






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  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Any:




       """simple docstring"""


       a						    = '''ylacombe/bark-small'''
       a						    = tempfile.mkdtemp()
       a						    = '''en_speaker_1'''
       a						    = '''This is a test string'''
       a						    = '''speaker_embeddings_path.json'''
       a						    = '''speaker_embeddings'''







      def  __lowerCAmelCase   ( self :		Tuple					,   **__UpperCAmelCase :		Any    )       ->Union[str, Any]:




       """simple docstring"""


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







      def  __lowerCAmelCase   ( self :		Optional[Any]    )       ->Tuple:




       """simple docstring"""


       shutil.rmtree(self.tmpdirname    )







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




       """simple docstring"""


       a						    = self.get_tokenizer()

       a						    = BarkProcessor(tokenizer=__lowerCamelCase    )

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

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







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




       """simple docstring"""


       a						    = 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					,   )

       a						    = self.get_tokenizer(bos_token='''(BOS)'''					,   eos_token='''(EOS)'''    )

       a						    = 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 :		str    )       ->str:




       """simple docstring"""


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

       a						    = 35
       a						    = 2
       a						    = 8

       a						    = {
           '''semantic_prompt''': np.ones(__lowerCamelCase    ),
           '''coarse_prompt''': np.ones((nb_codebooks_coarse, seq_len)    ),
           '''fine_prompt''': np.ones((nb_codebooks_total, seq_len)    ),
       }

       # test providing already loaded voice_preset
       a						    = processor(text=self.input_string					,   voice_preset=__lowerCamelCase    )

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

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

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

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







      def  __lowerCAmelCase   ( self :		Dict    )       ->Any:




       """simple docstring"""


       a						    = self.get_tokenizer()

       a						    = BarkProcessor(tokenizer=__lowerCamelCase    )

       a						    = processor(text=self.input_string    )

       a						    = tokenizer(
           self.input_string					,   padding='''max_length'''					,   max_length=256					,   add_special_tokens=__lowerCamelCase					,   return_attention_mask=__lowerCamelCase					,   return_token_type_ids=__lowerCamelCase					,   )

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









def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    int:
		# Return True if there is node that has not iterated.
		__lowercase											=     [False] * len(snake_case							)
		__lowercase											=     []
		queue.append(snake_case							)
		__lowercase											=     True

		while queue:
				__lowercase											=     queue.pop(0							)
				for ind in range(len(graph[u]							)							):
						if visited[ind] is False and graph[u][ind] > 0:
								queue.append(snake_case							)
								__lowercase											=     True
								__lowercase											=     u

		return visited[t]



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Dict:
		# This array is filled by BFS and to store path
		__lowercase											=     [-1] * (len(snake_case							))
		__lowercase											=     0
		while bfs(snake_case						,   snake_case						,   snake_case						,   snake_case							):
				__lowercase											=     float('Inf'							)
				__lowercase											=     sink

				while s != source:
						# Find the minimum value in select path
						__lowercase											=     min(snake_case						,   graph[parent[s]][s]							)
						__lowercase											=     parent[s]

				max_flow += path_flow
				__lowercase											=     sink

				while v != source:
						__lowercase											=     parent[v]
						graph[u][v] -= path_flow
						graph[v][u] += path_flow
						__lowercase											=     parent[v]
		return max_flow


SCREAMING_SNAKE_CASE_   :      Any			=   [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   0, 5


print(ford_fulkerson(graph, source, sink))




