



"""simple docstring"""

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


lowercase__			   =  random.Random()





def       __magic_name__    (   _lowerCamelCase				: Dict  ,       _lowerCamelCase				: List[str]=1.0  ,       _lowerCamelCase				: List[str]=None  ,       _lowerCamelCase				: List[Any]=None					):
       if rng is None:
              __a		:		Dict									=       global_rng

       __a		:		Any									=       []
       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						,     _lowercase						,     _lowercase=7						,     _lowercase=400						,     _lowercase=2000						,     _lowercase=1						,     _lowercase=0.0						,     _lowercase=16000						,     _lowercase=True						,     _lowercase=True						,     ):





             '''simple docstring'''

             __a		:		Optional[Any]									=       parent
             __a		:		str									=       batch_size
             __a		:		Optional[Any]									=       min_seq_length
             __a		:		str									=       max_seq_length
             __a		:		int									=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
             __a		:		List[Any]									=       feature_size
             __a		:		Optional[int]									=       padding_value
             __a		:		Dict									=       sampling_rate
             __a		:		Any									=       return_attention_mask
             __a		:		Optional[int]									=       do_normalize



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             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     lowerCAmelCase__(self						,     _lowercase=False						,     _lowercase=False							):





             '''simple docstring'''

             def _flatten(_lowercase							):
                    return list(itertools.chain(*_lowercase							)							)

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

             return speech_inputs







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



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		Optional[int]									=       WavaVecaFeatureExtractionTester(self							)



      def     lowerCAmelCase__(self						,     _lowercase							):





             '''simple docstring'''

             self.assertTrue(np.all(np.mean(_lowercase						,     axis=0							) < 1e-3							)							)
             self.assertTrue(np.all(np.abs(np.var(_lowercase						,     axis=0							) - 1							) < 1e-3							)							)



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		Dict									=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
             # create three inputs of length 800, 1000, and 1200
             __a		:		str									=       [floats_list((1, x)							)[0] for x in range(800						,     1400						,     200							)]
             __a		:		str									=       [np.asarray(_lowercase							) for speech_input in speech_inputs]

             # Test not batched input
             __a		:		int									=       feat_extract(speech_inputs[0]						,     return_tensors="""np"""							).input_values
             __a		:		List[Any]									=       feat_extract(np_speech_inputs[0]						,     return_tensors="""np"""							).input_values
             self.assertTrue(np.allclose(_lowercase						,     _lowercase						,     atol=1e-3							)							)

             # Test batched
             __a		:		Optional[Any]									=       feat_extract(_lowercase						,     return_tensors="""np"""							).input_values
             __a		:		Optional[Any]									=       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							)							)

             # Test 2-D numpy arrays are batched.
             __a		:		Optional[int]									=       [floats_list((1, x)							)[0] for x in (800, 800, 800)]
             __a		:		Dict									=       np.asarray(_lowercase							)
             __a		:		List[str]									=       feat_extract(_lowercase						,     return_tensors="""np"""							).input_values
             __a		:		Union[str, Any]									=       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							)							)



      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		str									=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
             __a		:		Dict									=       [floats_list((1, x)							)[0] for x in range(800						,     1400						,     200							)]

             __a		:		Optional[int]									=       ["""longest""", """max_length""", """do_not_pad"""]
             __a		:		int									=       [None, 1600, None]
             for max_length, padding in zip(_lowercase						,     _lowercase							):
                    __a		:		Optional[int]									=       feat_extract(_lowercase						,     padding=_lowercase						,     max_length=_lowercase						,     return_tensors="""np"""							)
                    __a		:		List[Any]									=       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     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		Dict									=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
             __a		:		Optional[int]									=       range(800						,     1400						,     200							)
             __a		:		Optional[Any]									=       [floats_list((1, x)							)[0] for x in lengths]

             __a		:		Any									=       ["""longest""", """max_length""", """do_not_pad"""]
             __a		:		Any									=       [None, 1600, None]

             for max_length, padding in zip(_lowercase						,     _lowercase							):
                    __a		:		Dict									=       feat_extract(_lowercase						,     max_length=_lowercase						,     padding=_lowercase							)
                    __a		:		str									=       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     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		List[Any]									=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
             __a		:		List[Any]									=       [floats_list((1, x)							)[0] for x in range(800						,     1400						,     200							)]
             __a		:		Dict									=       feat_extract(
                 _lowercase						,     truncation=_lowercase						,     max_length=1000						,     padding="""max_length"""						,     return_tensors="""np"""							)
             __a		:		Any									=       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     lowerCAmelCase__(self							):





             '''simple docstring'''

             __a		:		Optional[Any]									=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
             __a		:		Union[str, Any]									=       [floats_list((1, x)							)[0] for x in range(800						,     1400						,     200							)]
             __a		:		Union[str, Any]									=       feat_extract(
                 _lowercase						,     truncation=_lowercase						,     max_length=1000						,     padding="""longest"""						,     return_tensors="""np"""							)
             __a		:		Optional[int]									=       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)							)

             __a		:		List[str]									=       [floats_list((1, x)							)[0] for x in range(800						,     1400						,     200							)]
             __a		:		Union[str, Any]									=       feat_extract(
                 _lowercase						,     truncation=_lowercase						,     max_length=2000						,     padding="""longest"""						,     return_tensors="""np"""							)
             __a		:		str									=       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     lowerCAmelCase__(self							):





             '''simple docstring'''

             import torch

             __a		:		Dict									=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
             __a		:		List[str]									=       np.random.rand(100							).astype(np.floataa							)
             __a		:		Tuple									=       np_speech_inputs.tolist()

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





      @slow
      @require_torch
      def     lowerCAmelCase__(self							):





             '''simple docstring'''

             for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
                    __a		:		Dict									=       WavaVecaConfig.from_pretrained(_lowercase							)
                    __a		:		Any									=       WavaVecaFeatureExtractor.from_pretrained(_lowercase							)

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






"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


lowercase__			   =  {
    "configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__			   =  [
	    "ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "ErnieForCausalLM",
	    "ErnieForMaskedLM",
	    "ErnieForMultipleChoice",
	    "ErnieForNextSentencePrediction",
	    "ErnieForPreTraining",
	    "ErnieForQuestionAnswering",
	    "ErnieForSequenceClassification",
	    "ErnieForTokenClassification",
	    "ErnieModel",
	    "ErniePreTrainedModel",
	]

if TYPE_CHECKING:
	from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ernie import (
		    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ErnieForCausalLM,
		    ErnieForMaskedLM,
		    ErnieForMultipleChoice,
		    ErnieForNextSentencePrediction,
		    ErnieForPreTraining,
		    ErnieForQuestionAnswering,
		    ErnieForSequenceClassification,
		    ErnieForTokenClassification,
		    ErnieModel,
		    ErniePreTrainedModel,
		)

else:
	import sys

	lowercase__			   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

