



"""simple docstring"""




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


_lowerCAmelCase							:   Dict				=				logging.get_logger(__name__)






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					'AutoTokenizer'
      __SCREAMING_SNAKE_CASE		:  Tuple						=					['tokenizer']

      __SCREAMING_SNAKE_CASE		:  Any						=					{
          'semantic_prompt': 1,
          'coarse_prompt': 2,
          'fine_prompt': 2,
      }



      def __init__(	self	:      Optional[int]	,      A	:      List[str]	,      A	:      Any=None		):
           super().__init__(A		)

           _UpperCAmelCase     :      Any														=       speaker_embeddings



      @classmethod
      def 		snake_case_   (	cls	:      int	,      A	:      Optional[int]	,      A	:      str="speaker_embeddings_path.json"	,      **A	:      List[str]		):

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

           _UpperCAmelCase     :      Optional[int]														=       AutoTokenizer.from_pretrained(A	,      **A		)

           return cls(tokenizer=A	,      speaker_embeddings=A		)



      def 		snake_case_   (	self	:      List[Any]	,      A	:      Optional[Any]	,      A	:      List[str]="speaker_embeddings_path.json"	,      A	:      Union[str, Any]="speaker_embeddings"	,      A	:      bool = False	,      **A	:      List[str]	,      ):
           if self.speaker_embeddings is not None:
                os.makedirs(os.path.join(A	,      A	,      "v2"		)	,      exist_ok=A		)

                _UpperCAmelCase     :      List[str]														=       {}

                _UpperCAmelCase     :      Any														=       save_directory

                for prompt_key in self.speaker_embeddings:
                     if prompt_key != "repo_or_path":
                          _UpperCAmelCase     :      List[Any]														=       self._load_voice_preset(A		)

                          _UpperCAmelCase     :      str														=       {}
                          for key in self.speaker_embeddings[prompt_key]:
                               np.save(
                                   os.path.join(
                                       embeddings_dict["repo_or_path"]	,      A	,      f'{prompt_key}_{key}'		)	,      voice_preset[key]	,      allow_pickle=A	,      )
                               _UpperCAmelCase     :      Optional[Any]														=       os.path.join(A	,      f'{prompt_key}_{key}.npy'		)

                          _UpperCAmelCase     :      Dict														=       tmp_dict

                with open(os.path.join(A	,      A		)	,      "w"		) as fp:
                     json.dump(A	,      A		)

           super().save_pretrained(A	,      A	,      **A		)



      def 		snake_case_   (	self	:      str	,      A	:      str = None	,      **A	:      str		):
           _UpperCAmelCase     :      Dict														=       self.speaker_embeddings[voice_preset]

           _UpperCAmelCase     :      Tuple														=       {}
           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}].'		)

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

                _UpperCAmelCase     :      Tuple														=       np.load(A		)

           return voice_preset_dict



      def 		snake_case_   (	self	:      Dict	,      A	:      Optional[dict] = None		):
           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	:      List[str]	,      A	:      Optional[Any]=None	,      A	:      List[Any]=None	,      A	:      Tuple="pt"	,      A	:      Dict=2_5_6	,      A	:      Optional[int]=False	,      A	:      List[Any]=True	,      A	:      int=False	,      **A	:      List[str]	,      ):
           if voice_preset is not None and not isinstance(A	,      A		):
                if (
                    isinstance(A	,      A		)
                    and self.speaker_embeddings is not None
                    and voice_preset in self.speaker_embeddings
                ):
                     _UpperCAmelCase     :      str														=       self._load_voice_preset(A		)

                else:
                     if isinstance(A	,      A		) and not voice_preset.endswith(".npz"		):
                          _UpperCAmelCase     :      List[Any]														=       voice_preset + ".npz"

                     _UpperCAmelCase     :      List[Any]														=       np.load(A		)

           if voice_preset is not None:
                self._validate_voice_preset_dict(A	,      **A		)
                _UpperCAmelCase     :      Any														=       BatchFeature(data=A	,      tensor_type=A		)

           _UpperCAmelCase     :      str														=       self.tokenizer(
               A	,      return_tensors=A	,      padding="max_length"	,      max_length=A	,      return_attention_mask=A	,      return_token_type_ids=A	,      add_special_tokens=A	,      **A	,      )

           if voice_preset is not None:
                _UpperCAmelCase     :      Optional[Any]														=       voice_preset

           return encoded_text





"""simple docstring"""




import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


_lowerCAmelCase							:   Any				=				HfApi()

