






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 					_snake_case						(			unittest.TestCase ):






	def __init__(		self					,UpperCamelCase					,UpperCamelCase=100					,UpperCamelCase=13					,UpperCamelCase=30					,UpperCamelCase=2					,UpperCamelCase=3					,UpperCamelCase=True					,UpperCamelCase=True					,UpperCamelCase=32					,UpperCamelCase=5					,UpperCamelCase=4					,UpperCamelCase=37					,UpperCamelCase="gelu"					,UpperCamelCase=0.1					,UpperCamelCase=0.1					,UpperCamelCase=10					,UpperCamelCase=0.02					,UpperCamelCase=3					,)						->    Optional[int]:
					snake_case__      :Optional[int]         =   parent
					snake_case__      :Any         =   vocab_size
					snake_case__      :Any         =   batch_size
					snake_case__      :Union[str, Any]         =   image_size
					snake_case__      :Dict         =   patch_size
					snake_case__      :Optional[Any]         =   num_channels
					snake_case__      :Optional[int]         =   is_training
					snake_case__      :Optional[Any]         =   use_labels
					snake_case__      :Union[str, Any]         =   hidden_size
					snake_case__      :Optional[Any]         =   num_hidden_layers
					snake_case__      :Optional[int]         =   num_attention_heads
					snake_case__      :str         =   intermediate_size
					snake_case__      :int         =   hidden_act
					snake_case__      :str         =   hidden_dropout_prob
					snake_case__      :List[str]         =   attention_probs_dropout_prob
					snake_case__      :Optional[Any]         =   type_sequence_label_size
					snake_case__      :int         =   initializer_range

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






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :List[str]         =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

					snake_case__      :Tuple         =   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=UpperCamelCase					,initializer_range=self.initializer_range					,)

					return config, pixel_values, labels






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    Tuple:
					snake_case__      :Union[str, Any]         =   FlaxBeitModel(config=UpperCamelCase       )
					snake_case__      :Tuple         =   model(UpperCamelCase       )
					self.parent.assertEqual(result.last_hidden_state.shape					,(self.batch_size, self.seq_length, self.hidden_size)       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    Tuple:
					snake_case__      :Optional[Any]         =   FlaxBeitForMaskedImageModeling(config=UpperCamelCase       )
					snake_case__      :Optional[Any]         =   model(UpperCamelCase       )
					self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.seq_length - 1, self.vocab_size)       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase       )						->    int:
					snake_case__      :Any         =   self.type_sequence_label_size
					snake_case__      :List[Any]         =   FlaxBeitForImageClassification(config=UpperCamelCase       )
					snake_case__      :Optional[Any]         =   model(UpperCamelCase       )
					self.parent.assertEqual(result.logits.shape					,(self.batch_size, self.type_sequence_label_size)       )

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

					snake_case__      :List[Any]         =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]       )
					snake_case__      :Optional[Any]         =   model(UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					snake_case__      :Any         =   self.prepare_config_and_inputs()
					(
					    (
					    snake_case__
					)    ,						(
					    snake_case__
					)    ,						(
					    snake_case__
					)    ,						
					)      :List[Any]         =   config_and_inputs
					snake_case__      :str         =   {"pixel_values": pixel_values}
					return config, inputs_dict




@require_flax
class 					_snake_case						(			_A       ,    unittest.TestCase ):
	_A          =   (
	    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
	)






	def    lowerCAmelCase_							(		self       )						->    None:
					snake_case__      :List[str]         =   FlaxBeitModelTester(self       )
					snake_case__      :List[Any]         =   ConfigTester(self					,config_class=UpperCamelCase					,has_text_modality=UpperCamelCase					,hidden_size=37       )






	def    lowerCAmelCase_							(		self       )						->    Dict:
					self.config_tester.run_common_tests()






	def    lowerCAmelCase_							(		self       )						->    List[str]:
					snake_case__    ,						snake_case__      :Tuple         =   self.model_tester.prepare_config_and_inputs_for_common()

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

									snake_case__      :List[str]         =   ["pixel_values"]
									self.assertListEqual(arg_names[:1]					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__    ,						snake_case__      :Union[str, Any]         =   self.model_tester.prepare_config_and_inputs_for_common()

