Optional[Any]       ):


										'''simple docstring'''
										self.register_to_config()
										super().__init__()

				def     A	(       self    :      Dict    ,      _lowercase    :      List[str]    ,      _lowercase    :      Tuple       ):


										'''simple docstring'''
										if dicta == dicta:
																return True
										else:
																UpperCAmelCase__		, UpperCAmelCase__   		=						self._remove_meta_keys(_lowercase       )
																UpperCAmelCase__		, UpperCAmelCase__   		=						self._remove_meta_keys(_lowercase       )
																if configa == configa:
																						print(f'Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'       )
																						return True
										return False

				def     A	(       self    :      Optional[Any]    ,      _lowercase    :      Dict       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						[]
										UpperCAmelCase__   		=						config_dict.copy()
										for key in config_dict.keys():
																if key.startswith("""_"""       ):
																						temp_dict.pop(_lowercase       )
																						meta_keys.append(_lowercase       )
										return (temp_dict, meta_keys)

				@torch.no_grad()
				def     A	(       self    :      Optional[Any]    ,      _lowercase    :      List[Union[str, os.PathLike]]    ,      **_lowercase    :      Any       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						kwargs.pop("""cache_dir"""    ,      _lowercase       )
										UpperCAmelCase__   		=						kwargs.pop("""resume_download"""    ,      _lowercase       )
										UpperCAmelCase__   		=						kwargs.pop("""force_download"""    ,      _lowercase       )
										UpperCAmelCase__   		=						kwargs.pop("""proxies"""    ,      _lowercase       )
										UpperCAmelCase__   		=						kwargs.pop("""local_files_only"""    ,      _lowercase       )
										UpperCAmelCase__   		=						kwargs.pop("""use_auth_token"""    ,      _lowercase       )
										UpperCAmelCase__   		=						kwargs.pop("""revision"""    ,      _lowercase       )
										UpperCAmelCase__   		=						kwargs.pop("""torch_dtype"""    ,      _lowercase       )
										UpperCAmelCase__   		=						kwargs.pop("""device_map"""    ,      _lowercase       )

										UpperCAmelCase__   		=						kwargs.pop("""alpha"""    ,      0.5       )
										UpperCAmelCase__   		=						kwargs.pop("""interp"""    ,      _lowercase       )

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

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

										# 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 '_'
										UpperCAmelCase__   		=						[]
										for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
																UpperCAmelCase__   		=						DiffusionPipeline.load_config(
																    _lowercase    ,      cache_dir=_lowercase    ,      resume_download=_lowercase    ,      force_download=_lowercase    ,      proxies=_lowercase    ,      local_files_only=_lowercase    ,      use_auth_token=_lowercase    ,      revision=_lowercase    ,      )
																config_dicts.append(_lowercase       )

										UpperCAmelCase__   		=						True
										for idx in range(1    ,      len(_lowercase       )       ):
																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.
										UpperCAmelCase__   		=						[]
										for pretrained_model_name_or_path, config_dict in zip(_lowercase    ,      _lowercase       ):
																UpperCAmelCase__   		=						[k for k in config_dict.keys() if not k.startswith("""_"""       )]
																UpperCAmelCase__   		=						[os.path.join(_lowercase    ,      """*"""       ) for k in folder_names]
																allow_patterns += [
																    WEIGHTS_NAME,
																    SCHEDULER_CONFIG_NAME,
																    CONFIG_NAME,
																    ONNX_WEIGHTS_NAME,
																    DiffusionPipeline.config_name,
																]
																UpperCAmelCase__   		=						config_dict.get("""_class_name"""       )
																UpperCAmelCase__   		=						{"""diffusers""": __version__, """pipeline_class""": requested_pipeline_class}

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

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

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

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

										# Find each module's state dict.
										for attr in final_pipe.config.keys():
																if not attr.startswith("""_"""       ):
																						UpperCAmelCase__   		=						os.path.join(cached_folders[1]    ,      _lowercase       )
																						if os.path.exists(_lowercase       ):
																												UpperCAmelCase__   		=						[
																												    *glob.glob(os.path.join(_lowercase    ,      """*.safetensors"""       )       ),
																												    *glob.glob(os.path.join(_lowercase    ,      """*.bin"""       )       ),
																												]
																												UpperCAmelCase__   		=						files[0] if len(_lowercase       ) > 0 else None
																						if len(_lowercase       ) < 3:
																												UpperCAmelCase__   		=						None
																						else:
																												UpperCAmelCase__   		=						os.path.join(cached_folders[2]    ,      _lowercase       )
																												if os.path.exists(_lowercase       ):
																																		UpperCAmelCase__   		=						[
																																		    *glob.glob(os.path.join(_lowercase    ,      """*.safetensors"""       )       ),
																																		    *glob.glob(os.path.join(_lowercase    ,      """*.bin"""       )       ),
																																		]
																																		UpperCAmelCase__   		=						files[0] if len(_lowercase       ) > 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:
																												UpperCAmelCase__   		=						getattr(_lowercase    ,      _lowercase       )
																												if isinstance(_lowercase    ,      _lowercase       ):  # ignore requires_safety_checker boolean
																																		continue
																												UpperCAmelCase__   		=						getattr(_lowercase    ,      """state_dict"""       )
																												UpperCAmelCase__   		=						theta_a()

