"""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__  :  int						     =	random.Random()



def   a_						(					lowerCamelCase					,			lowerCamelCase=1.0					,			lowerCamelCase=None					,			lowerCamelCase=None       ):
				if rng is None:
								UpperCAmelCase__             =							global_rng

				UpperCAmelCase__             =							[]
				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 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					def __init__(				self   :	Optional[Any]    ,lowerCamelCase__   :	List[Any]    ,lowerCamelCase__   :	Optional[Any]=7    ,lowerCamelCase__   :	Dict=400    ,lowerCamelCase__   :	str=2_000    ,lowerCamelCase__   :	int=24    ,lowerCamelCase__   :	List[Any]=24    ,lowerCamelCase__   :	List[Any]=0.0    ,lowerCamelCase__   :	Dict=16_000    ,lowerCamelCase__   :	Union[str, Any]=True    ,lowerCamelCase__   :	Dict=True    ,):
									UpperCAmelCase__             =							parent
									UpperCAmelCase__             =							batch_size
									UpperCAmelCase__             =							min_seq_length
									UpperCAmelCase__             =							max_seq_length
									UpperCAmelCase__             =							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
									UpperCAmelCase__             =							feature_size
									UpperCAmelCase__             =							num_mel_bins
									UpperCAmelCase__             =							padding_value
									UpperCAmelCase__             =							sampling_rate
									UpperCAmelCase__             =							return_attention_mask
									UpperCAmelCase__             =							do_normalize







					def 		__lowerCAmelCase						(				self   :	List[str]			):
									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   :	List[Any]    ,lowerCamelCase__   :	Union[str, Any]=False    ,lowerCamelCase__   :	Optional[int]=False			):
									def _flatten(lowerCamelCase__   :	List[str]			):
													return list(itertools.chain(*lowerCamelCase__			)			)

									if equal_length:
													UpperCAmelCase__             =							[floats_list((self.max_seq_length, self.feature_size)			) for _ in range(self.batch_size			)]
									else:
													# make sure that inputs increase in size
													UpperCAmelCase__             =							[
													    floats_list((x, self.feature_size)			)
													    for x in range(self.min_seq_length    ,self.max_seq_length    ,self.seq_length_diff			)
													]
									if numpify:
													UpperCAmelCase__             =							[np.asarray(lowerCamelCase__			) for x in speech_inputs]
									return speech_inputs






@require_torch
@require_torchaudio
class 				snake_case       ( __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = SpeechaTextFeatureExtractor if is_speech_available() else None







					def 		__lowerCAmelCase						(				self   :	List[Any]			):
									UpperCAmelCase__             =							SpeechaTextFeatureExtractionTester(self			)







					def 		__lowerCAmelCase						(				self   :	Tuple    ,lowerCamelCase__   :	Dict			):
									self.assertTrue(np.all(np.mean(lowerCamelCase__    ,axis=0			) < 1e-3			)			)
									self.assertTrue(np.all(np.abs(np.var(lowerCamelCase__    ,axis=0			) - 1			) < 1e-3			)			)







					def 		__lowerCAmelCase						(				self   :	int			):
									# Tests that all call wrap to encode_plus and batch_encode_plus
									UpperCAmelCase__             =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
									# create three inputs of length 800, 1000, and 1200
									UpperCAmelCase__             =							[floats_list((1, x)			)[0] for x in range(800    ,1_400    ,200			)]
									UpperCAmelCase__             =							[np.asarray(lowerCamelCase__			) for speech_input in speech_inputs]

									# Test feature size
									UpperCAmelCase__             =							feature_extractor(lowerCamelCase__    ,padding=lowerCamelCase__    ,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
									UpperCAmelCase__             =							feature_extractor(speech_inputs[0]    ,return_tensors='np'			).input_features
									UpperCAmelCase__             =							feature_extractor(np_speech_inputs[0]    ,return_tensors='np'			).input_features
									self.assertTrue(np.allclose(lowerCamelCase__    ,lowerCamelCase__    ,atol=1e-3			)			)

									# Test batched
									UpperCAmelCase__             =							feature_extractor(lowerCamelCase__    ,return_tensors='np'			).input_features
									UpperCAmelCase__             =							feature_extractor(lowerCamelCase__    ,return_tensors='np'			).input_features
									for enc_seq_a, enc_seq_a in zip(lowerCamelCase__    ,lowerCamelCase__			):
													self.assertTrue(np.allclose(lowerCamelCase__    ,lowerCamelCase__    ,atol=1e-3			)			)