_lowerCAmelCase							:   int				=				{}
# fmt: off
_lowerCAmelCase							:   List[Any]				=				torch.tensor([
    -0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3.7_4_6_7,
    1.2_3_4_2, -2.2_4_8_5, 0.4_6_3_6, 0.8_0_7_6, -0.7_9_9_1, 0.3_9_6_9, 0.8_4_9_8, 0.9_1_8_9,
    -1.8_8_8_7, -3.3_5_2_2, 0.7_6_3_9, 0.2_0_4_0, 0.6_2_7_1, -2.7_1_4_8, -1.6_3_1_6, 3.0_8_3_9,
    0.3_1_8_6, 0.2_7_2_1, -0.9_7_5_9, -1.2_4_6_1, 2.6_2_5_7, 1.3_5_5_7
])
_lowerCAmelCase							:   Any				=				torch.tensor([
    -2.3_6_3_9, -2.5_3_4_4, 0.0_0_5_4, -0.6_6_7_4, 1.5_9_9_0, 1.0_1_5_8, 0.3_1_2_4, -2.1_4_3_6,
    1.8_7_9_5, -2.5_4_2_9, -0.1_5_6_6, -0.3_9_7_3, 1.2_4_9_0, 2.6_4_4_7, 1.2_2_8_3, -0.5_2_0_8,
    -2.8_1_5_4, -3.5_1_1_9, 2.3_8_3_8, 1.2_0_3_3, 1.7_2_0_1, -2.1_2_5_6, -1.4_5_7_6, 2.7_9_4_8,
    2.4_2_0_4, -0.9_7_5_2, -1.2_5_4_6, 0.8_0_2_7, 3.2_7_5_8, 3.1_3_6_5
])
_lowerCAmelCase							:   List[str]				=				torch.tensor([
    -0.6_5_3_1, -0.6_8_9_1, -0.3_1_7_2, -0.5_3_7_5, -0.9_1_4_0, -0.5_3_6_7, -0.1_1_7_5, -0.7_8_6_9,
    -0.3_8_0_8, -0.4_5_1_3, -0.2_0_9_8, -0.0_0_8_3, 0.3_1_8_3, 0.5_1_4_0, 0.2_2_4_7, -0.1_3_0_4,
    -0.1_3_0_2, -0.2_8_0_2, -0.2_0_8_4, -0.2_0_2_5, -0.4_9_6_7, -0.4_8_7_3, -0.0_8_6_1, 0.6_9_2_5,
    0.0_2_5_0, 0.1_2_9_0, -0.1_5_4_3, 0.6_3_1_6, 1.0_4_6_0, 1.4_9_4_3
])
_lowerCAmelCase							:   Optional[int]				=				torch.tensor([
    0.0_9_1_1, 0.1_1_0_7, 0.0_1_8_2, 0.0_4_3_5, -0.0_8_0_5, -0.0_6_0_8, 0.0_3_8_1, 0.2_1_7_2,
    -0.0_2_8_0, 0.1_3_2_7, -0.0_2_9_9, -0.0_2_5_5, -0.0_0_5_0, -0.1_1_7_0, -0.1_0_4_6, 0.0_3_0_9,
    0.1_3_6_7, 0.1_7_2_8, -0.0_5_3_3, -0.0_7_4_8, -0.0_5_3_4, 0.1_6_2_4, 0.0_3_8_4, -0.1_8_0_5,
    -0.0_7_0_7, 0.0_6_4_2, 0.0_2_2_0, -0.0_1_3_4, -0.1_3_3_3, -0.1_5_0_5
])
_lowerCAmelCase							:   List[Any]				=				torch.tensor([
    0.1_3_2_1, 0.1_3_3_7, 0.0_4_4_0, 0.0_6_2_2, -0.0_5_9_1, -0.0_3_7_0, 0.0_5_0_3, 0.2_1_3_3,
    -0.0_1_7_7, 0.1_4_1_5, -0.0_1_1_6, -0.0_1_1_2, 0.0_0_4_4, -0.0_9_8_0, -0.0_7_8_9, 0.0_3_9_5,
    0.1_5_0_2, 0.1_7_8_5, -0.0_4_8_8, -0.0_5_1_4, -0.0_4_0_4, 0.1_5_3_9, 0.0_4_5_4, -0.1_5_5_9,
    -0.0_6_6_5, 0.0_6_5_9, 0.0_3_8_3, -0.0_0_0_5, -0.1_2_6_6, -0.1_3_8_6
])
_lowerCAmelCase							:   Optional[int]				=				torch.tensor([
    0.1_1_5_4, 0.1_2_1_8, 0.0_3_0_7, 0.0_5_2_6, -0.0_7_1_1, -0.0_5_4_1, 0.0_3_6_6, 0.2_0_7_8,
    -0.0_2_6_7, 0.1_3_1_7, -0.0_2_2_6, -0.0_1_9_3, -0.0_0_1_4, -0.1_0_5_5, -0.0_9_0_2, 0.0_3_3_0,
    0.1_3_9_1, 0.1_7_0_9, -0.0_5_6_2, -0.0_6_9_3, -0.0_5_6_0, 0.1_4_8_2, 0.0_3_8_1, -0.1_6_8_3,
