
import unittest

import numpy as np

from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax.numpy as jnp

						from transformers.models.albert.modeling_flax_albert import (
						    FlaxAlbertForMaskedLM,
						    FlaxAlbertForMultipleChoice,
						    FlaxAlbertForPreTraining,
						    FlaxAlbertForQuestionAnswering,
						    FlaxAlbertForSequenceClassification,
						    FlaxAlbertForTokenClassification,
						    FlaxAlbertModel,
						)
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	def __init__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]       ,							SCREAMING_SNAKE_CASE_  :			Dict=1_3       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=7       ,							SCREAMING_SNAKE_CASE_  :			Any=True       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=True       ,							SCREAMING_SNAKE_CASE_  :			Any=True       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=True       ,							SCREAMING_SNAKE_CASE_  :			List[Any]=9_9       ,							SCREAMING_SNAKE_CASE_  :			Tuple=3_2       ,							SCREAMING_SNAKE_CASE_  :			int=5       ,							SCREAMING_SNAKE_CASE_  :			Dict=4       ,							SCREAMING_SNAKE_CASE_  :			Union[str, Any]=3_7       ,							SCREAMING_SNAKE_CASE_  :			Tuple="gelu"       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=0.1       ,							SCREAMING_SNAKE_CASE_  :			int=0.1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			List[str]=1_6       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=2       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=0.02       ,							SCREAMING_SNAKE_CASE_  :			Tuple=4       ,							)       ->     Dict:
					lowercase_       =					parent
					lowercase_       =					batch_size
					lowercase_       =					seq_length
					lowercase_       =					is_training
					lowercase_       =					use_attention_mask
					lowercase_       =					use_token_type_ids
					lowercase_       =					use_labels
					lowercase_       =					vocab_size
					lowercase_       =					hidden_size
					lowercase_       =					num_hidden_layers
					lowercase_       =					num_attention_heads
					lowercase_       =					intermediate_size
					lowercase_       =					hidden_act
					lowercase_       =					hidden_dropout_prob
					lowercase_       =					attention_probs_dropout_prob
					lowercase_       =					max_position_embeddings
					lowercase_       =					type_vocab_size
					lowercase_       =					type_sequence_label_size
					lowercase_       =					initializer_range
					lowercase_       =					num_choices
	def 						_lowercase  (       self  :			Optional[int]       )       ->     Union[str, Any]:
					lowercase_       =					ids_tensor([self.batch_size, self.seq_length]       ,							self.vocab_size       )

					lowercase_       =					None
					if self.use_attention_mask:
									lowercase_       =					random_attention_mask([self.batch_size, self.seq_length]       )

					lowercase_       =					None
					if self.use_token_type_ids:
									lowercase_       =					ids_tensor([self.batch_size, self.seq_length]       ,							self.type_vocab_size       )

					lowercase_       =					AlbertConfig(
					    vocab_size=self.vocab_size       ,							hidden_size=self.hidden_size       ,							num_hidden_layers=self.num_hidden_layers       ,							num_attention_heads=self.num_attention_heads       ,							intermediate_size=self.intermediate_size       ,							hidden_act=self.hidden_act       ,							hidden_dropout_prob=self.hidden_dropout_prob       ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,							max_position_embeddings=self.max_position_embeddings       ,							type_vocab_size=self.type_vocab_size       ,							is_decoder=SCREAMING_SNAKE_CASE_       ,							initializer_range=self.initializer_range       ,							)

					return config, input_ids, token_type_ids, attention_mask




	def 						_lowercase  (       self  :			Optional[Any]       )       ->     List[str]:
					lowercase_       =					self.prepare_config_and_inputs()
					lowercase_  ,	lowercase_  ,	lowercase_  ,	lowercase_       =					config_and_inputs
					lowercase_       =					{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
					return config, inputs_dict

@require_flax
class  lowercase__(				UpperCAmelCase ,						unittest.TestCase							):



	"""simple docstring"""




	a			:Optional[Any]         =	(
	    (
	        FlaxAlbertModel,
	        FlaxAlbertForPreTraining,
	        FlaxAlbertForMaskedLM,
	        FlaxAlbertForMultipleChoice,
	        FlaxAlbertForQuestionAnswering,
	        FlaxAlbertForSequenceClassification,
	        FlaxAlbertForTokenClassification,
	        FlaxAlbertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)
	def 						_lowercase  (       self  :			Tuple       )       ->     str:
					lowercase_       =					FlaxAlbertModelTester(self       )




