






'''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     a_						(      unittest.TestCase    ):

     def   A__						(				self				)  ->				List[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   A__						(				self      ,		**_SCREAMING_SNAKE_CASE				)  ->				str:



           """simple docstring"""






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

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



           """simple docstring"""






           shutil.rmtree(self.tmpdirname				)

     def   A__						(				self				)  ->				Any:



           """simple docstring"""






           UpperCamelCase      = self.get_tokenizer()

           UpperCamelCase      = BarkProcessor(tokenizer=lowercase_				)

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

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

     @slow
     def   A__						(				self				)  ->				Optional[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   A__						(				self				)  ->				str:



           """simple docstring"""






           UpperCamelCase      = BarkProcessor.from_pretrained(
               pretrained_processor_name_or_path=self.checkpoint      ,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path      ,		)

           UpperCamelCase      = 35
           UpperCamelCase      = 2
           UpperCamelCase      = 8

           UpperCamelCase      = {
               "semantic_prompt": np.ones(lowercase_				),
               "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=lowercase_				)

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

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

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

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


     def   A__						(				self				)  ->				Optional[Any]:



           """simple docstring"""






           UpperCamelCase      = self.get_tokenizer()

           UpperCamelCase      = BarkProcessor(tokenizer=lowercase_				)

           UpperCamelCase      = processor(text=self.input_string				)

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

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








'''simple docstring'''

import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)






class     a_						(      unittest.TestCase    ):

  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        UpperCamelCase      = ["""a""", """b""", """c"""]

        # Defaults to last layer if both are None
        UpperCamelCase  ,UpperCamelCase      = get_aligned_output_features_output_indices(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		["""c"""]				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		[2]				)

        # Out indices set to match out features
        UpperCamelCase  ,UpperCamelCase      = get_aligned_output_features_output_indices(["""a""", """c"""]      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		["""a""", """c"""]				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		[0, 2]				)

        # Out features set to match out indices
        UpperCamelCase  ,UpperCamelCase      = get_aligned_output_features_output_indices(_SCREAMING_SNAKE_CASE      ,		[0, 2]      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		["""a""", """c"""]				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		[0, 2]				)

        # Out features selected from negative indices
        UpperCamelCase  ,UpperCamelCase      = get_aligned_output_features_output_indices(_SCREAMING_SNAKE_CASE      ,		[-3, -1]      ,		_SCREAMING_SNAKE_CASE				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		["""a""", """c"""]				)
        self.assertEqual(_SCREAMING_SNAKE_CASE      ,		[-3, -1]				)

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        with self.assertRaises(_SCREAMING_SNAKE_CASE				):
              verify_out_features_out_indices(["""a""", """b"""]      ,		(0, 1)      ,		_SCREAMING_SNAKE_CASE				)

        # Out features must be a list
        with self.assertRaises(_SCREAMING_SNAKE_CASE				):
              verify_out_features_out_indices(("""a""", """b""")      ,		(0, 1)      ,		["""a""", """b"""]				)

        # Out features must be a subset of stage names
        with self.assertRaises(_SCREAMING_SNAKE_CASE				):
              verify_out_features_out_indices(["""a""", """b"""]      ,		(0, 1)      ,		["""a"""]				)

        # Out indices must be a list or tuple
        with self.assertRaises(_SCREAMING_SNAKE_CASE				):
              verify_out_features_out_indices(_SCREAMING_SNAKE_CASE      ,		0      ,		["""a""", """b"""]				)

        # Out indices must be a subset of stage names
        with self.assertRaises(_SCREAMING_SNAKE_CASE				):
              verify_out_features_out_indices(_SCREAMING_SNAKE_CASE      ,		(0, 1)      ,		["""a"""]				)

        # Out features and out indices must be the same length
        with self.assertRaises(_SCREAMING_SNAKE_CASE				):
              verify_out_features_out_indices(["""a""", """b"""]      ,		(0,)      ,		["""a""", """b""", """c"""]				)

        # Out features should match out indices
        with self.assertRaises(_SCREAMING_SNAKE_CASE				):
              verify_out_features_out_indices(["""a""", """b"""]      ,		(0, 2)      ,		["""a""", """b""", """c"""]				)

        # Out features and out indices should be in order
        with self.assertRaises(_SCREAMING_SNAKE_CASE				):
              verify_out_features_out_indices(["""b""", """a"""]      ,		(0, 1)      ,		["""a""", """b"""]				)

        # Check passes with valid inputs
        verify_out_features_out_indices(["""a""", """b""", """d"""]      ,		(0, 1, -1)      ,		["""a""", """b""", """c""", """d"""]				)


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



        """simple docstring"""






        UpperCamelCase      = BackboneMixin()

        UpperCamelCase      = ["""a""", """b""", """c"""]
        UpperCamelCase      = ["""a""", """c"""]
        UpperCamelCase      = [0, 2]

        # Check that the output features and indices are set correctly
        self.assertEqual(backbone.out_features      ,		["""a""", """c"""]				)
        self.assertEqual(backbone.out_indices      ,		[0, 2]				)

        # Check out features and indices are updated correctly
        UpperCamelCase      = ["""a""", """b"""]
        self.assertEqual(backbone.out_features      ,		["""a""", """b"""]				)
        self.assertEqual(backbone.out_indices      ,		[0, 1]				)

        UpperCamelCase      = [-3, -1]
        self.assertEqual(backbone.out_features      ,		["""a""", """c"""]				)
        self.assertEqual(backbone.out_indices      ,		[-3, -1]				)
