
'''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 				__lowerCAmelCase							(							self	) ->				List[Any]:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self						,			**SCREAMING_SNAKE_CASE	) ->				Tuple:






         """simple docstring"""






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





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






         """simple docstring"""






         shutil.rmtree(self.tmpdirname	)





     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         A       :   Any					=						self.get_tokenizer()

         A       :   str					=						BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE	)

         processor.save_pretrained(self.tmpdirname	)
         A       :   Tuple					=						BarkProcessor.from_pretrained(self.tmpdirname	)

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





     @slow
     def 				__lowerCAmelCase							(							self	) ->				int:






         """simple docstring"""






         A       :   List[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						,			)

         A       :   Dict					=						self.get_tokenizer(bos_token='''(BOS)'''						,			eos_token='''(EOS)'''	)

         A       :   Optional[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	) ->				Tuple:






         """simple docstring"""






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

         A       :   List[str]					=						35
         A       :   Optional[Any]					=						2
         A       :   str					=						8

         A       :   str					=						{
             '''semantic_prompt''': np.ones(SCREAMING_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
         A       :   Dict					=						processor(text=self.input_string						,			voice_preset=SCREAMING_SNAKE_CASE	)

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

         # test loading voice preset from npz file
         A       :   Optional[int]					=						os.path.join(self.tmpdirname						,			'''file.npz'''	)
         np.savez(SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)
         A       :   Dict					=						processor(text=self.input_string						,			voice_preset=SCREAMING_SNAKE_CASE	)
         A       :   Optional[int]					=						inputs['''history_prompt''']

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

         # test loading voice preset from the hub
         A       :   Optional[Any]					=						processor(text=self.input_string						,			voice_preset=self.voice_preset	)





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






         """simple docstring"""






         A       :   int					=						self.get_tokenizer()

         A       :   Dict					=						BarkProcessor(tokenizer=SCREAMING_SNAKE_CASE	)

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

         A       :   Union[str, Any]					=						tokenizer(
             self.input_string						,			padding='''max_length'''						,			max_length=256						,			add_special_tokens=SCREAMING_SNAKE_CASE						,			return_attention_mask=SCREAMING_SNAKE_CASE						,			return_token_type_ids=SCREAMING_SNAKE_CASE						,			)

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






'''simple docstring'''




import os
import sys
import unittest


lowercase			:				Dict    		=			os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


lowercase			:				Any    		=			os.path.join('tests', 'models', 'bert', 'test_modeling_bert.py')
lowercase			:				Optional[int]    		=			os.path.join('tests', 'models', 'blip', 'test_modeling_blip.py')





class  A				(							unittest.TestCase						):





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






         """simple docstring"""






         A       :   Tuple					=						get_test_to_tester_mapping(SCREAMING_SNAKE_CASE	)
         A       :   Any					=						get_test_to_tester_mapping(SCREAMING_SNAKE_CASE	)

         A       :   List[Any]					=						{'''BertModelTest''': '''BertModelTester'''}

         A       :   int					=						{
             '''BlipModelTest''': '''BlipModelTester''',
             '''BlipTextImageModelTest''': '''BlipTextImageModelsModelTester''',
             '''BlipTextModelTest''': '''BlipTextModelTester''',
             '''BlipTextRetrievalModelTest''': '''BlipTextRetrievalModelTester''',
             '''BlipVQAModelTest''': '''BlipVQAModelTester''',
             '''BlipVisionModelTest''': '''BlipVisionModelTester''',
         }

         self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)
         self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)





     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   Tuple					=						get_model_to_test_mapping(SCREAMING_SNAKE_CASE	)
         A       :   Tuple					=						get_model_to_test_mapping(SCREAMING_SNAKE_CASE	)

         A       :   List[str]					=						{
             '''BertForMaskedLM''': ['''BertModelTest'''],
             '''BertForMultipleChoice''': ['''BertModelTest'''],
             '''BertForNextSentencePrediction''': ['''BertModelTest'''],
             '''BertForPreTraining''': ['''BertModelTest'''],
             '''BertForQuestionAnswering''': ['''BertModelTest'''],
             '''BertForSequenceClassification''': ['''BertModelTest'''],
             '''BertForTokenClassification''': ['''BertModelTest'''],
             '''BertLMHeadModel''': ['''BertModelTest'''],
             '''BertModel''': ['''BertModelTest'''],
         }

         A       :   Union[str, Any]					=						{
             '''BlipForConditionalGeneration''': ['''BlipTextImageModelTest'''],
             '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTest'''],
             '''BlipForQuestionAnswering''': ['''BlipVQAModelTest'''],
             '''BlipModel''': ['''BlipModelTest'''],
             '''BlipTextModel''': ['''BlipTextModelTest'''],
             '''BlipVisionModel''': ['''BlipVisionModelTest'''],
         }

         self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)
         self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)





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






         """simple docstring"""






         A       :   int					=						get_model_to_tester_mapping(SCREAMING_SNAKE_CASE	)
         A       :   Union[str, Any]					=						get_model_to_tester_mapping(SCREAMING_SNAKE_CASE	)

         A       :   Dict					=						{
             '''BertForMaskedLM''': ['''BertModelTester'''],
             '''BertForMultipleChoice''': ['''BertModelTester'''],
             '''BertForNextSentencePrediction''': ['''BertModelTester'''],
             '''BertForPreTraining''': ['''BertModelTester'''],
             '''BertForQuestionAnswering''': ['''BertModelTester'''],
             '''BertForSequenceClassification''': ['''BertModelTester'''],
             '''BertForTokenClassification''': ['''BertModelTester'''],
             '''BertLMHeadModel''': ['''BertModelTester'''],
             '''BertModel''': ['''BertModelTester'''],
         }

         A       :   str					=						{
             '''BlipForConditionalGeneration''': ['''BlipTextImageModelsModelTester'''],
             '''BlipForImageTextRetrieval''': ['''BlipTextRetrievalModelTester'''],
             '''BlipForQuestionAnswering''': ['''BlipVQAModelTester'''],
             '''BlipModel''': ['''BlipModelTester'''],
             '''BlipTextModel''': ['''BlipTextModelTester'''],
             '''BlipVisionModel''': ['''BlipVisionModelTester'''],
         }

         self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)
         self.assertEqual(get_test_info.to_json(SCREAMING_SNAKE_CASE	)						,			SCREAMING_SNAKE_CASE	)




