


'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor





@require_tokenizers
@require_vision
class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    def  lowercase_							(				self      )							->	Union[str, Any]:
         __lowerCamelCase  :      List[Any]														=						tempfile.mkdtemp()

         # fmt: off
         __lowerCamelCase  :      Any														=						['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
         # fmt: on
         __lowerCamelCase  :      List[Any]														=						os.path.join(self.tmpdirname     ,	VOCAB_FILES_NAMES['vocab_file']      )
         with open(self.vocab_file     ,	'w'     ,	encoding='utf-8'      ) as vocab_writer:
              vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )

         __lowerCamelCase  :      Tuple														=						{
             'do_resize': True,
             'size': {'height': 18, 'width': 18},
             'do_normalize': True,
             'image_mean': [0.5, 0.5, 0.5],
             'image_std': [0.5, 0.5, 0.5],
         }
         __lowerCamelCase  :      Union[str, Any]														=						os.path.join(self.tmpdirname     ,	SCREAMING_SNAKE_CASE_      )
         with open(self.image_processor_file     ,	'w'     ,	encoding='utf-8'      ) as fp:
              json.dump(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	**SCREAMING_SNAKE_CASE_      )							->	Union[str, Any]:
         return BertTokenizer.from_pretrained(self.tmpdirname     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	**SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         return ViTImageProcessor.from_pretrained(self.tmpdirname     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Optional[Any]:
         shutil.rmtree(self.tmpdirname      )







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      List[str]														=						[np.random.randint(2_55     ,	size=(3, 30, 4_00)     ,	dtype=np.uinta      )]

         __lowerCamelCase  :      List[str]														=						[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_     ,	0     ,	-1      )      ) for x in image_inputs]

         return image_inputs







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      List[str]														=						self.get_tokenizer()
         __lowerCamelCase  :      Optional[int]														=						self.get_image_processor()

         __lowerCamelCase  :      Union[str, Any]														=						VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )

         processor.save_pretrained(self.tmpdirname      )
         __lowerCamelCase  :      Tuple														=						VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname      )

         self.assertEqual(processor.tokenizer.get_vocab()     ,	tokenizer.get_vocab()      )
         self.assertIsInstance(processor.tokenizer     ,	(BertTokenizer, BertTokenizerFast)      )

         self.assertEqual(processor.image_processor.to_json_string()     ,	image_processor.to_json_string()      )
         self.assertIsInstance(processor.image_processor     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      Optional[Any]														=						VisionTextDualEncoderProcessor(
             tokenizer=self.get_tokenizer()     ,	image_processor=self.get_image_processor()      )
         processor.save_pretrained(self.tmpdirname      )

         __lowerCamelCase  :      Any														=						self.get_tokenizer(bos_token='(BOS)'     ,	eos_token='(EOS)'      )
         __lowerCamelCase  :      Optional[int]														=						self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_     ,	padding_value=1.0      )

         __lowerCamelCase  :      List[str]														=						VisionTextDualEncoderProcessor.from_pretrained(
             self.tmpdirname     ,	bos_token='(BOS)'     ,	eos_token='(EOS)'     ,	do_normalize=SCREAMING_SNAKE_CASE_     ,	padding_value=1.0      )

         self.assertEqual(processor.tokenizer.get_vocab()     ,	tokenizer_add_kwargs.get_vocab()      )
         self.assertIsInstance(processor.tokenizer     ,	(BertTokenizer, BertTokenizerFast)      )

         self.assertEqual(processor.image_processor.to_json_string()     ,	image_processor_add_kwargs.to_json_string()      )
         self.assertIsInstance(processor.image_processor     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Union[str, Any]:
         __lowerCamelCase  :      Optional[int]														=						self.get_image_processor()
         __lowerCamelCase  :      List[str]														=						self.get_tokenizer()

         __lowerCamelCase  :      int														=						VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Any														=						self.prepare_image_inputs()

