






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


snake_case    				=		logging.get_logger(__name__)


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
					'''simple docstring'''


					UpperCamelCase_							:		Optional[Any]			=	'''AutoTokenizer'''
					UpperCamelCase_							:		Optional[int]			=	['''tokenizer''']

					UpperCamelCase_							:		Optional[int]			=	{
					    '''semantic_prompt''': 1,
					    '''coarse_prompt''': 2,
					    '''fine_prompt''': 2,
					}


					def __init__(				self       :	Tuple					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Tuple=None      ):
							super().__init__(UpperCAmelCase_      )

							SCREAMING_SNAKE_CASE       :      int							=    speaker_embeddings


					@classmethod
					def 				_A       (				cls       :	Dict					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Optional[Any]="speaker_embeddings_path.json"					, **UpperCAmelCase_       :	List[str]      ):

							if speaker_embeddings_dict_path is not None:
									SCREAMING_SNAKE_CASE       :      str							=    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`.'''      )
											SCREAMING_SNAKE_CASE       :      str							=    None
									else:
											with open(UpperCAmelCase_      ) as speaker_embeddings_json:
													SCREAMING_SNAKE_CASE       :      Tuple							=    json.load(UpperCAmelCase_      )
							else:
									SCREAMING_SNAKE_CASE       :      Optional[int]							=    None

							SCREAMING_SNAKE_CASE       :      int							=    AutoTokenizer.from_pretrained(UpperCAmelCase_					, **UpperCAmelCase_      )

							return cls(tokenizer=UpperCAmelCase_					, speaker_embeddings=UpperCAmelCase_      )


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

									SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {}

									SCREAMING_SNAKE_CASE       :      int							=    save_directory

									for prompt_key in self.speaker_embeddings:
											if prompt_key != "repo_or_path":
													SCREAMING_SNAKE_CASE       :      Any							=    self._load_voice_preset(UpperCAmelCase_      )

													SCREAMING_SNAKE_CASE       :      Tuple							=    {}
													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_					, )
															SCREAMING_SNAKE_CASE       :      Optional[Any]							=    os.path.join(UpperCAmelCase_					, f'''{prompt_key}_{key}.npy'''      )

													SCREAMING_SNAKE_CASE       :      Optional[Any]							=    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       :	int					, UpperCAmelCase_       :	str = None					, **UpperCAmelCase_       :	Tuple      ):
							SCREAMING_SNAKE_CASE       :      Tuple							=    self.speaker_embeddings[voice_preset]

							SCREAMING_SNAKE_CASE       :      Optional[int]							=    {}
							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}].'''      )

									SCREAMING_SNAKE_CASE       :      Dict							=    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.'''      )

									SCREAMING_SNAKE_CASE       :      Optional[int]							=    np.load(UpperCAmelCase_      )

							return voice_preset_dict


					def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	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       :	int					, UpperCAmelCase_       :	Union[str, Any]=None					, UpperCAmelCase_       :	str=None					, UpperCAmelCase_       :	List[str]="pt"					, UpperCAmelCase_       :	List[str]=256					, UpperCAmelCase_       :	Optional[int]=False					, UpperCAmelCase_       :	Optional[int]=True					, UpperCAmelCase_       :	Dict=False					, **UpperCAmelCase_       :	List[Any]					, ):
							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
									):
											SCREAMING_SNAKE_CASE       :      int							=    self._load_voice_preset(UpperCAmelCase_      )

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

											SCREAMING_SNAKE_CASE       :      int							=    np.load(UpperCAmelCase_      )

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

							SCREAMING_SNAKE_CASE       :      Tuple							=    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:
									SCREAMING_SNAKE_CASE       :      Tuple							=    voice_preset

							return encoded_text














import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		lowerCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Optional[Any]			=	IFImgaImgSuperResolutionPipeline
     UpperCamelCase_							:		Any			=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
     UpperCamelCase_							:		Optional[Any]			=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''original_image'''}					)
     UpperCamelCase_							:		str			=	PipelineTesterMixin.required_optional_params - {'''latents'''}


     def 				_A       (				self       :	List[Any]      ):
       return self._get_superresolution_dummy_components()


     def 				_A       (				self       :	Any					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	List[str]=0      ):
       if str(UpperCAmelCase_      ).startswith("mps"      ):
         SCREAMING_SNAKE_CASE       :      Any							=    torch.manual_seed(UpperCAmelCase_      )
       else:
         SCREAMING_SNAKE_CASE       :      List[str]							=    torch.Generator(device=UpperCAmelCase_      ).manual_seed(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      Any							=    floats_tensor((1, 3, 32, 32)					, rng=random.Random(UpperCAmelCase_      )      ).to(UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      Any							=    floats_tensor((1, 3, 16, 16)					, rng=random.Random(UpperCAmelCase_      )      ).to(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    {
           "prompt": "A painting of a squirrel eating a burger",
           "image": image,
           "original_image": original_image,
           "generator": generator,
           "num_inference_steps": 2,
           "output_type": "numpy",
       }

       return inputs


     @unittest.skipIf(
         torch_device != "cuda" or not is_xformers_available()					, reason="XFormers attention is only available with CUDA and `xformers` installed"					, )
     def 				_A       (				self       :	Tuple      ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3      )


     def 				_A       (				self       :	Optional[Any]      ):
       self._test_save_load_optional_components()


     @unittest.skipIf(torch_device != "cuda"					, reason="float16 requires CUDA"      )
     def 				_A       (				self       :	List[Any]      ):
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1E-1      )


     def 				_A       (				self       :	Optional[int]      ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2      )


     def 				_A       (				self       :	Tuple      ):
       self._test_save_load_local()






     def 				_A       (				self       :	List[str]      ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1E-2					, )






