
"""simple docstring"""







import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
							from transformers import SpeechaTextFeatureExtractor

lowerCamelCase__        =		random.Random()
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase=1.0	,  _UpperCamelCase=None	,  _UpperCamelCase=None		):
	if rng is None:
		__lowerCAmelCase      :						str						      =				global_rng

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

	return values




@require_torch
@require_torchaudio
class 			A__	(     unittest.TestCase):

		def __init__(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=7	,		_SCREAMING_SNAKE_CASE=4_00	,		_SCREAMING_SNAKE_CASE=20_00	,		_SCREAMING_SNAKE_CASE=24	,		_SCREAMING_SNAKE_CASE=24	,		_SCREAMING_SNAKE_CASE=0.0	,		_SCREAMING_SNAKE_CASE=1_60_00	,		_SCREAMING_SNAKE_CASE=True	,		_SCREAMING_SNAKE_CASE=True	,		):
			__lowerCAmelCase      :						List[Any]						      =				parent
			__lowerCAmelCase      :						int						      =				batch_size
			__lowerCAmelCase      :						List[Any]						      =				min_seq_length
			__lowerCAmelCase      :						List[Any]						      =				max_seq_length
			__lowerCAmelCase      :						Optional[int]						      =				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
			__lowerCAmelCase      :						Optional[Any]						      =				feature_size
			__lowerCAmelCase      :						Dict						      =				num_mel_bins
			__lowerCAmelCase      :						List[Any]						      =				padding_value
			__lowerCAmelCase      :						List[Any]						      =				sampling_rate
			__lowerCAmelCase      :						List[str]						      =				return_attention_mask
			__lowerCAmelCase      :						Optional[int]						      =				do_normalize

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







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE=False	,		_SCREAMING_SNAKE_CASE=False			):
			def _flatten(_SCREAMING_SNAKE_CASE			):
				return list(itertools.chain(*_a			)			)

			if equal_length:
				__lowerCAmelCase      :						Tuple						      =				[floats_list((self.max_seq_length, self.feature_size)			) for _ in range(self.batch_size			)]
			else:
				# make sure that inputs increase in size
				__lowerCAmelCase      :						Optional[int]						      =				[
				    floats_list((x, self.feature_size)			)
				    for x in range(self.min_seq_length	,		self.max_seq_length	,		self.seq_length_diff			)
				]
			if numpify:
				__lowerCAmelCase      :						Union[str, Any]						      =				[np.asarray(_a			) for x in speech_inputs]
			return speech_inputs




@require_torch
@require_torchaudio
class 			A__	(     __SCREAMING_SNAKE_CASE	,					unittest.TestCase):
		A_						:	str           =     SpeechaTextFeatureExtractor if is_speech_available() else None

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				SpeechaTextFeatureExtractionTester(self			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			self.assertTrue(np.all(np.mean(_a	,		axis=0			) < 1E-3			)			)
			self.assertTrue(np.all(np.abs(np.var(_a	,		axis=0			) - 1			) < 1E-3			)			)

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

			# Test feature size
			__lowerCAmelCase      :						Tuple						      =				feature_extractor(_a	,		padding=_a	,		return_tensors='np'			).input_features
			self.assertTrue(input_features.ndim == 3			)
			self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size			)

			# Test not batched input
			__lowerCAmelCase      :						List[Any]						      =				feature_extractor(speech_inputs[0]	,		return_tensors='np'			).input_features
			__lowerCAmelCase      :						Optional[int]						      =				feature_extractor(np_speech_inputs[0]	,		return_tensors='np'			).input_features
			self.assertTrue(np.allclose(_a	,		_a	,		atol=1E-3			)			)

			# Test batched
			__lowerCAmelCase      :						Union[str, Any]						      =				feature_extractor(_a	,		return_tensors='np'			).input_features
			__lowerCAmelCase      :						List[str]						      =				feature_extractor(_a	,		return_tensors='np'			).input_features
			for enc_seq_a, enc_seq_a in zip(_a	,		_a			):
				self.assertTrue(np.allclose(_a	,		_a	,		atol=1E-3			)			)

