
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 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def __init__(  self	)  ->List[str]:
									self.register_to_config()
									super().__init__()


						def    A__       (  self  , A  , A	)  ->str:
									if dicta == dicta:
												return True
									else:
												UpperCAmelCase__		,       UpperCAmelCase__		:Union[str, Any] 		=    self._remove_meta_keys(A	)
												UpperCAmelCase__		,       UpperCAmelCase__		:List[Any] 		=    self._remove_meta_keys(A	)
												if configa == configa:
															print(F"""Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."""	)
															return True
									return False


						def    A__       (  self  , A	)  ->Optional[Any]:
									UpperCAmelCase__		:Any 		=    []
									UpperCAmelCase__		:Dict 		=    config_dict.copy()
									for key in config_dict.keys():
												if key.startswith('_'	):
															temp_dict.pop(A	)
															meta_keys.append(A	)
									return (temp_dict, meta_keys)


						@torch.no_grad()
						def    A__       (  self  , A  , **A	)  ->Union[str, Any]:
									UpperCAmelCase__		:Tuple 		=    kwargs.pop('cache_dir'  , A	)
									UpperCAmelCase__		:List[str] 		=    kwargs.pop('resume_download'  , A	)
									UpperCAmelCase__		:str 		=    kwargs.pop('force_download'  , A	)
									UpperCAmelCase__		:Any 		=    kwargs.pop('proxies'  , A	)
									UpperCAmelCase__		:Any 		=    kwargs.pop('local_files_only'  , A	)
									UpperCAmelCase__		:Tuple 		=    kwargs.pop('use_auth_token'  , A	)
									UpperCAmelCase__		:Dict 		=    kwargs.pop('revision'  , A	)
									UpperCAmelCase__		:Any 		=    kwargs.pop('torch_dtype'  , A	)
									UpperCAmelCase__		:Any 		=    kwargs.pop('device_map'  , A	)

									UpperCAmelCase__		:str 		=    kwargs.pop('alpha'  , 0.5	)
									UpperCAmelCase__		:Tuple 		=    kwargs.pop('interp'  , A	)

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

									UpperCAmelCase__		:Optional[Any] 		=    len(A	)
									# Ignore result from model_index_json comparision of the two checkpoints
									UpperCAmelCase__		:Union[str, Any] 		=    kwargs.pop('force'  , A	)

									# 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__		:Any 		=    []
									for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
												UpperCAmelCase__		:Optional[Any] 		=    DiffusionPipeline.load_config(
												    A  , cache_dir=A  , resume_download=A  , force_download=A  , proxies=A  , local_files_only=A  , use_auth_token=A  , revision=A  , )
												config_dicts.append(A	)

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

												UpperCAmelCase__		:List[Any] 		=    (
												    pretrained_model_name_or_path
												    if os.path.isdir(A	)
												    else snapshot_download(
												        A  , cache_dir=A  , resume_download=A  , proxies=A  , local_files_only=A  , use_auth_token=A  , revision=A  , allow_patterns=A  , user_agent=A  , )
												)
												print('Cached Folder'  , A	)
												cached_folders.append(A	)

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

									UpperCAmelCase__		:Tuple 		=    None
									if len(A	) > 2:
												UpperCAmelCase__		:Optional[int] 		=    os.path.join(cached_folders[2]	)

									if interp == "sigmoid":
												UpperCAmelCase__		:str 		=    CheckpointMergerPipeline.sigmoid
									elif interp == "inv_sigmoid":
												UpperCAmelCase__		:Union[str, Any] 		=    CheckpointMergerPipeline.inv_sigmoid
									elif interp == "add_diff":
												UpperCAmelCase__		:Union[str, Any] 		=    CheckpointMergerPipeline.add_difference
									else:
												UpperCAmelCase__		:Any 		=    CheckpointMergerPipeline.weighted_sum

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

																		UpperCAmelCase__		:Optional[Any] 		=    getattr(A  , 'load_state_dict'	)
																		UpperCAmelCase__		:List[Any] 		=    (
																		    safetensors.torch.load_file(A	)
																		    if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'	))
																		    else torch.load(A  , map_location='cpu'	)
																		)
																		UpperCAmelCase__		:Union[str, Any] 		=    None
																		if checkpoint_path_a:
																					UpperCAmelCase__		:Tuple 		=    (
																					    safetensors.torch.load_file(A	)
																					    if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'	))
																					    else torch.load(A  , 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(A	)}"""	)
																		continue
															print(F"""MERGING {attr}"""	)

															for key in theta_a.keys():
																		if theta_a:
																					UpperCAmelCase__		:Union[str, Any] 		=    theta_func(theta_a[key]  , theta_a[key]  , theta_a[key]  , A	)
																		else:
																					UpperCAmelCase__		:Any 		=    theta_func(theta_a[key]  , theta_a[key]  , A  , A	)

															del theta_a
															del theta_a
															update_theta_a(A	)

															del theta_a
									return final_pipe


						@staticmethod
						def    A__       (  A  , A  , A  , A	)  ->Optional[Any]:
									return ((1 - alpha) * thetaa) + (alpha * thetaa)


						@staticmethod
						def    A__       (  A  , A  , A  , A	)  ->Optional[Any]:
									UpperCAmelCase__		:List[Any] 		=    alpha * alpha * (3 - (2 * alpha))
									return thetaa + ((thetaa - thetaa) * alpha)


						@staticmethod
						def    A__       (  A  , A  , A  , A	)  ->Optional[Any]:
									import math

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


						@staticmethod
						def    A__       (  A  , A  , A  , A	)  ->Any:
									return thetaa + (thetaa - thetaa) * (1.0 - alpha)







import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__snake_case					:							List[Any]           =							logging.getLogger()




def 		A						(  ):




			"""simple docstring"""


			UpperCAmelCase__		:Any 		=    argparse.ArgumentParser()
			parser.add_argument('-f'							)
			UpperCAmelCase__		:List[Any] 		=    parser.parse_args()
			return args.f




class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def    A__       (  self	)  ->str:
									UpperCAmelCase__		:Optional[int] 		=    logging.StreamHandler(sys.stdout	)
									logger.addHandler(A	)

									UpperCAmelCase__		:List[str] 		=    self.get_auto_remove_tmp_dir()
									UpperCAmelCase__		:List[str] 		=    F"""
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

									with patch.object(A  , 'argv'  , A	):
												UpperCAmelCase__		:Any 		=    run_glue_with_pabee.main()
												for value in result.values():
															self.assertGreaterEqual(A  , 0.75	)





