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__   (     A_				):






     def __init__(				self	:	Optional[Any]					):





         """simple docstring"""



         self.register_to_config()
         super().__init__()






     def      SCREAMING_SNAKE_CASE__     (				self	:	Optional[Any]						,       a	:	Tuple						,       a	:	List[str]					):





         """simple docstring"""



         if dicta == dicta:
             return True
         else:
             __lowerCamelCase			,						__lowerCamelCase						=						self._remove_meta_keys(a					)
             __lowerCamelCase			,						__lowerCamelCase						=						self._remove_meta_keys(a					)
             if configa == configa:
                 print(f"""Warning !: Mismatch in keys {meta_keysa} and {meta_keysa}."""					)
                 return True
         return False






     def      SCREAMING_SNAKE_CASE__     (				self	:	Dict						,       a	:	int					):





         """simple docstring"""



         __lowerCamelCase						=						[]
         __lowerCamelCase						=						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      SCREAMING_SNAKE_CASE__     (				self	:	List[Any]						,       a	:	Optional[int]						,       **a	:	str					):





         """simple docstring"""



         __lowerCamelCase						=						kwargs.pop('''cache_dir'''						,       a					)
         __lowerCamelCase						=						kwargs.pop('''resume_download'''						,       a					)
         __lowerCamelCase						=						kwargs.pop('''force_download'''						,       a					)
         __lowerCamelCase						=						kwargs.pop('''proxies'''						,       a					)
         __lowerCamelCase						=						kwargs.pop('''local_files_only'''						,       a					)
         __lowerCamelCase						=						kwargs.pop('''use_auth_token'''						,       a					)
         __lowerCamelCase						=						kwargs.pop('''revision'''						,       a					)
         __lowerCamelCase						=						kwargs.pop('''torch_dtype'''						,       a					)
         __lowerCamelCase						=						kwargs.pop('''device_map'''						,       a					)

         __lowerCamelCase						=						kwargs.pop('''alpha'''						,       0.5					)
         __lowerCamelCase						=						kwargs.pop('''interp'''						,       a					)

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

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

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

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

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

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

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

                     __lowerCamelCase						=						getattr(a						,       '''load_state_dict'''					)
                     __lowerCamelCase						=						(
                         safetensors.torch.load_file(a					)
                         if (is_safetensors_available() and checkpoint_path_a.endswith('''.safetensors'''					))
                         else torch.load(a						,       map_location='''cpu'''					)
                     )
                     __lowerCamelCase						=						None
                     if checkpoint_path_a:
                         __lowerCamelCase						=						(
                             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:
                         __lowerCamelCase						=						theta_func(theta_a[key]						,       theta_a[key]						,       theta_a[key]						,       a					)
                     else:
                         __lowerCamelCase						=						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      SCREAMING_SNAKE_CASE__     (				a	:	str						,       a	:	Optional[int]						,       a	:	int						,       a	:	int					):





         """simple docstring"""



         return ((1 - alpha) * thetaa) + (alpha * thetaa)






     @staticmethod
     def      SCREAMING_SNAKE_CASE__     (				a	:	int						,       a	:	Dict						,       a	:	List[Any]						,       a	:	Optional[Any]					):





         """simple docstring"""



         __lowerCamelCase						=						alpha * alpha * (3 - (2 * alpha))
         return thetaa + ((thetaa - thetaa) * alpha)






     @staticmethod
     def      SCREAMING_SNAKE_CASE__     (				a	:	List[str]						,       a	:	int						,       a	:	Tuple						,       a	:	int					):





         """simple docstring"""



         import math

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






     @staticmethod
     def      SCREAMING_SNAKE_CASE__     (				a	:	List[Any]						,       a	:	Tuple						,       a	:	Optional[int]						,       a	:	Optional[Any]					):





         """simple docstring"""



         return thetaa + (thetaa - thetaa) * (1.0 - alpha)

'''simple docstring'''




import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCAmelCase  							=logging.get_logger(__name__)

__UpperCAmelCase  							={
    "source_spm": "source.spm",
    "target_spm": "target.spm",
    "vocab": "vocab.json",
    "target_vocab_file": "target_vocab.json",
    "tokenizer_config_file": "tokenizer_config.json",
}

__UpperCAmelCase  							={
    "source_spm": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"
    },
    "target_spm": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"
    },
    "vocab": {
        "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"
    },
    "tokenizer_config_file": {
        "Helsinki-NLP/opus-mt-en-de": (
            "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"
        )
    },
}