         __lowerCamelCase  :      Optional[int]														=						image_processor(SCREAMING_SNAKE_CASE_     ,	return_tensors='np'      )
         __lowerCamelCase  :      Union[str, Any]														=						processor(images=SCREAMING_SNAKE_CASE_     ,	return_tensors='np'      )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()     ,	input_processor[key].sum()     ,	delta=1E-2      )







    def  lowercase_							(				self      )							->	List[str]:
         __lowerCamelCase  :      Dict														=						self.get_image_processor()
         __lowerCamelCase  :      str														=						self.get_tokenizer()

         __lowerCamelCase  :      Any														=						VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      List[Any]														=						'lower newer'

         __lowerCamelCase  :      Optional[int]														=						processor(text=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      List[Any]														=						tokenizer(SCREAMING_SNAKE_CASE_      )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]     ,	encoded_processor[key]      )







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      List[Any]														=						self.get_image_processor()
         __lowerCamelCase  :      Any														=						self.get_tokenizer()

         __lowerCamelCase  :      int														=						VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      int														=						'lower newer'
         __lowerCamelCase  :      List[str]														=						self.prepare_image_inputs()

         __lowerCamelCase  :      Any														=						processor(text=SCREAMING_SNAKE_CASE_     ,	images=SCREAMING_SNAKE_CASE_      )

         self.assertListEqual(list(inputs.keys()      )     ,	['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']      )

         # test if it raises when no input is passed
         with self.assertRaises(SCREAMING_SNAKE_CASE_      ):
              processor()







    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase  :      Tuple														=						self.get_image_processor()
         __lowerCamelCase  :      int														=						self.get_tokenizer()

         __lowerCamelCase  :      Optional[Any]														=						VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      int														=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __lowerCamelCase  :      int														=						processor.batch_decode(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Tuple														=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE_      )

         self.assertListEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      List[str]														=						self.get_image_processor()
         __lowerCamelCase  :      List[str]														=						self.get_tokenizer()

         __lowerCamelCase  :      int														=						VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Any														=						'lower newer'
         __lowerCamelCase  :      int														=						self.prepare_image_inputs()

         __lowerCamelCase  :      Any														=						processor(text=SCREAMING_SNAKE_CASE_     ,	images=SCREAMING_SNAKE_CASE_      )

         self.assertListEqual(list(inputs.keys()      )     ,	processor.model_input_names      )










'''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    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=13     ,	SCREAMING_SNAKE_CASE_=30     ,	SCREAMING_SNAKE_CASE_=2     ,	SCREAMING_SNAKE_CASE_=3     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=32     ,	SCREAMING_SNAKE_CASE_=5     ,	SCREAMING_SNAKE_CASE_=4     ,	SCREAMING_SNAKE_CASE_=37     ,	SCREAMING_SNAKE_CASE_="gelu"     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=10     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	)							->	Optional[int]:
         __lowerCamelCase  :      Optional[int]														=						parent
         __lowerCamelCase  :      Dict														=						batch_size
         __lowerCamelCase  :      int														=						image_size
         __lowerCamelCase  :      List[str]														=						patch_size
         __lowerCamelCase  :      Optional[int]														=						num_channels
         __lowerCamelCase  :      Any														=						is_training
         __lowerCamelCase  :      Dict														=						use_labels
         __lowerCamelCase  :      List[Any]														=						hidden_size
         __lowerCamelCase  :      List[Any]														=						num_hidden_layers
         __lowerCamelCase  :      Optional[Any]														=						num_attention_heads
         __lowerCamelCase  :      Dict														=						intermediate_size
         __lowerCamelCase  :      Union[str, Any]														=						hidden_act
         __lowerCamelCase  :      Optional[int]														=						hidden_dropout_prob
         __lowerCamelCase  :      Tuple														=						attention_probs_dropout_prob
         __lowerCamelCase  :      str														=						type_sequence_label_size
         __lowerCamelCase  :      List[str]														=						initializer_range

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







    def  lowercase_							(				self      )							->	Union[str, Any]:
         __lowerCamelCase  :      int														=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

