
"""simple docstring"""

import inspect
import unittest

import numpy as np

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

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
   import jax

   from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class      _UpperCAmelCase							(		unittest.TestCase							):




      def __init__(							self   ,       lowercase_   ,       lowercase_=1_3   ,       lowercase_=3_0   ,       lowercase_=2   ,       lowercase_=3   ,       lowercase_=True   ,       lowercase_=True   ,       lowercase_=3_2   ,       lowercase_=5   ,       lowercase_=4   ,       lowercase_=3_7   ,       lowercase_="gelu"   ,       lowercase_=0.1   ,       lowercase_=0.1   ,       lowercase_=1_0   ,       lowercase_=0.0_2   ,       )						->					Optional[Any]:
          UpperCAmelCase									=					parent
          UpperCAmelCase									=					batch_size
          UpperCAmelCase									=					image_size
          UpperCAmelCase									=					patch_size
          UpperCAmelCase									=					num_channels
          UpperCAmelCase									=					is_training
          UpperCAmelCase									=					use_labels
          UpperCAmelCase									=					hidden_size
          UpperCAmelCase									=					num_hidden_layers
          UpperCAmelCase									=					num_attention_heads
          UpperCAmelCase									=					intermediate_size
          UpperCAmelCase									=					hidden_act
          UpperCAmelCase									=					hidden_dropout_prob
          UpperCAmelCase									=					attention_probs_dropout_prob
          UpperCAmelCase									=					type_sequence_label_size
          UpperCAmelCase									=					initializer_range

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




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

          UpperCAmelCase									=					ViTConfig(
              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   ,       is_decoder=lowercase_   ,       initializer_range=self.initializer_range   ,       )

          return config, pixel_values




      def     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					Union[str, Any]:
          UpperCAmelCase									=					FlaxViTModel(config=lowercase_							)
          UpperCAmelCase									=					model(lowercase_							)
          # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
          UpperCAmelCase									=					(self.image_size, self.image_size)
          UpperCAmelCase									=					(self.patch_size, self.patch_size)
          UpperCAmelCase									=					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
          self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, num_patches + 1, self.hidden_size)							)




      def     a_      (							self   ,       lowercase_   ,       lowercase_							)						->					Optional[Any]:
          UpperCAmelCase									=					self.type_sequence_label_size
          UpperCAmelCase									=					FlaxViTForImageClassification(config=lowercase_							)
          UpperCAmelCase									=					model(lowercase_							)
          self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.type_sequence_label_size)							)

          # test greyscale images
          UpperCAmelCase									=					1
          UpperCAmelCase									=					FlaxViTForImageClassification(lowercase_							)

          UpperCAmelCase									=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
          UpperCAmelCase									=					model(lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					self.prepare_config_and_inputs()
          (
              (
              UpperCAmelCase
          )			,							(
              UpperCAmelCase
          )			,							
          )									=					config_and_inputs
          UpperCAmelCase									=					{'pixel_values': pixel_values}
          return config, inputs_dict

@require_flax
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()




      def     a_      (							self							)						->					None:
          UpperCAmelCase									=					FlaxViTModelTester(self							)
          UpperCAmelCase									=					ConfigTester(self   ,       config_class=lowercase_   ,       has_text_modality=lowercase_   ,       hidden_size=3_7							)




      def     a_      (							self							)						->					Any:
          self.config_tester.run_common_tests()




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_							)




      def     a_      (							self							)						->					Dict:
          UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase_							)




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs_for_common()

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

              UpperCAmelCase									=					['pixel_values']
              self.assertListEqual(arg_names[:1]   ,       lowercase_							)




      def     a_      (							self							)						->					List[str]:
          UpperCAmelCase			,							UpperCAmelCase									=					self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__							):
                  UpperCAmelCase									=					self._prepare_for_class(lowercase_   ,       lowercase_							)
                  UpperCAmelCase									=					model_class(lowercase_							)

                  @jax.jit
                  def model_jitted(lowercase_   ,       **lowercase_							):
                      return model(pixel_values=lowercase_   ,       **lowercase_							)

                  with self.subTest('JIT Enabled'							):
                      UpperCAmelCase									=					model_jitted(**lowercase_							).to_tuple()

                  with self.subTest('JIT Disabled'							):
                      with jax.disable_jit():
                          UpperCAmelCase									=					model_jitted(**lowercase_							).to_tuple()

                  self.assertEqual(len(lowercase_							)   ,       len(lowercase_							)							)
                  for jitted_output, output in zip(lowercase_   ,       lowercase_							):
                      self.assertEqual(jitted_output.shape   ,       output.shape							)




      @slow
      def     a_      (							self							)						->					Dict:
          for model_class_name in self.all_model_classes:
              UpperCAmelCase									=					model_class_name.from_pretrained('google/vit-base-patch16-224'							)
              UpperCAmelCase									=					model(np.ones((1, 3, 2_2_4, 2_2_4)							)							)
              self.assertIsNotNone(lowercase_							)





"""simple docstring"""

import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_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_torch_available():
   import torch

if is_datasets_available():
   from datasets import load_dataset

SCREAMING_SNAKE_CASE_    					=						random.Random()




def     lowercase__						(					lowerCAmelCase				:					Any ,				lowerCAmelCase				:					Tuple=1.0 ,				lowerCAmelCase				:					Tuple=None ,				lowerCAmelCase				:					List[str]=None ) ->      Dict:






    """simple docstring"""