__UpperCAmelCase  							={"Helsinki-NLP/opus-mt-en-de": 5_1_2}
__UpperCAmelCase  							={}




class 					a__   (     UpperCAmelCase__				):
     lowerCamelCase						:  str							=VOCAB_FILES_NAMES
     lowerCamelCase						:  Union[str, Any]							=PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase						:  Optional[int]							=PRETRAINED_INIT_CONFIGURATION
     lowerCamelCase						:  str							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase						:  Union[str, Any]							=["input_ids", "attention_mask"]
     lowerCamelCase						:  List[Any]							=re.compile(">>.+<<"				)  # type: re.Pattern






     def __init__(				self	:	Dict						,       a	:	Tuple						,       a	:	List[Any]						,       a	:	int						,       a	:	Any=None						,       a	:	str=None						,       a	:	int=None						,       a	:	Optional[int]="<unk>"						,       a	:	Optional[int]="</s>"						,       a	:	Optional[int]="<pad>"						,       a	:	Dict=5_12						,       a	:	Optional[Dict[str, Any]] = None						,       a	:	Optional[int]=False						,       **a	:	List[Any]						,       ):





         """simple docstring"""



         __lowerCamelCase						=						{} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
             source_lang=a						,       target_lang=a						,       unk_token=a						,       eos_token=a						,       pad_token=a						,       model_max_length=a						,       sp_model_kwargs=self.sp_model_kwargs						,       target_vocab_file=a						,       separate_vocabs=a						,       **a						,       )
         assert Path(a					).exists(), f"""cannot find spm source {source_spm}"""

         __lowerCamelCase						=						separate_vocabs
         __lowerCamelCase						=						load_json(a					)
         if self.unk_token not in self.encoder:
             raise KeyError('''<unk> token must be in vocab'''					)
         assert self.pad_token in self.encoder

         if separate_vocabs:
             __lowerCamelCase						=						load_json(a					)
             __lowerCamelCase						=						{v: k for k, v in self.target_encoder.items()}
             __lowerCamelCase						=						[]
         else:
             __lowerCamelCase						=						{v: k for k, v in self.encoder.items()}
             __lowerCamelCase						=						[k for k in self.encoder if k.startswith('''>>'''					) and k.endswith('''<<'''					)]

         __lowerCamelCase						=						source_lang
         __lowerCamelCase						=						target_lang
         __lowerCamelCase						=						[source_spm, target_spm]

         # load SentencePiece model for pre-processing
         __lowerCamelCase						=						load_spm(a						,       self.sp_model_kwargs					)
         __lowerCamelCase						=						load_spm(a						,       self.sp_model_kwargs					)
         __lowerCamelCase						=						self.spm_source
         __lowerCamelCase						=						self.encoder

         # Multilingual target side: default to using first supported language code.

         self._setup_normalizer()






     def      SCREAMING_SNAKE_CASE__     (				self	:	Dict					):





         """simple docstring"""



         try:
             from sacremoses import MosesPunctNormalizer

             __lowerCamelCase						=						MosesPunctNormalizer(self.source_lang					).normalize
         except (ImportError, FileNotFoundError):
             warnings.warn('''Recommended: pip install sacremoses.'''					)
             __lowerCamelCase						=						lambda a					: x






     def      SCREAMING_SNAKE_CASE__     (				self	:	Optional[Any]						,       a	:	str					):





         """simple docstring"""



         return self.punc_normalizer(a					) if x else ""






     def      SCREAMING_SNAKE_CASE__     (				self	:	Optional[Any]						,       a	:	Any					):





         """simple docstring"""



         return self.current_encoder.get(a						,       self.current_encoder[self.unk_token]					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	Optional[Any]						,       a	:	str					):





         """simple docstring"""



         __lowerCamelCase						=						self.language_code_re.match(a					)
         __lowerCamelCase						=						[match.group(0					)] if match else []
         return code, self.language_code_re.sub(''''''						,       a					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	Dict						,       a	:	str					):





         """simple docstring"""



         __lowerCamelCase			,						__lowerCamelCase						=						self.remove_language_code(a					)
         __lowerCamelCase						=						self.current_spm.encode(a						,       out_type=a					)
         return code + pieces






     def      SCREAMING_SNAKE_CASE__     (				self	:	Dict						,       a	:	int					):





         """simple docstring"""



         return self.decoder.get(a						,       self.unk_token					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[Any]						,       a	:	Optional[Any]						,       **a	:	Dict					):





         """simple docstring"""



         return super().batch_decode(a						,       **a					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	Union[str, Any]						,       a	:	int						,       **a	:	int					):





         """simple docstring"""



         return super().decode(a						,       **a					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	Any						,       a	:	List[str]					):





         """simple docstring"""



         __lowerCamelCase						=						self.spm_source if self._decode_use_source_tokenizer else self.spm_target
         __lowerCamelCase						=						[]
         __lowerCamelCase						=						''''''
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 out_string += sp_model.decode_pieces(a					) + token + " "
                 __lowerCamelCase						=						[]
             else:
                 current_sub_tokens.append(a					)
         out_string += sp_model.decode_pieces(a					)
         return out_string.strip()






     def      SCREAMING_SNAKE_CASE__     (				self	:	Any						,       a	:	Union[str, Any]						,       a	:	Tuple=None					):





