

'''simple docstring'''







from io import BytesIO
from typing import List, Union

import requests

from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_decord_available():
      import numpy as np
      from decord import VideoReader


if is_torch_available():
      from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING

__lowerCamelCase										=			logging.get_logger(__name__)


@add_end_docstrings(_snake_case	)
class     A__					(					_snake_case	):



    def __init__(    self						, *UpperCamelCase__						, **UpperCamelCase__				)  ->					List[Any]:





           '''simple docstring'''
           super().__init__(*UpperCamelCase__						, **UpperCamelCase__				)
           requires_backends(self						, """decord"""				)
           self.check_model_type(UpperCamelCase__				)



    def     snake_case_	(    self						, UpperCamelCase__=None						, UpperCamelCase__=None						, UpperCamelCase__=None				)  ->					Tuple:





           '''simple docstring'''
           A_		    =					{}
           if frame_sampling_rate is not None:
                  A_		    =					frame_sampling_rate
           if num_frames is not None:
                  A_		    =					num_frames

           A_		    =					{}
           if top_k is not None:
                  A_		    =					top_k
           return preprocess_params, {}, postprocess_params



    def __call__(    self						, UpperCamelCase__						, **UpperCamelCase__				)  ->					Any:





           '''simple docstring'''
           return super().__call__(UpperCamelCase__						, **UpperCamelCase__				)



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__=None						, UpperCamelCase__=1				)  ->					List[Any]:





           '''simple docstring'''






           if num_frames is None:
                  A_		    =					self.model.config.num_frames

           if video.startswith("""http://"""				) or video.startswith("""https://"""				):
                  A_		    =					BytesIO(requests.get(UpperCamelCase__				).content				)

           A_		    =					VideoReader(UpperCamelCase__				)
           videoreader.seek(0				)

           A_		    =					0
           A_		    =					num_frames * frame_sampling_rate - 1
           A_		    =					np.linspace(UpperCamelCase__						, UpperCamelCase__						, num=UpperCamelCase__						, dtype=np.intaa				)

           A_		    =					videoreader.get_batch(UpperCamelCase__				).asnumpy()
           A_		    =					list(UpperCamelCase__				)

           A_		    =					self.image_processor(UpperCamelCase__						, return_tensors=self.framework				)
           return model_inputs



    def     snake_case_	(    self						, UpperCamelCase__				)  ->					Any:





           '''simple docstring'''
           A_		    =					self.model(**UpperCamelCase__				)
           return model_outputs







    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__=5				)  ->					int:





           '''simple docstring'''






           if top_k > self.model.config.num_labels:
                  A_		    =					self.model.config.num_labels

           if self.framework == "pt":
                  A_		    =					model_outputs.logits.softmax(-1				)[0]
                  A_		    =					probs.topk(UpperCamelCase__				)
           else:
                  raise ValueError(f'''Unsupported framework: {self.framework}'''				)

           A_		    =					scores.tolist()
           A_		    =					ids.tolist()
           return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(UpperCamelCase__						, UpperCamelCase__				)]




"""simple docstring"""






import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow





@require_torch
class      lowerCamelCase__   (     unittest.TestCase				):







					"""simple docstring"""


					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					"""ylacombe/bark-small"""
												__UpperCAmelCase   :						List[Any]      							=					tempfile.mkdtemp()
												__UpperCAmelCase   :						Optional[Any]      							=					"""en_speaker_1"""
												__UpperCAmelCase   :						Union[str, Any]      							=					"""This is a test string"""
												__UpperCAmelCase   :						Dict      							=					"""speaker_embeddings_path.json"""
												__UpperCAmelCase   :						Any      							=					"""speaker_embeddings"""






					def lowerCamelCase__							(       self    : Dict ,       **UpperCamelCase    : Union[str, Any]  ):
												'''simple docstring'''






												return AutoTokenizer.from_pretrained(self.checkpoint ,       **UpperCamelCase  )






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												shutil.rmtree(self.tmpdirname  )






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					self.get_tokenizer()

												__UpperCAmelCase   :						Any      							=					BarkProcessor(tokenizer=UpperCamelCase  )

												processor.save_pretrained(self.tmpdirname  )
												__UpperCAmelCase   :						Union[str, Any]      							=					BarkProcessor.from_pretrained(self.tmpdirname  )

												self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer.get_vocab()  )






					@slow
					def lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					BarkProcessor.from_pretrained(
												    pretrained_processor_name_or_path=self.checkpoint ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,       )
												processor.save_pretrained(
												    self.tmpdirname ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,       speaker_embeddings_directory=self.speaker_embeddings_directory ,       )

												__UpperCAmelCase   :						Tuple      							=					self.get_tokenizer(bos_token="""(BOS)""" ,       eos_token="""(EOS)"""  )

												__UpperCAmelCase   :						Any      							=					BarkProcessor.from_pretrained(
												    self.tmpdirname ,       self.speaker_embeddings_dict_path ,       bos_token="""(BOS)""" ,       eos_token="""(EOS)""" ,       )

												self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer_add_kwargs.get_vocab()  )






					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					BarkProcessor.from_pretrained(
												    pretrained_processor_name_or_path=self.checkpoint ,       speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,       )

												__UpperCAmelCase   :						List[str]      							=					35
												__UpperCAmelCase   :						Tuple      							=					2
												__UpperCAmelCase   :						Union[str, Any]      							=					8

												__UpperCAmelCase   :						Optional[Any]      							=					{
												    """semantic_prompt""": np.ones(UpperCamelCase  ),
												    """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)  ),
												    """fine_prompt""": np.ones((nb_codebooks_total, seq_len)  ),
												}

												# test providing already loaded voice_preset
												__UpperCAmelCase   :						Dict      							=					processor(text=self.input_string ,       voice_preset=UpperCamelCase  )

												__UpperCAmelCase   :						Union[str, Any]      							=					inputs["""history_prompt"""]
												for key in voice_preset:
																			self.assertListEqual(voice_preset[key].tolist() ,       processed_voice_preset.get(UpperCamelCase ,       np.array([]  )  ).tolist()  )

												# test loading voice preset from npz file
												__UpperCAmelCase   :						Union[str, Any]      							=					os.path.join(self.tmpdirname ,       """file.npz"""  )
												np.savez(UpperCamelCase ,       **UpperCamelCase  )
												__UpperCAmelCase   :						Optional[int]      							=					processor(text=self.input_string ,       voice_preset=UpperCamelCase  )
												__UpperCAmelCase   :						List[Any]      							=					inputs["""history_prompt"""]

												for key in voice_preset:
																			self.assertListEqual(voice_preset[key].tolist() ,       processed_voice_preset.get(UpperCamelCase ,       np.array([]  )  ).tolist()  )

												# test loading voice preset from the hub
												__UpperCAmelCase   :						Dict      							=					processor(text=self.input_string ,       voice_preset=self.voice_preset  )






					def lowerCamelCase__							(       self    : Dict  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					self.get_tokenizer()

												__UpperCAmelCase   :						Union[str, Any]      							=					BarkProcessor(tokenizer=UpperCamelCase  )

												__UpperCAmelCase   :						List[str]      							=					processor(text=self.input_string  )

												__UpperCAmelCase   :						Tuple      							=					tokenizer(
												    self.input_string ,       padding="""max_length""" ,       max_length=256 ,       add_special_tokens=UpperCamelCase ,       return_attention_mask=UpperCamelCase ,       return_token_type_ids=UpperCamelCase ,       )

												for key in encoded_tok.keys():
																			self.assertListEqual(encoded_tok[key] ,       encoded_processor[key].squeeze().tolist()  )
