






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 ):







		"""simple docstring"""



		def __init__(  self		:       str						,_SCREAMING_SNAKE_CASE		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       str=1_0_0						,_SCREAMING_SNAKE_CASE		:       List[str]=1_3						,_SCREAMING_SNAKE_CASE		:       List[Any]=3_0						,_SCREAMING_SNAKE_CASE		:       Tuple=2						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=3						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=True						,_SCREAMING_SNAKE_CASE		:       List[str]=True						,_SCREAMING_SNAKE_CASE		:       Tuple=3_2						,_SCREAMING_SNAKE_CASE		:       Tuple=5						,_SCREAMING_SNAKE_CASE		:       List[str]=4						,_SCREAMING_SNAKE_CASE		:       Tuple=3_7						,_SCREAMING_SNAKE_CASE		:       Any="gelu"						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=0.1						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=0.1						,_SCREAMING_SNAKE_CASE		:       Tuple=1_0						,_SCREAMING_SNAKE_CASE		:       Optional[int]=0.02						,_SCREAMING_SNAKE_CASE		:       Optional[int]=3						,)    ->   Optional[int]:


						'''simple docstring'''
						A	 =      parent
						A	 =      vocab_size
						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

						# in BeiT, 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       A(  self		:       List[Any]		)    ->   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	 =      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       A(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       int						,_SCREAMING_SNAKE_CASE		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Dict		)    ->   List[Any]:


						'''simple docstring'''
						A	 =      FlaxBeitModel(config=snake_case_		)
						A	 =      model(snake_case_		)
						self.parent.assertEqual(result.last_hidden_state.shape						,(self.batch_size, self.seq_length, self.hidden_size)		)
		def       A(  self		:       List[Any]						,_SCREAMING_SNAKE_CASE		:       Optional[Any]						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Optional[int]		)    ->   List[Any]:


						'''simple docstring'''
						A	 =      FlaxBeitForMaskedImageModeling(config=snake_case_		)
						A	 =      model(snake_case_		)
						self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.seq_length - 1, self.vocab_size)		)
		def       A(  self		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]						,_SCREAMING_SNAKE_CASE		:       Optional[int]						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]		)    ->   List[str]:


						'''simple docstring'''
						A	 =      self.type_sequence_label_size
						A	 =      FlaxBeitForImageClassification(config=snake_case_		)
						A	 =      model(snake_case_		)
						self.parent.assertEqual(result.logits.shape						,(self.batch_size, self.type_sequence_label_size)		)

						# test greyscale images
						A	 =      1
						A	 =      FlaxBeitForImageClassification(snake_case_		)

						A	 =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
						A	 =      model(snake_case_		)







		def       A(  self		:       Any		)    ->   Dict:


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


@require_flax
class      UpperCamelCase (				_a			,       unittest.TestCase ):







		"""simple docstring"""



		snake_case    =   (
		    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
		)
		def       A(  self		:       int		)    ->   None:


						'''simple docstring'''
						A	 =      FlaxBeitModelTester(self		)
						A	 =      ConfigTester(self						,config_class=snake_case_						,has_text_modality=snake_case_						,hidden_size=3_7		)
		def       A(  self		:       List[str]		)    ->   Dict:


						'''simple docstring'''
						self.config_tester.run_common_tests()
		def       A(  self		:       Dict		)    ->   Optional[int]:


						'''simple docstring'''
						A ,		A	 =      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										A	 =      model_class(snake_case_		)
										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]						,snake_case_		)
		def       A(  self		:       Optional[int]		)    ->   Dict:


						'''simple docstring'''
						A ,		A	 =      self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										with self.subTest(model_class.__name__		):
														A	 =      self._prepare_for_class(snake_case_						,snake_case_		)
														A	 =      model_class(snake_case_		)

														@jax.jit
														def model_jitted(_SCREAMING_SNAKE_CASE		:       str						,**_SCREAMING_SNAKE_CASE		:       Optional[Any]		):
																		return model(pixel_values=snake_case_						,**snake_case_		)

														with self.subTest('JIT Enabled'		):
																		A	 =      model_jitted(**snake_case_		).to_tuple()

														with self.subTest('JIT Disabled'		):
																		with jax.disable_jit():
																						A	 =      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       A(  self		:       int		)    ->   Dict:


						'''simple docstring'''
						A	 =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case_		)
		def       A(  self		:       Any		)    ->   Any:


						'''simple docstring'''
						A	 =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*snake_case_		)
		def       A(  self		:       Union[str, Any]		)    ->   Optional[int]:


						'''simple docstring'''
						A	 =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*snake_case_		)







		@slow
		def       A(  self		:       Any		)    ->   List[Any]:


