
from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
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 (
          TFDeiTForImageClassification,
          TFDeiTForImageClassificationWithTeacher,
          TFDeiTForMaskedImageModeling,
          TFDeiTModel,
      )
      from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import DeiTImageProcessor





class 							A__		:
      def __init__(      self    ,  __magic_name__    ,  __magic_name__=1_3    ,  __magic_name__=3_0    ,  __magic_name__=2    ,  __magic_name__=3    ,  __magic_name__=True    ,  __magic_name__=True    ,  __magic_name__=3_2    ,  __magic_name__=2    ,  __magic_name__=4    ,  __magic_name__=3_7    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=1_0    ,  __magic_name__=0.02    ,  __magic_name__=3    ,  __magic_name__=None    ,  __magic_name__=2    ,  ):
       lowerCamelCase		:	Any							=      parent
       lowerCamelCase		:	Union[str, Any]							=      batch_size
       lowerCamelCase		:	str							=      image_size
       lowerCamelCase		:	str							=      patch_size
       lowerCamelCase		:	Optional[int]							=      num_channels
       lowerCamelCase		:	Union[str, Any]							=      is_training
       lowerCamelCase		:	Optional[Any]							=      use_labels
       lowerCamelCase		:	Union[str, Any]							=      hidden_size
       lowerCamelCase		:	Union[str, Any]							=      num_hidden_layers
       lowerCamelCase		:	Optional[int]							=      num_attention_heads
       lowerCamelCase		:	List[str]							=      intermediate_size
       lowerCamelCase		:	Union[str, Any]							=      hidden_act
       lowerCamelCase		:	Optional[Any]							=      hidden_dropout_prob
       lowerCamelCase		:	Dict							=      attention_probs_dropout_prob
       lowerCamelCase		:	str							=      type_sequence_label_size
       lowerCamelCase		:	Optional[Any]							=      initializer_range
       lowerCamelCase		:	Dict							=      scope
       lowerCamelCase		:	Tuple							=      encoder_stride

       # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
       lowerCamelCase		:	Tuple							=      (image_size // patch_size) ** 2
       lowerCamelCase		:	List[Any]							=      num_patches + 2
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

       lowerCamelCase		:	Optional[int]							=      None
       if self.use_labels:
        lowerCamelCase		:	Any							=      ids_tensor([self.batch_size]    ,  self.type_sequence_label_size      )

       lowerCamelCase		:	Tuple							=      self.get_config()

       return config, pixel_values, labels
      def 						UpperCamelCase__							(      self      ):
       return DeiTConfig(
           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=__magic_name__    ,  initializer_range=self.initializer_range    ,  encoder_stride=self.encoder_stride    ,  )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	Dict							=      TFDeiTModel(config=__magic_name__      )
       lowerCamelCase		:	Any							=      model(__magic_name__      )
       self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, self.seq_length, self.hidden_size)      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	List[Any]							=      TFDeiTForMaskedImageModeling(config=__magic_name__      )
       lowerCamelCase		:	List[str]							=      model(__magic_name__      )
       self.parent.assertEqual(
           result.reconstruction.shape    ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

       # test greyscale images
       lowerCamelCase		:	int							=      1
       lowerCamelCase		:	Optional[int]							=      TFDeiTForMaskedImageModeling(__magic_name__      )

       lowerCamelCase		:	Optional[int]							=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       lowerCamelCase		:	List[str]							=      model(__magic_name__      )
       self.parent.assertEqual(result.reconstruction.shape    ,  (self.batch_size, 1, self.image_size, self.image_size)      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	Dict							=      self.type_sequence_label_size
       lowerCamelCase		:	Optional[Any]							=      TFDeiTForImageClassification(__magic_name__      )
       lowerCamelCase		:	List[str]							=      model(__magic_name__    ,  labels=__magic_name__      )
       self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       lowerCamelCase		:	str							=      1
       lowerCamelCase		:	List[Any]							=      TFDeiTForImageClassification(__magic_name__      )

       lowerCamelCase		:	Optional[int]							=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
       lowerCamelCase		:	Tuple							=      model(__magic_name__    ,  labels=__magic_name__      )
       self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.type_sequence_label_size)      )






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[Any]							=      self.prepare_config_and_inputs()
       lowerCamelCase			,    lowerCamelCase			,    lowerCamelCase		:	Dict							=      config_and_inputs
       lowerCamelCase		:	Any							=      {"""pixel_values""": pixel_values}
       return config, inputs_dict