         __lowerCamelCase  :      Optional[int]														=						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=SCREAMING_SNAKE_CASE_     ,	initializer_range=self.initializer_range     ,	)

         return config, pixel_values







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Union[str, Any]:
         __lowerCamelCase  :      Union[str, Any]														=						FlaxViTModel(config=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      str														=						model(SCREAMING_SNAKE_CASE_      )
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         __lowerCamelCase  :      str														=						(self.image_size, self.image_size)
         __lowerCamelCase  :      str														=						(self.patch_size, self.patch_size)
         __lowerCamelCase  :      Any														=						(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  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         __lowerCamelCase  :      Tuple														=						self.type_sequence_label_size
         __lowerCamelCase  :      Any														=						FlaxViTForImageClassification(config=SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Dict														=						model(SCREAMING_SNAKE_CASE_      )
         self.parent.assertEqual(result.logits.shape     ,	(self.batch_size, self.type_sequence_label_size)      )

         # test greyscale images
         __lowerCamelCase  :      List[str]														=						1
         __lowerCamelCase  :      List[Any]														=						FlaxViTForImageClassification(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      str														=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
         __lowerCamelCase  :      List[Any]														=						model(SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Optional[int]:
         __lowerCamelCase  :      List[Any]														=						self.prepare_config_and_inputs()
         (
             (
             __lowerCamelCase
         )							,			(
             __lowerCamelCase
         )							,			
         )  :      int														=						config_and_inputs
         __lowerCamelCase  :      Union[str, Any]														=						{'pixel_values': pixel_values}
         return config, inputs_dict







@require_flax
class   UpperCAmelCase_    (_UpperCAmelCase   ,						unittest.TestCase    ):





    """simple docstring"""






    lowerCamelCase    :   str					=     (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()







    def  lowercase_							(				self      )							->	None:
         __lowerCamelCase  :      str														=						FlaxViTModelTester(self      )
         __lowerCamelCase  :      Union[str, Any]														=						ConfigTester(self     ,	config_class=SCREAMING_SNAKE_CASE_     ,	has_text_modality=SCREAMING_SNAKE_CASE_     ,	hidden_size=37      )







    def  lowercase_							(				self      )							->	List[Any]:
         self.config_tester.run_common_tests()







    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase  :      Optional[int]														=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      List[Any]														=						self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase							,			__lowerCamelCase  :      Optional[int]														=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              __lowerCamelCase  :      Optional[Any]														=						model_class(SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Dict														=						inspect.signature(model.__call__      )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              __lowerCamelCase  :      List[str]														=						[*signature.parameters.keys()]

              __lowerCamelCase  :      Optional[int]														=						['pixel_values']
              self.assertListEqual(arg_names[:1]     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase							,			__lowerCamelCase  :      Dict														=						self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__      ):
                   __lowerCamelCase  :      List[Any]														=						self._prepare_for_class(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
                   __lowerCamelCase  :      Optional[int]														=						model_class(SCREAMING_SNAKE_CASE_      )

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

                   with self.subTest('JIT Enabled'      ):
                        __lowerCamelCase  :      Optional[int]														=						model_jitted(**SCREAMING_SNAKE_CASE_      ).to_tuple()

                   with self.subTest('JIT Disabled'      ):
                        with jax.disable_jit():
                             __lowerCamelCase  :      Union[str, Any]														=						model_jitted(**SCREAMING_SNAKE_CASE_      ).to_tuple()

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







    @slow
    def  lowercase_							(				self      )							->	List[Any]:
         for model_class_name in self.all_model_classes:
              __lowerCamelCase  :      Union[str, Any]														=						model_class_name.from_pretrained('google/vit-base-patch16-224'      )
              __lowerCamelCase  :      Union[str, Any]														=						model(np.ones((1, 3, 2_24, 2_24)      )      )
              self.assertIsNotNone(SCREAMING_SNAKE_CASE_      )