									# Test 2-D numpy arrays are batched.
									UpperCAmelCase__             =							[floats_list((1, x)			)[0] for x in (800, 800, 800)]
									UpperCAmelCase__             =							np.asarray(lowerCamelCase__			)
									UpperCAmelCase__             =							feature_extractor(lowerCamelCase__    ,return_tensors='np'			).input_features
									UpperCAmelCase__             =							feature_extractor(lowerCamelCase__    ,return_tensors='np'			).input_features
									for enc_seq_a, enc_seq_a in zip(lowerCamelCase__    ,lowerCamelCase__			):
													self.assertTrue(np.allclose(lowerCamelCase__    ,lowerCamelCase__    ,atol=1e-3			)			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
									UpperCAmelCase__             =							[floats_list((1, x)			)[0] for x in range(800    ,1_400    ,200			)]

									UpperCAmelCase__             =							['longest', 'max_length', 'do_not_pad']
									UpperCAmelCase__             =							[None, 16, None]
									for max_length, padding in zip(lowerCamelCase__    ,lowerCamelCase__			):
													UpperCAmelCase__             =							feature_extractor(
													    lowerCamelCase__    ,padding=lowerCamelCase__    ,max_length=lowerCamelCase__    ,return_attention_mask=lowerCamelCase__			)
													UpperCAmelCase__             =							inputs.input_features
													UpperCAmelCase__             =							inputs.attention_mask
													UpperCAmelCase__             =							[np.sum(lowerCamelCase__			) 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   :	str			):
									UpperCAmelCase__             =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
									UpperCAmelCase__             =							[floats_list((1, x)			)[0] for x in range(800    ,1_400    ,200			)]

									UpperCAmelCase__             =							['longest', 'max_length', 'do_not_pad']
									UpperCAmelCase__             =							[None, 16, None]
									for max_length, padding in zip(lowerCamelCase__    ,lowerCamelCase__			):
													UpperCAmelCase__             =							feature_extractor(
													    lowerCamelCase__    ,max_length=lowerCamelCase__    ,padding=lowerCamelCase__    ,return_tensors='np'    ,return_attention_mask=lowerCamelCase__			)
													UpperCAmelCase__             =							inputs.input_features
													UpperCAmelCase__             =							inputs.attention_mask
													UpperCAmelCase__             =							[np.sum(lowerCamelCase__			) 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   :	List[Any]			):
									UpperCAmelCase__             =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
									UpperCAmelCase__             =							[floats_list((1, x)			)[0] for x in range(800    ,1_400    ,200			)]
									UpperCAmelCase__             =							feature_extractor(
									    lowerCamelCase__    ,padding='max_length'    ,max_length=4    ,truncation=lowerCamelCase__    ,return_tensors='np'    ,return_attention_mask=lowerCamelCase__    ,)
									UpperCAmelCase__             =							inputs.input_features
									UpperCAmelCase__             =							inputs.attention_mask
									UpperCAmelCase__             =							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   :	int			):
									UpperCAmelCase__             =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
									UpperCAmelCase__             =							[floats_list((1, x)			)[0] for x in range(800    ,1_400    ,200			)]
									UpperCAmelCase__             =							feature_extractor(
									    lowerCamelCase__    ,padding='longest'    ,max_length=4    ,truncation=lowerCamelCase__    ,return_tensors='np'    ,return_attention_mask=lowerCamelCase__    ,)
									UpperCAmelCase__             =							inputs.input_features
									UpperCAmelCase__             =							inputs.attention_mask
									UpperCAmelCase__             =							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)			)

									UpperCAmelCase__             =							[floats_list((1, x)			)[0] for x in range(800    ,1_400    ,200			)]
									UpperCAmelCase__             =							feature_extractor(
									    lowerCamelCase__    ,padding='longest'    ,max_length=16    ,truncation=lowerCamelCase__    ,return_tensors='np'    ,return_attention_mask=lowerCamelCase__    ,)
									UpperCAmelCase__             =							inputs.input_features
									UpperCAmelCase__             =							inputs.attention_mask
									UpperCAmelCase__             =							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   :	Optional[Any]			):
									import torch

									UpperCAmelCase__             =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
									UpperCAmelCase__             =							np.random.rand(100    ,32			).astype(np.floataa			)
									UpperCAmelCase__             =							np_speech_inputs.tolist()

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







					def 		__lowerCAmelCase						(				self   :	List[str]    ,lowerCamelCase__   :	Tuple			):
									from datasets import load_dataset

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

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







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									# fmt: off
									UpperCAmelCase__             =							np.array([
									    -1.5_7_4_5, -1.7_7_1_3, -1.7_0_2_0, -1.6_0_6_9, -1.2_2_5_0, -1.1_1_0_5, -0.9_0_7_2, -0.8_2_4_1,
									    -1.2_3_1_0, -0.8_0_9_8, -0.3_3_2_0, -0.4_1_0_1, -0.7_9_8_5, -0.4_9_9_6, -0.8_2_1_3, -0.9_1_2_8,
									    -1.0_4_2_0, -1.1_2_8_6, -1.0_4_4_0, -0.7_9_9_9, -0.8_4_0_5, -1.2_2_7_5, -1.5_4_4_3, -1.4_6_2_5,
									]			)
									# fmt: on

