

'''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 							lowercase_						(		unittest.TestCase			):

  '''simple docstring'''





  def     snake_case_      (  self  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						'ylacombe/bark-small'
    UpperCAmelCase    				=						tempfile.mkdtemp()
    UpperCAmelCase    				=						'en_speaker_1'
    UpperCAmelCase    				=						'This is a test string'
    UpperCAmelCase    				=						'speaker_embeddings_path.json'
    UpperCAmelCase    				=						'speaker_embeddings'
  def     snake_case_      (  self ,			**a_  )				->					List[str]:


    """simple docstring"""
    return AutoTokenizer.from_pretrained(self.checkpoint ,			**a_  )
  def     snake_case_      (  self  )				->					str:


    """simple docstring"""
    shutil.rmtree(self.tmpdirname  )
  def     snake_case_      (  self  )				->					Optional[int]:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						BarkProcessor(tokenizer=a_  )

    processor.save_pretrained(self.tmpdirname  )
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(self.tmpdirname  )

    self.assertEqual(processor.tokenizer.get_vocab() ,			tokenizer.get_vocab()  )
  @slow
  def     snake_case_      (  self  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						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    				=						self.get_tokenizer(bos_token='(BOS)' ,			eos_token='(EOS)'  )

    UpperCAmelCase    				=						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     snake_case_      (  self  )				->					Dict:


    """simple docstring"""
    UpperCAmelCase    				=						BarkProcessor.from_pretrained(
        pretrained_processor_name_or_path=self.checkpoint ,			speaker_embeddings_dict_path=self.speaker_embeddings_dict_path ,			)

    UpperCAmelCase    				=						3_5
    UpperCAmelCase    				=						2
    UpperCAmelCase    				=						8

    UpperCAmelCase    				=						{
        'semantic_prompt': np.ones(a_  ),
        '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    				=						processor(text=self.input_string ,			voice_preset=a_  )

    UpperCAmelCase    				=						inputs['history_prompt']
    for key in voice_preset:
      self.assertListEqual(voice_preset[key].tolist() ,			processed_voice_preset.get(a_ ,			np.array([]  )  ).tolist()  )

    # test loading voice preset from npz file
    UpperCAmelCase    				=						os.path.join(self.tmpdirname ,			'file.npz'  )
    np.savez(a_ ,			**a_  )
    UpperCAmelCase    				=						processor(text=self.input_string ,			voice_preset=a_  )
    UpperCAmelCase    				=						inputs['history_prompt']

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

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





  def     snake_case_      (  self  )				->					List[Any]:


    """simple docstring"""
    UpperCAmelCase    				=						self.get_tokenizer()

    UpperCAmelCase    				=						BarkProcessor(tokenizer=a_  )

    UpperCAmelCase    				=						processor(text=self.input_string  )

    UpperCAmelCase    				=						tokenizer(
        self.input_string ,			padding='max_length' ,			max_length=2_5_6 ,			add_special_tokens=a_ ,			return_attention_mask=a_ ,			return_token_type_ids=a_ ,			)

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





'''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

_a					:			List[Any]							=    logging.get_logger(__name__)



@add_end_docstrings(a			)
class 							lowercase_						(		a			):

  '''simple docstring'''





  def __init__(  self ,			*a_ ,			**a_  )				->					str:


    """simple docstring"""
    super().__init__(*a_ ,			**a_  )
    requires_backends(self ,			'decord'  )
    self.check_model_type(a_  )
  def     snake_case_      (  self ,			a_=None ,			a_=None ,			a_=None  )				->					int:


    """simple docstring"""
    UpperCAmelCase    				=						{}
    if frame_sampling_rate is not None:
      UpperCAmelCase    				=						frame_sampling_rate
    if num_frames is not None:
      UpperCAmelCase    				=						num_frames

    UpperCAmelCase    				=						{}
    if top_k is not None:
      UpperCAmelCase    				=						top_k
    return preprocess_params, {}, postprocess_params
  def __call__(  self ,			a_ ,			**a_  )				->					Union[str, Any]:


    """simple docstring"""
    return super().__call__(a_ ,			**a_  )
  def     snake_case_      (  self ,			a_ ,			a_=None ,			a_=1  )				->					Tuple:


    """simple docstring"""
    if num_frames is None:
      UpperCAmelCase    				=						self.model.config.num_frames

    if video.startswith('http://'  ) or video.startswith('https://'  ):
      UpperCAmelCase    				=						BytesIO(requests.get(a_  ).content  )

    UpperCAmelCase    				=						VideoReader(a_  )
    videoreader.seek(0  )

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

    UpperCAmelCase    				=						videoreader.get_batch(a_  ).asnumpy()
    UpperCAmelCase    				=						list(a_  )

    UpperCAmelCase    				=						self.image_processor(a_ ,			return_tensors=self.framework  )
    return model_inputs
  def     snake_case_      (  self ,			a_  )				->					Union[str, Any]:


    """simple docstring"""
    UpperCAmelCase    				=						self.model(**a_  )
    return model_outputs





  def     snake_case_      (  self ,			a_ ,			a_=5  )				->					Union[str, Any]:


    """simple docstring"""
    if top_k > self.model.config.num_labels:
      UpperCAmelCase    				=						self.model.config.num_labels

    if self.framework == "pt":
      UpperCAmelCase    				=						model_outputs.logits.softmax(-1  )[0]
      UpperCAmelCase				,   UpperCAmelCase    				=						probs.topk(a_  )
    else:
      raise ValueError(F'''Unsupported framework: {self.framework}'''  )

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


