

"""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      __a (   snake_case__     ):

			"""simple docstring"""

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






			def  _lowerCAmelCase      (				self  :       Optional[int]    ,	lowercase_  :       Tuple    ,	lowercase_  :       Optional[int]    ):
								if dicta == dicta:
													return True
								else:
													UpperCamelCase__	,      UpperCamelCase__		:     Optional[int]								=self._remove_meta_keys(lowercase_    )
													UpperCamelCase__	,      UpperCamelCase__		:     Union[str, Any]								=self._remove_meta_keys(lowercase_    )
													if configa == configa:
																		print(f'''Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}.'''    )
																		return True
								return False






			def  _lowerCAmelCase      (				self  :       List[str]    ,	lowercase_  :       Dict    ):
								UpperCamelCase__		:     Optional[Any]								=[]
								UpperCamelCase__		:     str								=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  _lowerCAmelCase      (				self  :       str    ,	lowercase_  :       List[Union[str, os.PathLike]]    ,	**lowercase_  :       int    ):
								UpperCamelCase__		:     List[str]								=kwargs.pop('''cache_dir'''    ,	lowercase_    )
								UpperCamelCase__		:     int								=kwargs.pop('''resume_download'''    ,	lowercase_    )
								UpperCamelCase__		:     str								=kwargs.pop('''force_download'''    ,	lowercase_    )
								UpperCamelCase__		:     Tuple								=kwargs.pop('''proxies'''    ,	lowercase_    )
								UpperCamelCase__		:     Any								=kwargs.pop('''local_files_only'''    ,	lowercase_    )
								UpperCamelCase__		:     List[str]								=kwargs.pop('''use_auth_token'''    ,	lowercase_    )
								UpperCamelCase__		:     Any								=kwargs.pop('''revision'''    ,	lowercase_    )
								UpperCamelCase__		:     str								=kwargs.pop('''torch_dtype'''    ,	lowercase_    )
								UpperCamelCase__		:     Optional[int]								=kwargs.pop('''device_map'''    ,	lowercase_    )

								UpperCamelCase__		:     str								=kwargs.pop('''alpha'''    ,	0.5    )
								UpperCamelCase__		:     Any								=kwargs.pop('''interp'''    ,	lowercase_    )

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

								UpperCamelCase__		:     List[str]								=len(lowercase_    )
								# Ignore result from model_index_json comparision of the two checkpoints
								UpperCamelCase__		:     str								=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__		:     str								=[]
								for pretrained_model_name_or_path in pretrained_model_name_or_path_list:
													UpperCamelCase__		:     List[str]								=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__		:     Union[str, Any]								=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__		:     Optional[Any]								=[]
								for pretrained_model_name_or_path, config_dict in zip(lowercase_    ,	lowercase_    ):
													UpperCamelCase__		:     str								=[k for k in config_dict.keys() if not k.startswith('''_'''    )]
													UpperCamelCase__		:     str								=[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__		:     List[Any]								=config_dict.get('''_class_name'''    )
													UpperCamelCase__		:     Optional[int]								={'''diffusers''': __version__, '''pipeline_class''': requested_pipeline_class}

													UpperCamelCase__		:     Union[str, Any]								=(
													    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__		:     Any								=DiffusionPipeline.from_pretrained(
								    cached_folders[0]    ,	torch_dtype=lowercase_    ,	device_map=lowercase_    )
								final_pipe.to(self.device    )

								UpperCamelCase__		:     Dict								=None
								if len(lowercase_    ) > 2:
													UpperCamelCase__		:     Dict								=os.path.join(cached_folders[2]    )

								if interp == "sigmoid":
													UpperCamelCase__		:     str								=CheckpointMergerPipeline.sigmoid
								elif interp == "inv_sigmoid":
													UpperCamelCase__		:     int								=CheckpointMergerPipeline.inv_sigmoid
								elif interp == "add_diff":
													UpperCamelCase__		:     Optional[Any]								=CheckpointMergerPipeline.add_difference
								else:
													UpperCamelCase__		:     str								=CheckpointMergerPipeline.weighted_sum

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

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






			@staticmethod
			def  _lowerCAmelCase      (				lowercase_  :       Optional[Any]    ,	lowercase_  :       str    ,	lowercase_  :       Optional[Any]    ,	lowercase_  :       Any    ):
								UpperCamelCase__		:     str								=alpha * alpha * (3 - (2 * alpha))
								return thetaa + ((thetaa - thetaa) * alpha)






			@staticmethod
			def  _lowerCAmelCase      (				lowercase_  :       Optional[int]    ,	lowercase_  :       int    ,	lowercase_  :       Optional[int]    ,	lowercase_  :       Any    ):
								import math