	@slow
	def 						_lowercase  (       self  :			List[Any]       )       ->     int:
					for model_class_name in self.all_model_classes:
									lowercase_       =					model_class_name.from_pretrained('''albert-base-v2'''       )
									lowercase_       =					model(np.ones((1, 1)       )       )
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_       )

@require_flax
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	@slow
	def 						_lowercase  (       self  :			Any       )       ->     Dict:
					lowercase_       =					FlaxAlbertModel.from_pretrained('''albert-base-v2'''       )
					lowercase_       =					np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]       )
					lowercase_       =					np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
					lowercase_       =					model(SCREAMING_SNAKE_CASE_       ,							attention_mask=SCREAMING_SNAKE_CASE_       )[0]
					lowercase_       =					(1, 1_1, 7_6_8)
					self.assertEqual(output.shape       ,							SCREAMING_SNAKE_CASE_       )
					lowercase_       =					np.array(
					    [[[-0.65_13, 1.50_35, -0.27_66], [-0.65_15, 1.50_46, -0.27_80], [-0.65_12, 1.50_49, -0.27_84]]]       )

					self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]       ,							SCREAMING_SNAKE_CASE_       ,							atol=1e-4       )       )







'''simple docstring'''






import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
 from transformers import WhisperFeatureExtractor

if is_torch_available():
 import torch

lowerCAmelCase__											=	random.Random()


def 		_A     (    A__     ,				A__=1.0     ,				A__=None     ,				A__=None      ):






 """simple docstring"""



 if rng is None:
  __lowercase							  = global_rng

 __lowercase							  = []
 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 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  def __init__(      self   :     List[str]					,lowercase__   :     List[str]					,lowercase__   :     List[Any]=7					,lowercase__   :     Dict=4_0_0					,lowercase__   :     Tuple=2_0_0_0					,lowercase__   :     Optional[int]=1_0					,lowercase__   :     Optional[int]=1_6_0					,lowercase__   :     Dict=8					,lowercase__   :     str=0.0					,lowercase__   :     Union[str, Any]=4_0_0_0					,lowercase__   :     Optional[int]=False					,lowercase__   :     List[str]=True					,):
   __lowercase							  = parent
   __lowercase							  = batch_size
   __lowercase							  = min_seq_length
   __lowercase							  = max_seq_length
   __lowercase							  = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   __lowercase							  = padding_value
   __lowercase							  = sampling_rate
   __lowercase							  = return_attention_mask
   __lowercase							  = do_normalize
   __lowercase							  = feature_size
   __lowercase							  = chunk_length
   __lowercase							  = hop_length






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   return {
       "feature_size": self.feature_size,
       "hop_length": self.hop_length,
       "chunk_length": self.chunk_length,
       "padding_value": self.padding_value,
       "sampling_rate": self.sampling_rate,
       "return_attention_mask": self.return_attention_mask,
       "do_normalize": self.do_normalize,
   }






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,lowercase__   :     List[str]=False					,lowercase__   :     Dict=False			):
   def _flatten(lowercase__   :     Optional[Any]			):
    return list(itertools.chain(*lowercase__			)			)

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






@require_torch
@require_torchaudio
class 			lowercase_	(lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[int]							=			WhisperFeatureExtractor if is_speech_available() else None






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   __lowercase							  = WhisperFeatureExtractionTester(self			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_dict			)

   with tempfile.TemporaryDirectory() as tmpdirname:
    __lowercase							  = feat_extract_first.save_pretrained(lowercase__			)[0]
    check_json_file_has_correct_format(lowercase__			)
    __lowercase							  = self.feature_extraction_class.from_pretrained(lowercase__			)

   __lowercase							  = feat_extract_first.to_dict()
   __lowercase							  = feat_extract_second.to_dict()
   __lowercase							  = feat_extract_first.mel_filters
   __lowercase							  = feat_extract_second.mel_filters
   self.assertTrue(np.allclose(lowercase__					,lowercase__			)			)
   self.assertEqual(lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_dict			)

   with tempfile.TemporaryDirectory() as tmpdirname:
    __lowercase							  = os.path.join(lowercase__					,'''feat_extract.json'''			)
    feat_extract_first.to_json_file(lowercase__			)
    __lowercase							  = self.feature_extraction_class.from_json_file(lowercase__			)