									UpperCAmelCase__             =							self._load_datasamples(1			)
									UpperCAmelCase__             =							self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
									UpperCAmelCase__             =							feature_extractor(lowerCamelCase__    ,return_tensors='pt'			).input_features
									self.assertEquals(input_features.shape    ,(1, 584, 24)			)
									self.assertTrue(np.allclose(input_features[0, 0, :30]    ,lowerCamelCase__    ,atol=1e-4			)			)




"""simple docstring"""






import os
import sys
import unittest


lowerCAmelCase__  :  Tuple						     =	os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


lowerCAmelCase__  :  Tuple						     =	os.path.join('tests', 'models', 'bert', 'test_modeling_bert.py')
lowerCAmelCase__  :  Optional[Any]						     =	os.path.join('tests', 'models', 'blip', 'test_modeling_blip.py')
class 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					def 		__lowerCAmelCase						(				self   :	List[str]			):
									UpperCAmelCase__             =							get_test_to_tester_mapping(lowerCamelCase__			)
									UpperCAmelCase__             =							get_test_to_tester_mapping(lowerCamelCase__			)

									UpperCAmelCase__             =							{'BertModelTest': 'BertModelTester'}

									UpperCAmelCase__             =							{
									    'BlipModelTest': 'BlipModelTester',
									    'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
									    'BlipTextModelTest': 'BlipTextModelTester',
									    'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
									    'BlipVQAModelTest': 'BlipVQAModelTester',
									    'BlipVisionModelTest': 'BlipVisionModelTester',
									}

									self.assertEqual(get_test_info.to_json(lowerCamelCase__			)    ,lowerCamelCase__			)
									self.assertEqual(get_test_info.to_json(lowerCamelCase__			)    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							get_model_to_test_mapping(lowerCamelCase__			)
									UpperCAmelCase__             =							get_model_to_test_mapping(lowerCamelCase__			)

									UpperCAmelCase__             =							{
									    'BertForMaskedLM': ['BertModelTest'],
									    'BertForMultipleChoice': ['BertModelTest'],
									    'BertForNextSentencePrediction': ['BertModelTest'],
									    'BertForPreTraining': ['BertModelTest'],
									    'BertForQuestionAnswering': ['BertModelTest'],
									    'BertForSequenceClassification': ['BertModelTest'],
									    'BertForTokenClassification': ['BertModelTest'],
									    'BertLMHeadModel': ['BertModelTest'],
									    'BertModel': ['BertModelTest'],
									}

									UpperCAmelCase__             =							{
									    'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
									    'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
									    'BlipForQuestionAnswering': ['BlipVQAModelTest'],
									    'BlipModel': ['BlipModelTest'],
									    'BlipTextModel': ['BlipTextModelTest'],
									    'BlipVisionModel': ['BlipVisionModelTest'],
									}

									self.assertEqual(get_test_info.to_json(lowerCamelCase__			)    ,lowerCamelCase__			)
									self.assertEqual(get_test_info.to_json(lowerCamelCase__			)    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__             =							get_model_to_tester_mapping(lowerCamelCase__			)
									UpperCAmelCase__             =							get_model_to_tester_mapping(lowerCamelCase__			)

									UpperCAmelCase__             =							{
									    'BertForMaskedLM': ['BertModelTester'],
									    'BertForMultipleChoice': ['BertModelTester'],
									    'BertForNextSentencePrediction': ['BertModelTester'],
									    'BertForPreTraining': ['BertModelTester'],
									    'BertForQuestionAnswering': ['BertModelTester'],
									    'BertForSequenceClassification': ['BertModelTester'],
									    'BertForTokenClassification': ['BertModelTester'],
									    'BertLMHeadModel': ['BertModelTester'],
									    'BertModel': ['BertModelTester'],
									}

									UpperCAmelCase__             =							{
									    'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
									    'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
									    'BlipForQuestionAnswering': ['BlipVQAModelTester'],
									    'BlipModel': ['BlipModelTester'],
									    'BlipTextModel': ['BlipTextModelTester'],
									    'BlipVisionModel': ['BlipVisionModelTester'],
									}

									self.assertEqual(get_test_info.to_json(lowerCamelCase__			)    ,lowerCamelCase__			)
									self.assertEqual(get_test_info.to_json(lowerCamelCase__			)    ,lowerCamelCase__			)



