

"""simple docstring"""




import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
						import torch

if is_datasets_available():
						from datasets import load_dataset

A_				:      str           =			random.Random()

def 						A							(  snake_case__		,					snake_case__=1.0		,					snake_case__=None		,					snake_case__=None     ):




			'''simple docstring'''


			if rng is None:
						SCREAMING_SNAKE_CASE__										=   global_rng

			SCREAMING_SNAKE_CASE__										=   []
			for batch_idx in range(shape[0]     ):
						values.append([]     )
						for _ in range(shape[1]     ):
									values[-1].append(rng.random() * scale     )

			return values





class  lowerCamelCase    (unittest.TestCase      ):

			def __init__(       self					:    Optional[Any]     ,					__UpperCAmelCase					:    Tuple     ,					__UpperCAmelCase					:    int=7     ,					__UpperCAmelCase					:    Any=4_0_0     ,					__UpperCAmelCase					:    Any=2_0_0_0     ,					__UpperCAmelCase					:    List[Any]=2_0_4_8     ,					__UpperCAmelCase					:    List[str]=1_2_8     ,					__UpperCAmelCase					:    Optional[Any]=1     ,					__UpperCAmelCase					:    Any=5_1_2     ,					__UpperCAmelCase					:    str=3_0     ,					__UpperCAmelCase					:    Tuple=4_4_1_0_0     ,					) ->     int:
						SCREAMING_SNAKE_CASE__										=   parent
						SCREAMING_SNAKE_CASE__										=   batch_size
						SCREAMING_SNAKE_CASE__										=   min_seq_length
						SCREAMING_SNAKE_CASE__										=   max_seq_length
						SCREAMING_SNAKE_CASE__										=   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
						SCREAMING_SNAKE_CASE__										=   spectrogram_length
						SCREAMING_SNAKE_CASE__										=   feature_size
						SCREAMING_SNAKE_CASE__										=   num_audio_channels
						SCREAMING_SNAKE_CASE__										=   hop_length
						SCREAMING_SNAKE_CASE__										=   chunk_length
						SCREAMING_SNAKE_CASE__										=   sampling_rate

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Optional[int]:
						return {
						    "spectrogram_length": self.spectrogram_length,
						    "feature_size": self.feature_size,
						    "num_audio_channels": self.num_audio_channels,
						    "hop_length": self.hop_length,
						    "chunk_length": self.chunk_length,
						    "sampling_rate": self.sampling_rate,
						}





			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]     ,					__UpperCAmelCase					:    Dict=False     ,					__UpperCAmelCase					:    int=False							) ->     List[str]:
						def _flatten(__UpperCAmelCase					:    Any							):
									return list(itertools.chain(*__UpperCAmelCase							)							)

						if equal_length:
									SCREAMING_SNAKE_CASE__										=   [floats_list((self.max_seq_length, self.feature_size)							) for _ in range(self.batch_size							)]
						else:
									# make sure that inputs increase in size
									SCREAMING_SNAKE_CASE__										=   [
									    floats_list((x, self.feature_size)							)
									    for x in range(self.min_seq_length     ,					self.max_seq_length     ,					self.seq_length_diff							)
									]
						if numpify:
									SCREAMING_SNAKE_CASE__										=   [np.asarray(__UpperCAmelCase							) for x in speech_inputs]
						return speech_inputs





@require_torch
@require_torchaudio
class  lowerCamelCase    (A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		Tuple       		=				TvltFeatureExtractor

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   TvltFeatureExtractionTester(self							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]							) ->     Tuple:
						SCREAMING_SNAKE_CASE__										=   self.feature_extraction_class(**self.feat_extract_dict							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""spectrogram_length"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""feature_size"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""num_audio_channels"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""hop_length"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""chunk_length"""							)							)
						self.assertTrue(hasattr(__UpperCAmelCase     ,					"""sampling_rate"""							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     int:
						SCREAMING_SNAKE_CASE__										=   self.feature_extraction_class(**self.feat_extract_dict							)

