


'''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        UpperCAmelCase_  (A__  ):





    """simple docstring"""






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







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )    ->					Optional[int]:
         if dicta == dicta:
              return True
         else:
              __lowerCamelCase    ,	__lowerCamelCase       :     int        =		self._remove_meta_keys(SCREAMING_SNAKE_CASE_     )
              __lowerCamelCase    ,	__lowerCamelCase       :     Tuple        =		self._remove_meta_keys(SCREAMING_SNAKE_CASE_     )
              if configa == configa:
                   print(f'Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'     )
                   return True
         return False







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







    @torch.no_grad()
    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					**SCREAMING_SNAKE_CASE_     )    ->					Dict:
         __lowerCamelCase       :     Union[str, Any]        =		kwargs.pop('cache_dir'      ,					SCREAMING_SNAKE_CASE_     )
         __lowerCamelCase       :     Optional[int]        =		kwargs.pop('resume_download'      ,					SCREAMING_SNAKE_CASE_     )
         __lowerCamelCase       :     int        =		kwargs.pop('force_download'      ,					SCREAMING_SNAKE_CASE_     )
         __lowerCamelCase       :     str        =		kwargs.pop('proxies'      ,					SCREAMING_SNAKE_CASE_     )
         __lowerCamelCase       :     Dict        =		kwargs.pop('local_files_only'      ,					SCREAMING_SNAKE_CASE_     )
         __lowerCamelCase       :     Optional[Any]        =		kwargs.pop('use_auth_token'      ,					SCREAMING_SNAKE_CASE_     )
         __lowerCamelCase       :     Optional[Any]        =		kwargs.pop('revision'      ,					SCREAMING_SNAKE_CASE_     )
         __lowerCamelCase       :     str        =		kwargs.pop('torch_dtype'      ,					SCREAMING_SNAKE_CASE_     )
         __lowerCamelCase       :     Optional[int]        =		kwargs.pop('device_map'      ,					SCREAMING_SNAKE_CASE_     )

         __lowerCamelCase       :     str        =		kwargs.pop('alpha'      ,					0.5     )
         __lowerCamelCase       :     Tuple        =		kwargs.pop('interp'      ,					SCREAMING_SNAKE_CASE_     )

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

         __lowerCamelCase       :     List[Any]        =		len(SCREAMING_SNAKE_CASE_     )
         # Ignore result from model_index_json comparision of the two checkpoints
         __lowerCamelCase       :     Optional[Any]        =		kwargs.pop('force'      ,					SCREAMING_SNAKE_CASE_     )

         # 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 '_'
         __lowerCamelCase       :     List[str]        =		[]
         for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
              __lowerCamelCase       :     Optional[Any]        =		DiffusionPipeline.load_config(
                  SCREAMING_SNAKE_CASE_      ,					cache_dir=SCREAMING_SNAKE_CASE_      ,					resume_download=SCREAMING_SNAKE_CASE_      ,					force_download=SCREAMING_SNAKE_CASE_      ,					proxies=SCREAMING_SNAKE_CASE_      ,					local_files_only=SCREAMING_SNAKE_CASE_      ,					use_auth_token=SCREAMING_SNAKE_CASE_      ,					revision=SCREAMING_SNAKE_CASE_      ,					)
              config_dicts.append(SCREAMING_SNAKE_CASE_     )

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

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

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

         __lowerCamelCase       :     Dict        =		None
         if len(SCREAMING_SNAKE_CASE_     ) > 2:
              __lowerCamelCase       :     Optional[int]        =		os.path.join(cached_folders[2]     )

         if interp == "sigmoid":
              __lowerCamelCase       :     str        =		CheckpointMergerPipeline.sigmoid
         elif interp == "inv_sigmoid":
              __lowerCamelCase       :     str        =		CheckpointMergerPipeline.inv_sigmoid
         elif interp == "add_diff":
              __lowerCamelCase       :     str        =		CheckpointMergerPipeline.add_difference
         else:
              __lowerCamelCase       :     Any        =		CheckpointMergerPipeline.weighted_sum

