

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


snake_case_        =			random.Random()

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   Any=1.0     ,					SCREAMING_SNAKE_CASE_		:   Tuple=None     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]=None      ):



     '''simple docstring'''





     if rng is None:
          lowercase__   :       List[str]				=	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 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    def __init__(				self						,       a						,       a=7						,       a=400						,       a=2000						,       a=1						,       a=0.0						,       a=1_6000						,       a=True						,       a=True						,       ):
         lowercase__   :       Any				=	parent
         lowercase__   :       List[str]				=	batch_size
         lowercase__   :       Union[str, Any]				=	min_seq_length
         lowercase__   :       str				=	max_seq_length
         lowercase__   :       Tuple				=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         lowercase__   :       Any				=	feature_size
         lowercase__   :       Tuple				=	padding_value
         lowercase__   :       List[Any]				=	sampling_rate
         lowercase__   :       Any				=	return_attention_mask
         lowercase__   :       List[Any]				=	do_normalize





    def 				snake_case_    (				self):
         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						,       a=False						,       a=False):
         def _flatten(a):
              return list(itertools.chain(*a))

         if equal_length:
              lowercase__   :       Union[str, Any]				=	floats_list((self.batch_size, self.max_seq_length))
         else:
              # make sure that inputs increase in size
              lowercase__   :       Union[str, Any]				=	[
                  _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__   :       str				=	[np.asarray(a) for x in speech_inputs]

         return speech_inputs



class 				SCREAMING_SNAKE_CASE__ (__snake_case		,	unittest.TestCase					):
    __lowerCamelCase						:	Any														=					WavaVecaFeatureExtractor





    def 				snake_case_    (				self):
         lowercase__   :       Optional[int]				=	WavaVecaFeatureExtractionTester(self)





    def 				snake_case_    (				self						,       a):
         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 				snake_case_    (				self):
         # 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						,       1400						,       200)]
         lowercase__   :       int				=	[np.asarray(a) for speech_input in speech_inputs]

         # Test not batched input
         lowercase__   :       int				=	feat_extract(speech_inputs[0]						,       return_tensors='np').input_values
         lowercase__   :       List[str]				=	feat_extract(np_speech_inputs[0]						,       return_tensors='np').input_values
         self.assertTrue(np.allclose(a						,       a						,       atol=1e-3))

         # Test batched
         lowercase__   :       str				=	feat_extract(a						,       return_tensors='np').input_values
         lowercase__   :       List[Any]				=	feat_extract(a						,       return_tensors='np').input_values
         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.
         lowercase__   :       Tuple				=	[floats_list((1, x))[0] for x in (800, 800, 800)]
         lowercase__   :       List[str]				=	np.asarray(a)
         lowercase__   :       Union[str, Any]				=	feat_extract(a						,       return_tensors='np').input_values
         lowercase__   :       List[str]				=	feat_extract(a						,       return_tensors='np').input_values
         for enc_seq_a, enc_seq_a in zip(a						,       a):
              self.assertTrue(np.allclose(a						,       a						,       atol=1e-3))





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	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						,       1400						,       200)]

         lowercase__   :       Union[str, Any]				=	['longest', 'max_length', 'do_not_pad']
         lowercase__   :       Tuple				=	[None, 1600, None]
         for max_length, padding in zip(a						,       a):
              lowercase__   :       List[Any]				=	feat_extract(a						,       padding=a						,       max_length=a						,       return_tensors='np')
              lowercase__   :       Optional[int]				=	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][:1000])
              self.assertTrue(input_values[0][1000:].sum() < 1e-6)
              self._check_zero_mean_unit_variance(input_values[2][:1200])





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
         lowercase__   :       Tuple				=	range(800						,       1400						,       200)
         lowercase__   :       int				=	[floats_list((1, x))[0] for x in lengths]

         lowercase__   :       Dict				=	['longest', 'max_length', 'do_not_pad']
         lowercase__   :       Optional[int]				=	[None, 1600, None]

         for max_length, padding in zip(a						,       a):
              lowercase__   :       str				=	feat_extract(a						,       max_length=a						,       padding=a)
              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][:1000])
              self._check_zero_mean_unit_variance(input_values[2][:1200])





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
         lowercase__   :       Optional[int]				=	[floats_list((1, x))[0] for x in range(800						,       1400						,       200)]
         lowercase__   :       Optional[int]				=	feat_extract(
             a						,       truncation=a						,       max_length=1000						,       padding='max_length'						,       return_tensors='np')
         lowercase__   :       List[str]				=	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):
         lowercase__   :       Tuple				=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
         lowercase__   :       str				=	[floats_list((1, x))[0] for x in range(800						,       1400						,       200)]
         lowercase__   :       List[Any]				=	feat_extract(
             a						,       truncation=a						,       max_length=1000						,       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, :1000])
         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, 1000))

         lowercase__   :       Optional[Any]				=	[floats_list((1, x))[0] for x in range(800						,       1400						,       200)]
         lowercase__   :       Any				=	feat_extract(
             a						,       truncation=a						,       max_length=2000						,       padding='longest'						,       return_tensors='np')
         lowercase__   :       List[Any]				=	processed.input_values

         self._check_zero_mean_unit_variance(input_values[0, :800])
         self._check_zero_mean_unit_variance(input_values[1, :1000])
         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, 1200))





    @require_torch
    def 				snake_case_    (				self):
         import torch

         lowercase__   :       Dict				=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
         lowercase__   :       Dict				=	np.random.rand(100).astype(np.floataa)
         lowercase__   :       Dict				=	np_speech_inputs.tolist()

         for inputs in [py_speech_inputs, np_speech_inputs]:
              lowercase__   :       Optional[Any]				=	feature_extractor.pad([{'input_values': inputs}]						,       return_tensors='np')
              self.assertTrue(np_processed.input_values.dtype == np.floataa)
              lowercase__   :       Dict				=	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):
         # 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(a)
              lowercase__   :       int				=	WavaVecaFeatureExtractor.from_pretrained(a)

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




from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):





    def __init__(				self						,       a						,       a = None						,       a = None						,       a = True						,       a = None						,       a = False						,       a = None						,       a = True						,       a = "arrow"						,       **a						,       ):
         super().__init__(
             split=a						,       features=a						,       cache_dir=a						,       keep_in_memory=a						,       streaming=a						,       **a						,       )
         lowercase__   :       Optional[int]				=	load_from_cache_file
         lowercase__   :       Optional[int]				=	file_format
         lowercase__   :       int				=	Spark(
             df=a						,       features=a						,       cache_dir=a						,       working_dir=a						,       **a						,       )





    def 				snake_case_    (				self):
         if self.streaming:
              return self.builder.as_streaming_dataset(split=self.split)
         lowercase__   :       Dict				=	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
         self.builder.download_and_prepare(
             download_mode=a						,       file_format=self._file_format						,       )
         return self.builder.as_dataset(split=self.split)