         """simple docstring"""



         if token_ids_a is None:
             return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[Any]					):





         """simple docstring"""



         __lowerCamelCase						=						self.spm_source
         __lowerCamelCase						=						self.encoder






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[str]					):





         """simple docstring"""



         __lowerCamelCase						=						self.spm_target
         if self.separate_vocabs:
             __lowerCamelCase						=						self.target_encoder






     @property
     def      SCREAMING_SNAKE_CASE__     (				self	:	int					):





         """simple docstring"""



         return len(self.encoder					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	Any						,       a	:	str						,       a	:	Optional[str] = None					):





         """simple docstring"""



         if not os.path.isdir(a					):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
             return
         __lowerCamelCase						=						[]

         if self.separate_vocabs:
             __lowerCamelCase						=						os.path.join(
                 a						,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']						,       )
             __lowerCamelCase						=						os.path.join(
                 a						,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''target_vocab_file''']						,       )
             save_json(self.encoder						,       a					)
             save_json(self.target_encoder						,       a					)
             saved_files.append(a					)
             saved_files.append(a					)
         else:
             __lowerCamelCase						=						os.path.join(
                 a						,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']					)
             save_json(self.encoder						,       a					)
             saved_files.append(a					)

         for spm_save_filename, spm_orig_path, spm_model in zip(
             [VOCAB_FILES_NAMES['''source_spm'''], VOCAB_FILES_NAMES['''target_spm''']]						,       self.spm_files						,       [self.spm_source, self.spm_target]						,       ):
             __lowerCamelCase						=						os.path.join(
                 a						,       (filename_prefix + '''-''' if filename_prefix else '''''') + spm_save_filename					)
             if os.path.abspath(a					) != os.path.abspath(a					) and os.path.isfile(a					):
                 copyfile(a						,       a					)
                 saved_files.append(a					)
             elif not os.path.isfile(a					):
                 with open(a						,       '''wb'''					) as fi:
                     __lowerCamelCase						=						spm_model.serialized_model_proto()
                     fi.write(a					)
                 saved_files.append(a					)

         return tuple(a					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[Any]					):





         """simple docstring"""



         return self.get_src_vocab()






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[Any]					):





         """simple docstring"""



         return dict(self.encoder						,       **self.added_tokens_encoder					)






     def      SCREAMING_SNAKE_CASE__     (				self	:	Dict					):





         """simple docstring"""



         return dict(self.target_encoder						,       **self.added_tokens_decoder					)






     def __getstate__(				self	:	str					):





         """simple docstring"""



         __lowerCamelCase						=						self.__dict__.copy()
         state.update(
             {k: None for k in ['''spm_source''', '''spm_target''', '''current_spm''', '''punc_normalizer''', '''target_vocab_file''']}					)
         return state






     def __setstate__(				self	:	Tuple						,       a	:	Dict					):





         """simple docstring"""



         __lowerCamelCase						=						d

         # for backward compatibility
         if not hasattr(self						,       '''sp_model_kwargs'''					):
             __lowerCamelCase						=						{}

         __lowerCamelCase			,						__lowerCamelCase						=						(load_spm(a						,       self.sp_model_kwargs					) for f in self.spm_files)
         __lowerCamelCase						=						self.spm_source
         self._setup_normalizer()






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[Any]						,       *a	:	Tuple						,       **a	:	int					):





         """simple docstring"""



         return 1






     def      SCREAMING_SNAKE_CASE__     (				self	:	Optional[Any]						,       a	:	Optional[Any]					):





         """simple docstring"""



         __lowerCamelCase						=						set(self.all_special_ids					)  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id					)  # <unk> is only sometimes special
         return [1 if x in all_special_ids else 0 for x in seq]






     def      SCREAMING_SNAKE_CASE__     (				self	:	List[str]						,       a	:	List						,       a	:	Optional[List] = None						,       a	:	bool = False					):





         """simple docstring"""



         if already_has_special_tokens:
             return self._special_token_mask(a					)
         elif token_ids_a is None:
             return self._special_token_mask(a					) + [1]
         else:
             return self._special_token_mask(token_ids_a + token_ids_a					) + [1]






def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       )			->					sentencepiece.SentencePieceProcessor:
    __lowerCamelCase						=						sentencepiece.SentencePieceProcessor(**UpperCamelCase__       )
    spm.Load(UpperCamelCase__       )
    return spm






def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       )			->					None:
    with open(UpperCamelCase__       ,						'''w'''       ) as f:
        json.dump(UpperCamelCase__       ,						UpperCamelCase__       ,						indent=2       )






def __lowerCAmelCase				(							UpperCamelCase__       )			->					Union[Dict, List]:
    with open(UpperCamelCase__       ,						'''r'''       ) as f:
        return json.load(UpperCamelCase__       )