						with tempfile.TemporaryDirectory() as tmpdirname:
									SCREAMING_SNAKE_CASE__										=   feat_extract_first.save_pretrained(__UpperCAmelCase							)[0]
									check_json_file_has_correct_format(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   self.feature_extraction_class.from_pretrained(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   feat_extract_first.to_dict()
						SCREAMING_SNAKE_CASE__										=   feat_extract_second.to_dict()
						SCREAMING_SNAKE_CASE__										=   dict_first.pop("""mel_filters"""							)
						SCREAMING_SNAKE_CASE__										=   dict_second.pop("""mel_filters"""							)
						self.assertTrue(np.allclose(__UpperCAmelCase     ,					__UpperCAmelCase							)							)
						self.assertEqual(__UpperCAmelCase     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Any							) ->     Any:
						SCREAMING_SNAKE_CASE__										=   self.feature_extraction_class(**self.feat_extract_dict							)

						with tempfile.TemporaryDirectory() as tmpdirname:
									SCREAMING_SNAKE_CASE__										=   os.path.join(__UpperCAmelCase     ,					"""feat_extract.json"""							)
									feat_extract_first.to_json_file(__UpperCAmelCase							)
									SCREAMING_SNAKE_CASE__										=   self.feature_extraction_class.from_json_file(__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   feat_extract_first.to_dict()
						SCREAMING_SNAKE_CASE__										=   feat_extract_second.to_dict()
						SCREAMING_SNAKE_CASE__										=   dict_first.pop("""mel_filters"""							)
						SCREAMING_SNAKE_CASE__										=   dict_second.pop("""mel_filters"""							)
						self.assertTrue(np.allclose(__UpperCAmelCase     ,					__UpperCAmelCase							)							)
						self.assertEqual(__UpperCAmelCase     ,					__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Tuple:
						# Initialize feature_extractor
						SCREAMING_SNAKE_CASE__										=   self.feature_extraction_class(**self.feat_extract_dict							)

						# create three inputs of length 800, 1000, and 1200
						SCREAMING_SNAKE_CASE__										=   [floats_list((1, x)							)[0] for x in range(8_0_0     ,					1_4_0_0     ,					2_0_0							)]
						SCREAMING_SNAKE_CASE__										=   [np.asarray(__UpperCAmelCase							) for speech_input in speech_inputs]

						# Test not batched input
						SCREAMING_SNAKE_CASE__										=   feature_extractor(np_speech_inputs[0]     ,					return_tensors="""np"""     ,					sampling_rate=4_4_1_0_0							).audio_values

						self.assertTrue(encoded_audios.ndim == 4							)
						self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
						self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
						self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

						# Test batched
						SCREAMING_SNAKE_CASE__										=   feature_extractor(__UpperCAmelCase     ,					return_tensors="""np"""     ,					sampling_rate=4_4_1_0_0							).audio_values

						self.assertTrue(encoded_audios.ndim == 4							)
						self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
						self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
						self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

						# Test audio masking
						SCREAMING_SNAKE_CASE__										=   feature_extractor(
						    __UpperCAmelCase     ,					return_tensors="""np"""     ,					sampling_rate=4_4_1_0_0     ,					mask_audio=__UpperCAmelCase							).audio_values

						self.assertTrue(encoded_audios.ndim == 4							)
						self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
						self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
						self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

						# Test 2-D numpy arrays are batched.
						SCREAMING_SNAKE_CASE__										=   [floats_list((1, x)							)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
						SCREAMING_SNAKE_CASE__										=   np.asarray(__UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__										=   feature_extractor(__UpperCAmelCase     ,					return_tensors="""np"""     ,					sampling_rate=4_4_1_0_0							).audio_values
						self.assertTrue(encoded_audios.ndim == 4							)
						self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
						self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
						self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Union[str, Any]     ,					__UpperCAmelCase					:    Union[str, Any]							) ->     List[Any]:
						SCREAMING_SNAKE_CASE__										=   load_dataset("""hf-internal-testing/librispeech_asr_dummy"""     ,					"""clean"""     ,					split="""validation"""							)
						# automatic decoding with librispeech
						SCREAMING_SNAKE_CASE__										=   ds.sort("""id"""							).select(range(__UpperCAmelCase							)							)[:num_samples]["""audio"""]

						return [x["array"] for x in speech_samples]





			def        SCREAMING_SNAKE_CASE					(       self					:    str							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   self._load_datasamples(1							)
						SCREAMING_SNAKE_CASE__										=   TvltFeatureExtractor()
						SCREAMING_SNAKE_CASE__										=   feature_extractor(__UpperCAmelCase     ,					return_tensors="""pt"""							).audio_values

						self.assertEquals(audio_values.shape     ,					(1, 1, 1_9_2, 1_2_8)							)