    -0.0_6_8_1, 0.0_6_6_1, 0.0_3_3_1, -0.0_0_4_6, -0.1_2_6_8, -0.1_4_3_1
])
_lowerCAmelCase							:   Optional[Any]				=				torch.tensor([
    0.1_1_9_2, 0.1_2_4_0, 0.0_4_1_4, 0.0_6_0_6, -0.0_5_5_7, -0.0_4_1_2, 0.0_4_3_0, 0.2_0_4_2,
    -0.0_2_0_0, 0.1_3_8_5, -0.0_1_1_5, -0.0_1_3_2, 0.0_0_1_7, -0.0_9_6_5, -0.0_8_0_2, 0.0_3_9_8,
    0.1_4_3_3, 0.1_7_4_7, -0.0_4_5_8, -0.0_5_3_3, -0.0_4_0_7, 0.1_5_4_5, 0.0_4_1_9, -0.1_5_7_4,
    -0.0_6_4_5, 0.0_6_2_6, 0.0_3_4_1, -0.0_0_1_0, -0.1_1_9_9, -0.1_3_9_0
])
_lowerCAmelCase							:   List[str]				=				torch.tensor([
    0.1_0_7_5, 0.1_0_7_4, 0.0_2_0_5, 0.0_4_3_1, -0.0_7_7_4, -0.0_6_0_7, 0.0_2_9_8, 0.2_0_4_2,
    -0.0_3_2_0, 0.1_2_6_7, -0.0_2_8_1, -0.0_2_5_0, -0.0_0_6_4, -0.1_0_9_1, -0.0_9_4_6, 0.0_2_9_0,
    0.1_3_2_8, 0.1_6_5_0, -0.0_5_8_0, -0.0_7_3_8, -0.0_5_8_6, 0.1_4_4_0, 0.0_3_3_7, -0.1_7_4_6,
    -0.0_7_1_2, 0.0_6_0_5, 0.0_2_5_0, -0.0_0_9_9, -0.1_3_1_6, -0.1_4_7_3
])
_lowerCAmelCase							:   Any				=				torch.tensor([
    -1.4_5_7_2, -2.0_4_8_1, -0.0_4_1_4, -0.6_0_0_5, 1.4_1_3_6, 0.5_8_4_8, 0.4_0_2_8, -2.7_3_3_0,
    1.2_2_1_2, -2.1_2_2_8, 0.2_1_5_5, 0.4_0_3_9, 0.7_6_6_2, 2.0_5_3_5, 0.7_4_7_7, -0.3_2_4_3,
    -2.1_7_5_8, -2.7_6_4_8, 1.6_9_4_7, 0.7_0_2_6, 1.2_3_3_8, -1.6_0_7_8, -0.8_6_8_2, 2.2_8_1_0,
    1.8_5_7_4, -0.5_7_1_8, -0.5_5_8_6, -0.0_1_8_6, 2.3_4_1_5, 2.1_2_5_1])
_lowerCAmelCase							:   int				=				torch.tensor([
    -1.3_6_9_0, -1.9_7_2_0, -0.4_0_9_0, -0.6_9_6_6, 1.4_6_6_0, 0.9_9_3_8, -0.1_3_8_5, -2.7_3_2_4,
    0.7_7_3_6, -1.8_9_1_7, 0.2_9_2_3, 0.4_2_9_3, 0.1_6_9_3, 1.4_1_1_2, 1.1_8_8_7, -0.3_1_8_1,
    -2.2_1_6_0, -2.6_3_8_1, 1.3_1_7_0, 0.8_1_6_3, 0.9_2_4_0, -1.6_5_4_4, -0.6_0_9_9, 2.5_2_5_9,
    1.6_4_3_0, -0.9_0_9_0, -0.9_3_9_2, -0.0_1_2_6, 2.4_2_6_8, 2.3_2_6_6
])
_lowerCAmelCase							:   Union[str, Any]				=				torch.tensor([
    -1.3_5_2_5, -1.9_6_2_8, -0.3_9_5_6, -0.6_8_6_0, 1.4_6_6_4, 1.0_0_1_4, -0.1_2_5_9, -2.7_2_1_2,
    0.7_7_7_2, -1.8_8_1_1, 0.2_9_9_6, 0.4_3_8_8, 0.1_7_0_4, 1.4_0_2_9, 1.1_7_0_1, -0.3_0_2_7,
    -2.2_0_5_3, -2.6_2_8_7, 1.3_3_5_0, 0.8_1_3_1, 0.9_2_7_4, -1.6_2_9_2, -0.6_0_9_8, 2.5_1_3_1,
    1.6_5_0_5, -0.8_9_5_8, -0.9_2_9_8, -0.0_1_5_1, 2.4_2_5_7, 2.3_3_5_5
])
_lowerCAmelCase							:   str				=				torch.tensor([
    -2.0_5_8_5, -2.7_8_9_7, -0.2_8_5_0, -0.8_9_4_0, 1.9_0_5_2, 0.5_7_0_2, 0.6_3_4_5, -3.8_9_5_9,
    1.5_9_3_2, -3.2_3_1_9, 0.1_9_7_4, 0.0_2_8_7, 1.7_5_6_6, 2.6_5_4_3, 0.8_3_8_7, -0.5_3_5_1,
