





import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


_UpperCAmelCase       : List[str]			     =       logging.get_logger(__name__)

class      lowerCAmelCase							(      __UpperCamelCase    ):
 UpperCAmelCase__									=	"""AutoTokenizer"""
 UpperCAmelCase__									=	["""tokenizer"""]

 UpperCAmelCase__									=	{
     """semantic_prompt""": 1,
     """coarse_prompt""": 2,
     """fine_prompt""": 2,
 }
 def __init__(   self						:   Optional[int]    ,  UpperCAmelCase						:   str    ,  UpperCAmelCase						:   Tuple=None			)    ->			List[Any]:
     super().__init__(UpperCAmelCase			)

     lowerCamelCase__					:   Optional[int]      				=	speaker_embeddings
 @classmethod
 def   A_			(   cls						:   List[Any]    ,  UpperCAmelCase						:   Dict    ,  UpperCAmelCase						:   Dict="speaker_embeddings_path.json"    ,  **UpperCAmelCase						:   int			)    ->			Optional[Any]:

     if speaker_embeddings_dict_path is not None:
         lowerCamelCase__					:   Tuple      				=	get_file_from_repo(
             UpperCAmelCase    ,  UpperCAmelCase    ,  subfolder=kwargs.pop('subfolder'    ,  UpperCAmelCase			)    ,  cache_dir=kwargs.pop('cache_dir'    ,  UpperCAmelCase			)    ,  force_download=kwargs.pop('force_download'    ,  UpperCAmelCase			)    ,  proxies=kwargs.pop('proxies'    ,  UpperCAmelCase			)    ,  resume_download=kwargs.pop('resume_download'    ,  UpperCAmelCase			)    ,  local_files_only=kwargs.pop('local_files_only'    ,  UpperCAmelCase			)    ,  use_auth_token=kwargs.pop('use_auth_token'    ,  UpperCAmelCase			)    ,  revision=kwargs.pop('revision'    ,  UpperCAmelCase			)    ,  )
         if speaker_embeddings_path is None:
             logger.warning(
                 F"""`{os.path.join(UpperCAmelCase    ,  UpperCAmelCase			)}` does not exists
                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`."""			)
             lowerCamelCase__					:   Optional[Any]      				=	None
         else:
             with open(UpperCAmelCase			) as speaker_embeddings_json:
                 lowerCamelCase__					:   Optional[int]      				=	json.load(UpperCAmelCase			)
     else:
         lowerCamelCase__					:   int      				=	None

     lowerCamelCase__					:   Union[str, Any]      				=	AutoTokenizer.from_pretrained(UpperCAmelCase    ,  **UpperCAmelCase			)

     return cls(tokenizer=UpperCAmelCase    ,  speaker_embeddings=UpperCAmelCase			)
 def   A_			(   self						:   Union[str, Any]    ,  UpperCAmelCase						:   int    ,  UpperCAmelCase						:   Tuple="speaker_embeddings_path.json"    ,  UpperCAmelCase						:   Union[str, Any]="speaker_embeddings"    ,  UpperCAmelCase						:   bool = False    ,  **UpperCAmelCase						:   int    ,  )    ->			List[str]:
     if self.speaker_embeddings is not None:
         os.makedirs(os.path.join(UpperCAmelCase    ,  UpperCAmelCase    ,  'v2'			)    ,  exist_ok=UpperCAmelCase			)

         lowerCamelCase__					:   Any      				=	{}

         lowerCamelCase__					:   Optional[Any]      				=	save_directory

         for prompt_key in self.speaker_embeddings:
             if prompt_key != "repo_or_path":
                 lowerCamelCase__					:   str      				=	self._load_voice_preset(UpperCAmelCase			)

                 lowerCamelCase__					:   Any      				=	{}
                 for key in self.speaker_embeddings[prompt_key]:
                     np.save(
                         os.path.join(
                             embeddings_dict['repo_or_path']    ,  UpperCAmelCase    ,  F"""{prompt_key}_{key}"""			)    ,  voice_preset[key]    ,  allow_pickle=UpperCAmelCase    ,  )
                     lowerCamelCase__					:   Any      				=	os.path.join(UpperCAmelCase    ,  F"""{prompt_key}_{key}.npy"""			)

                 lowerCamelCase__					:   Optional[int]      				=	tmp_dict

         with open(os.path.join(UpperCAmelCase    ,  UpperCAmelCase			)    ,  'w'			) as fp:
             json.dump(UpperCAmelCase    ,  UpperCAmelCase			)

     super().save_pretrained(UpperCAmelCase    ,  UpperCAmelCase    ,  **UpperCAmelCase			)
 def   A_			(   self						:   List[str]    ,  UpperCAmelCase						:   str = None    ,  **UpperCAmelCase						:   Tuple			)    ->			int:
     lowerCamelCase__					:   Dict      				=	self.speaker_embeddings[voice_preset]