			# Test 2-D numpy arrays are batched.
			__lowerCAmelCase      :						Optional[int]						      =				[floats_list((1, x)			)[0] for x in (8_00, 8_00, 8_00)]
			__lowerCAmelCase      :						int						      =				np.asarray(_a			)
			__lowerCAmelCase      :						Any						      =				feature_extractor(_a	,		return_tensors='np'			).input_features
			__lowerCAmelCase      :						str						      =				feature_extractor(_a	,		return_tensors='np'			).input_features
			for enc_seq_a, enc_seq_a in zip(_a	,		_a			):
				self.assertTrue(np.allclose(_a	,		_a	,		atol=1E-3			)			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						str						      =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
			__lowerCAmelCase      :						Any						      =				[floats_list((1, x)			)[0] for x in range(8_00	,		14_00	,		2_00			)]

			__lowerCAmelCase      :						Union[str, Any]						      =				['longest', 'max_length', 'do_not_pad']
			__lowerCAmelCase      :						Tuple						      =				[None, 16, None]
			for max_length, padding in zip(_a	,		_a			):
				__lowerCAmelCase      :						Tuple						      =				feature_extractor(
				    _a	,		padding=_a	,		max_length=_a	,		return_attention_mask=_a			)
				__lowerCAmelCase      :						Tuple						      =				inputs.input_features
				__lowerCAmelCase      :						Any						      =				inputs.attention_mask
				__lowerCAmelCase      :						int						      =				[np.sum(_a			) for x in attention_mask]

				self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]			)
				self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]			)
				self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
			__lowerCAmelCase      :						Optional[Any]						      =				[floats_list((1, x)			)[0] for x in range(8_00	,		14_00	,		2_00			)]

			__lowerCAmelCase      :						Dict						      =				['longest', 'max_length', 'do_not_pad']
			__lowerCAmelCase      :						Any						      =				[None, 16, None]
			for max_length, padding in zip(_a	,		_a			):
				__lowerCAmelCase      :						Optional[int]						      =				feature_extractor(
				    _a	,		max_length=_a	,		padding=_a	,		return_tensors='np'	,		return_attention_mask=_a			)
				__lowerCAmelCase      :						Dict						      =				inputs.input_features
				__lowerCAmelCase      :						str						      =				inputs.attention_mask
				__lowerCAmelCase      :						Union[str, Any]						      =				[np.sum(_a			) for x in attention_mask]

				self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]			)
				self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6			)
				self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]			)
				self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6			)
				self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
			__lowerCAmelCase      :						Union[str, Any]						      =				[floats_list((1, x)			)[0] for x in range(8_00	,		14_00	,		2_00			)]
			__lowerCAmelCase      :						Tuple						      =				feature_extractor(
			    _a	,		padding='max_length'	,		max_length=4	,		truncation=_a	,		return_tensors='np'	,		return_attention_mask=_a	,		)
			__lowerCAmelCase      :						Optional[int]						      =				inputs.input_features
			__lowerCAmelCase      :						Optional[Any]						      =				inputs.attention_mask
			__lowerCAmelCase      :						Optional[Any]						      =				np.sum(attention_mask == 1	,		axis=1			)

			self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]			)
			self._check_zero_mean_unit_variance(input_features[1]			)
			self._check_zero_mean_unit_variance(input_features[2]			)

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[str]						      =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
			__lowerCAmelCase      :						str						      =				[floats_list((1, x)			)[0] for x in range(8_00	,		14_00	,		2_00			)]
			__lowerCAmelCase      :						Optional[Any]						      =				feature_extractor(
			    _a	,		padding='longest'	,		max_length=4	,		truncation=_a	,		return_tensors='np'	,		return_attention_mask=_a	,		)
			__lowerCAmelCase      :						List[Any]						      =				inputs.input_features
			__lowerCAmelCase      :						Dict						      =				inputs.attention_mask
			__lowerCAmelCase      :						Any						      =				np.sum(attention_mask == 1	,		axis=1			)

			self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]			)
			self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]			)
			self._check_zero_mean_unit_variance(input_features[2]			)

			# make sure that if max_length < longest -> then pad to max_length
			self.assertEqual(input_features.shape	,		(3, 4, 24)			)