    -3.2_7_3_6, -4.3_3_7_5, 2.9_0_2_9, 1.6_3_9_0, 1.4_6_4_0, -2.1_7_0_1, -1.9_0_1_3, 2.9_3_4_1,
    3.4_9_8_1, -0.6_2_5_5, -1.1_6_4_4, -0.1_5_9_1, 3.7_0_9_7, 3.2_0_6_6
])
_lowerCAmelCase							:   Optional[int]				=				torch.tensor([
    -2.3_1_3_9, -2.5_5_9_4, -0.0_1_9_7, -0.6_7_8_5, 1.7_0_0_1, 1.1_6_0_6, 0.3_0_7_5, -2.1_7_4_0,
    1.8_0_7_1, -2.5_6_3_0, -0.0_9_2_6, -0.3_8_1_1, 1.2_1_1_6, 2.6_2_4_6, 1.2_7_3_1, -0.5_3_9_8,
    -2.8_1_5_3, -3.6_1_4_0, 2.3_8_9_3, 1.3_2_6_2, 1.6_2_5_8, -2.1_8_5_6, -1.3_2_6_7, 2.8_3_9_5,
    2.3_7_7_9, -1.0_6_2_3, -1.2_4_6_8, 0.8_9_5_9, 3.3_3_6_7, 3.2_2_4_3
])
_lowerCAmelCase							:   Union[str, Any]				=				torch.tensor([
    -2.0_6_2_8, -2.7_6_6_7, -0.2_0_8_9, -0.8_2_6_3, 2.0_5_3_9, 0.5_9_9_2, 0.6_4_9_5, -3.8_3_3_6,
    1.6_0_2_5, -3.2_8_1_7, 0.1_7_2_1, -0.0_6_3_3, 1.7_5_1_6, 2.7_0_3_9, 0.8_1_0_0, -0.5_9_0_8,
    -3.2_1_1_3, -4.4_3_4_3, 2.9_2_5_7, 1.3_6_3_2, 1.5_5_6_2, -2.1_4_8_9, -1.9_8_9_4, 3.0_5_6_0,
    3.3_3_9_6, -0.7_3_2_8, -1.0_4_1_7, 0.0_3_8_3, 3.7_0_9_3, 3.2_3_4_3
])
_lowerCAmelCase							:   Any				=				torch.tensor([
    -1.4_5_7_4, -2.0_5_6_9, -0.0_4_7_3, -0.6_1_1_7, 1.4_0_1_8, 0.5_7_6_9, 0.4_1_2_9, -2.7_3_4_4,
    1.2_2_4_1, -2.1_3_9_7, 0.2_0_0_0, 0.3_9_3_7, 0.7_6_1_6, 2.0_4_5_3, 0.7_3_2_4, -0.3_3_9_1,
    -2.1_7_4_6, -2.7_7_4_4, 1.6_9_6_3, 0.6_9_2_1, 1.2_1_8_7, -1.6_1_7_2, -0.8_8_7_7, 2.2_4_3_9,
    1.8_4_7_1, -0.5_8_3_9, -0.5_6_0_5, -0.0_4_6_4, 2.3_2_5_0, 2.1_2_1_9
])
# fmt: on

_lowerCAmelCase							:   Optional[Any]				=				api.list_models(filter="diffusers")
for mod in models:
 if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
  _lowerCAmelCase							:   List[Any]				=				"/home/patrick/google_checkpoints/" + mod.modelId.split("/")[-1]

  print(F"Started running {mod.modelId}!!!")

  if mod.modelId.startswith("CompVis"):
   _lowerCAmelCase							:   Tuple				=				UNetaDModel.from_pretrained(local_checkpoint, subfolder="unet")
  else:
   _lowerCAmelCase							:   List[Any]				=				UNetaDModel.from_pretrained(local_checkpoint)

  torch.manual_seed(0)
  random.seed(0)

  _lowerCAmelCase							:   Optional[Any]				=				torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
  _lowerCAmelCase							:   Tuple				=				torch.tensor([10] * noise.shape[0])
  with torch.no_grad():
   _lowerCAmelCase							:   Optional[Any]				=				model(noise, time_step).sample

  assert torch.allclose(
      logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1E-3
  )
  print(F"{mod.modelId} has passed successfully!!!")
