



"""simple docstring"""


import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


__A      :							Any   =      random.Random()

if is_torch_available():
					import torch







def        lowercase		(					_SCREAMING_SNAKE_CASE      :  Dict    ,   _SCREAMING_SNAKE_CASE      :  Dict=1.0    ,   _SCREAMING_SNAKE_CASE      :  List[Any]=None    ,   _SCREAMING_SNAKE_CASE      :  Union[str, Any]=None ):






				'''simple docstring'''



				if rng is None:
								_UpperCAmelCase							=			global_rng

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

				return values
class 	_a       (    unittest.TestCase):
			"""simple docstring"""



			def __init__(						self   :			List[Any] ,      __UpperCamelCase   :			Optional[Any] ,      __UpperCamelCase   :			List[str]=7 ,      __UpperCamelCase   :			int=4_0_0 ,      __UpperCamelCase   :			int=2_0_0_0 ,      __UpperCamelCase   :			List[str]=1 ,      __UpperCamelCase   :			Tuple=0.0 ,      __UpperCamelCase   :			Tuple=1_6_0_0_0 ,      __UpperCamelCase   :			Tuple=True ,      __UpperCamelCase   :			Union[str, Any]=True ,      )->Any:
							_UpperCAmelCase							=			parent
							_UpperCAmelCase							=			batch_size
							_UpperCAmelCase							=			min_seq_length
							_UpperCAmelCase							=			max_seq_length
							_UpperCAmelCase							=			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							_UpperCAmelCase							=			feature_size
							_UpperCAmelCase							=			padding_value
							_UpperCAmelCase							=			sampling_rate
							_UpperCAmelCase							=			return_attention_mask
							_UpperCAmelCase							=			do_normalize



			def        lowercase__  (						self   :			Optional[Any]      )->List[str]:
							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        lowercase__  (						self   :			str ,      __UpperCamelCase   :			Union[str, Any]=False ,      __UpperCamelCase   :			Union[str, Any]=False      )->Optional[Any]:
							def _flatten(__UpperCamelCase   :			int      ):
											return list(itertools.chain(*UpperCAmelCase__      )      )

							if equal_length:
											_UpperCAmelCase							=			floats_list((self.batch_size, self.max_seq_length)      )
							else:
											# make sure that inputs increase in size
											_UpperCAmelCase							=			[
											    _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:
											_UpperCAmelCase							=			[np.asarray(UpperCAmelCase__      ) for x in speech_inputs]

							return speech_inputs



@require_torch
@require_torchaudio
class 	_a       (    __lowercase	,							unittest.TestCase):
			"""simple docstring"""
			UpperCamelCase__						=		ASTFeatureExtractor



			def        lowercase__  (						self   :			str      )->Optional[int]:
							_UpperCAmelCase							=			ASTFeatureExtractionTester(self      )



			def        lowercase__  (						self   :			Optional[int]      )->Optional[int]:
							# Tests that all call wrap to encode_plus and batch_encode_plus
							_UpperCAmelCase							=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
							# create three inputs of length 800, 1000, and 1200
							_UpperCAmelCase							=			[floats_list((1, x)      )[0] for x in range(8_0_0 ,      1_4_0_0 ,      2_0_0      )]
							_UpperCAmelCase							=			[np.asarray(UpperCAmelCase__      ) for speech_input in speech_inputs]

							# Test not batched input
							_UpperCAmelCase							=			feat_extract(speech_inputs[0] ,      return_tensors='''np'''      ).input_values
							_UpperCAmelCase							=			feat_extract(np_speech_inputs[0] ,      return_tensors='''np'''      ).input_values
							self.assertTrue(np.allclose(UpperCAmelCase__ ,      UpperCAmelCase__ ,      atol=1e-3      )      )

							# Test batched
							_UpperCAmelCase							=			feat_extract(UpperCAmelCase__ ,      padding=UpperCAmelCase__ ,      return_tensors='''np'''      ).input_values
							_UpperCAmelCase							=			feat_extract(UpperCAmelCase__ ,      padding=UpperCAmelCase__ ,      return_tensors='''np'''      ).input_values
							for enc_seq_a, enc_seq_a in zip(UpperCAmelCase__ ,      UpperCAmelCase__      ):
											self.assertTrue(np.allclose(UpperCAmelCase__ ,      UpperCAmelCase__ ,      atol=1e-3      )      )

							# Test 2-D numpy arrays are batched.
							_UpperCAmelCase							=			[floats_list((1, x)      )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
							_UpperCAmelCase							=			np.asarray(UpperCAmelCase__      )
							_UpperCAmelCase							=			feat_extract(UpperCAmelCase__ ,      return_tensors='''np'''      ).input_values
							_UpperCAmelCase							=			feat_extract(UpperCAmelCase__ ,      return_tensors='''np'''      ).input_values
							for enc_seq_a, enc_seq_a in zip(UpperCAmelCase__ ,      UpperCAmelCase__      ):
											self.assertTrue(np.allclose(UpperCAmelCase__ ,      UpperCAmelCase__ ,      atol=1e-3      )      )



			@require_torch
			def        lowercase__  (						self   :			Union[str, Any]      )->Optional[int]:
							import torch

							_UpperCAmelCase							=			self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
							_UpperCAmelCase							=			np.random.rand(1_0_0      ).astype(np.floataa      )
							_UpperCAmelCase							=			np_speech_inputs.tolist()

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



			def        lowercase__  (						self   :			int ,      __UpperCamelCase   :			str      )->Tuple:
							from datasets import load_dataset

							_UpperCAmelCase							=			load_dataset('''hf-internal-testing/librispeech_asr_dummy''' ,      '''clean''' ,      split='''validation'''      )
							# automatic decoding with librispeech
							_UpperCAmelCase							=			ds.sort('''id'''      ).select(range(UpperCAmelCase__      )      )[:num_samples]['''audio''']

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



			@require_torch
			def        lowercase__  (						self   :			str      )->Optional[Any]:
							# fmt: off
							_UpperCAmelCase							=			torch.tensor(
							    [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
							     -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
							     -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
							     -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]      )
							# fmt: on

							_UpperCAmelCase							=			self._load_datasamples(1      )
							_UpperCAmelCase							=			ASTFeatureExtractor()
							_UpperCAmelCase							=			feature_extractor(UpperCAmelCase__ ,      return_tensors='''pt'''      ).input_values
							self.assertEquals(input_values.shape ,      (1, 1_0_2_4, 1_2_8)      )
							self.assertTrue(torch.allclose(input_values[0, 0, :3_0] ,      UpperCAmelCase__ ,      atol=1e-4      )      )







'''simple docstring'''




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 	a_					( lowerCamelCase	:     Dict				):
   lowerCAmelCase     =			{}
   lowerCAmelCase     =			tokenizer(example['content']	, truncation=lowerCamelCase				)['input_ids']
   lowerCAmelCase     =			len(example['content']				) / len(output['input_ids']				)
   return output


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

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

__snake_case    						=time.time()
__snake_case    						=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''')

__snake_case    						=time.time()
ds.push_to_hub(args.tokenized_data_repo)




print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')