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






			@staticmethod
			def  _lowerCAmelCase      (				lowercase_  :       Any    ,	lowercase_  :       Union[str, Any]    ,	lowercase_  :       Optional[Any]    ,	lowercase_  :       Union[str, Any]    ):
								return thetaa + (thetaa - thetaa) * (1.0 - alpha)





"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


_SCREAMING_SNAKE_CASE							:	Union[str, Any]										=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:	Union[str, Any]										=			{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

_SCREAMING_SNAKE_CASE							:	List[Any]										=			{
    """vocab_file""": {
        """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """microsoft/mpnet-base""": """https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json""",
    },
}

_SCREAMING_SNAKE_CASE							:	List[Any]										=			{
    """microsoft/mpnet-base""": 5_1_2,
}

_SCREAMING_SNAKE_CASE							:	Optional[int]										=			{
    """microsoft/mpnet-base""": {"""do_lower_case""": True},
}

class      __a (   snake_case__     ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    VOCAB_FILES_NAMES
			SCREAMING_SNAKE_CASE_      =    PRETRAINED_VOCAB_FILES_MAP
			SCREAMING_SNAKE_CASE_      =    PRETRAINED_INIT_CONFIGURATION
			SCREAMING_SNAKE_CASE_      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			SCREAMING_SNAKE_CASE_      =    MPNetTokenizer
			SCREAMING_SNAKE_CASE_      =    ['input_ids', 'attention_mask']






			def __init__(				self  :       List[str]    ,	lowercase_  :       List[Any]=None    ,	lowercase_  :       Optional[int]=None    ,	lowercase_  :       Optional[int]=True    ,	lowercase_  :       str="<s>"    ,	lowercase_  :       str="</s>"    ,	lowercase_  :       List[Any]="</s>"    ,	lowercase_  :       Tuple="<s>"    ,	lowercase_  :       Any="[UNK]"    ,	lowercase_  :       Dict="<pad>"    ,	lowercase_  :       Optional[Any]="<mask>"    ,	lowercase_  :       List[str]=True    ,	lowercase_  :       Optional[Any]=None    ,	**lowercase_  :       Dict    ,	):
								super().__init__(
								    lowercase_    ,	tokenizer_file=lowercase_    ,	do_lower_case=lowercase_    ,	bos_token=lowercase_    ,	eos_token=lowercase_    ,	sep_token=lowercase_    ,	cls_token=lowercase_    ,	unk_token=lowercase_    ,	pad_token=lowercase_    ,	mask_token=lowercase_    ,	tokenize_chinese_chars=lowercase_    ,	strip_accents=lowercase_    ,	**lowercase_    ,	)

								UpperCamelCase__		:     List[str]								=json.loads(self.backend_tokenizer.normalizer.__getstate__()    )
								if (
								    pre_tok_state.get('''lowercase'''    ,	lowercase_    ) != do_lower_case
								    or pre_tok_state.get('''strip_accents'''    ,	lowercase_    ) != strip_accents
								):
													UpperCamelCase__		:     int								=getattr(lowercase_    ,	pre_tok_state.pop('''type'''    )    )
													UpperCamelCase__		:     List[Any]								=do_lower_case
													UpperCamelCase__		:     Optional[Any]								=strip_accents
													UpperCamelCase__		:     Optional[int]								=pre_tok_class(**lowercase_    )

								UpperCamelCase__		:     Tuple								=do_lower_case






			@property
			def  _lowerCAmelCase      (				self  :       List[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  _lowerCAmelCase      (				self  :       int    ,	lowercase_  :       str    ):
								UpperCamelCase__		:     int								=AddedToken(lowercase_    ,	lstrip=lowercase_    ,	rstrip=lowercase_    ) if isinstance(lowercase_    ,	lowercase_    ) else value
								UpperCamelCase__		:     str								=value






			def  _lowerCAmelCase      (				self  :       Tuple    ,	lowercase_  :       Any    ,	lowercase_  :       Tuple=None    ):
								UpperCamelCase__		:     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  _lowerCAmelCase      (				self  :       List[str]    ,	lowercase_  :       List[int]    ,	lowercase_  :       Optional[List[int]] = None    ):
								UpperCamelCase__		:     str								=[self.sep_token_id]
								UpperCamelCase__		:     List[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]






			def  _lowerCAmelCase      (				self  :       str    ,	lowercase_  :       str    ,	lowercase_  :       Optional[str] = None    ):
								UpperCamelCase__		:     List[Any]								=self._tokenizer.model.save(lowercase_    ,	name=lowercase_    )
								return tuple(lowercase_    )


