



'''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 			__magic_name__		(					_UpperCamelCase     ):







       def __init__(							self      : Any	):
         self.register_to_config()
         super().__init__()







       def 							__lowercase    (							self      : List[str]			,_UpperCAmelCase      : List[str]			,_UpperCAmelCase      : str	):
         if dicta == dicta:
           return True
         else:
           _a  :   List[Any]				 =							self._remove_meta_keys(__lowerCamelCase	)
           _a  :   Tuple				 =							self._remove_meta_keys(__lowerCamelCase	)
           if configa == configa:
             print(F"""Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."""	)
             return True
         return False







       def 							__lowercase    (							self      : Any			,_UpperCAmelCase      : Any	):
         _a  :   Any				 =							[]
         _a  :   List[Any]				 =							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 							__lowercase    (							self      : Dict			,_UpperCAmelCase      : Any			,**_UpperCAmelCase      : List[str]	):
         _a  :   Any				 =							kwargs.pop('cache_dir'			,__lowerCamelCase	)
         _a  :   List[Any]				 =							kwargs.pop('resume_download'			,__lowerCamelCase	)
         _a  :   int				 =							kwargs.pop('force_download'			,__lowerCamelCase	)
         _a  :   List[Any]				 =							kwargs.pop('proxies'			,__lowerCamelCase	)
         _a  :   Optional[int]				 =							kwargs.pop('local_files_only'			,__lowerCamelCase	)
         _a  :   Union[str, Any]				 =							kwargs.pop('use_auth_token'			,__lowerCamelCase	)
         _a  :   int				 =							kwargs.pop('revision'			,__lowerCamelCase	)
         _a  :   Tuple				 =							kwargs.pop('torch_dtype'			,__lowerCamelCase	)
         _a  :   List[Any]				 =							kwargs.pop('device_map'			,__lowerCamelCase	)

         _a  :   List[Any]				 =							kwargs.pop('alpha'			,0.5	)
         _a  :   Any				 =							kwargs.pop('interp'			,__lowerCamelCase	)

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

         _a  :   Tuple				 =							len(__lowerCamelCase	)
         # Ignore result from model_index_json comparision of the two checkpoints
         _a  :   Tuple				 =							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 '_'
         _a  :   Union[str, Any]				 =							[]
         for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
           _a  :   Dict				 =							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	)

         _a  :   Any				 =							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.
         _a  :   Optional[Any]				 =							[]
         for pretrained_model_name_or_path, config_dict in zip(__lowerCamelCase			,__lowerCamelCase	):
           _a  :   int				 =							[k for k in config_dict.keys() if not k.startswith('_'	)]
           _a  :   Any				 =							[os.path.join(__lowerCamelCase			,'*'	) for k in folder_names]
           allow_patterns += [
               WEIGHTS_NAME,
               SCHEDULER_CONFIG_NAME,
               CONFIG_NAME,
               ONNX_WEIGHTS_NAME,
               DiffusionPipeline.config_name,
           ]
           _a  :   int				 =							config_dict.get('_class_name'	)
           _a  :   int				 =							{"diffusers": __version__, "pipeline_class": requested_pipeline_class}

           _a  :   Tuple				 =							(
               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
         _a  :   str				 =							DiffusionPipeline.from_pretrained(
             cached_folders[0]			,torch_dtype=__lowerCamelCase			,device_map=__lowerCamelCase	)
         final_pipe.to(self.device	)

         _a  :   Any				 =							None
         if len(__lowerCamelCase	) > 2:
           _a  :   Optional[Any]				 =							os.path.join(cached_folders[2]	)

         if interp == "sigmoid":
           _a  :   List[str]				 =							CheckpointMergerPipeline.sigmoid
         elif interp == "inv_sigmoid":
           _a  :   Dict				 =							CheckpointMergerPipeline.inv_sigmoid
         elif interp == "add_diff":
           _a  :   Dict				 =							CheckpointMergerPipeline.add_difference
         else:
           _a  :   List[str]				 =							CheckpointMergerPipeline.weighted_sum

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

               _a  :   List[str]				 =							getattr(__lowerCamelCase			,'load_state_dict'	)
               _a  :   List[Any]				 =							(
                   safetensors.torch.load_file(__lowerCamelCase	)
                   if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'	))
                   else torch.load(__lowerCamelCase			,map_location='cpu'	)
               )
               _a  :   List[str]				 =							None
               if checkpoint_path_a:
                 _a  :   Union[str, Any]				 =							(
                     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:
                 _a  :   List[str]				 =							theta_func(theta_a[key]			,theta_a[key]			,theta_a[key]			,__lowerCamelCase	)
               else:
                 _a  :   Optional[int]				 =							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 							__lowercase    (							_UpperCAmelCase      : Union[str, Any]			,_UpperCAmelCase      : Dict			,_UpperCAmelCase      : int			,_UpperCAmelCase      : Optional[int]	):
         return ((1 - alpha) * thetaa) + (alpha * thetaa)







       @staticmethod
       def 							__lowercase    (							_UpperCAmelCase      : Union[str, Any]			,_UpperCAmelCase      : int			,_UpperCAmelCase      : Tuple			,_UpperCAmelCase      : int	):
         _a  :   Tuple				 =							alpha * alpha * (3 - (2 * alpha))
         return thetaa + ((thetaa - thetaa) * alpha)







       @staticmethod
       def 							__lowercase    (							_UpperCAmelCase      : Optional[int]			,_UpperCAmelCase      : Union[str, Any]			,_UpperCAmelCase      : int			,_UpperCAmelCase      : Optional[Any]	):
         import math

         _a  :   Optional[int]				 =							0.5 - math.sin(math.asin(1.0 - 2.0 * alpha	) / 3.0	)
         return thetaa + ((thetaa - thetaa) * alpha)







       @staticmethod
       def 							__lowercase    (							_UpperCAmelCase      : int			,_UpperCAmelCase      : List[str]			,_UpperCAmelCase      : Optional[int]			,_UpperCAmelCase      : Any	):
         return thetaa + (thetaa - thetaa) * (1.0 - alpha)









'''simple docstring'''

from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest

@require_tokenizers
class 			__magic_name__		(					_UpperCamelCase     ):
							lowerCAmelCase :					int            =      SqueezeBertTokenizer
							lowerCAmelCase :					int            =      SqueezeBertTokenizerFast
							lowerCAmelCase :					List[Any]            =      True







							def 							__lowercase    (							self      : List[str]			,**_UpperCAmelCase      : Tuple	):
									return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname			,**_UpperCAmelCase	)







							@slow
							def 							__lowercase    (							self      : Tuple	):
									_a  :   Optional[int]				 =							SqueezeBertTokenizer.from_pretrained('squeezebert/squeezebert-mnli-headless'	)

									_a  :   int				 =							tokenizer.encode('sequence builders'			,add_special_tokens=_UpperCAmelCase	)
									_a  :   List[Any]				 =							tokenizer.encode('multi-sequence build'			,add_special_tokens=_UpperCAmelCase	)

									_a  :   Union[str, Any]				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase	)
									_a  :   Dict				 =							tokenizer.build_inputs_with_special_tokens(_UpperCAmelCase			,_UpperCAmelCase	)

									assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
									assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
									    tokenizer.sep_token_id
									]




