


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_ :str  							=			random.Random()

if is_torch_available():
    import torch




def    a   (							A__      ,    A__=1.0      ,    A__=None      ,    A__=None							)	-> Optional[Any]:




      '''simple docstring'''

      if rng is None:
            SCREAMING_SNAKE_CASE__       :  Tuple						     =		global_rng

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

      return values

class 							lowercase  (						unittest.TestCase			):







     def __init__(						self :						Optional[Any]   ,   _lowercase :						Tuple   ,   _lowercase :						Optional[Any]=7   ,   _lowercase :						List[str]=4_00   ,   _lowercase :						Optional[Any]=20_00   ,   _lowercase :						Optional[int]=1   ,   _lowercase :						Optional[Any]=0.0   ,   _lowercase :						Union[str, Any]=1_60_00   ,   _lowercase :						str=True   ,   _lowercase :						List[str]=True   ,   ):
           SCREAMING_SNAKE_CASE__       :  Any						     =		parent
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		batch_size
           SCREAMING_SNAKE_CASE__       :  str						     =		min_seq_length
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		max_seq_length
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		feature_size
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		padding_value
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		sampling_rate
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		return_attention_mask
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		do_normalize







     def 							lowercase__       (						self :						Tuple      ):
           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 :						int   ,   _lowercase :						Tuple=False   ,   _lowercase :						Optional[Any]=False      ):
           def _flatten(_lowercase :						Union[str, Any]      ):
                 return list(itertools.chain(*_lowercase      )      )

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

           return speech_inputs




@require_torch
@require_torchaudio
class 							lowercase  (						_UpperCAmelCase    ,       unittest.TestCase			):
     lowerCamelCase			:			Any      = ASTFeatureExtractor







     def 							lowercase__       (						self :						Optional[int]      ):
           SCREAMING_SNAKE_CASE__       :  str						     =		ASTFeatureExtractionTester(self      )







     def 							lowercase__       (						self :						str      ):
           # Tests that all call wrap to encode_plus and batch_encode_plus
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
           # create three inputs of length 800, 1000, and 1200
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		[floats_list((1, x)      )[0] for x in range(8_00   ,   14_00   ,   2_00      )]
           SCREAMING_SNAKE_CASE__       :  Any						     =		[np.asarray(_lowercase      ) for speech_input in speech_inputs]

           # Test not batched input
           SCREAMING_SNAKE_CASE__       :  Any						     =		feat_extract(speech_inputs[0]   ,   return_tensors='''np'''      ).input_values
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		feat_extract(np_speech_inputs[0]   ,   return_tensors='''np'''      ).input_values
           self.assertTrue(np.allclose(_lowercase   ,   _lowercase   ,   atol=1E-3      )      )

           # Test batched
           SCREAMING_SNAKE_CASE__       :  Any						     =		feat_extract(_lowercase   ,   padding=_lowercase   ,   return_tensors='''np'''      ).input_values
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		feat_extract(_lowercase   ,   padding=_lowercase   ,   return_tensors='''np'''      ).input_values
           for enc_seq_a, enc_seq_a in zip(_lowercase   ,   _lowercase      ):
                 self.assertTrue(np.allclose(_lowercase   ,   _lowercase   ,   atol=1E-3      )      )

           # Test 2-D numpy arrays are batched.
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		[floats_list((1, x)      )[0] for x in (8_00, 8_00, 8_00)]
           SCREAMING_SNAKE_CASE__       :  str						     =		np.asarray(_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Dict						     =		feat_extract(_lowercase   ,   return_tensors='''np'''      ).input_values
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		feat_extract(_lowercase   ,   return_tensors='''np'''      ).input_values
           for enc_seq_a, enc_seq_a in zip(_lowercase   ,   _lowercase      ):
                 self.assertTrue(np.allclose(_lowercase   ,   _lowercase   ,   atol=1E-3      )      )







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

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()      )
           SCREAMING_SNAKE_CASE__       :  Any						     =		np.random.rand(1_00      ).astype(np.floataa      )
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		np_speech_inputs.tolist()

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







     def 							lowercase__       (						self :						Optional[Any]   ,   _lowercase :						int      ):
           from datasets import load_dataset

           SCREAMING_SNAKE_CASE__       :  int						     =		load_dataset('''hf-internal-testing/librispeech_asr_dummy'''   ,   '''clean'''   ,   split='''validation'''      )
           # automatic decoding with librispeech
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		ds.sort('''id'''      ).select(range(_lowercase      )      )[:num_samples]['''audio''']

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







     @require_torch
     def 							lowercase__       (						self :						Optional[int]      ):
           # fmt: off
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		torch.tensor(
               [-0.9894, -1.2776, -0.9066, -1.2776, -0.9349, -1.2609, -1.0386, -1.2776,
                -1.1561, -1.2776, -1.2052, -1.2723, -1.2190, -1.2132, -1.2776, -1.1133,
                -1.1953, -1.1343, -1.1584, -1.2203, -1.1770, -1.2474, -1.2381, -1.1936,
                -0.9270, -0.8317, -0.8049, -0.7706, -0.7565, -0.7869]      )
           # fmt: on

           SCREAMING_SNAKE_CASE__       :  str						     =		self._load_datasamples(1      )
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		ASTFeatureExtractor()
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		feature_extractor(_lowercase   ,   return_tensors='''pt'''      ).input_values
           self.assertEquals(input_values.shape   ,   (1, 10_24, 1_28)      )
           self.assertTrue(torch.allclose(input_values[0, 0, :30]   ,   _lowercase   ,   atol=1E-4      )      )













import pytest


_UpperCAmelCase							:			Optional[int]        =							"""__dummy_dataset1__"""

_UpperCAmelCase							:			Dict        =							"""
import json
import os

import datasets


REPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"
URLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self):
        features = datasets.Features(
            {
                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),
                \"ner_tags\": datasets.Sequence(
                    datasets.features.ClassLabel(
                        names=[
                            \"O\",
                            \"B-PER\",
                            \"I-PER\",
                            \"B-ORG\",
                            \"I-ORG\",
                            \"B-LOC\",
                            \"I-LOC\",
                        ]
                    )
                ),
                \"langs\": datasets.Sequence(datasets.Value(\"string\")),
                \"spans\": datasets.Sequence(datasets.Value(\"string\")),
            }
        )
        return datasets.DatasetInfo(features=features)

    def _split_generators(self, dl_manager):
        dl_path = dl_manager.download(URLS)
        return [
            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),
            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),
        ]

    def _generate_examples(self, filepath):
        with open(filepath, \"r\", encoding=\"utf-8\") as f:
            for i, line in enumerate(f):
                yield i, json.loads(line)
"""


@pytest.fixture
def 			SCREAMING_SNAKE_CASE (			)     ->	Dict:
    return DATASET_LOADING_SCRIPT_NAME


@pytest.fixture
def 			SCREAMING_SNAKE_CASE (			)     ->	List[Any]:
    return DATASET_LOADING_SCRIPT_CODE


@pytest.fixture
def 			SCREAMING_SNAKE_CASE (			_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase					)     ->	List[Any]:
    lowerCamelCase__       : int   					=						dataset_loading_script_name
    lowerCamelCase__       : Any   					=						tmp_path / 'datasets' / script_name
    script_dir.mkdir(parents=_UpperCAmelCase					)
    lowerCamelCase__       : str   					=						script_dir / F"""{script_name}.py"""
    with open(_UpperCAmelCase		,      'w'					) as f:
        f.write(_UpperCAmelCase					)
    return str(_UpperCAmelCase					)





