



import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowerCAmelCase__					  =  random.Random()


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__=1.0					,			lowerCamelCase__=None					,			lowerCamelCase__=None						):

				"""simple docstring"""


				if rng is None:
								lowercase__					:   Optional[int]													=					global_rng

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

				return values

class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def __init__(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Optional[int]				,       SCREAMING_SNAKE_CASE :     List[str]=7				,       SCREAMING_SNAKE_CASE :     Optional[int]=400				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=2_000				,       SCREAMING_SNAKE_CASE :     int=1				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=0.0				,       SCREAMING_SNAKE_CASE :     List[str]=16_000				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=True				,       SCREAMING_SNAKE_CASE :     Optional[Any]=True				,       ):
									lowercase__					:   Tuple													=					parent
									lowercase__					:   Union[str, Any]													=					batch_size
									lowercase__					:   Optional[int]													=					min_seq_length
									lowercase__					:   int													=					max_seq_length
									lowercase__					:   Optional[int]													=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									lowercase__					:   List[str]													=					feature_size
									lowercase__					:   str													=					padding_value
									lowercase__					:   Optional[int]													=					sampling_rate
									lowercase__					:   List[Any]													=					return_attention_mask
									lowercase__					:   List[Any]													=					do_normalize







					def 			snake_case					(							self :     Dict					):
									return {
									    "feature_size": self.feature_size,
									    "padding_value": self.padding_value,
									    "sampling_rate": self.sampling_rate,
									    "return_attention_mask": self.return_attention_mask,
									    "do_normalize": self.do_normalize,
									}







					def 			snake_case					(							self :     Tuple				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=False				,       SCREAMING_SNAKE_CASE :     List[Any]=False					):
									def _flatten(SCREAMING_SNAKE_CASE :     str					):
													return list(itertools.chain(*SCREAMING_SNAKE_CASE					)					)

									if equal_length:
													lowercase__					:   int													=					floats_list((self.batch_size, self.max_seq_length)					)
									else:
													# make sure that inputs increase in size
													lowercase__					:   List[str]													=					[
													    _flatten(floats_list((x, self.feature_size)					)					)
													    for x in range(self.min_seq_length				,       self.max_seq_length				,       self.seq_length_diff					)
													]

									if numpify:
													lowercase__					:   int													=					[np.asarray(SCREAMING_SNAKE_CASE					) for x in speech_inputs]

									return speech_inputs






class 				snake_case__(_UpperCamelCase    ,      unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		WavaVecaFeatureExtractor







					def 			snake_case					(							self :     int					):
									lowercase__					:   Tuple													=					WavaVecaFeatureExtractionTester(self					)







					def 			snake_case					(							self :     int				,       SCREAMING_SNAKE_CASE :     List[Any]					):
									self.assertTrue(np.all(np.mean(SCREAMING_SNAKE_CASE				,       axis=0					) < 1E-3					)					)
									self.assertTrue(np.all(np.abs(np.var(SCREAMING_SNAKE_CASE				,       axis=0					) - 1					) < 1E-3					)					)







					def 			snake_case					(							self :     Any					):
									# Tests that all call wrap to encode_plus and batch_encode_plus
									lowercase__					:   Optional[int]													=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
									# create three inputs of length 800, 1000, and 1200
									lowercase__					:   str													=					[floats_list((1, x)					)[0] for x in range(800				,       1_400				,       200					)]
									lowercase__					:   List[str]													=					[np.asarray(SCREAMING_SNAKE_CASE					) for speech_input in speech_inputs]

									# Test not batched input
									lowercase__					:   Dict													=					feat_extract(speech_inputs[0]				,       return_tensors="np"					).input_values
									lowercase__					:   List[Any]													=					feat_extract(np_speech_inputs[0]				,       return_tensors="np"					).input_values
									self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=1E-3					)					)

									# Test batched
									lowercase__					:   List[Any]													=					feat_extract(SCREAMING_SNAKE_CASE				,       return_tensors="np"					).input_values
									lowercase__					:   Union[str, Any]													=					feat_extract(SCREAMING_SNAKE_CASE				,       return_tensors="np"					).input_values
									for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					):
													self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=1E-3					)					)

									# Test 2-D numpy arrays are batched.
									lowercase__					:   Dict													=					[floats_list((1, x)					)[0] for x in (800, 800, 800)]
									lowercase__					:   List[str]													=					np.asarray(SCREAMING_SNAKE_CASE					)
									lowercase__					:   Dict													=					feat_extract(SCREAMING_SNAKE_CASE				,       return_tensors="np"					).input_values
									lowercase__					:   Any													=					feat_extract(SCREAMING_SNAKE_CASE				,       return_tensors="np"					).input_values
									for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					):
													self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE				,       atol=1E-3					)					)







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Optional[int]													=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
									lowercase__					:   Optional[Any]													=					[floats_list((1, x)					)[0] for x in range(800				,       1_400				,       200					)]

									lowercase__					:   Union[str, Any]													=					["longest", "max_length", "do_not_pad"]
									lowercase__					:   int													=					[None, 1_600, None]
									for max_length, padding in zip(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					):
													lowercase__					:   str													=					feat_extract(SCREAMING_SNAKE_CASE				,       padding=SCREAMING_SNAKE_CASE				,       max_length=SCREAMING_SNAKE_CASE				,       return_tensors="np"					)
													lowercase__					:   List[str]													=					processed.input_values

													self._check_zero_mean_unit_variance(input_values[0][:800]					)
													self.assertTrue(input_values[0][800:].sum() < 1E-6					)
													self._check_zero_mean_unit_variance(input_values[1][:1_000]					)
													self.assertTrue(input_values[0][1_000:].sum() < 1E-6					)
													self._check_zero_mean_unit_variance(input_values[2][:1_200]					)