   __lowercase							  = feat_extract_first.to_dict()
   __lowercase							  = feat_extract_second.to_dict()
   __lowercase							  = feat_extract_first.mel_filters
   __lowercase							  = feat_extract_second.mel_filters
   self.assertTrue(np.allclose(lowercase__					,lowercase__			)			)
   self.assertEqual(lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   # Tests that all call wrap to encode_plus and batch_encode_plus
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
   # create three inputs of length 800, 1000, and 1200
   __lowercase							  = [floats_list((1, x)			)[0] for x in range(8_0_0					,1_4_0_0					,2_0_0			)]
   __lowercase							  = [np.asarray(lowercase__			) for speech_input in speech_inputs]

   # Test feature size
   __lowercase							  = feature_extractor(lowercase__					,padding='''max_length'''					,return_tensors='''np'''			).input_features
   self.assertTrue(input_features.ndim == 3			)
   self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames			)
   self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size			)

   # Test not batched input
   __lowercase							  = feature_extractor(speech_inputs[0]					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(np_speech_inputs[0]					,return_tensors='''np'''			).input_features
   self.assertTrue(np.allclose(lowercase__					,lowercase__					,atol=1e-3			)			)

   # Test batched
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   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.
   __lowercase							  = [floats_list((1, x)			)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
   __lowercase							  = np.asarray(lowercase__			)
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   for enc_seq_a, enc_seq_a in zip(lowercase__					,lowercase__			):
    self.assertTrue(np.allclose(lowercase__					,lowercase__					,atol=1e-3			)			)

   # Test truncation required
   __lowercase							  = [floats_list((1, x)			)[0] for x in range(2_0_0					,(feature_extractor.n_samples + 5_0_0)					,2_0_0			)]
   __lowercase							  = [np.asarray(lowercase__			) for speech_input in speech_inputs]

   __lowercase							  = [x[: feature_extractor.n_samples] for x in speech_inputs]
   __lowercase							  = [np.asarray(lowercase__			) for speech_input in speech_inputs_truncated]

   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   for enc_seq_a, enc_seq_a in zip(lowercase__					,lowercase__			):
    self.assertTrue(np.allclose(lowercase__					,lowercase__					,atol=1e-3			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   import torch

   __lowercase							  = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
   __lowercase							  = np.random.rand(1_0_0					,3_2			).astype(np.floataa			)
   __lowercase							  = np_speech_inputs.tolist()

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     int			):
   __lowercase							  = load_dataset('''hf-internal-testing/librispeech_asr_dummy'''					,'''clean'''					,split='''validation'''			)
   # automatic decoding with librispeech
   __lowercase							  = ds.sort('''id'''			).select(range(lowercase__			)			)[:num_samples]['''audio''']

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   # fmt: off
   __lowercase							  = torch.tensor(
       [
           0.1_1_9_3, -0.0_9_4_6, -0.1_0_9_8, -0.0_1_9_6, 0.0_2_2_5, -0.0_6_9_0, -0.1_7_3_6, 0.0_9_5_1,
           0.0_9_7_1, -0.0_8_1_7, -0.0_7_0_2, 0.0_1_6_2, 0.0_2_6_0, 0.0_0_1_7, -0.0_1_9_2, -0.1_6_7_8,
           0.0_7_0_9, -0.1_8_6_7, -0.0_6_5_5, -0.0_2_7_4, -0.0_2_3_4, -0.1_8_8_4, -0.0_5_1_6, -0.0_5_5_4,
           -0.0_2_7_4, -0.1_4_2_5, -0.1_4_2_3, 0.0_8_3_7, 0.0_3_7_7, -0.0_8_5_4
       ]			)
   # fmt: on

   __lowercase							  = self._load_datasamples(1			)
   __lowercase							  = WhisperFeatureExtractor()
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''pt'''			).input_features
   self.assertEqual(input_features.shape					,(1, 8_0, 3_0_0_0)			)
   self.assertTrue(torch.allclose(input_features[0, 0, :3_0]					,lowercase__					,atol=1e-4			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
   __lowercase							  = self._load_datasamples(1			)[0]
   __lowercase							  = ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5_5_3_5  # Rescale to [0, 65535] to show issue
   __lowercase							  = feat_extract.zero_mean_unit_var_norm([audio]					,attention_mask=lowercase__			)[0]

   self.assertTrue(np.all(np.mean(lowercase__			) < 1e-3			)			)
   self.assertTrue(np.all(np.abs(np.var(lowercase__			) - 1			) < 1e-3			)			)

