
"""simple docstring"""
import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

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


if is_flax_available():
						import jax

						from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
						from PIL import Image

						from transformers import BeitImageProcessor
class 			UpperCamelCase      (							unittest.TestCase  ):
					def __init__(     self  ,     snake_case__  ,     snake_case__=100  ,     snake_case__=13  ,     snake_case__=30  ,     snake_case__=2  ,     snake_case__=3  ,     snake_case__=True  ,     snake_case__=True  ,     snake_case__=32  ,     snake_case__=5  ,     snake_case__=4  ,     snake_case__=37  ,     snake_case__="gelu"  ,     snake_case__=0.1  ,     snake_case__=0.1  ,     snake_case__=10  ,     snake_case__=0.02  ,     snake_case__=3  ,     ):




							"""simple docstring"""


							_SCREAMING_SNAKE_CASE   :				Optional[int]							    = parent
							_SCREAMING_SNAKE_CASE   :				List[str]							    = vocab_size
							_SCREAMING_SNAKE_CASE   :				Tuple							    = batch_size
							_SCREAMING_SNAKE_CASE   :				str							    = image_size
							_SCREAMING_SNAKE_CASE   :				Dict							    = patch_size
							_SCREAMING_SNAKE_CASE   :				Dict							    = num_channels
							_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = is_training
							_SCREAMING_SNAKE_CASE   :				List[str]							    = use_labels
							_SCREAMING_SNAKE_CASE   :				int							    = hidden_size
							_SCREAMING_SNAKE_CASE   :				int							    = num_hidden_layers
							_SCREAMING_SNAKE_CASE   :				Any							    = num_attention_heads
							_SCREAMING_SNAKE_CASE   :				Dict							    = intermediate_size
							_SCREAMING_SNAKE_CASE   :				Dict							    = hidden_act
							_SCREAMING_SNAKE_CASE   :				Any							    = hidden_dropout_prob
							_SCREAMING_SNAKE_CASE   :				List[Any]							    = attention_probs_dropout_prob
							_SCREAMING_SNAKE_CASE   :				Optional[int]							    = type_sequence_label_size
							_SCREAMING_SNAKE_CASE   :				List[Any]							    = initializer_range

							# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = (image_size // patch_size) ** 2
							_SCREAMING_SNAKE_CASE   :				List[str]							    = num_patches + 1
					def 				__SCREAMING_SNAKE_CASE							(     self				):




							"""simple docstring"""


							_SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

							_SCREAMING_SNAKE_CASE   :				Optional[int]							    = BeitConfig(
							    vocab_size=self.vocab_size  ,     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=snake_case__  ,     initializer_range=self.initializer_range  ,     )

							return config, pixel_values, labels
					def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




							"""simple docstring"""


							_SCREAMING_SNAKE_CASE   :				Dict							    = FlaxBeitModel(config=snake_case__				)
							_SCREAMING_SNAKE_CASE   :				Dict							    = model(snake_case__				)
							self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)				)
					def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




							"""simple docstring"""


							_SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = FlaxBeitForMaskedImageModeling(config=snake_case__				)
							_SCREAMING_SNAKE_CASE   :				int							    = model(snake_case__				)
							self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length - 1, self.vocab_size)				)
					def 				__SCREAMING_SNAKE_CASE							(     self  ,     snake_case__  ,     snake_case__  ,     snake_case__				):




							"""simple docstring"""


							_SCREAMING_SNAKE_CASE   :				List[str]							    = self.type_sequence_label_size
							_SCREAMING_SNAKE_CASE   :				str							    = FlaxBeitForImageClassification(config=snake_case__				)
							_SCREAMING_SNAKE_CASE   :				str							    = model(snake_case__				)
							self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.type_sequence_label_size)				)

							# test greyscale images
							_SCREAMING_SNAKE_CASE   :				List[str]							    = 1
							_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = FlaxBeitForImageClassification(snake_case__				)

							_SCREAMING_SNAKE_CASE   :				Dict							    = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
							_SCREAMING_SNAKE_CASE   :				int							    = model(snake_case__				)




					def 				__SCREAMING_SNAKE_CASE							(     self				):




