
"""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						=			random.Random()

if is_torch_available():
				import torch
def 		UpperCAmelCase (		a_,						a_=1.0,						a_=None,						a_=None      ):





		'''simple docstring'''


		if rng is None:
				lowerCamelCase					:      Any          =   global_rng

		lowerCamelCase					:      Union[str, 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		,			UpperCAmelCase_		,			UpperCAmelCase_=7		,			UpperCAmelCase_=400		,			UpperCAmelCase_=2000		,			UpperCAmelCase_=1		,			UpperCAmelCase_=0.0		,			UpperCAmelCase_=16000		,			UpperCAmelCase_=True		,			UpperCAmelCase_=True		,			)		->							List[Any]:
			lowerCamelCase					:      List[str]          =   parent
			lowerCamelCase					:      int          =   batch_size
			lowerCamelCase					:      List[str]          =   min_seq_length
			lowerCamelCase					:      List[Any]          =   max_seq_length
			lowerCamelCase					:      List[str]          =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
			lowerCamelCase					:      str          =   feature_size
			lowerCamelCase					:      List[Any]          =   padding_value
			lowerCamelCase					:      Tuple          =   sampling_rate
			lowerCamelCase					:      Dict          =   return_attention_mask
			lowerCamelCase					:      Optional[Any]          =   do_normalize




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			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       _UpperCamelCase   (			self		,			UpperCAmelCase_=False		,			UpperCAmelCase_=False )		->							str:
			def _flatten(UpperCAmelCase_ ):
					return list(itertools.chain(*UpperCAmelCase_ ) )

			if equal_length:
					lowerCamelCase					:      Optional[int]          =   floats_list((self.batch_size, self.max_seq_length) )
			else:
					# make sure that inputs increase in size
					lowerCamelCase					:      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:
					lowerCamelCase					:      List[str]          =   [np.asarray(UpperCAmelCase_ ) for x in speech_inputs]

			return speech_inputs






@require_torch
@require_torchaudio
class 	_lowercase       (    __UpperCAmelCase    ,	unittest.TestCase		):
	lowercase_				=     ASTFeatureExtractor




	def       _UpperCamelCase   (			self )		->							List[str]:
			lowerCamelCase					:      Optional[Any]          =   ASTFeatureExtractionTester(self )




	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			# Tests that all call wrap to encode_plus and batch_encode_plus
			lowerCamelCase					:      Union[str, Any]          =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
			# create three inputs of length 800, 1000, and 1200
			lowerCamelCase					:      Union[str, Any]          =   [floats_list((1, x) )[0] for x in range(800		,			1400		,			200 )]
			lowerCamelCase					:      List[Any]          =   [np.asarray(UpperCAmelCase_ ) for speech_input in speech_inputs]

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

			# Test batched
			lowerCamelCase					:      Dict          =   feat_extract(UpperCAmelCase_		,			padding=UpperCAmelCase_		,			return_tensors='np' ).input_values
			lowerCamelCase					:      int          =   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.
			lowerCamelCase					:      List[Any]          =   [floats_list((1, x) )[0] for x in (800, 800, 800)]
			lowerCamelCase					:      Optional[Any]          =   np.asarray(UpperCAmelCase_ )
			lowerCamelCase					:      str          =   feat_extract(UpperCAmelCase_		,			return_tensors='np' ).input_values
			lowerCamelCase					:      Any          =   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       _UpperCamelCase   (			self )		->							Union[str, Any]:
			import torch

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

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




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							List[str]:
			from datasets import load_dataset

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

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




	@require_torch
	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			# fmt: off
			lowerCamelCase					:      str          =   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

			lowerCamelCase					:      int          =   self._load_datasamples(1 )
			lowerCamelCase					:      int          =   ASTFeatureExtractor()
			lowerCamelCase					:      List[str]          =   feature_extractor(UpperCAmelCase_		,			return_tensors='pt' ).input_values
			self.assertEquals(input_values.shape		,			(1, 1024, 128) )
			self.assertTrue(torch.allclose(input_values[0, 0, :30]		,			UpperCAmelCase_		,			atol=1E-4 ) )








"""simple docstring"""

# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


_A						=			re.compile(r'^(?P<major>\d+)' r'\.(?P<minor>\d+)' r'\.(?P<patch>\d+)$')






@total_ordering
@dataclass
class 	_lowercase       :
	lowercase_				=     42
	lowercase_				=     None
	lowercase_				=     None
	lowercase_				=     None
	lowercase_				=     None




	def       _UpperCamelCase   (			self )		->							List[str]:
			lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase					:      List[str]          =   _str_to_version_tuple(self.version_str )




	def __repr__(			self )		->							int:
			return F"""{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}"""




	@property
	def       _UpperCamelCase   (			self )		->							Dict:
			return self.major, self.minor, self.patch




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_ )		->							Optional[int]:
			if isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ):
					return Version(UpperCAmelCase_ )
			elif isinstance(UpperCAmelCase_		,			UpperCAmelCase_ ):
					return other
			raise TypeError(F"""{other} (type {type(UpperCAmelCase_ )}) cannot be compared to version.""" )




	def __eq__(			self		,			UpperCAmelCase_ )		->							Optional[Any]:
			try:
					lowerCamelCase					:      List[str]          =   self._validate_operand(UpperCAmelCase_ )
			except (TypeError, ValueError):
					return False
			else:
					return self.tuple == other.tuple




	def __lt__(			self		,			UpperCAmelCase_ )		->							Optional[int]:
			lowerCamelCase					:      Optional[int]          =   self._validate_operand(UpperCAmelCase_ )
			return self.tuple < other.tuple




	def __hash__(			self )		->							Optional[Any]:
			return hash(_version_tuple_to_str(self.tuple ) )




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_ )		->							Union[str, Any]:
			lowerCamelCase					:      str          =   {f.name for f in dataclasses.fields(cls )}
			return cls(**{k: v for k, v in dic.items() if k in field_names} )




	def       _UpperCamelCase   (			self )		->							str:
			return self.version_str


def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		lowerCamelCase					:      Tuple          =   _VERSION_REG.match(a_      )
		if not res:
				raise ValueError(F"""Invalid version '{version_str}'. Format should be x.y.z with {{x,y,z}} being digits."""      )
		return tuple(int(a_      ) for v in [res.group('major'      ), res.group('minor'      ), res.group('patch'      )]      )





def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		return ".".join(str(a_      ) for v in version_tuple      )