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

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

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

													with self.subTest("JIT Disabled"       ):
																	with jax.disable_jit():
																					snake_case__      :Optional[int]         =   model_jitted(**UpperCamelCase       ).to_tuple()

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






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :List[Any]         =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :Union[str, Any]         =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :List[str]         =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*UpperCamelCase       )






	@slow
	def    lowerCAmelCase_							(		self       )						->    Dict:
					for model_class_name in self.all_model_classes:
									snake_case__      :str         =   model_class_name.from_pretrained("microsoft/beit-base-patch16-224"       )
									snake_case__      :List[str]         =   model(np.ones((1, 3, 224, 224)       )       )
									self.assertIsNotNone(UpperCamelCase       )





def 					lowercase_ (    )     -> List[str]:





				'''simple docstring'''


				snake_case__      :Dict         =   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
				return image


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






	@cached_property
	def    lowerCAmelCase_							(		self       )						->    Any:
					return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"       ) if is_vision_available() else None






	@slow
	def    lowerCAmelCase_							(		self       )						->    List[str]:
					snake_case__      :int         =   FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"       )

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

					# prepare bool_masked_pos
					snake_case__      :Any         =   np.ones((1, 196)					,dtype=UpperCamelCase       )

					# forward pass
					snake_case__      :Optional[int]         =   model(pixel_values=UpperCamelCase					,bool_masked_pos=UpperCamelCase       )
					snake_case__      :int         =   outputs.logits

					# verify the logits
					snake_case__      :Optional[Any]         =   (1, 196, 8_192)
					self.assertEqual(logits.shape					,UpperCamelCase       )

					snake_case__      :Any         =   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]					,UpperCamelCase					,atol=1E-2       )       )






	@slow
	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :Optional[int]         =   FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"       )

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

					# forward pass
					snake_case__      :Dict         =   model(**UpperCamelCase       )
					snake_case__      :Optional[Any]         =   outputs.logits

					# verify the logits
					snake_case__      :List[str]         =   (1, 1_000)
					self.assertEqual(logits.shape					,UpperCamelCase       )

					snake_case__      :Dict         =   np.array([-1.2385, -1.0987, -1.0108]       )

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

					snake_case__      :Tuple         =   281
					self.assertEqual(logits.argmax(-1       ).item()					,UpperCamelCase       )






	@slow
	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :Dict         =   FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"       )

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

					# forward pass
					snake_case__      :Any         =   model(**UpperCamelCase       )
					snake_case__      :List[Any]         =   outputs.logits

					# verify the logits
					snake_case__      :int         =   (1, 21_841)
					self.assertEqual(logits.shape					,UpperCamelCase       )

					snake_case__      :List[Any]         =   np.array([1.6881, -0.2787, 0.5901]       )

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

					snake_case__      :Dict         =   2_396
					self.assertEqual(logits.argmax(-1       ).item()					,UpperCamelCase       )







import pytest


__UpperCAmelCase							:   int							=  "__dummy_dataset1__"

__UpperCAmelCase							:   int							=  "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n                \"ner_tags\": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            \"O\",\n                            \"B-PER\",\n                            \"I-PER\",\n                            \"B-ORG\",\n                            \"I-ORG\",\n                            \"B-LOC\",\n                            \"I-LOC\",\n                        ]\n                    )\n                ),\n                \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n                \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n"





@pytest.fixture
def 					lowercase_ (    )     -> Optional[Any]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_NAME





@pytest.fixture
def 					lowercase_ (    )     -> Optional[int]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_CODE





@pytest.fixture
def 					lowercase_ (    __snake_case     :							Optional[int]	,	__snake_case     :							List[Any]	,	__snake_case     :							Any      )     -> Dict:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   dataset_loading_script_name
				snake_case__      :Optional[Any]         =   tmp_path / "datasets" / script_name
				script_dir.mkdir(parents=__snake_case      )
				snake_case__      :List[Any]         =   script_dir / F'{script_name}.py'
				with open(__snake_case	,	"w"      ) as f:
								f.write(__snake_case      )
				return str(__snake_case      )