							"""simple docstring"""


							_SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.prepare_config_and_inputs()
							(
							    _SCREAMING_SNAKE_CASE		
							)   :				str							    = config_and_inputs
							_SCREAMING_SNAKE_CASE   :				int							    = {"pixel_values": pixel_values}
							return config, inputs_dict
@require_flax
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE      ,       unittest.TestCase  ):
					A__      =			(
					    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
					)
					def 				__SCREAMING_SNAKE_CASE							(     self				):




							"""simple docstring"""


							_SCREAMING_SNAKE_CASE   :				Tuple							    = FlaxBeitModelTester(self				)
							_SCREAMING_SNAKE_CASE   :				Dict							    = ConfigTester(self  ,     config_class=snake_case__  ,     has_text_modality=snake_case__  ,     hidden_size=37				)
					def 				__SCREAMING_SNAKE_CASE							(     self				):




							"""simple docstring"""


							self.config_tester.run_common_tests()
					def 				__SCREAMING_SNAKE_CASE							(     self				):




							"""simple docstring"""


							_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self.model_tester.prepare_config_and_inputs_for_common()

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

									_SCREAMING_SNAKE_CASE   :				Tuple							    = ["pixel_values"]
									self.assertListEqual(arg_names[:1]  ,     snake_case__				)
					def 				__SCREAMING_SNAKE_CASE							(     self				):




							"""simple docstring"""


							_SCREAMING_SNAKE_CASE   :				int							    = self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
									with self.subTest(model_class.__name__				):
											_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = self._prepare_for_class(snake_case__  ,     snake_case__				)
											_SCREAMING_SNAKE_CASE   :				List[str]							    = model_class(snake_case__				)

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

											with self.subTest("JIT Enabled"				):
													_SCREAMING_SNAKE_CASE   :				List[Any]							    = model_jitted(**snake_case__				).to_tuple()

											with self.subTest("JIT Disabled"				):
													with jax.disable_jit():
															_SCREAMING_SNAKE_CASE   :				int							    = model_jitted(**snake_case__				).to_tuple()

											self.assertEqual(len(snake_case__				)  ,     len(snake_case__				)				)
											for jitted_output, output in zip(snake_case__  ,     snake_case__				):
													self.assertEqual(jitted_output.shape  ,     output.shape				)
					def 				__SCREAMING_SNAKE_CASE							(     self				):




							"""simple docstring"""


							_SCREAMING_SNAKE_CASE   :				Tuple							    = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*snake_case__				)
					def 				__SCREAMING_SNAKE_CASE							(     self				):




							"""simple docstring"""


							_SCREAMING_SNAKE_CASE   :				List[Any]							    = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*snake_case__				)
					def 				__SCREAMING_SNAKE_CASE							(     self				):




							"""simple docstring"""


							_SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*snake_case__				)




					@slow
					def 				__SCREAMING_SNAKE_CASE							(     self				):




							"""simple docstring"""


							for model_class_name in self.all_model_classes:
									_SCREAMING_SNAKE_CASE   :				Tuple							    = model_class_name.from_pretrained("microsoft/beit-base-patch16-224"				)
									_SCREAMING_SNAKE_CASE   :				List[str]							    = model(np.ones((1, 3, 224, 224)				)				)
									self.assertIsNotNone(snake_case__				)





def    _lowerCAmelCase    (     )  ->    Union[str, Any]:
		_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
		return image





@require_vision
@require_flax
class 			UpperCamelCase      (							unittest.TestCase  ):
					@cached_property
					def 				__SCREAMING_SNAKE_CASE							(     self				):




							"""simple docstring"""


							return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"				) if is_vision_available() else None
					@slow
					def 				__SCREAMING_SNAKE_CASE							(     self				):




							"""simple docstring"""


							_SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"				)

							_SCREAMING_SNAKE_CASE   :				int							    = self.default_image_processor
							_SCREAMING_SNAKE_CASE   :				Any							    = prepare_img()
							_SCREAMING_SNAKE_CASE   :				List[Any]							    = image_processor(images=snake_case__  ,     return_tensors="np"				).pixel_values

