
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase  :					Optional[int]						    =							{"""configuration_fnet""": ["""FNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FNetConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					Union[str, Any]						    =							["""FNetTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					List[str]						    =							["""FNetTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowercase  :					str						    =							[
      """FNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """FNetForMaskedLM""",
      """FNetForMultipleChoice""",
      """FNetForNextSentencePrediction""",
      """FNetForPreTraining""",
      """FNetForQuestionAnswering""",
      """FNetForSequenceClassification""",
      """FNetForTokenClassification""",
      """FNetLayer""",
      """FNetModel""",
      """FNetPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet import FNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_fnet_fast import FNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_fnet import (
        FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        FNetForMaskedLM,
        FNetForMultipleChoice,
        FNetForNextSentencePrediction,
        FNetForPreTraining,
        FNetForQuestionAnswering,
        FNetForSequenceClassification,
        FNetForTokenClassification,
        FNetLayer,
        FNetModel,
        FNetPreTrainedModel,
    )


else:
  import sys

  lowercase  :					Dict						    =							_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor


class  A						:







    """simple docstring"""





    def __init__(      self :       Union[str, Any],lowercase_ :       Any,lowercase_ :       Union[str, Any]=1_3,lowercase_ :       Tuple=3_0,lowercase_ :       List[Any]=2,lowercase_ :       Optional[int]=3,lowercase_ :       Union[str, Any]=True,lowercase_ :       Tuple=True,lowercase_ :       Any=3_2,lowercase_ :       List[str]=2,lowercase_ :       Optional[int]=4,lowercase_ :       Union[str, Any]=3_7,lowercase_ :       Tuple="gelu",lowercase_ :       str=0.1,lowercase_ :       Tuple=0.1,lowercase_ :       Union[str, Any]=1_0,lowercase_ :       int=0.02,lowercase_ :       List[Any]=3,lowercase_ :       Any=None,)->    Dict:





        '''simple docstring'''







        A__          =   parent
        A__          =   batch_size
        A__          =   image_size
        A__          =   patch_size
        A__          =   num_channels
        A__          =   is_training
        A__          =   use_labels
        A__          =   hidden_size
        A__          =   num_hidden_layers
        A__          =   num_attention_heads
        A__          =   intermediate_size
        A__          =   hidden_act
        A__          =   hidden_dropout_prob
        A__          =   attention_probs_dropout_prob
        A__          =   type_sequence_label_size
        A__          =   initializer_range
        A__          =   scope

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


    def 						snake_case__     (      self :       int		)->    List[str]:





        '''simple docstring'''







        A__          =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

        A__          =   self.get_config()

        return config, pixel_values, labels


    def 						snake_case__     (      self :       Tuple		)->    List[Any]:





        '''simple docstring'''







        return 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,)


    def 						snake_case__     (      self :       List[str],lowercase_ :       int,lowercase_ :       Union[str, Any],lowercase_ :       Tuple		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   TFViTModel(config=lowercase_		)
        A__          =   model(lowercase_,training=lowercase_		)
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)		)

        # Test with an image with different size than the one specified in config.
        A__          =   self.image_size // 2
        A__          =   pixel_values[:, :, :image_size, :image_size]
        A__          =   model(lowercase_,interpolate_pos_encoding=lowercase_,training=lowercase_		)
        A__          =   (image_size // self.patch_size) ** 2 + 1
        self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, seq_length, self.hidden_size)		)


    def 						snake_case__     (      self :       List[Any],lowercase_ :       List[Any],lowercase_ :       List[Any],lowercase_ :       List[Any]		)->    Dict:





        '''simple docstring'''







        A__          =   self.type_sequence_label_size
        A__          =   TFViTForImageClassification(lowercase_		)
        A__          =   model(lowercase_,labels=lowercase_,training=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)		)

        # Test with an image with different size than the one specified in config.
        A__          =   self.image_size // 2
        A__          =   pixel_values[:, :, :image_size, :image_size]
        A__          =   model(lowercase_,interpolate_pos_encoding=lowercase_,training=lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)		)

        # test greyscale images
        A__          =   1
        A__          =   TFViTForImageClassification(lowercase_		)
        A__          =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
        A__          =   model(lowercase_		)
        self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size)		)


    def 						snake_case__     (      self :       Any		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.prepare_config_and_inputs()
        A__      ,						A__      ,						A__          =   config_and_inputs
        A__          =   {'pixel_values': pixel_values}
        return config, inputs_dict




@require_tf
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
    lowerCamelCase			=							(
        {'feature-extraction': TFViTModel, 'image-classification': TFViTForImageClassification}
        if is_tf_available()
        else {}
    )

    lowerCamelCase			=							False
    lowerCamelCase			=							False
    lowerCamelCase			=							False


    def 						snake_case__     (      self :       int		)->    List[Any]:





        '''simple docstring'''







        A__          =   TFViTModelTester(self		)
        A__          =   ConfigTester(self,config_class=lowercase_,has_text_modality=lowercase_,hidden_size=3_7		)


    def 						snake_case__     (      self :       Any		)->    Optional[Any]:





        '''simple docstring'''







        self.config_tester.run_common_tests()


    @unittest.skip(reason='ViT does not use inputs_embeds'		)
    def 						snake_case__     (      self :       Optional[Any]		)->    str:





        '''simple docstring'''







        pass


    @unittest.skip(reason='ViT does not use inputs_embeds'		)
    def 						snake_case__     (      self :       Any		)->    int:





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       str		)->    Dict:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            A__          =   model_class(lowercase_		)
            self.assertIsInstance(model.get_input_embeddings(),(tf.keras.layers.Layer)		)
            A__          =   model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(lowercase_,tf.keras.layers.Layer		)		)


    def 						snake_case__     (      self :       int		)->    List[str]:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

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

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


    def 						snake_case__     (      self :       Union[str, Any]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase_		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase_		)


    @slow
    def 						snake_case__     (      self :       Union[str, Any]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   TFViTModel.from_pretrained('google/vit-base-patch16-224'		)
        self.assertIsNotNone(lowercase_		)




def 						_snake_case(    )							->      str:





    '''simple docstring'''






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


@require_tf
@require_vision
class  A						(							unittest.TestCase ):







    """simple docstring"""





    @cached_property
    def 						snake_case__     (      self :       List[Any]		)->    str:





        '''simple docstring'''







        return ViTImageProcessor.from_pretrained('google/vit-base-patch16-224'		) if is_vision_available() else None


    @slow
    def 						snake_case__     (      self :       Any		)->    Dict:





        '''simple docstring'''







        A__          =   TFViTForImageClassification.from_pretrained('google/vit-base-patch16-224'		)

        A__          =   self.default_image_processor
        A__          =   prepare_img()
        A__          =   image_processor(images=lowercase_,return_tensors='tf'		)

        # forward pass
        A__          =   model(**lowercase_		)

        # verify the logits
        A__          =   tf.TensorShape((1, 1_0_0_0)		)
        self.assertEqual(outputs.logits.shape,lowercase_		)

        A__          =   tf.constant([-0.2_744, 0.8_215, -0.0_836]		)

        tf.debugging.assert_near(outputs.logits[0, :3],lowercase_,atol=1E-4		)

