'''simple docstring'''




import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
  import tensorflow as tf

if is_tensorflow_text_available():
  from transformers.models.bert import TFBertTokenizer


__SCREAMING_SNAKE_CASE       :     Union[str, Any]     =  ["""bert-base-uncased""", """bert-base-cased"""]
__SCREAMING_SNAKE_CASE       :     List[str]     =  """hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():

  class 	lowerCamelCase_	(tf.keras.Model    ):
        '''simple docstring'''

        def __init__(       self						:				Union[str, Any]						,					A						:				Tuple       ):
               super().__init__()
               _UpperCAmelCase     :  Any							      =    tokenizer
               _UpperCAmelCase     :  int							      =    AutoConfig.from_pretrained(A       )
               _UpperCAmelCase     :  str							      =    TFAutoModel.from_config(A       )





        def      _A	(       self						:				Union[str, Any]						,					A						:				Dict       ):
               _UpperCAmelCase     :  Any							      =    self.tokenizer(A       )
               _UpperCAmelCase     :  Union[str, Any]							      =    self.bert(**A       )
               return out["pooler_output"]

@require_tf
@require_tensorflow_text
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def      _A	(       self						:				List[str]       ):
             super().setUp()

             _UpperCAmelCase     :  Union[str, Any]							      =    [
                 BertTokenizer.from_pretrained(A       ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
             ]  # repeat for when fast_bert_tokenizer=false
             _UpperCAmelCase     :  List[Any]							      =    [TFBertTokenizer.from_pretrained(A       ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
                 TFBertTokenizer.from_pretrained(A						,					use_fast_bert_tokenizer=A       )
                 for checkpoint in TOKENIZER_CHECKPOINTS
             ]
             assert len(self.tokenizers       ) == len(self.tf_tokenizers       )

             _UpperCAmelCase     :  Dict							      =    [
                 "This is a straightforward English test sentence.",
                 "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
                 "Now we're going to add some Chinese: 一 二 三 一二三",
                 "And some much more rare Chinese: 齉 堃 齉堃",
                 "Je vais aussi écrire en français pour tester les accents",
                 "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
             ]
             _UpperCAmelCase     :  str							      =    list(zip(self.test_sentences						,					self.test_sentences[::-1]       )       )




      def      _A	(       self						:				Optional[Any]       ):
             for tokenizer, tf_tokenizer in zip(self.tokenizers						,					self.tf_tokenizers       ):
                    for test_inputs in (self.test_sentences, self.paired_sentences):
                           _UpperCAmelCase     :  Any							      =    tokenizer(A						,					return_tensors="tf"						,					padding="longest"       )
                           _UpperCAmelCase     :  str							      =    tf_tokenizer(A       )

                           for key in python_outputs.keys():
                                  self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape       )       )
                                  self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]						,					tf.intaa       ) == tf_outputs[key]       )       )




      @slow
      def      _A	(       self						:				int       ):
             for tf_tokenizer in self.tf_tokenizers:
                    _UpperCAmelCase     :  str							      =    tf_tokenizer(self.paired_sentences       )
                    _UpperCAmelCase     :  int							      =    tf_tokenizer(
                        text=[sentence[0] for sentence in self.paired_sentences]						,					text_pair=[sentence[1] for sentence in self.paired_sentences]						,					)
                    for key in merged_outputs.keys():
                           self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]						,					tf.intaa       ) == separated_outputs[key]       )       )




      @slow
      def      _A	(       self						:				Tuple       ):
             for tf_tokenizer in self.tf_tokenizers:
                    _UpperCAmelCase     :  List[Any]							      =    tf.function(A       )
                    for test_inputs in (self.test_sentences, self.paired_sentences):
                           _UpperCAmelCase     :  int							      =    tf.constant(A       )
                           _UpperCAmelCase     :  Optional[Any]							      =    compiled_tokenizer(A       )
                           _UpperCAmelCase     :  int							      =    tf_tokenizer(A       )

                           for key in eager_outputs.keys():
                                  self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]       )       )





      @slow
      def      _A	(       self						:				List[Any]       ):
             for tf_tokenizer in self.tf_tokenizers:
                    _UpperCAmelCase     :  Any							      =    ModelToSave(tokenizer=A       )
                    _UpperCAmelCase     :  Optional[int]							      =    tf.convert_to_tensor(self.test_sentences       )
                    _UpperCAmelCase     :  Optional[Any]							      =    model(A       )  # Build model with some sample inputs
                    with TemporaryDirectory() as tempdir:
                           _UpperCAmelCase     :  Union[str, Any]							      =    Path(A       ) / "saved.model"
                           model.save(A       )
                           _UpperCAmelCase     :  Dict							      =    tf.keras.models.load_model(A       )
                    _UpperCAmelCase     :  str							      =    loaded_model(A       )
                    # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
                    self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output       )       )						,					1E-5       )


