
'''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 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






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

  def 		_lowerCamelCase						(self	:			List[str]						,			lowerCAmelCase_	:			Tuple						,			lowerCAmelCase_	:			Union[str, Any]     ) ->							List[Any]:
        if dicta == dicta:
              return True
        else:
              snake_case_							,						snake_case_    							=				self._remove_meta_keys(lowerCAmelCase_     )
              snake_case_							,						snake_case_    							=				self._remove_meta_keys(lowerCAmelCase_     )
              if configa == configa:
                    print(f'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'''     )
                    return True
        return False

  def 		_lowerCamelCase						(self	:			str						,			lowerCAmelCase_	:			Dict     ) ->							Union[str, Any]:
        snake_case_    							=				[]
        snake_case_    							=				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 		_lowerCamelCase						(self	:			Dict						,			lowerCAmelCase_	:			List[Union[str, os.PathLike]]						,			**lowerCAmelCase_	:			Union[str, Any]     ) ->							Tuple:
        snake_case_    							=				kwargs.pop("cache_dir"						,			lowerCAmelCase_     )
        snake_case_    							=				kwargs.pop("resume_download"						,			lowerCAmelCase_     )
        snake_case_    							=				kwargs.pop("force_download"						,			lowerCAmelCase_     )
        snake_case_    							=				kwargs.pop("proxies"						,			lowerCAmelCase_     )
        snake_case_    							=				kwargs.pop("local_files_only"						,			lowerCAmelCase_     )
        snake_case_    							=				kwargs.pop("use_auth_token"						,			lowerCAmelCase_     )
        snake_case_    							=				kwargs.pop("revision"						,			lowerCAmelCase_     )
        snake_case_    							=				kwargs.pop("torch_dtype"						,			lowerCAmelCase_     )
        snake_case_    							=				kwargs.pop("device_map"						,			lowerCAmelCase_     )

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

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

        snake_case_    							=				len(lowerCAmelCase_     )
        # Ignore result from model_index_json comparision of the two checkpoints
        snake_case_    							=				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 '_'
        snake_case_    							=				[]
        for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
              snake_case_    							=				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_     )

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

              snake_case_    							=				(
                  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
        snake_case_    							=				DiffusionPipeline.from_pretrained(
            cached_folders[0]						,			torch_dtype=lowerCAmelCase_						,			device_map=lowerCAmelCase_     )
        final_pipe.to(self.device     )

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

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

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

                          snake_case_    							=				getattr(lowerCAmelCase_						,			"load_state_dict"     )
                          snake_case_    							=				(
                              safetensors.torch.load_file(lowerCAmelCase_     )
                              if (is_safetensors_available() and checkpoint_path_a.endswith(".safetensors"     ))
                              else torch.load(lowerCAmelCase_						,			map_location="cpu"     )
                          )
                          snake_case_    							=				None
                          if checkpoint_path_a:
                                snake_case_    							=				(
                                    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:
                                snake_case_    							=				theta_func(theta_a[key]						,			theta_a[key]						,			theta_a[key]						,			lowerCAmelCase_     )
                          else:
                                snake_case_    							=				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 		_lowerCamelCase						(lowerCAmelCase_	:			Any						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			List[Any]     ) ->							Tuple:
        return ((1 - alpha) * thetaa) + (alpha * thetaa)

  @staticmethod
  def 		_lowerCamelCase						(lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Optional[int]     ) ->							List[str]:
        snake_case_    							=				alpha * alpha * (3 - (2 * alpha))
        return thetaa + ((thetaa - thetaa) * alpha)

  @staticmethod
  def 		_lowerCamelCase						(lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			Dict						,			lowerCAmelCase_	:			Union[str, Any]     ) ->							Dict:
        import math

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


  @staticmethod
  def 		_lowerCamelCase						(lowerCAmelCase_	:			Dict						,			lowerCAmelCase_	:			Tuple						,			lowerCAmelCase_	:			Tuple						,			lowerCAmelCase_	:			Optional[int]     ) ->							List[str]:
        return thetaa + (thetaa - thetaa) * (1.0 - alpha)






"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


_lowercase					:					str 		=		logging.get_logger(__name__)

_lowercase					:					List[str] 		=		{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowercase					:					Tuple 		=		{
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "allenai/longformer-base-4096": (
            "https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
    },
}

_lowercase					:					str 		=		{
    "allenai/longformer-base-4096": 4_0_9_6,
    "allenai/longformer-large-4096": 4_0_9_6,
    "allenai/longformer-large-4096-finetuned-triviaqa": 4_0_9_6,
    "allenai/longformer-base-4096-extra.pos.embd.only": 4_0_9_6,
    "allenai/longformer-large-4096-extra.pos.embd.only": 4_0_9_6,
}

class 			__SCREAMING_SNAKE_CASE      ( lowerCAmelCase_  ):


					'''simple docstring'''





