






import shutil
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import SpeechaTextTokenizer, is_speech_available
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, require_torchaudio
from transformers.utils import FEATURE_EXTRACTOR_NAME

from .test_feature_extraction_speech_to_text import floats_list


if is_speech_available():
   from transformers import SpeechaTextFeatureExtractor, SpeechaTextProcessor


snake_case			 =						get_tests_dir("""fixtures/test_sentencepiece.model""")


@require_torch
@require_torchaudio
@require_sentencepiece
class        SCREAMING_SNAKE_CASE	(	unittest.TestCase    ):
     '''simple docstring'''


     def  _A				( self				:		Tuple					):
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			tempfile.mkdtemp()

       SCREAMING_SNAKE_CASE				:					List[str]		  =			["<s>", "<pad>", "</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est"]
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			dict(zip(A_    ,       range(len(A_					)					)					)					)
       SCREAMING_SNAKE_CASE				:					Dict		  =			Path(self.tmpdirname					)
       save_json(A_    ,       save_dir / VOCAB_FILES_NAMES["vocab_file"]					)
       if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
         copyfile(A_    ,       save_dir / VOCAB_FILES_NAMES["spm_file"]					)

       SCREAMING_SNAKE_CASE				:					List[str]		  =			SpeechaTextTokenizer.from_pretrained(self.tmpdirname					)
       tokenizer.save_pretrained(self.tmpdirname					)

       SCREAMING_SNAKE_CASE				:					int		  =			{
           "feature_size": 24,
           "num_mel_bins": 24,
           "padding_value": 0.0,
           "sampling_rate": 1_6000,
           "return_attention_mask": False,
           "do_normalize": True,
       }
       save_json(A_    ,       save_dir / FEATURE_EXTRACTOR_NAME					)


     def  _A				( self				:		int    ,       **UpperCAmelCase_				:		int					):
       return SpeechaTextTokenizer.from_pretrained(self.tmpdirname    ,       **A_					)


     def  _A				( self				:		Optional[int]    ,       **UpperCAmelCase_				:		Union[str, Any]					):
       return SpeechaTextFeatureExtractor.from_pretrained(self.tmpdirname    ,       **A_					)


     def  _A				( self				:		Tuple					):
       shutil.rmtree(self.tmpdirname					)


     def  _A				( self				:		Optional[int]					):
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			self.get_tokenizer()
       SCREAMING_SNAKE_CASE				:					Any		  =			self.get_feature_extractor()

       SCREAMING_SNAKE_CASE				:					Dict		  =			SpeechaTextProcessor(tokenizer=A_    ,       feature_extractor=A_					)

       processor.save_pretrained(self.tmpdirname					)
       SCREAMING_SNAKE_CASE				:					str		  =			SpeechaTextProcessor.from_pretrained(self.tmpdirname					)

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

       self.assertEqual(processor.feature_extractor.to_json_string()    ,       feature_extractor.to_json_string()					)
       self.assertIsInstance(processor.feature_extractor    ,       A_					)


     def  _A				( self				:		int					):
       SCREAMING_SNAKE_CASE				:					Union[str, Any]		  =			SpeechaTextProcessor(
           tokenizer=self.get_tokenizer()    ,       feature_extractor=self.get_feature_extractor()					)
       processor.save_pretrained(self.tmpdirname					)

       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.get_tokenizer(bos_token="(BOS)"    ,       eos_token="(EOS)"					)
       SCREAMING_SNAKE_CASE				:					int		  =			self.get_feature_extractor(do_normalize=A_    ,       padding_value=1.0					)

       SCREAMING_SNAKE_CASE				:					Tuple		  =			SpeechaTextProcessor.from_pretrained(
           self.tmpdirname    ,       bos_token="(BOS)"    ,       eos_token="(EOS)"    ,       do_normalize=A_    ,       padding_value=1.0					)

       self.assertEqual(processor.tokenizer.get_vocab()    ,       tokenizer_add_kwargs.get_vocab()					)
       self.assertIsInstance(processor.tokenizer    ,       A_					)

       self.assertEqual(processor.feature_extractor.to_json_string()    ,       feature_extractor_add_kwargs.to_json_string()					)
       self.assertIsInstance(processor.feature_extractor    ,       A_					)


     def  _A				( self				:		Any					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.get_feature_extractor()
       SCREAMING_SNAKE_CASE				:					List[str]		  =			self.get_tokenizer()

       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			SpeechaTextProcessor(tokenizer=A_    ,       feature_extractor=A_					)

       SCREAMING_SNAKE_CASE				:					Tuple		  =			floats_list((3, 1000)					)

       SCREAMING_SNAKE_CASE				:					List[str]		  =			feature_extractor(A_    ,       return_tensors="np"					)
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			processor(A_    ,       return_tensors="np"					)

       for key in input_feat_extract.keys():
         self.assertAlmostEqual(input_feat_extract[key].sum()    ,       input_processor[key].sum()    ,       delta=1E-2					)


     def  _A				( self				:		List[Any]					):
       SCREAMING_SNAKE_CASE				:					List[str]		  =			self.get_feature_extractor()
       SCREAMING_SNAKE_CASE				:					str		  =			self.get_tokenizer()