																												UpperCAmelCase__   		=						getattr(_lowercase    ,      """load_state_dict"""       )
																												UpperCAmelCase__   		=						(
																												    safetensors.torch.load_file(_lowercase       )
																												    if (is_safetensors_available() and checkpoint_path_a.endswith(""".safetensors"""       ))
																												    else torch.load(_lowercase    ,      map_location="""cpu"""       )
																												)
																												UpperCAmelCase__   		=						None
																												if checkpoint_path_a:
																																		UpperCAmelCase__   		=						(
																																		    safetensors.torch.load_file(_lowercase       )
																																		    if (is_safetensors_available() and checkpoint_path_a.endswith(""".safetensors"""       ))
																																		    else torch.load(_lowercase    ,      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(_lowercase       )}'       )
																												continue
																						print(f'MERGING {attr}'       )

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

																						del theta_a
																						del theta_a
																						update_theta_a(_lowercase       )

																						del theta_a
										return final_pipe

				@staticmethod
				def     A	(       _lowercase    :      List[str]    ,      _lowercase    :      Tuple    ,      _lowercase    :      int    ,      _lowercase    :      Tuple       ):


										'''simple docstring'''
										return ((1 - alpha) * thetaa) + (alpha * thetaa)

				@staticmethod
				def     A	(       _lowercase    :      Dict    ,      _lowercase    :      Dict    ,      _lowercase    :      Optional[Any]    ,      _lowercase    :      Union[str, Any]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						alpha * alpha * (3 - (2 * alpha))
										return thetaa + ((thetaa - thetaa) * alpha)

				@staticmethod
				def     A	(       _lowercase    :      Tuple    ,      _lowercase    :      List[str]    ,      _lowercase    :      List[Any]    ,      _lowercase    :      Optional[int]       ):


										'''simple docstring'''
										import math

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

				@staticmethod
				def     A	(       _lowercase    :      List[Any]    ,      _lowercase    :      Union[str, Any]    ,      _lowercase    :      int    ,      _lowercase    :      int       ):


										'''simple docstring'''
										return thetaa + (thetaa - thetaa) * (1.0 - alpha)


"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_    :    Optional[Any]											=						logging.get_logger(__name__)

UpperCamelCase_    :    Optional[int]											=						{
    """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/config.json""",
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}


class      _lowercase						(				__lowerCamelCase					):
				lowerCamelCase__									=    'rembert'

				def __init__(       self    :      List[Any]    ,      _lowercase    :      Tuple=2_5_0_3_0_0    ,      _lowercase    :      Tuple=1_1_5_2    ,      _lowercase    :      List[Any]=3_2    ,      _lowercase    :      Dict=1_8    ,      _lowercase    :      str=2_5_6    ,      _lowercase    :      Optional[Any]=1_6_6_4    ,      _lowercase    :      Union[str, Any]=4_6_0_8    ,      _lowercase    :      Optional[Any]="gelu"    ,      _lowercase    :      Optional[Any]=0.0    ,      _lowercase    :      Tuple=0.0    ,      _lowercase    :      Dict=0.1    ,      _lowercase    :      str=5_1_2    ,      _lowercase    :      Tuple=2    ,      _lowercase    :      int=0.02    ,      _lowercase    :      Tuple=1e-12    ,      _lowercase    :      Union[str, Any]=True    ,      _lowercase    :      Optional[int]=0    ,      _lowercase    :      Any=3_1_2    ,      _lowercase    :      Optional[Any]=3_1_3    ,      **_lowercase    :      str    ,      ):


										'''simple docstring'''
										super().__init__(pad_token_id=_lowercase    ,      bos_token_id=_lowercase    ,      eos_token_id=_lowercase    ,      **_lowercase       )

										UpperCAmelCase__   		=						vocab_size
										UpperCAmelCase__   		=						input_embedding_size
										UpperCAmelCase__   		=						output_embedding_size
										UpperCAmelCase__   		=						max_position_embeddings
										UpperCAmelCase__   		=						hidden_size
										UpperCAmelCase__   		=						num_hidden_layers
										UpperCAmelCase__   		=						num_attention_heads
										UpperCAmelCase__   		=						intermediate_size
										UpperCAmelCase__   		=						hidden_act
										UpperCAmelCase__   		=						hidden_dropout_prob
										UpperCAmelCase__   		=						attention_probs_dropout_prob
										UpperCAmelCase__   		=						classifier_dropout_prob
										UpperCAmelCase__   		=						initializer_range
										UpperCAmelCase__   		=						type_vocab_size
										UpperCAmelCase__   		=						layer_norm_eps
										UpperCAmelCase__   		=						use_cache
										UpperCAmelCase__   		=						False


class      _lowercase						(				__lowerCamelCase					):

				@property
				def     A	(       self    :      str       ):


										'''simple docstring'''
										if self.task == "multiple-choice":
																UpperCAmelCase__   		=						{0: """batch""", 1: """choice""", 2: """sequence"""}
										else:
																UpperCAmelCase__   		=						{0: """batch""", 1: """sequence"""}
										return OrderedDict(
										    [
										        ("""input_ids""", dynamic_axis),
										        ("""attention_mask""", dynamic_axis),
										        ("""token_type_ids""", dynamic_axis),
										    ]       )

				@property
				def     A	(       self    :      Tuple       ):


										'''simple docstring'''
										return 1e-4