						SCREAMING_SNAKE_CASE__										=   torch.tensor([[-0.3_032, -0.2_708], [-0.4_434, -0.4_007]]							)
						self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]     ,					__UpperCAmelCase     ,					atol=1e-4							)							)





"""simple docstring"""




import os
import unittest

from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer

from ...test_tokenization_common import TokenizerTesterMixin





class  lowerCamelCase    (A__      ,unittest.TestCase      ):
			lowerCamelCase__ :		List[str]       		=				PhobertTokenizer
			lowerCamelCase__ :		Union[str, Any]       		=				False

			def        SCREAMING_SNAKE_CASE					(       self					:    int							) ->     Any:
						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						SCREAMING_SNAKE_CASE__										=   ["""T@@""", """i""", """I""", """R@@""", """r""", """e@@"""]
						SCREAMING_SNAKE_CASE__										=   dict(zip(__UpperCAmelCase     ,					range(len(__UpperCAmelCase							)							)							)							)
						SCREAMING_SNAKE_CASE__										=   ["""#version: 0.2""", """l à</w>"""]
						SCREAMING_SNAKE_CASE__										=   {"""unk_token""": """<unk>"""}

						SCREAMING_SNAKE_CASE__										=   os.path.join(self.tmpdirname     ,					VOCAB_FILES_NAMES["""vocab_file"""]							)
						SCREAMING_SNAKE_CASE__										=   os.path.join(self.tmpdirname     ,					VOCAB_FILES_NAMES["""merges_file"""]							)

						with open(self.vocab_file     ,					"""w"""     ,					encoding="""utf-8"""							) as fp:
									for token in vocab_tokens:
												fp.write(F"""{token} {vocab_tokens[token]}\n"""							)
						with open(self.merges_file     ,					"""w"""     ,					encoding="""utf-8"""							) as fp:
									fp.write("""\n""".join(__UpperCAmelCase							)							)

			def        SCREAMING_SNAKE_CASE					(       self					:    List[Any]     ,					**__UpperCAmelCase					:    int							) ->     Optional[int]:
						kwargs.update(self.special_tokens_map							)
						return PhobertTokenizer.from_pretrained(self.tmpdirname     ,					**__UpperCAmelCase							)

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict     ,					__UpperCAmelCase					:    int							) ->     Optional[Any]:
						SCREAMING_SNAKE_CASE__										=   """Tôi là VinAI Research"""
						SCREAMING_SNAKE_CASE__										=   """T<unk> i <unk> <unk> <unk> <unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
						return input_text, output_text





			def        SCREAMING_SNAKE_CASE					(       self					:    Optional[int]							) ->     int:
						SCREAMING_SNAKE_CASE__										=   PhobertTokenizer(self.vocab_file     ,					self.merges_file     ,					**self.special_tokens_map							)
						SCREAMING_SNAKE_CASE__										=   """Tôi là VinAI Research"""
						SCREAMING_SNAKE_CASE__										=   """T@@ ô@@ i l@@ à V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
						SCREAMING_SNAKE_CASE__										=   tokenizer.tokenize(__UpperCAmelCase							)
						print(__UpperCAmelCase							)
						self.assertListEqual(__UpperCAmelCase     ,					__UpperCAmelCase							)

						SCREAMING_SNAKE_CASE__										=   tokens + [tokenizer.unk_token]

						SCREAMING_SNAKE_CASE__										=   [4, 3, 5, 3, 3, 3, 3, 3, 3, 6, 7, 9, 3, 9, 3, 3, 3, 3, 3]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase							)     ,					__UpperCAmelCase							)