     lowerCamelCase__					:   List[Any]      				=	{}
     for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
         if key not in voice_preset_paths:
             raise ValueError(
                 F"""Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]."""			)

         lowerCamelCase__					:   str      				=	get_file_from_repo(
             self.speaker_embeddings.get('repo_or_path'    ,  '/'			)    ,  voice_preset_paths[key]    ,  subfolder=kwargs.pop('subfolder'    ,  UpperCAmelCase			)    ,  cache_dir=kwargs.pop('cache_dir'    ,  UpperCAmelCase			)    ,  force_download=kwargs.pop('force_download'    ,  UpperCAmelCase			)    ,  proxies=kwargs.pop('proxies'    ,  UpperCAmelCase			)    ,  resume_download=kwargs.pop('resume_download'    ,  UpperCAmelCase			)    ,  local_files_only=kwargs.pop('local_files_only'    ,  UpperCAmelCase			)    ,  use_auth_token=kwargs.pop('use_auth_token'    ,  UpperCAmelCase			)    ,  revision=kwargs.pop('revision'    ,  UpperCAmelCase			)    ,  )
         if path is None:
             raise ValueError(
                 F"""`{os.path.join(self.speaker_embeddings.get("repo_or_path"    ,  "/"			)    ,  voice_preset_paths[key]			)}` does not exists
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings."""			)

         lowerCamelCase__					:   Any      				=	np.load(UpperCAmelCase			)

     return voice_preset_dict
 def   A_			(   self						:   List[str]    ,  UpperCAmelCase						:   Optional[dict] = None			)    ->			int:
     for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
         if key not in voice_preset:
             raise ValueError(F"""Voice preset unrecognized, missing {key} as a key."""			)

         if not isinstance(voice_preset[key]    ,  np.ndarray			):
             raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key]			)}D ndarray."""			)

         if len(voice_preset[key].shape			) != self.preset_shape[key]:
             raise ValueError(F"""{key} voice preset must be a {str(self.preset_shape[key]			)}D ndarray."""			)







 def __call__(   self						:   Optional[int]    ,  UpperCAmelCase						:   List[str]=None    ,  UpperCAmelCase						:   Optional[int]=None    ,  UpperCAmelCase						:   Tuple="pt"    ,  UpperCAmelCase						:   str=256    ,  UpperCAmelCase						:   Dict=False    ,  UpperCAmelCase						:   str=True    ,  UpperCAmelCase						:   Dict=False    ,  **UpperCAmelCase						:   str    ,  )    ->			int:
     if voice_preset is not None and not isinstance(UpperCAmelCase    ,  UpperCAmelCase			):
         if (
             isinstance(UpperCAmelCase    ,  UpperCAmelCase			)
             and self.speaker_embeddings is not None
             and voice_preset in self.speaker_embeddings
         ):
             lowerCamelCase__					:   List[Any]      				=	self._load_voice_preset(UpperCAmelCase			)

         else:
             if isinstance(UpperCAmelCase    ,  UpperCAmelCase			) and not voice_preset.endswith('.npz'			):
                 lowerCamelCase__					:   List[Any]      				=	voice_preset + '.npz'

             lowerCamelCase__					:   List[Any]      				=	np.load(UpperCAmelCase			)

     if voice_preset is not None:
         self._validate_voice_preset_dict(UpperCAmelCase    ,  **UpperCAmelCase			)
         lowerCamelCase__					:   int      				=	BatchFeature(data=UpperCAmelCase    ,  tensor_type=UpperCAmelCase			)

     lowerCamelCase__					:   List[Any]      				=	self.tokenizer(
         UpperCAmelCase    ,  return_tensors=UpperCAmelCase    ,  padding='max_length'    ,  max_length=UpperCAmelCase    ,  return_attention_mask=UpperCAmelCase    ,  return_token_type_ids=UpperCAmelCase    ,  add_special_tokens=UpperCAmelCase    ,  **UpperCAmelCase    ,  )

     if voice_preset is not None:
         lowerCamelCase__					:   Optional[int]      				=	voice_preset

     return encoded_text












from collections import defaultdict
from math import ceil, sqrt


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase = 100_0000		,			_UpperCAmelCase = 10				)    ->  int:
    lowerCamelCase__					:   defaultdict      				=	defaultdict(_UpperCAmelCase				)

    for outer_width in range(3		,			(t_limit // 4) + 2				):
        if outer_width * outer_width > t_limit:
            lowerCamelCase__					:   List[str]      				=	max(
                ceil(sqrt(outer_width * outer_width - t_limit				)				)		,			1				)
        else:
            lowerCamelCase__					:   int      				=	1

        hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

        for hole_width in range(_UpperCAmelCase		,			outer_width - 1		,			2				):
            count[outer_width * outer_width - hole_width * hole_width] += 1

    return sum(1 for n in count.values() if 1 <= n <= 10				)


if __name__ == "__main__":
    print(F"""{solution() = }""")