@require_tf
class 							A__		(	__SCREAMING_SNAKE_CASE  ,   __SCREAMING_SNAKE_CASE  ,   unittest.TestCase):
      _UpperCAmelCase				:    Union[str, Any]           =			(
          (
              TFDeiTModel,
              TFDeiTForImageClassification,
              TFDeiTForImageClassificationWithTeacher,
              TFDeiTForMaskedImageModeling,
          )
          if is_tf_available()
          else ()
      )
      _UpperCAmelCase				:    Union[str, Any]           =			(
          {
              """feature-extraction""": TFDeiTModel,
              """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
          }
          if is_tf_available()
          else {}
      )

      _UpperCAmelCase				:    str           =			False
      _UpperCAmelCase				:    Dict           =			False
      _UpperCAmelCase				:    str           =			False
      _UpperCAmelCase				:    Optional[Any]           =			False
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Tuple							=      TFDeiTModelTester(self      )
       lowerCamelCase		:	List[Any]							=      ConfigTester(self    ,  config_class=__magic_name__    ,  has_text_modality=__magic_name__    ,  hidden_size=3_7      )
      def 						UpperCamelCase__							(      self      ):
       self.config_tester.run_common_tests()
      @unittest.skip(reason="""DeiT does not use inputs_embeds"""      )
      def 						UpperCamelCase__							(      self      ):
       pass
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase		:	Optional[int]							=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        lowerCamelCase		:	List[str]							=      model_class(__magic_name__      )
        self.assertIsInstance(model.get_input_embeddings()    ,  (tf.keras.layers.Layer)      )
        lowerCamelCase		:	Dict							=      model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(__magic_name__    ,  tf.keras.layers.Dense      )      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase			,    lowerCamelCase		:	Tuple							=      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
        lowerCamelCase		:	Union[str, Any]							=      model_class(__magic_name__      )
        lowerCamelCase		:	str							=      inspect.signature(model.call      )
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        lowerCamelCase		:	List[Any]							=      [*signature.parameters.keys()]

        lowerCamelCase		:	Union[str, Any]							=      ["""pixel_values"""]
        self.assertListEqual(arg_names[:1]    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[Any]							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__=False      ):
       lowerCamelCase		:	Union[str, Any]							=      super()._prepare_for_class(__magic_name__    ,  __magic_name__    ,  return_labels=__magic_name__      )

       if return_labels:
        if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call      ).parameters:
         del inputs_dict["labels"]

       return inputs_dict






      @slow
      def 						UpperCamelCase__							(      self      ):
       for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        lowerCamelCase		:	str							=      TFDeiTModel.from_pretrained(__magic_name__      )
        self.assertIsNotNone(__magic_name__      )





def     _a					(						):
 lowerCamelCase		:	Optional[int]							=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
 return image





@require_tf
@require_vision
class 							A__		(	unittest.TestCase):
      @cached_property
      def 						UpperCamelCase__							(      self      ):
       return (
           DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""      )
           if is_vision_available()
           else None
       )






      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""      )

       lowerCamelCase		:	Dict							=      self.default_image_processor
       lowerCamelCase		:	List[str]							=      prepare_img()
       lowerCamelCase		:	str							=      image_processor(images=__magic_name__    ,  return_tensors="""tf"""      )

       # forward pass
       lowerCamelCase		:	Optional[Any]							=      model(**__magic_name__      )

       # verify the logits
       lowerCamelCase		:	str							=      tf.TensorShape((1, 1_0_0_0)      )
       self.assertEqual(outputs.logits.shape    ,  __magic_name__      )

       lowerCamelCase		:	Optional[Any]							=      tf.constant([-1.0_266, 0.1_912, -1.2_861]      )

       self.assertTrue(np.allclose(outputs.logits[0, :3]    ,  __magic_name__    ,  atol=1e-4      )      )






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
      import numpy as np
      import tensorflow as tf

      from transformers import TFCamembertModel





@require_tf
@require_sentencepiece
@require_tokenizers
class 							A__		(	unittest.TestCase):
      @slow
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Union[str, Any]							=      TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""      )

       lowerCamelCase		:	Any							=      tf.convert_to_tensor(
           [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]    ,  dtype=tf.intaa    ,  )  # J'aime le camembert !"

       lowerCamelCase		:	str							=      model(__magic_name__      )["""last_hidden_state"""]
       lowerCamelCase		:	Union[str, Any]							=      tf.TensorShape((1, 1_0, 7_6_8)      )
       self.assertEqual(output.shape    ,  __magic_name__      )
       # compare the actual values for a slice.
       lowerCamelCase		:	Dict							=      tf.convert_to_tensor(
           [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]    ,  dtype=tf.floataa    ,  )
       # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
       # camembert.eval()
       # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

       self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,  expected_slice.numpy()    ,  atol=1e-4      )      )