       SCREAMING_SNAKE_CASE				:					str		  =			SpeechaTextProcessor(tokenizer=A_    ,       feature_extractor=A_					)

       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			"This is a test string"

       SCREAMING_SNAKE_CASE				:					Dict		  =			processor(text=A_					)

       SCREAMING_SNAKE_CASE				:					Any		  =			tokenizer(A_					)

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


     def  _A				( self				:		Optional[int]					):
       SCREAMING_SNAKE_CASE				:					Any		  =			self.get_feature_extractor()
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.get_tokenizer()

       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			SpeechaTextProcessor(tokenizer=A_    ,       feature_extractor=A_					)

       SCREAMING_SNAKE_CASE				:					List[Any]		  =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       SCREAMING_SNAKE_CASE				:					Tuple		  =			processor.batch_decode(A_					)
       SCREAMING_SNAKE_CASE				:					List[str]		  =			tokenizer.batch_decode(A_					)

       self.assertListEqual(A_    ,       A_					)






     def  _A				( self				:		str					):
       SCREAMING_SNAKE_CASE				:					Any		  =			self.get_feature_extractor()
       SCREAMING_SNAKE_CASE				:					Dict		  =			self.get_tokenizer()

       SCREAMING_SNAKE_CASE				:					Tuple		  =			SpeechaTextProcessor(tokenizer=A_    ,       feature_extractor=A_					)

       self.assertListEqual(
           processor.model_input_names    ,       feature_extractor.model_input_names    ,       msg="`processor` and `feature_extractor` model input names do not match"    ,       )








'''simple docstring'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm







def 						A_(			A		:						int						,			A		:						bool = False):
							if device_specific:
														seed += AcceleratorState().process_index
							random.seed(A)
							np.random.seed(A)
							torch.manual_seed(A)
							if is_xpu_available():
														torch.xpu.manual_seed_all(A)
							elif is_npu_available():
														torch.npu.manual_seed_all(A)
							else:
														torch.cuda.manual_seed_all(A)
							# ^^ safe to call this function even if cuda is not available
							if is_tpu_available():
														xm.set_rng_state(A)







def 						A_(			A		:						Optional[RNGType] = None						,			A		:						Optional[torch.Generator] = None):
							# Get the proper rng state
							if rng_type == RNGType.TORCH:
														UpperCamelCase					       =			torch.get_rng_state()
							elif rng_type == RNGType.CUDA:
														UpperCamelCase					       =			torch.cuda.get_rng_state()
							elif rng_type == RNGType.XLA:
														assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
														UpperCamelCase					       =			torch.tensor(xm.get_rng_state())
							elif rng_type == RNGType.NPU:
														assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
														UpperCamelCase					       =			torch.npu.get_rng_state()
							elif rng_type == RNGType.XPU:
														assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
														UpperCamelCase					       =			torch.xpu.get_rng_state()
							elif rng_type == RNGType.GENERATOR:
														assert generator is not None, "Need a generator to synchronize its seed."
														UpperCamelCase					       =			generator.get_state()

							# Broadcast the rng state from device 0 to other devices
							UpperCamelCase					       =			AcceleratorState()
							if state.distributed_type == DistributedType.TPU:
														UpperCamelCase					       =			rng_state.to(xm.xla_device())
														xm.collective_broadcast([rng_state])
														xm.mark_step()
														UpperCamelCase					       =			rng_state.cpu()
							elif (
							    state.distributed_type in CUDA_DISTRIBUTED_TYPES
							    or state.distributed_type == DistributedType.MULTI_NPU
							    or state.distributed_type == DistributedType.MULTI_XPU
							):
														UpperCamelCase					       =			rng_state.to(state.device)
														torch.distributed.broadcast(A						,			0)
														UpperCamelCase					       =			rng_state.cpu()
							elif state.distributed_type == DistributedType.MULTI_CPU:
														torch.distributed.broadcast(A						,			0)

							# Set the broadcast rng state
							if rng_type == RNGType.TORCH:
														torch.set_rng_state(A)
							elif rng_type == RNGType.CUDA:
														torch.cuda.set_rng_state(A)
							elif rng_type == RNGType.NPU:
														torch.npu.set_rng_state(A)
							elif rng_type == RNGType.XPU:
														torch.xpu.set_rng_state(A)
							elif rng_type == RNGType.XLA:
														xm.set_rng_state(rng_state.item())
							elif rng_type == RNGType.GENERATOR:
														generator.set_state(A)







def 						A_(			A		:						List[Union[str, RNGType]]						,			A		:						Optional[torch.Generator] = None):
							for rng_type in rng_types:
														synchronize_rng_state(RNGType(A)						,			generator=A)




