
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     UpperCAmelCase						(		unittest.TestCase					):



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      int:


          '''simple docstring'''


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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]       ,		**snake_case__						:   List[str]					)						->      Tuple:


          '''simple docstring'''


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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Dict					)						->      Any:


          '''simple docstring'''


          shutil.rmtree(self.tmpdirname					)



   def 							_SCREAMING_SNAKE_CASE  (self						:   Union[str, Any]					)						->      Optional[int]:


          '''simple docstring'''


          snake_case			:  Optional[Any]     							= self.get_tokenizer()

          snake_case			:  Optional[Any]     							= BarkProcessor(tokenizer=snake_case__					)

          processor.save_pretrained(self.tmpdirname					)
          snake_case			:  Any     							= BarkProcessor.from_pretrained(self.tmpdirname					)

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



   @slow
   def 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      Any:


          '''simple docstring'''


          snake_case			:  Tuple     							= 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       ,		)

          snake_case			:  Optional[int]     							= self.get_tokenizer(bos_token="(BOS)"       ,		eos_token="(EOS)"					)

          snake_case			:  int     							= 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 							_SCREAMING_SNAKE_CASE  (self						:   Optional[Any]					)						->      Any:


          '''simple docstring'''


          snake_case			:  Any     							= BarkProcessor.from_pretrained(
              pretrained_processor_name_or_path=self.checkpoint       ,		speaker_embeddings_dict_path=self.speaker_embeddings_dict_path       ,		)

          snake_case			:  List[Any]     							= 35
          snake_case			:  Tuple     							= 2
          snake_case			:  Dict     							= 8

          snake_case			:  List[str]     							= {
              "semantic_prompt": np.ones(snake_case__					),
              "coarse_prompt": np.ones((nb_codebooks_coarse, seq_len)					),
              "fine_prompt": np.ones((nb_codebooks_total, seq_len)					),
          }

          # test providing already loaded voice_preset
          snake_case			:  str     							= processor(text=self.input_string       ,		voice_preset=snake_case__					)

          snake_case			:  List[Any]     							= inputs["history_prompt"]
          for key in voice_preset:
                 self.assertListEqual(voice_preset[key].tolist()       ,		processed_voice_preset.get(snake_case__       ,		np.array([]					)					).tolist()					)

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

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

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



   def 							_SCREAMING_SNAKE_CASE  (self						:   Any					)						->      List[str]:


          '''simple docstring'''


          snake_case			:  Union[str, Any]     							= self.get_tokenizer()

          snake_case			:  Optional[Any]     							= BarkProcessor(tokenizer=snake_case__					)

          snake_case			:  Dict     							= processor(text=self.input_string					)

          snake_case			:  int     							= tokenizer(
              self.input_string       ,		padding="max_length"       ,		max_length=2_56       ,		add_special_tokens=snake_case__       ,		return_attention_mask=snake_case__       ,		return_token_type_ids=snake_case__       ,		)

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







from math import factorial


def      UpperCamelCase   (       __lowerCamelCase	:					int							,   __lowerCamelCase	:					int							,   __lowerCamelCase	:					float       ):
       if successes > trials:
              raise ValueError("successes must be lower or equal to trials"       )
       if trials < 0 or successes < 0:
              raise ValueError("the function is defined for non-negative integers"       )
       if not isinstance(__lowerCamelCase							,   __lowerCamelCase       ) or not isinstance(__lowerCamelCase							,   __lowerCamelCase       ):
              raise ValueError("the function is defined for non-negative integers"       )
       if not 0 < prob < 1:
              raise ValueError("prob has to be in range of 1 - 0"       )
       snake_case			:  Tuple     							= (prob**successes) * ((1 - prob) ** (trials - successes))
       # Calculate the binomial coefficient: n! / k!(n-k)!
       snake_case			:  str     							= float(factorial(__lowerCamelCase       )       )
       coefficient /= factorial(__lowerCamelCase       ) * factorial(trials - successes       )
       return probability * coefficient


if __name__ == "__main__":
   from doctest import testmod

   testmod()
   print("""Probability of 2 successes out of 4 trails""")
   print("""with probability of 0.75 is:""", end=""" """)
   print(binomial_distribution(2, 4, 0.75))