    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
class      _UpperCAmelCase							(		unittest.TestCase							):




      def __init__(							self   ,       lowercase_   ,       lowercase_=7   ,       lowercase_=4_0_0   ,       lowercase_=2_0_0_0   ,       lowercase_=2_0_4_8   ,       lowercase_=1_2_8   ,       lowercase_=1   ,       lowercase_=5_1_2   ,       lowercase_=3_0   ,       lowercase_=4_4_1_0_0   ,       )						->					str:
          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									=					spectrogram_length
          UpperCAmelCase									=					feature_size
          UpperCAmelCase									=					num_audio_channels
          UpperCAmelCase									=					hop_length
          UpperCAmelCase									=					chunk_length
          UpperCAmelCase									=					sampling_rate




      def     a_      (							self							)						->					str:
          return {
              "spectrogram_length": self.spectrogram_length,
              "feature_size": self.feature_size,
              "num_audio_channels": self.num_audio_channels,
              "hop_length": self.hop_length,
              "chunk_length": self.chunk_length,
              "sampling_rate": self.sampling_rate,
          }




      def     a_      (							self   ,       lowercase_=False   ,       lowercase_=False							)						->					Optional[Any]:
          def _flatten(lowercase_							):
              return list(itertools.chain(*lowercase_							)							)

          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(lowercase_							) for x in speech_inputs]
          return speech_inputs

@require_torch
@require_torchaudio
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	TvltFeatureExtractor




      def     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					TvltFeatureExtractionTester(self							)




      def     a_      (							self							)						->					Optional[Any]:
          UpperCAmelCase									=					self.feature_extraction_class(**self.feat_extract_dict							)
          self.assertTrue(hasattr(lowercase_   ,       'spectrogram_length'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'feature_size'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'num_audio_channels'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'hop_length'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'chunk_length'							)							)
          self.assertTrue(hasattr(lowercase_   ,       'sampling_rate'							)							)




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					self.feature_extraction_class(**self.feat_extract_dict							)

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

          UpperCAmelCase									=					feat_extract_first.to_dict()
          UpperCAmelCase									=					feat_extract_second.to_dict()
          UpperCAmelCase									=					dict_first.pop('mel_filters'							)
          UpperCAmelCase									=					dict_second.pop('mel_filters'							)
          self.assertTrue(np.allclose(lowercase_   ,       lowercase_							)							)
          self.assertEqual(lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					str:
          UpperCAmelCase									=					self.feature_extraction_class(**self.feat_extract_dict							)

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

          UpperCAmelCase									=					feat_extract_first.to_dict()
          UpperCAmelCase									=					feat_extract_second.to_dict()
          UpperCAmelCase									=					dict_first.pop('mel_filters'							)
          UpperCAmelCase									=					dict_second.pop('mel_filters'							)
          self.assertTrue(np.allclose(lowercase_   ,       lowercase_							)							)
          self.assertEqual(lowercase_   ,       lowercase_							)




      def     a_      (							self							)						->					int:
          # Initialize feature_extractor
          UpperCAmelCase									=					self.feature_extraction_class(**self.feat_extract_dict							)

          # create three inputs of length 800, 1000, and 1200
          UpperCAmelCase									=					[floats_list((1, x)							)[0] for x in range(8_0_0   ,       1_4_0_0   ,       2_0_0							)]
          UpperCAmelCase									=					[np.asarray(lowercase_							) for speech_input in speech_inputs]

          # Test not batched input
          UpperCAmelCase									=					feature_extractor(np_speech_inputs[0]   ,       return_tensors='np'   ,       sampling_rate=4_4_1_0_0							).audio_values

          self.assertTrue(encoded_audios.ndim == 4							)
          self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
          self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
          self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

          # Test batched
          UpperCAmelCase									=					feature_extractor(lowercase_   ,       return_tensors='np'   ,       sampling_rate=4_4_1_0_0							).audio_values

          self.assertTrue(encoded_audios.ndim == 4							)
          self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
          self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
          self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

          # Test audio masking
          UpperCAmelCase									=					feature_extractor(
              lowercase_   ,       return_tensors='np'   ,       sampling_rate=4_4_1_0_0   ,       mask_audio=lowercase_							).audio_values

          self.assertTrue(encoded_audios.ndim == 4							)
          self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
          self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
          self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

          # Test 2-D numpy arrays are batched.
          UpperCAmelCase									=					[floats_list((1, x)							)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
          UpperCAmelCase									=					np.asarray(lowercase_							)
          UpperCAmelCase									=					feature_extractor(lowercase_   ,       return_tensors='np'   ,       sampling_rate=4_4_1_0_0							).audio_values
          self.assertTrue(encoded_audios.ndim == 4							)
          self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
          self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
          self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)




      def     a_      (							self   ,       lowercase_							)						->					Optional[Any]:
          UpperCAmelCase									=					load_dataset('hf-internal-testing/librispeech_asr_dummy'   ,       'clean'   ,       split='validation'							)
          # automatic decoding with librispeech
          UpperCAmelCase									=					ds.sort('id'							).select(range(lowercase_							)							)[:num_samples]['audio']

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




      def     a_      (							self							)						->					Tuple:
          UpperCAmelCase									=					self._load_datasamples(1							)
          UpperCAmelCase									=					TvltFeatureExtractor()
          UpperCAmelCase									=					feature_extractor(lowercase_   ,       return_tensors='pt'							).audio_values

          self.assertEquals(audio_values.shape   ,       (1, 1, 1_9_2, 1_2_8)							)

          UpperCAmelCase									=					torch.tensor([[-0.3_0_3_2, -0.2_7_0_8], [-0.4_4_3_4, -0.4_0_0_7]]							)
          self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]   ,       lowercase_   ,       atol=1E-4							)							)



