

'''simple docstring'''






import inspect
import unittest

from transformers import ViTMSNConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import ViTMSNForImageClassification, ViTMSNModel
 from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor






class 			lowercase_	:




  """simple docstring"""




  def __init__(      self   :     str					,lowercase__   :     Tuple					,lowercase__   :     Optional[int]=1_3					,lowercase__   :     List[Any]=3_0					,lowercase__   :     str=2					,lowercase__   :     Optional[int]=3					,lowercase__   :     Dict=True					,lowercase__   :     Optional[Any]=True					,lowercase__   :     Dict=3_2					,lowercase__   :     Union[str, Any]=5					,lowercase__   :     Any=4					,lowercase__   :     Dict=3_7					,lowercase__   :     List[Any]="gelu"					,lowercase__   :     Optional[int]=0.1					,lowercase__   :     str=0.1					,lowercase__   :     int=1_0					,lowercase__   :     Tuple=0.0_2					,lowercase__   :     List[Any]=None					,):
   __lowercase							  = parent
   __lowercase							  = batch_size
   __lowercase							  = image_size
   __lowercase							  = patch_size
   __lowercase							  = num_channels
   __lowercase							  = is_training
   __lowercase							  = use_labels
   __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							  = type_sequence_label_size
   __lowercase							  = initializer_range
   __lowercase							  = scope

   # in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
   __lowercase							  = (image_size // patch_size) ** 2
   __lowercase							  = num_patches + 1






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

   __lowercase							  = None
   if self.use_labels:
    __lowercase							  = ids_tensor([self.batch_size]					,self.type_sequence_label_size			)

   __lowercase							  = self.get_config()

   return config, pixel_values, labels






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   return ViTMSNConfig(
       image_size=self.image_size					,patch_size=self.patch_size					,num_channels=self.num_channels					,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					,initializer_range=self.initializer_range					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     int					,lowercase__   :     Tuple					,lowercase__   :     List[str]			):
   __lowercase							  = ViTMSNModel(config=lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__			)
   self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]					,lowercase__   :     str					,lowercase__   :     Optional[int]					,lowercase__   :     Any			):
   __lowercase							  = self.type_sequence_label_size
   __lowercase							  = ViTMSNForImageClassification(lowercase__			)
   model.to(lowercase__			)
   model.eval()
   __lowercase							  = model(lowercase__					,labels=lowercase__			)
   print('''Pixel and labels shape: {pixel_values.shape}, {labels.shape}'''			)
   print('''Labels: {labels}'''			)
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.type_sequence_label_size)			)

   # test greyscale images
   __lowercase							  = 1
   __lowercase							  = ViTMSNForImageClassification(lowercase__			)
   model.to(lowercase__			)
   model.eval()

   __lowercase							  = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
   __lowercase							  = model(lowercase__			)
   self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.type_sequence_label_size)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]			):
   __lowercase							  = self.prepare_config_and_inputs()
   __lowercase  ,	__lowercase  ,	__lowercase							  = config_and_inputs
   __lowercase							  = {'''pixel_values''': pixel_values}
   return config, inputs_dict






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




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Any							=			(ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
  SCREAMING_SNAKE_CASE    :  str							=			(
      {'feature-extraction': ViTMSNModel, 'image-classification': ViTMSNForImageClassification}
      if is_torch_available()
      else {}
  )

  SCREAMING_SNAKE_CASE    :  Optional[Any]							=			False
  SCREAMING_SNAKE_CASE    :  Tuple							=			False
  SCREAMING_SNAKE_CASE    :  Any							=			False
  SCREAMING_SNAKE_CASE    :  Union[str, Any]							=			False






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = ViTMSNModelTester(self			)
   __lowercase							  = ConfigTester(self					,config_class=lowercase__					,has_text_modality=lowercase__					,hidden_size=3_7			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   self.config_tester.run_common_tests()






  @unittest.skip(reason='''ViTMSN does not use inputs_embeds'''			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   pass






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase							  = model_class(lowercase__			)
    self.assertIsInstance(model.get_input_embeddings()					,(nn.Module)			)
    __lowercase							  = model.get_output_embeddings()
    self.assertTrue(x is None or isinstance(lowercase__					,nn.Linear			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase  ,	__lowercase							  = self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __lowercase							  = model_class(lowercase__			)
    __lowercase							  = inspect.signature(model.forward			)
    # signature.parameters is an OrderedDict => so arg_names order is deterministic
    __lowercase							  = [*signature.parameters.keys()]

    __lowercase							  = ['''pixel_values''']
    self.assertListEqual(arg_names[:1]					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*lowercase__			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase							  = ViTMSNModel.from_pretrained(lowercase__			)
    self.assertIsNotNone(lowercase__			)


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
 return image






@require_torch
@require_vision
class 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  @cached_property
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   return ViTImageProcessor.from_pretrained('''facebook/vit-msn-small'''			) if is_vision_available() else None






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   torch.manual_seed(2			)
   __lowercase							  = ViTMSNForImageClassification.from_pretrained('''facebook/vit-msn-small'''			).to(lowercase__			)

   __lowercase							  = self.default_image_processor
   __lowercase							  = prepare_img()
   __lowercase							  = image_processor(images=lowercase__					,return_tensors='''pt'''			).to(lowercase__			)

   # forward pass
   with torch.no_grad():
    __lowercase							  = model(**lowercase__			)

   # verify the logits
   __lowercase							  = torch.Size((1, 1_0_0_0)			)
   self.assertEqual(outputs.logits.shape					,lowercase__			)

   __lowercase							  = torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]			).to(lowercase__			)

   self.assertTrue(torch.allclose(outputs.logits[0, :3]					,lowercase__					,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			)			)