'''simple docstring'''




import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import VivitImageProcessor

class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def __init__(       self						:				int						,					A						:				Dict						,					A						:				Optional[int]=7						,					A						:				Tuple=3						,					A						:				Optional[Any]=10						,					A						:				int=18						,					A						:				Dict=30						,					A						:				List[str]=400						,					A						:				int=True						,					A						:				Optional[Any]=None						,					A						:				Optional[Any]=True						,					A						:				List[Any]=[0.5, 0.5, 0.5]						,					A						:				List[str]=[0.5, 0.5, 0.5]						,					A						:				Optional[int]=None						,					):
             _UpperCAmelCase     :  Dict							      =    size if size is not None else {"shortest_edge": 18}
             _UpperCAmelCase     :  Optional[Any]							      =    crop_size if crop_size is not None else {"height": 18, "width": 18}

             _UpperCAmelCase     :  Tuple							      =    parent
             _UpperCAmelCase     :  Any							      =    batch_size
             _UpperCAmelCase     :  Optional[int]							      =    num_channels
             _UpperCAmelCase     :  Optional[Any]							      =    num_frames
             _UpperCAmelCase     :  Any							      =    image_size
             _UpperCAmelCase     :  Dict							      =    min_resolution
             _UpperCAmelCase     :  Any							      =    max_resolution
             _UpperCAmelCase     :  Optional[int]							      =    do_resize
             _UpperCAmelCase     :  str							      =    size
             _UpperCAmelCase     :  List[Any]							      =    do_normalize
             _UpperCAmelCase     :  Any							      =    image_mean
             _UpperCAmelCase     :  Tuple							      =    image_std
             _UpperCAmelCase     :  Any							      =    crop_size





      def      _A	(       self						:				List[Any]       ):
             return {
                 "image_mean": self.image_mean,
                 "image_std": self.image_std,
                 "do_normalize": self.do_normalize,
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "crop_size": self.crop_size,
             }

@require_torch
@require_vision
class 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						VivitImageProcessor if is_vision_available() else None




      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Tuple							      =    VivitImageProcessingTester(self       )




      @property
      def      _A	(       self						:				Optional[Any]       ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  str							      =    self.image_processing_class(**self.image_processor_dict       )
             self.assertTrue(hasattr(A						,					"image_mean"       )       )
             self.assertTrue(hasattr(A						,					"image_std"       )       )
             self.assertTrue(hasattr(A						,					"do_normalize"       )       )
             self.assertTrue(hasattr(A						,					"do_resize"       )       )
             self.assertTrue(hasattr(A						,					"do_center_crop"       )       )
             self.assertTrue(hasattr(A						,					"size"       )       )




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    self.image_processing_class.from_dict(self.image_processor_dict       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 18}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 18, "width": 18}       )

             _UpperCAmelCase     :  Optional[int]							      =    self.image_processing_class.from_dict(self.image_processor_dict						,					size=42						,					crop_size=84       )
             self.assertEqual(image_processor.size						,					{"shortest_edge": 42}       )
             self.assertEqual(image_processor.crop_size						,					{"height": 84, "width": 84}       )




      def      _A	(       self						:				Tuple       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Optional[Any]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PIL videos
             _UpperCAmelCase     :  Any							      =    prepare_video_inputs(self.image_processor_tester						,					equal_resolution=A       )
             for video in video_inputs:
                    self.assertIsInstance(A						,					A       )
                    self.assertIsInstance(video[0]						,					Image.Image       )

             # Test not batched input
             _UpperCAmelCase     :  str							      =    image_processing(video_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     1,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  List[Any]							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)




      def      _A	(       self						:				List[Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             _UpperCAmelCase     :  List[Any]							      =    prepare_video_inputs(self.image_processor_tester						,					equal_resolution=A						,					numpify=A       )
             for video in video_inputs:
                    self.assertIsInstance(A						,					A       )
                    self.assertIsInstance(video[0]						,					np.ndarray       )

             # Test not batched input
             _UpperCAmelCase     :  Tuple							      =    image_processing(video_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     1,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  Optional[int]							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)





      def      _A	(       self						:				List[Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Optional[Any]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             _UpperCAmelCase     :  Optional[int]							      =    prepare_video_inputs(self.image_processor_tester						,					equal_resolution=A						,					torchify=A       )
             for video in video_inputs:
                    self.assertIsInstance(A						,					A       )
                    self.assertIsInstance(video[0]						,					torch.Tensor       )

             # Test not batched input
             _UpperCAmelCase     :  Optional[Any]							      =    image_processing(video_inputs[0]						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     1,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

             # Test batched
             _UpperCAmelCase     :  List[Any]							      =    image_processing(A						,					return_tensors="pt"       ).pixel_values
             self.assertEqual(
                 encoded_videos.shape						,					(
                     self.image_processor_tester.batch_size,
                     self.image_processor_tester.num_frames,
                     self.image_processor_tester.num_channels,
                     self.image_processor_tester.crop_size["height"],
                     self.image_processor_tester.crop_size["width"],
                 )						,					)