					_a       =				VOCAB_FILES_NAMES
					_a       =				PRETRAINED_VOCAB_FILES_MAP
					_a       =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_a       =				['input_ids', 'attention_mask']
					_a       =				LongformerTokenizer



					def __init__(			self				:    Dict,    lowerCamelCase				:    Any=None,    lowerCamelCase				:    List[Any]=None,    lowerCamelCase				:    List[Any]=None,    lowerCamelCase				:    Optional[Any]="replace",    lowerCamelCase				:    List[str]="<s>",    lowerCamelCase				:    Tuple="</s>",    lowerCamelCase				:    Union[str, Any]="</s>",    lowerCamelCase				:    str="<s>",    lowerCamelCase				:    Union[str, Any]="<unk>",    lowerCamelCase				:    Optional[Any]="<pad>",    lowerCamelCase				:    Optional[Any]="<mask>",    lowerCamelCase				:    int=False,    lowerCamelCase				:    Optional[Any]=True,    **lowerCamelCase				:    Union[str, Any],    )->   List[str]:
								super().__init__(
								    lowerCamelCase,    lowerCamelCase,    tokenizer_file=lowerCamelCase,    errors=lowerCamelCase,    bos_token=lowerCamelCase,    eos_token=lowerCamelCase,    sep_token=lowerCamelCase,    cls_token=lowerCamelCase,    unk_token=lowerCamelCase,    pad_token=lowerCamelCase,    mask_token=lowerCamelCase,    add_prefix_space=lowerCamelCase,    trim_offsets=lowerCamelCase,    **lowerCamelCase,    )

								lowerCamelCase__						:						List[str]						      =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
								if pre_tok_state.get('''add_prefix_space''',    lowerCamelCase	) != add_prefix_space:
											lowerCamelCase__						:						Dict						      =getattr(lowerCamelCase,    pre_tok_state.pop('''type'''	)	)
											lowerCamelCase__						:						int						      =add_prefix_space
											lowerCamelCase__						:						int						      =pre_tok_class(**lowerCamelCase	)

								lowerCamelCase__						:						Optional[int]						      =add_prefix_space

								lowerCamelCase__						:						str						      ='''post_processor'''
								lowerCamelCase__						:						int						      =getattr(self.backend_tokenizer,    lowerCamelCase,    lowerCamelCase	)
								if tokenizer_component_instance:
											lowerCamelCase__						:						Union[str, Any]						      =json.loads(tokenizer_component_instance.__getstate__()	)

											# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
											if "sep" in state:
														lowerCamelCase__						:						List[str]						      =tuple(state['''sep''']	)
											if "cls" in state:
														lowerCamelCase__						:						List[str]						      =tuple(state['''cls''']	)

											lowerCamelCase__						:						Dict						      =False

											if state.get('''add_prefix_space''',    lowerCamelCase	) != add_prefix_space:
														lowerCamelCase__						:						int						      =add_prefix_space
														lowerCamelCase__						:						int						      =True

											if state.get('''trim_offsets''',    lowerCamelCase	) != trim_offsets:
														lowerCamelCase__						:						Optional[int]						      =trim_offsets
														lowerCamelCase__						:						Union[str, Any]						      =True

											if changes_to_apply:
														lowerCamelCase__						:						Dict						      =getattr(lowerCamelCase,    state.pop('''type'''	)	)
														lowerCamelCase__						:						Dict						      =component_class(**lowerCamelCase	)
														setattr(self.backend_tokenizer,    lowerCamelCase,    lowerCamelCase	)



					@property
					def 						snake_case			(			self				:    str	)->   str:
								if self._mask_token is None:
											if self.verbose:
														logger.error('''Using mask_token, but it is not set yet.'''	)
											return None
								return str(self._mask_token	)



					@mask_token.setter
					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    List[Any]	)->   List[str]:
								lowerCamelCase__						:						List[str]						      =AddedToken(lowerCamelCase,    lstrip=lowerCamelCase,    rstrip=lowerCamelCase	) if isinstance(lowerCamelCase,    lowerCamelCase	) else value
								lowerCamelCase__						:						Any						      =value



					def 						snake_case			(			self				:    Optional[Any],    *lowerCamelCase				:    Tuple,    **lowerCamelCase				:    Dict	)->   BatchEncoding:
								lowerCamelCase__						:						Union[str, Any]						      =kwargs.get('''is_split_into_words''',    lowerCamelCase	)
								assert self.add_prefix_space or not is_split_into_words, (
								    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
								    "to use it with pretokenized inputs."
								)

								return super()._batch_encode_plus(*lowerCamelCase,    **lowerCamelCase	)



					def 						snake_case			(			self				:    List[str],    *lowerCamelCase				:    Tuple,    **lowerCamelCase				:    List[Any]	)->   BatchEncoding:
								lowerCamelCase__						:						List[Any]						      =kwargs.get('''is_split_into_words''',    lowerCamelCase	)

								assert self.add_prefix_space or not is_split_into_words, (
								    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
								    "to use it with pretokenized inputs."
								)

								return super()._encode_plus(*lowerCamelCase,    **lowerCamelCase	)



					def 						snake_case			(			self				:    str,    lowerCamelCase				:    str,    lowerCamelCase				:    Optional[str] = None	)->   Tuple[str]:
								lowerCamelCase__						:						str						      =self._tokenizer.model.save(lowerCamelCase,    name=lowerCamelCase	)
								return tuple(lowerCamelCase	)



					def 						snake_case			(			self				:    Dict,    lowerCamelCase				:    int,    lowerCamelCase				:    int=None	)->   str:
								lowerCamelCase__						:						int						      =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
											return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




					def 						snake_case			(			self				:    Optional[int],    lowerCamelCase				:    List[int],    lowerCamelCase				:    Optional[List[int]] = None	)->   List[int]:
								lowerCamelCase__						:						Tuple						      =[self.sep_token_id]
								lowerCamelCase__						:						str						      =[self.cls_token_id]

								if token_ids_a is None:
											return len(cls + token_ids_a + sep	) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]




