






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								=						logging.get_logger(__name__)



class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



		_snake_case    :    Optional[int]       						=  '''AutoTokenizer'''
		_snake_case    :    List[str]       						=  ['''tokenizer''']

		_snake_case    :    List[str]       						=  {
		    '''semantic_prompt''': 1,
		    '''coarse_prompt''': 2,
		    '''fine_prompt''': 2,
		}



		def __init__(							self				,		_UpperCamelCase				,		_UpperCamelCase=None   )	->							int:
							super().__init__(_UpperCamelCase   )

							UpperCAmelCase_    :      List[str]            =						speaker_embeddings



		@classmethod
		def 					__UpperCAmelCase			(							cls				,		_UpperCamelCase				,		_UpperCamelCase="speaker_embeddings_path.json"				,		**_UpperCamelCase   )	->							Any:

							if speaker_embeddings_dict_path is not None:
												UpperCAmelCase_    :      List[Any]            =						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\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_    :      Tuple            =						None
												else:
																	with open(_UpperCamelCase   ) as speaker_embeddings_json:
																						UpperCAmelCase_    :      int            =						json.load(_UpperCamelCase   )
							else:
												UpperCAmelCase_    :      Optional[Any]            =						None

							UpperCAmelCase_    :      List[str]            =						AutoTokenizer.from_pretrained(_UpperCamelCase				,		**_UpperCamelCase   )

							return cls(tokenizer=_UpperCamelCase				,		speaker_embeddings=_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase="speaker_embeddings_path.json"				,		_UpperCamelCase="speaker_embeddings"				,		_UpperCamelCase = False				,		**_UpperCamelCase				,		)	->							Optional[int]:
							if self.speaker_embeddings is not None:
												os.makedirs(os.path.join(_UpperCamelCase				,		_UpperCamelCase				,		'v2'   )				,		exist_ok=_UpperCamelCase   )

												UpperCAmelCase_    :      Dict            =						{}

												UpperCAmelCase_    :      Tuple            =						save_directory

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

																						UpperCAmelCase_    :      List[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				,		)
																											UpperCAmelCase_    :      Optional[Any]            =						os.path.join(_UpperCamelCase				,		f"{prompt_key}_{key}.npy"   )

																						UpperCAmelCase_    :      str            =						tmp_dict

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

							super().save_pretrained(_UpperCamelCase				,		_UpperCamelCase				,		**_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase = None				,		**_UpperCamelCase   )	->							int:
							UpperCAmelCase_    :      str            =						self.speaker_embeddings[voice_preset]

							UpperCAmelCase_    :      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}]."   )

												UpperCAmelCase_    :      Optional[Any]            =						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\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings."   )

												UpperCAmelCase_    :      List[str]            =						np.load(_UpperCamelCase   )

							return voice_preset_dict



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase = None   )	->							Optional[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				,		_UpperCamelCase=None				,		_UpperCamelCase=None				,		_UpperCamelCase="pt"				,		_UpperCamelCase=2_5_6				,		_UpperCamelCase=False				,		_UpperCamelCase=True				,		_UpperCamelCase=False				,		**_UpperCamelCase				,		)	->							List[str]:
							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
												):
																	UpperCAmelCase_    :      List[Any]            =						self._load_voice_preset(_UpperCamelCase   )

												else:
																	if isinstance(_UpperCamelCase				,		_UpperCamelCase   ) and not voice_preset.endswith('.npz'   ):
																						UpperCAmelCase_    :      str            =						voice_preset + '.npz'

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

							if voice_preset is not None:
												self._validate_voice_preset_dict(_UpperCamelCase				,		**_UpperCamelCase   )
												UpperCAmelCase_    :      Optional[int]            =						BatchFeature(data=_UpperCamelCase				,		tensor_type=_UpperCamelCase   )

							UpperCAmelCase_    :      Union[str, 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:
												UpperCAmelCase_    :      int            =						voice_preset

							return encoded_text









"""simple docstring"""





import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY







@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class 				SCREAMING_SNAKE_CASE					(		unittest.TestCase       ):






					"""simple docstring"""



					lowercase__				 =       MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING







					def        __lowerCAmelCase (       self			:				Optional[Any]   ,lowercase_			:				Optional[Any]   ,lowercase_			:				int   ,lowercase_			:				str		):
						lowerCAmelCase__	:						Optional[int]  				=	hf_hub_download(
						    repo_id='''nateraw/video-demo'''   ,filename='''archery.mp4'''   ,repo_type='''dataset'''		)
						lowerCAmelCase__	:						Tuple  				=	VideoClassificationPipeline(model=lowercase_   ,image_processor=lowercase_   ,top_k=2		)
						lowerCAmelCase__	:						Optional[int]  				=	[
						    example_video_filepath,
						    '''https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4''',
						]
						return video_classifier, examples







					def        __lowerCAmelCase (       self			:				str   ,lowercase_			:				int   ,lowercase_			:				Dict		):
						for example in examples:
							lowerCAmelCase__	:						Dict  				=	video_classifier(lowercase_		)

							self.assertEqual(
							    lowercase_   ,[
							        {'''score''': ANY(lowercase_		), '''label''': ANY(lowercase_		)},
							        {'''score''': ANY(lowercase_		), '''label''': ANY(lowercase_		)},
							    ]   ,)







					@require_torch
					def        __lowerCAmelCase (       self			:				Optional[int]		):
						lowerCAmelCase__	:						Optional[int]  				=	'''hf-internal-testing/tiny-random-VideoMAEForVideoClassification'''
						lowerCAmelCase__	:						List[str]  				=	VideoMAEFeatureExtractor(
						    size={'''shortest_edge''': 1_0}   ,crop_size={'''height''': 1_0, '''width''': 1_0}		)
						lowerCAmelCase__	:						Optional[Any]  				=	pipeline(
						    '''video-classification'''   ,model=lowercase_   ,feature_extractor=lowercase_   ,frame_sampling_rate=4		)

						lowerCAmelCase__	:						Optional[int]  				=	hf_hub_download(repo_id='''nateraw/video-demo'''   ,filename='''archery.mp4'''   ,repo_type='''dataset'''		)
						lowerCAmelCase__	:						Optional[int]  				=	video_classifier(lowercase_   ,top_k=2		)
						self.assertEqual(
						    nested_simplify(lowercase_   ,decimals=4		)   ,[{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}]   ,)

						lowerCAmelCase__	:						Dict  				=	video_classifier(
						    [
						        video_file_path,
						        video_file_path,
						    ]   ,top_k=2   ,)
						self.assertEqual(
						    nested_simplify(lowercase_   ,decimals=4		)   ,[
						        [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
						        [{'''score''': 0.5199, '''label''': '''LABEL_0'''}, {'''score''': 0.4801, '''label''': '''LABEL_1'''}],
						    ]   ,)







					@require_tf
					def        __lowerCAmelCase (       self			:				int		):
						pass