						'''simple docstring'''
						for model_class_name in self.all_model_classes:
										A	 =      model_class_name.from_pretrained('microsoft/beit-base-patch16-224'		)
										A	 =      model(np.ones((1, 3, 2_2_4, 2_2_4)		)		)
										self.assertIsNotNone(snake_case_		)







def      snake_case		(							):
				A	 =      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
				return image



@require_vision
@require_flax
class      UpperCamelCase (				unittest.TestCase ):







		"""simple docstring"""



		@cached_property
		def       A(  self		:       Tuple		)    ->   List[Any]:


						'''simple docstring'''
						return BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224'		) if is_vision_available() else None
		@slow
		def       A(  self		:       Optional[Any]		)    ->   str:


						'''simple docstring'''
						A	 =      FlaxBeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'		)

						A	 =      self.default_image_processor
						A	 =      prepare_img()
						A	 =      image_processor(images=snake_case_						,return_tensors='np'		).pixel_values

						# prepare bool_masked_pos
						A	 =      np.ones((1, 1_9_6)						,dtype=snake_case_		)

						# forward pass
						A	 =      model(pixel_values=snake_case_						,bool_masked_pos=snake_case_		)
						A	 =      outputs.logits

						# verify the logits
						A	 =      (1, 1_9_6, 8_1_9_2)
						self.assertEqual(logits.shape						,snake_case_		)

						A	 =      np.array(
						    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]		)

						self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]						,snake_case_						,atol=1E-2		)		)
		@slow
		def       A(  self		:       str		)    ->   Optional[int]:


						'''simple docstring'''
						A	 =      FlaxBeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'		)

						A	 =      self.default_image_processor
						A	 =      prepare_img()
						A	 =      image_processor(images=snake_case_						,return_tensors='np'		)

						# forward pass
						A	 =      model(**snake_case_		)
						A	 =      outputs.logits

						# verify the logits
						A	 =      (1, 1_0_0_0)
						self.assertEqual(logits.shape						,snake_case_		)

						A	 =      np.array([-1.2385, -1.0987, -1.0108]		)

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

						A	 =      2_8_1
						self.assertEqual(logits.argmax(-1		).item()						,snake_case_		)







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


						'''simple docstring'''
						A	 =      FlaxBeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'		)

						A	 =      self.default_image_processor
						A	 =      prepare_img()
						A	 =      image_processor(images=snake_case_						,return_tensors='np'		)

						# forward pass
						A	 =      model(**snake_case_		)
						A	 =      outputs.logits

						# verify the logits
						A	 =      (1, 2_1_8_4_1)
						self.assertEqual(logits.shape						,snake_case_		)

						A	 =      np.array([1.6881, -0.2787, 0.5901]		)

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

						A	 =      2_3_9_6
						self.assertEqual(logits.argmax(-1		).item()						,snake_case_		)













from ...configuration_utils import PretrainedConfig
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		snake_case    =   "bert-generation"
		def __init__(  self		:       Tuple						,_SCREAMING_SNAKE_CASE		:       Tuple=5_0_3_5_8						,_SCREAMING_SNAKE_CASE		:       str=1_0_2_4						,_SCREAMING_SNAKE_CASE		:       List[str]=2_4						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=1_6						,_SCREAMING_SNAKE_CASE		:       Dict=4_0_9_6						,_SCREAMING_SNAKE_CASE		:       str="gelu"						,_SCREAMING_SNAKE_CASE		:       str=0.1						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=0.1						,_SCREAMING_SNAKE_CASE		:       Tuple=5_1_2						,_SCREAMING_SNAKE_CASE		:       Optional[Any]=0.02						,_SCREAMING_SNAKE_CASE		:       List[str]=1E-12						,_SCREAMING_SNAKE_CASE		:       Tuple=0						,_SCREAMING_SNAKE_CASE		:       Union[str, Any]=2						,_SCREAMING_SNAKE_CASE		:       Optional[int]=1						,_SCREAMING_SNAKE_CASE		:       str="absolute"						,_SCREAMING_SNAKE_CASE		:       Optional[int]=True						,**_SCREAMING_SNAKE_CASE		:       Optional[int]						,)    ->   Dict:


						'''simple docstring'''
						super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE						,bos_token_id=_SCREAMING_SNAKE_CASE						,eos_token_id=_SCREAMING_SNAKE_CASE						,**_SCREAMING_SNAKE_CASE		)

						A	 =      vocab_size
						A	 =      hidden_size
						A	 =      num_hidden_layers
						A	 =      num_attention_heads
						A	 =      hidden_act
						A	 =      intermediate_size
						A	 =      hidden_dropout_prob
						A	 =      attention_probs_dropout_prob
						A	 =      max_position_embeddings
						A	 =      initializer_range
						A	 =      layer_norm_eps
						A	 =      position_embedding_type
						A	 =      use_cache