			__lowerCAmelCase      :						Dict						      =				[floats_list((1, x)			)[0] for x in range(8_00	,		14_00	,		2_00			)]
			__lowerCAmelCase      :						Optional[Any]						      =				feature_extractor(
			    _a	,		padding='longest'	,		max_length=16	,		truncation=_a	,		return_tensors='np'	,		return_attention_mask=_a	,		)
			__lowerCAmelCase      :						int						      =				inputs.input_features
			__lowerCAmelCase      :						List[Any]						      =				inputs.attention_mask
			__lowerCAmelCase      :						List[Any]						      =				np.sum(attention_mask == 1	,		axis=1			)

			self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]			)
			self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]			)
			self._check_zero_mean_unit_variance(input_features[2]			)

			# make sure that if max_length < longest -> then pad to max_length
			self.assertEqual(input_features.shape	,		(3, 6, 24)			)

		def 							__lowerCamelCase							(				self			):
			import torch

			__lowerCAmelCase      :						List[str]						      =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
			__lowerCAmelCase      :						str						      =				np.random.rand(1_00	,		32			).astype(np.floataa			)
			__lowerCAmelCase      :						Union[str, Any]						      =				np_speech_inputs.tolist()

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

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE			):
			from datasets import load_dataset

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

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







		def 							__lowerCamelCase							(				self			):
			# fmt: off
			__lowerCAmelCase      :						Dict						      =				np.array([
			    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
			    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
			    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
			]			)
			# fmt: on

			__lowerCAmelCase      :						List[Any]						      =				self._load_datasamples(1			)
			__lowerCAmelCase      :						Optional[Any]						      =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
			__lowerCAmelCase      :						int						      =				feature_extractor(_a	,		return_tensors='pt'			).input_features
			self.assertEquals(input_features.shape	,		(1, 5_84, 24)			)
			self.assertTrue(np.allclose(input_features[0, 0, :30]	,		_a	,		atol=1E-4			)			)

"""simple docstring"""







# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowerCamelCase__        =		logging.get_logger(__name__)

lowerCamelCase__        =		{}
lowerCamelCase__        =		{}
lowerCamelCase__        =		{}
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = None	,  ):
	__lowerCAmelCase      :						Union[str, Any]						      =				aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    F"Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"		)
	__lowerCAmelCase      :						str						      =				formatter_cls
	for alias in set(aliases + [format_type]		):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    F"Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"		)
		__lowerCAmelCase      :						Any						      =				format_type
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = None		):
	__lowerCAmelCase      :						int						      =				aliases if aliases is not None else []
	for alias in set(aliases + [format_type]		):
		__lowerCAmelCase      :						str						      =				unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["""python"""])
_register_formatter(ArrowFormatter, """arrow""", aliases=["""pa""", """pyarrow"""])
_register_formatter(NumpyFormatter, """numpy""", aliases=["""np"""])
_register_formatter(PandasFormatter, """pandas""", aliases=["""pd"""])
_register_formatter(CustomFormatter, """custom""")

if config.TORCH_AVAILABLE:
							from .torch_formatter import TorchFormatter

							_register_formatter(TorchFormatter, """torch""", aliases=["""pt""", """pytorch"""])
else:
							lowerCamelCase__        =		ValueError("""PyTorch needs to be installed to be able to return PyTorch tensors.""")
							_register_unavailable_formatter(_torch_error, """torch""", aliases=["""pt""", """pytorch"""])

if config.TF_AVAILABLE:
							from .tf_formatter import TFFormatter

							_register_formatter(TFFormatter, """tensorflow""", aliases=["""tf"""])
else:
							lowerCamelCase__        =		ValueError("""Tensorflow needs to be installed to be able to return Tensorflow tensors.""")
							_register_unavailable_formatter(_tf_error, """tensorflow""", aliases=["""tf"""])

if config.JAX_AVAILABLE:
							from .jax_formatter import JaxFormatter

							_register_formatter(JaxFormatter, """jax""", aliases=[])
else:
							lowerCamelCase__        =		ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
							_register_unavailable_formatter(_jax_error, """jax""", aliases=[])
def    __lowerCAmelCase    (_UpperCamelCase		):
	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type
def    __lowerCAmelCase    (_UpperCamelCase	,  **_UpperCamelCase		):
	__lowerCAmelCase      :						Any						      =				get_format_type_from_alias(_UpperCamelCase		)
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**_UpperCamelCase		)
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    F"Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None		)}, but got '{format_type}'"		)