


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

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




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Any        =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

								UpperCAmelCase__  :					str        =     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=_lowerCamelCase							,							initializer_range=self.initializer_range							,							)

								return config, pixel_values, labels




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[Any]        =     FlaxBeitModel(config=_lowerCamelCase)
								UpperCAmelCase__  :					Optional[Any]        =     model(_lowerCamelCase)
								self.parent.assertEqual(result.last_hidden_state.shape							,							(self.batch_size, self.seq_length, self.hidden_size))




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					Optional[int]        =     FlaxBeitForMaskedImageModeling(config=_lowerCamelCase)
								UpperCAmelCase__  :					List[Any]        =     model(_lowerCamelCase)
								self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.seq_length - 1, self.vocab_size))




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase):
								UpperCAmelCase__  :					Tuple        =     self.type_sequence_label_size
								UpperCAmelCase__  :					str        =     FlaxBeitForImageClassification(config=_lowerCamelCase)
								UpperCAmelCase__  :					List[Any]        =     model(_lowerCamelCase)
								self.parent.assertEqual(result.logits.shape							,							(self.batch_size, self.type_sequence_label_size))

								# test greyscale images
								UpperCAmelCase__  :					Union[str, Any]        =     1
								UpperCAmelCase__  :					Union[str, Any]        =     FlaxBeitForImageClassification(_lowerCamelCase)

								UpperCAmelCase__  :					int        =     floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
								UpperCAmelCase__  :					Optional[Any]        =     model(_lowerCamelCase)




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


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




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					int        =     FlaxBeitModelTester(self)
								UpperCAmelCase__  :					str        =     ConfigTester(self							,							config_class=_lowerCamelCase							,							has_text_modality=_lowerCamelCase							,							hidden_size=37)




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




					def 				snake_case__						(      self):
								UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[int]        =     self.model_tester.prepare_config_and_inputs_for_common()

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

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




					def 				snake_case__						(      self):
								UpperCAmelCase__   ,       UpperCAmelCase__  :					Optional[Any]        =     self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__):
														UpperCAmelCase__  :					Tuple        =     self._prepare_for_class(_lowerCamelCase							,							_lowerCamelCase)
														UpperCAmelCase__  :					Any        =     model_class(_lowerCamelCase)

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

														with self.subTest("""JIT Enabled"""):
																	UpperCAmelCase__  :					Any        =     model_jitted(**_lowerCamelCase).to_tuple()

														with self.subTest("""JIT Disabled"""):
																	with jax.disable_jit():
																				UpperCAmelCase__  :					Any        =     model_jitted(**_lowerCamelCase).to_tuple()

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




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Any        =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_lowerCamelCase)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Union[str, Any]        =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase)




					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[Any]        =     self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase)




					@slow
					def 				snake_case__						(      self):
								for model_class_name in self.all_model_classes:
											UpperCAmelCase__  :					str        =     model_class_name.from_pretrained("""microsoft/beit-base-patch16-224""")
											UpperCAmelCase__  :					Optional[Any]        =     model(np.ones((1, 3, 224, 224)))
											self.assertIsNotNone(_lowerCamelCase)







def     _UpperCamelCase      (		):
			UpperCAmelCase__  :					Any        =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
			return image


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




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




					@slow
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Optional[Any]        =     FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k""")

								UpperCAmelCase__  :					int        =     self.default_image_processor
								UpperCAmelCase__  :					Union[str, Any]        =     prepare_img()
								UpperCAmelCase__  :					Optional[Any]        =     image_processor(images=_lowerCamelCase							,							return_tensors="""np""").pixel_values

								# prepare bool_masked_pos
								UpperCAmelCase__  :					Dict        =     np.ones((1, 196)							,							dtype=_lowerCamelCase)

								# forward pass
								UpperCAmelCase__  :					str        =     model(pixel_values=_lowerCamelCase							,							bool_masked_pos=_lowerCamelCase)
								UpperCAmelCase__  :					int        =     outputs.logits

								# verify the logits
								UpperCAmelCase__  :					str        =     (1, 196, 8192)
								self.assertEqual(logits.shape							,							_lowerCamelCase)

								UpperCAmelCase__  :					int        =     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]							,							_lowerCamelCase							,							atol=1e-2))




					@slow
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Dict        =     FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224""")

								UpperCAmelCase__  :					str        =     self.default_image_processor
								UpperCAmelCase__  :					Any        =     prepare_img()
								UpperCAmelCase__  :					Tuple        =     image_processor(images=_lowerCamelCase							,							return_tensors="""np""")