							# prepare bool_masked_pos
							_SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = np.ones((1, 196)  ,     dtype=snake_case__				)

							# forward pass
							_SCREAMING_SNAKE_CASE   :				Dict							    = model(pixel_values=snake_case__  ,     bool_masked_pos=snake_case__				)
							_SCREAMING_SNAKE_CASE   :				str							    = outputs.logits

							# verify the logits
							_SCREAMING_SNAKE_CASE   :				Optional[int]							    = (1, 196, 8192)
							self.assertEqual(logits.shape  ,     snake_case__				)

							_SCREAMING_SNAKE_CASE   :				Optional[int]							    = np.array(
							    [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]				)

							self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]  ,     snake_case__  ,     atol=1E-2				)				)
					@slow
					def 				__SCREAMING_SNAKE_CASE							(     self				):




							"""simple docstring"""


							_SCREAMING_SNAKE_CASE   :				Dict							    = FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"				)

							_SCREAMING_SNAKE_CASE   :				int							    = self.default_image_processor
							_SCREAMING_SNAKE_CASE   :				List[str]							    = prepare_img()
							_SCREAMING_SNAKE_CASE   :				Tuple							    = image_processor(images=snake_case__  ,     return_tensors="np"				)

							# forward pass
							_SCREAMING_SNAKE_CASE   :				Any							    = model(**snake_case__				)
							_SCREAMING_SNAKE_CASE   :				int							    = outputs.logits

							# verify the logits
							_SCREAMING_SNAKE_CASE   :				List[Any]							    = (1, 1000)
							self.assertEqual(logits.shape  ,     snake_case__				)

							_SCREAMING_SNAKE_CASE   :				Optional[Any]							    = np.array([-1.2_385, -1.0_987, -1.0_108]				)

							self.assertTrue(np.allclose(logits[0, :3]  ,     snake_case__  ,     atol=1E-4				)				)

							_SCREAMING_SNAKE_CASE   :				Dict							    = 281
							self.assertEqual(logits.argmax(-1				).item()  ,     snake_case__				)




					@slow
					def 				__SCREAMING_SNAKE_CASE							(     self				):




							"""simple docstring"""


							_SCREAMING_SNAKE_CASE   :				Tuple							    = FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"				)

							_SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.default_image_processor
							_SCREAMING_SNAKE_CASE   :				Dict							    = prepare_img()
							_SCREAMING_SNAKE_CASE   :				Tuple							    = image_processor(images=snake_case__  ,     return_tensors="np"				)

							# forward pass
							_SCREAMING_SNAKE_CASE   :				Any							    = model(**snake_case__				)
							_SCREAMING_SNAKE_CASE   :				Any							    = outputs.logits

							# verify the logits
							_SCREAMING_SNAKE_CASE   :				Tuple							    = (1, 21841)
							self.assertEqual(logits.shape  ,     snake_case__				)

							_SCREAMING_SNAKE_CASE   :				Any							    = np.array([1.6_881, -0.2_787, 0.5_901]				)

							self.assertTrue(np.allclose(logits[0, :3]  ,     snake_case__  ,     atol=1E-4				)				)

							_SCREAMING_SNAKE_CASE   :				Any							    = 2396
							self.assertEqual(logits.argmax(-1				).item()  ,     snake_case__				)





"""simple docstring"""
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 			UpperCamelCase      (							unittest.TestCase  ):
     @slow
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = TFCamembertModel.from_pretrained("jplu/tf-camembert-base"				)

       _SCREAMING_SNAKE_CASE   :				str							    = tf.convert_to_tensor(
           [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]  ,     dtype=tf.intaa  ,     )  # J'aime le camembert !"

       _SCREAMING_SNAKE_CASE   :				str							    = model(snake_case__				)["last_hidden_state"]
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = tf.TensorShape((1, 10, 768)				)
       self.assertEqual(output.shape  ,     snake_case__				)
       # compare the actual values for a slice.
       _SCREAMING_SNAKE_CASE   :				int							    = 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				)				)



