





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        __SCREAMING_SNAKE_CASE					(     unittest.TestCase       ):






							def __init__(						self  , __lowerCAmelCase  , __lowerCAmelCase=100  , __lowerCAmelCase=13  , __lowerCAmelCase=30  , __lowerCAmelCase=2  , __lowerCAmelCase=3  , __lowerCAmelCase=True  , __lowerCAmelCase=True  , __lowerCAmelCase=32  , __lowerCAmelCase=5  , __lowerCAmelCase=4  , __lowerCAmelCase=37  , __lowerCAmelCase="gelu"  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=10  , __lowerCAmelCase=0.02  , __lowerCAmelCase=3  , ):
														UpperCamelCase__				     =			parent
														UpperCamelCase__				     =			vocab_size
														UpperCamelCase__				     =			batch_size
														UpperCamelCase__				     =			image_size
														UpperCamelCase__				     =			patch_size
														UpperCamelCase__				     =			num_channels
														UpperCamelCase__				     =			is_training
														UpperCamelCase__				     =			use_labels
														UpperCamelCase__				     =			hidden_size
														UpperCamelCase__				     =			num_hidden_layers
														UpperCamelCase__				     =			num_attention_heads
														UpperCamelCase__				     =			intermediate_size
														UpperCamelCase__				     =			hidden_act
														UpperCamelCase__				     =			hidden_dropout_prob
														UpperCamelCase__				     =			attention_probs_dropout_prob
														UpperCamelCase__				     =			type_sequence_label_size
														UpperCamelCase__				     =			initializer_range

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






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

														UpperCamelCase__				     =			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 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			FlaxBeitModel(config=snake_case_  )
														UpperCamelCase__				     =			model(snake_case_  )
														self.parent.assertEqual(result.last_hidden_state.shape  , (self.batch_size, self.seq_length, self.hidden_size)  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			FlaxBeitForMaskedImageModeling(config=snake_case_  )
														UpperCamelCase__				     =			model(snake_case_  )
														self.parent.assertEqual(result.logits.shape  , (self.batch_size, self.seq_length - 1, self.vocab_size)  )






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			self.type_sequence_label_size
														UpperCamelCase__				     =			FlaxBeitForImageClassification(config=snake_case_  )
														UpperCamelCase__				     =			model(snake_case_  )
														self.parent.assertEqual(result.logits.shape  , (self.batch_size, self.type_sequence_label_size)  )

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

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







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.prepare_config_and_inputs()
														(
														    (
														    UpperCamelCase__						
														)     ,     (
														    UpperCamelCase__						
														)     ,     (
														    UpperCamelCase__						
														)     ,     						
														)				     =			config_and_inputs
														UpperCamelCase__				     =			{"""pixel_values""": pixel_values}
														return config, inputs_dict






@require_flax
class        __SCREAMING_SNAKE_CASE					(     _a   ,		unittest.TestCase       ):
							snake_case     :      int						     =       (
							    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
							)






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			FlaxBeitModelTester(self  )
														UpperCamelCase__				     =			ConfigTester(self  , config_class=snake_case_  , has_text_modality=snake_case_  , hidden_size=37  )






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






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			self.model_tester.prepare_config_and_inputs_for_common()

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

																					UpperCamelCase__				     =			["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]  , snake_case_  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__     ,     UpperCamelCase__				     =			self.model_tester.prepare_config_and_inputs_for_common()

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

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

																												with self.subTest("""JIT Enabled"""  ):
																																			UpperCamelCase__				     =			model_jitted(**snake_case_  ).to_tuple()

																												with self.subTest("""JIT Disabled"""  ):
																																			with jax.disable_jit():
																																										UpperCamelCase__				     =			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 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*snake_case_  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_masked_lm(*snake_case_  )






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*snake_case_  )







							@slow
							def 				_lowerCamelCase		(						self  ):
														for model_class_name in self.all_model_classes:
																					UpperCamelCase__				     =			model_class_name.from_pretrained("""microsoft/beit-base-patch16-224"""  )
																					UpperCamelCase__				     =			model(np.ones((1, 3, 224, 224)  )  )
																					self.assertIsNotNone(snake_case_  )






def     _UpperCamelCase     ():






							"""simple docstring"""





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

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






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






							@slow
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""  )

														UpperCamelCase__				     =			self.default_image_processor
														UpperCamelCase__				     =			prepare_img()
														UpperCamelCase__				     =			image_processor(images=snake_case_  , return_tensors="""np"""  ).pixel_values

														# prepare bool_masked_pos
														UpperCamelCase__				     =			np.ones((1, 196)  , dtype=snake_case_  )

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

														# verify the logits
														UpperCamelCase__				     =			(1, 196, 8192)
														self.assertEqual(logits.shape  , snake_case_  )

														UpperCamelCase__				     =			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 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""  )

														UpperCamelCase__				     =			self.default_image_processor
														UpperCamelCase__				     =			prepare_img()
														UpperCamelCase__				     =			image_processor(images=snake_case_  , return_tensors="""np"""  )

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

														# verify the logits
														UpperCamelCase__				     =			(1, 1000)
														self.assertEqual(logits.shape  , snake_case_  )

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

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

														UpperCamelCase__				     =			281
														self.assertEqual(logits.argmax(-1  ).item()  , snake_case_  )







							@slow
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k"""  )

														UpperCamelCase__				     =			self.default_image_processor
														UpperCamelCase__				     =			prepare_img()
														UpperCamelCase__				     =			image_processor(images=snake_case_  , return_tensors="""np"""  )

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

														# verify the logits
														UpperCamelCase__				     =			(1, 21841)
														self.assertEqual(logits.shape  , snake_case_  )

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

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

														UpperCamelCase__				     =			2396
														self.assertEqual(logits.argmax(-1  ).item()  , snake_case_  )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase__											= {
    "configuration_pix2struct": [
        "PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Pix2StructConfig",
        "Pix2StructTextConfig",
        "Pix2StructVisionConfig",
    ],
    "processing_pix2struct": ["Pix2StructProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__											= ["Pix2StructImageProcessor"]


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__											= [
					    "PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "Pix2StructPreTrainedModel",
					    "Pix2StructForConditionalGeneration",
					    "Pix2StructVisionModel",
					    "Pix2StructTextModel",
					]

if TYPE_CHECKING:
					from .configuration_pixastruct import (
					    PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    PixaStructConfig,
					    PixaStructTextConfig,
					    PixaStructVisionConfig,
					)
					from .processing_pixastruct import PixaStructProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_pixastruct import PixaStructImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_pixastruct import (
										    PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    PixaStructForConditionalGeneration,
										    PixaStructPreTrainedModel,
										    PixaStructTextModel,
										    PixaStructVisionModel,
										)

else:
					import sys

					UpperCamelCase__											= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