								# forward pass
								UpperCAmelCase__  :					Union[str, Any]        =     model(**_lowerCamelCase)
								UpperCAmelCase__  :					Any        =     outputs.logits

								# verify the logits
								UpperCAmelCase__  :					int        =     (1, 1000)
								self.assertEqual(logits.shape							,							_lowerCamelCase)

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

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

								UpperCAmelCase__  :					Optional[int]        =     281
								self.assertEqual(logits.argmax(-1).item()							,							_lowerCamelCase)




					@slow
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					List[str]        =     FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-large-patch16-224-pt22k-ft22k""")

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

								# forward pass
								UpperCAmelCase__  :					Optional[int]        =     model(**_lowerCamelCase)
								UpperCAmelCase__  :					Any        =     outputs.logits

								# verify the logits
								UpperCAmelCase__  :					Union[str, Any]        =     (1, 2_1841)
								self.assertEqual(logits.shape							,							_lowerCamelCase)

								UpperCAmelCase__  :					Union[str, Any]        =     np.array([1.6881, -0.2787, 0.5901])

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

								UpperCAmelCase__  :					Optional[int]        =     2396
								self.assertEqual(logits.argmax(-1).item()							,							_lowerCamelCase)



'''simple docstring'''



import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__A	=logging.get_logger(__name__)







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					List[str]        =     R"""\w+[.]\d+"""
			UpperCAmelCase__  :					List[Any]        =     re.findall(UpperCamelCase__ ,      UpperCamelCase__					)
			for pat in pats:
						UpperCAmelCase__  :					str        =     key.replace(UpperCamelCase__ ,      """_""".join(pat.split("""."""					)					)					)
			return key







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					str        =     pt_tuple_key[:-1] + ("""scale""",)
			if (
			    any("""norm""" in str_ for str_ in pt_tuple_key					)
			    and (pt_tuple_key[-1] == "bias")
			    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
			    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
			):
						UpperCAmelCase__  :					Union[str, Any]        =     pt_tuple_key[:-1] + ("""scale""",)
						return renamed_pt_tuple_key, pt_tensor
			elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
						UpperCAmelCase__  :					str        =     pt_tuple_key[:-1] + ("""scale""",)
						return renamed_pt_tuple_key, pt_tensor

			# embedding
			if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
						UpperCAmelCase__  :					Union[str, Any]        =     pt_tuple_key[:-1] + ("""embedding""",)
						return renamed_pt_tuple_key, pt_tensor

			# conv layer
			UpperCAmelCase__  :					Any        =     pt_tuple_key[:-1] + ("""kernel""",)
			if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
						UpperCAmelCase__  :					Any        =     pt_tensor.transpose(2 ,      3 ,      1 ,      0					)
						return renamed_pt_tuple_key, pt_tensor

			# linear layer
			UpperCAmelCase__  :					Optional[int]        =     pt_tuple_key[:-1] + ("""kernel""",)
			if pt_tuple_key[-1] == "weight":
						UpperCAmelCase__  :					Any        =     pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm weight
			UpperCAmelCase__  :					Optional[Any]        =     pt_tuple_key[:-1] + ("""weight""",)
			if pt_tuple_key[-1] == "gamma":
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm bias
			UpperCAmelCase__  :					Dict        =     pt_tuple_key[:-1] + ("""bias""",)
			if pt_tuple_key[-1] == "beta":
						return renamed_pt_tuple_key, pt_tensor

			return pt_tuple_key, pt_tensor







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__=4_2					):
			# Step 1: Convert pytorch tensor to numpy
			UpperCAmelCase__  :					str        =     {k: v.numpy() for k, v in pt_state_dict.items()}

			# Step 2: Since the model is stateless, get random Flax params
			UpperCAmelCase__  :					str        =     flax_model.init_weights(PRNGKey(UpperCamelCase__					)					)

			UpperCAmelCase__  :					Union[str, Any]        =     flatten_dict(UpperCamelCase__					)
			UpperCAmelCase__  :					int        =     {}

			# Need to change some parameters name to match Flax names
			for pt_key, pt_tensor in pt_state_dict.items():
						UpperCAmelCase__  :					str        =     rename_key(UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[Any]        =     tuple(renamed_pt_key.split("""."""					)					)

						# Correctly rename weight parameters
						UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     rename_key_and_reshape_tensor(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)

						if flax_key in random_flax_state_dict:
									if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
												raise ValueError(
												    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
												    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''					)

        # also add unexpected weight so that warning is thrown
						UpperCAmelCase__  :					str        =     jnp.asarray(UpperCamelCase__					)

			return unflatten_dict(UpperCamelCase__					)