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

                        __lowerCamelCase       :     List[str]        =		getattr(SCREAMING_SNAKE_CASE_      ,					'load_state_dict'     )
                        __lowerCamelCase       :     List[Any]        =		(
                            safetensors.torch.load_file(SCREAMING_SNAKE_CASE_     )
                            if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'     ))
                            else torch.load(SCREAMING_SNAKE_CASE_      ,					map_location='cpu'     )
                        )
                        __lowerCamelCase       :     Union[str, Any]        =		None
                        if checkpoint_path_a:
                             __lowerCamelCase       :     Union[str, Any]        =		(
                                 safetensors.torch.load_file(SCREAMING_SNAKE_CASE_     )
                                 if (is_safetensors_available() and checkpoint_path_a.endswith('.safetensors'     ))
                                 else torch.load(SCREAMING_SNAKE_CASE_      ,					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(SCREAMING_SNAKE_CASE_     )}'     )
                        continue
                   print(f'MERGING {attr}'     )

                   for key in theta_a.keys():
                        if theta_a:
                             __lowerCamelCase       :     Union[str, Any]        =		theta_func(theta_a[key]      ,					theta_a[key]      ,					theta_a[key]      ,					SCREAMING_SNAKE_CASE_     )
                        else:
                             __lowerCamelCase       :     List[str]        =		theta_func(theta_a[key]      ,					theta_a[key]      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )

                   del theta_a
                   del theta_a
                   update_theta_a(SCREAMING_SNAKE_CASE_     )

                   del theta_a
         return final_pipe







    @staticmethod
    def 				lowercase_    (							SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )    ->					List[str]:
         return ((1 - alpha) * thetaa) + (alpha * thetaa)







    @staticmethod
    def 				lowercase_    (							SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )    ->					str:
         __lowerCamelCase       :     List[Any]        =		alpha * alpha * (3 - (2 * alpha))
         return thetaa + ((thetaa - thetaa) * alpha)







    @staticmethod
    def 				lowercase_    (							SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     )    ->					List[Any]:
         import math

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







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










'''simple docstring'''



import torch

from transformers import AutoTokenizer





class        UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    def __init__(							self      ,					SCREAMING_SNAKE_CASE_     )    ->					int:
         __lowerCamelCase       :     Dict        =		AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_     )







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_     )    ->					int:

         if isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) and all(isinstance(_x      ,					SCREAMING_SNAKE_CASE_     ) for _x in x     ):
              __lowerCamelCase       :     Union[str, Any]        =		None
              for l in x:
                   __lowerCamelCase       :     Tuple        =		self.tokenizer(
                       SCREAMING_SNAKE_CASE_      ,					padding='max_length'      ,					max_length=3_84      ,					truncation=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'      ,					)
                   __lowerCamelCase       :     str        =		torch.tensor([len(SCREAMING_SNAKE_CASE_     )]     )
                   if d is not None:
                        for k in d.keys():
                             __lowerCamelCase       :     List[Any]        =		torch.cat((d[k], t[k])      ,					0     )
                   else:
                        __lowerCamelCase       :     str        =		t

              __lowerCamelCase       :     List[Any]        =		torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'     )     )
              __lowerCamelCase       :     Tuple        =		torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'     )     )

         elif isinstance(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_     ) and all(isinstance(_x      ,					SCREAMING_SNAKE_CASE_     ) for _x in x     ):
              __lowerCamelCase       :     Optional[Any]        =		self.tokenizer(
                  SCREAMING_SNAKE_CASE_      ,					padding='max_length'      ,					max_length=3_84      ,					truncation=SCREAMING_SNAKE_CASE_      ,					return_tensors='pt'      ,					)

         else:
              raise Exception(
                  'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
                  ' strings` for supports are supported.'     )

         return d







    def 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_=0.7_0     )    ->					str:
         __lowerCamelCase       :     str        =		[]
         for idx in range(len(W_query['input_ids']     )     ):
              __lowerCamelCase       :     Optional[Any]        =		range(p_start.shape[1]     )

              __lowerCamelCase       :     List[Any]        =		[]
              for start_id in start_indexes:
                   for end_id in end_indexes:
                        if start_id < end_id:
                             output.append(
                                 (
                                     start_id,
                                     end_id,
                                     p_start[idx][start_id].item(),
                                     p_end[idx][end_id].item(),
                                 )     )

              output.sort(key=lambda SCREAMING_SNAKE_CASE_     : (tup[2] * tup[3])      ,					reverse=SCREAMING_SNAKE_CASE_     )
              __lowerCamelCase       :     str        =		[]
              for k in range(len(SCREAMING_SNAKE_CASE_     )     ):
                   if output[k][2] * output[k][3] >= thresh:
                        __lowerCamelCase    ,	__lowerCamelCase       :     Union[str, Any]        =		output[k][0], output[k][1]
                        __lowerCamelCase       :     List[str]        =		self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos]     )
                        temp.append((decoded, output[k][2] * output[k][3])     )

              final_outputs.append(SCREAMING_SNAKE_CASE_     )

         return final_outputs






