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   snake_case_       (		unittest.TestCase       ):






   def       __UpperCamelCase     (      self				:     List[str]  )					->						int:
       lowercase__ :   str    					=				"ylacombe/bark-small"
       lowercase__ :   Optional[int]    					=				tempfile.mkdtemp()
       lowercase__ :   str    					=				"en_speaker_1"
       lowercase__ :   List[str]    					=				"This is a test string"
       lowercase__ :   Dict    					=				"speaker_embeddings_path.json"
       lowercase__ :   List[str]    					=				"speaker_embeddings"






   def       __UpperCamelCase     (      self				:     Optional[int]	,						**lowercase_				:     Union[str, Any]  )					->						Optional[int]:
       return AutoTokenizer.from_pretrained(self.checkpoint	,						**lowercase_  )






   def       __UpperCamelCase     (      self				:     List[str]  )					->						int:
       shutil.rmtree(self.tmpdirname  )






   def       __UpperCamelCase     (      self				:     List[str]  )					->						List[Any]:
       lowercase__ :   List[Any]    					=				self.get_tokenizer()

       lowercase__ :   Optional[int]    					=				BarkProcessor(tokenizer=lowercase_  )

       processor.save_pretrained(self.tmpdirname  )
       lowercase__ :   List[Any]    					=				BarkProcessor.from_pretrained(self.tmpdirname  )

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






   @slow
   def       __UpperCamelCase     (      self				:     Tuple  )					->						List[Any]:
       lowercase__ :   Dict    					=				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	,						)

       lowercase__ :   str    					=				self.get_tokenizer(bos_token="(BOS)"	,						eos_token="(EOS)"  )

       lowercase__ :   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       __UpperCamelCase     (      self				:     List[Any]  )					->						List[str]:
       lowercase__ :   int    					=				BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint	,						speaker_embeddings_dict_path=self.speaker_embeddings_dict_path	,						)

       lowercase__ :   List[Any]    					=				35
       lowercase__ :   Any    					=				2
       lowercase__ :   List[str]    					=				8

       lowercase__ :   int    					=				{
           "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
       lowercase__ :   List[str]    					=				processor(text=self.input_string	,						voice_preset=lowercase_  )

       lowercase__ :   Union[str, Any]    					=				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
       lowercase__ :   Tuple    					=				os.path.join(self.tmpdirname	,						"file.npz"  )
       np.savez(lowercase_	,						**lowercase_  )
       lowercase__ :   Tuple    					=				processor(text=self.input_string	,						voice_preset=lowercase_  )
       lowercase__ :   Dict    					=				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
       lowercase__ :   Any    					=				processor(text=self.input_string	,						voice_preset=self.voice_preset  )







   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Any:
       lowercase__ :   Tuple    					=				self.get_tokenizer()

       lowercase__ :   Optional[int]    					=				BarkProcessor(tokenizer=lowercase_  )

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

       lowercase__ :   int    					=				tokenizer(
           self.input_string	,						padding="max_length"	,						max_length=2_56	,						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 subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch


class 						__UpperCAmelCase      (					_lowerCamelCase						):


							@require_torch
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

								_snake_case   						=		'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

								_snake_case   						=		'\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError("Offline mode is enabled, we shouldn\'t access internet")\nsocket.socket = offline_socket\n        '

								# Force fetching the files so that we can use the cache
								_snake_case   						=		'hf-internal-testing/tiny-random-bert'
								BertConfig.from_pretrained(lowerCAmelCase_  )
								BertModel.from_pretrained(lowerCAmelCase_  )
								BertTokenizer.from_pretrained(lowerCAmelCase_  )
								pipeline(task='fill-mask'    ,   model=lowerCAmelCase_  )

								# baseline - just load from_pretrained with normal network
								_snake_case   						=		[sys.executable, '-c', '\n'.join([load, run, mock]  )]

								# should succeed
								_snake_case   						=		self.get_env()
								# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
								_snake_case   						=		'1'
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   0    ,   result.stderr  )
								self.assertIn('success'    ,   result.stdout.decode()  )


							@require_torch
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

								_snake_case   						=		'\nmname = "hf-internal-testing/tiny-random-bert"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task="fill-mask", model=mname)\nprint("success")\n        '

								_snake_case   						=		'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n        '

								# Force fetching the files so that we can use the cache
								_snake_case   						=		'hf-internal-testing/tiny-random-bert'
								BertConfig.from_pretrained(lowerCAmelCase_  )
								BertModel.from_pretrained(lowerCAmelCase_  )
								BertTokenizer.from_pretrained(lowerCAmelCase_  )
								pipeline(task='fill-mask'    ,   model=lowerCAmelCase_  )

								# baseline - just load from_pretrained with normal network
								_snake_case   						=		[sys.executable, '-c', '\n'.join([load, run, mock]  )]

								# should succeed
								_snake_case   						=		self.get_env()
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   0    ,   result.stderr  )
								self.assertIn('success'    ,   result.stdout.decode()  )


							@require_torch
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		'\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

								_snake_case   						=		'\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n        '

								_snake_case   						=		'\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError("Offline mode is enabled")\nsocket.socket = offline_socket\n        '

								# baseline - just load from_pretrained with normal network
								_snake_case   						=		[sys.executable, '-c', '\n'.join([load, run]  )]

								# should succeed
								_snake_case   						=		self.get_env()
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   0    ,   result.stderr  )
								self.assertIn('success'    ,   result.stdout.decode()  )

								# next emulate no network
								_snake_case   						=		[sys.executable, '-c', '\n'.join([load, mock, run]  )]

								# Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
								# env["TRANSFORMERS_OFFLINE"] = "0"
								# result = subprocess.run(cmd, env=env, check=False, capture_output=True)
								# self.assertEqual(result.returncode, 1, result.stderr)

								# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
								_snake_case   						=		'1'
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   0    ,   result.stderr  )
								self.assertIn('success'    ,   result.stdout.decode()  )


							@require_torch
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		'\nfrom transformers import pipeline\n        '
								_snake_case   						=		'\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

								_snake_case   						=		'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
								_snake_case   						=		self.get_env()
								_snake_case   						=		'1'
								_snake_case   						=		[sys.executable, '-c', '\n'.join([load, mock, run]  )]
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   1    ,   result.stderr  )
								self.assertIn(
								    'You cannot infer task automatically within `pipeline` when using offline mode'    ,   result.stderr.decode().replace('\n'    ,   ''  )    ,   )




							@require_torch
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		'\nfrom transformers import AutoModel\n        '
								_snake_case   						=		'\nmname = "hf-internal-testing/test_dynamic_model"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint("success")\n        '

								# baseline - just load from_pretrained with normal network
								_snake_case   						=		[sys.executable, '-c', '\n'.join([load, run]  )]

								# should succeed
								_snake_case   						=		self.get_env()
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   0    ,   result.stderr  )
								self.assertIn('success'    ,   result.stdout.decode()  )

								# should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
								_snake_case   						=		'1'
								_snake_case   						=		subprocess.run(lowerCAmelCase_    ,   env=lowerCAmelCase_    ,   check=lowerCAmelCase_    ,   capture_output=lowerCAmelCase_  )
								self.assertEqual(result.returncode    ,   0    ,   result.stderr  )
								self.assertIn('success'    ,   result.stdout.decode()  )