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   List[Any]													=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
									lowercase__					:   Optional[int]													=					range(800				,       1_400				,       200					)
									lowercase__					:   Optional[Any]													=					[floats_list((1, x)					)[0] for x in lengths]

									lowercase__					:   Optional[Any]													=					["longest", "max_length", "do_not_pad"]
									lowercase__					:   Tuple													=					[None, 1_600, None]

									for max_length, padding in zip(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					):
													lowercase__					:   Optional[Any]													=					feat_extract(SCREAMING_SNAKE_CASE				,       max_length=SCREAMING_SNAKE_CASE				,       padding=SCREAMING_SNAKE_CASE					)
													lowercase__					:   Union[str, Any]													=					processed.input_values

													self._check_zero_mean_unit_variance(input_values[0][:800]					)
													self._check_zero_mean_unit_variance(input_values[1][:1_000]					)
													self._check_zero_mean_unit_variance(input_values[2][:1_200]					)







					def 			snake_case					(							self :     str					):
									lowercase__					:   str													=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
									lowercase__					:   Tuple													=					[floats_list((1, x)					)[0] for x in range(800				,       1_400				,       200					)]
									lowercase__					:   List[Any]													=					feat_extract(
									    SCREAMING_SNAKE_CASE				,       truncation=SCREAMING_SNAKE_CASE				,       max_length=1_000				,       padding="max_length"				,       return_tensors="np"					)
									lowercase__					:   Optional[Any]													=					processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]					)
									self._check_zero_mean_unit_variance(input_values[1]					)
									self._check_zero_mean_unit_variance(input_values[2]					)







					def 			snake_case					(							self :     Dict					):
									lowercase__					:   Union[str, Any]													=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
									lowercase__					:   List[Any]													=					[floats_list((1, x)					)[0] for x in range(800				,       1_400				,       200					)]
									lowercase__					:   Optional[Any]													=					feat_extract(
									    SCREAMING_SNAKE_CASE				,       truncation=SCREAMING_SNAKE_CASE				,       max_length=1_000				,       padding="longest"				,       return_tensors="np"					)
									lowercase__					:   Optional[int]													=					processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]					)
									self._check_zero_mean_unit_variance(input_values[1, :1_000]					)
									self._check_zero_mean_unit_variance(input_values[2]					)

									# make sure that if max_length < longest -> then pad to max_length
									self.assertTrue(input_values.shape == (3, 1_000)					)

									lowercase__					:   List[Any]													=					[floats_list((1, x)					)[0] for x in range(800				,       1_400				,       200					)]
									lowercase__					:   Tuple													=					feat_extract(
									    SCREAMING_SNAKE_CASE				,       truncation=SCREAMING_SNAKE_CASE				,       max_length=2_000				,       padding="longest"				,       return_tensors="np"					)
									lowercase__					:   Union[str, Any]													=					processed.input_values

									self._check_zero_mean_unit_variance(input_values[0, :800]					)
									self._check_zero_mean_unit_variance(input_values[1, :1_000]					)
									self._check_zero_mean_unit_variance(input_values[2]					)

									# make sure that if max_length > longest -> then pad to longest
									self.assertTrue(input_values.shape == (3, 1_200)					)







					@require_torch
					def 			snake_case					(							self :     Tuple					):
									import torch

									lowercase__					:   Optional[Any]													=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
									lowercase__					:   str													=					np.random.rand(100					).astype(np.floataa					)
									lowercase__					:   List[Any]													=					np_speech_inputs.tolist()

									for inputs in [py_speech_inputs, np_speech_inputs]:
													lowercase__					:   Dict													=					feature_extractor.pad([{"input_values": inputs}]				,       return_tensors="np"					)
													self.assertTrue(np_processed.input_values.dtype == np.floataa					)
													lowercase__					:   int													=					feature_extractor.pad([{"input_values": inputs}]				,       return_tensors="pt"					)
													self.assertTrue(pt_processed.input_values.dtype == torch.floataa					)







					@slow
					@require_torch
					def 			snake_case					(							self :     Dict					):
									# this test makes sure that models that are using
									# group norm don't have their feature extractor return the
									# attention_mask
									for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
													lowercase__					:   int													=					WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE					)
													lowercase__					:   List[Any]													=					WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE					)

													# only "layer" feature extraction norm should make use of
													# attention_mask
													self.assertEqual(feat_extract.return_attention_mask				,       config.feat_extract_norm == "layer"					)









import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Optional[Any]													=					{}
				lowercase__					:   Tuple													=					tokenizer(example["content"]					,			truncation=lowerCamelCase__						)["input_ids"]
				lowercase__					:   Optional[int]													=					len(example["content"]						) / len(output["input_ids"]						)
				return output


lowerCAmelCase__					  =  HfArgumentParser(PretokenizationArguments)
lowerCAmelCase__					  =  parser.parse_args()
if args.num_workers is None:
				lowerCAmelCase__					  =  multiprocessing.cpu_count()
lowerCAmelCase__					  =  AutoTokenizer.from_pretrained(args.tokenizer_dir)

lowerCAmelCase__					  =  time.time()
lowerCAmelCase__					  =  load_dataset(args.dataset_name, split='''train''')
print(f'''Dataset loaded in {time.time()-t_start:.2f}s''')

lowerCAmelCase__					  =  time.time()
lowerCAmelCase__					  =  ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        '''repo_name''',
        '''path''',
        '''copies''',
        '''size''',
        '''content''',
        '''license''',
        '''hash''',
        '''line_mean''',
        '''line_max''',
        '''alpha_frac''',
        '''autogenerated''',
    ],
)
print(f'''Dataset tokenized in {time.time()-t_start:.2f}s''')

lowerCAmelCase__					  =  time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')




