



'''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	):
    """simple docstring"""







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

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




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

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

          snake_case__  :    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=snake_case_ ,     initializer_range=self.initializer_range ,     )

          return config, pixel_values, labels




    def lowerCamelCase       (    self					:     Any ,     snake_case_					:     Dict ,     snake_case_					:     Tuple ,     snake_case_					:     List[str]						):
          snake_case__  :    Union[str, Any]    						=      FlaxBeitModel(config=snake_case_						)
          snake_case__  :    Optional[int]    						=      model(snake_case_						)
          self.parent.assertEqual(result.last_hidden_state.shape ,     (self.batch_size, self.seq_length, self.hidden_size)						)




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




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     List[Any] ,     snake_case_					:     List[Any] ,     snake_case_					:     Tuple						):
          snake_case__  :    Dict    						=      self.type_sequence_label_size
          snake_case__  :    int    						=      FlaxBeitForImageClassification(config=snake_case_						)
          snake_case__  :    Optional[int]    						=      model(snake_case_						)
          self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.type_sequence_label_size)						)

          # test greyscale images
          snake_case__  :    str    						=      1
          snake_case__  :    List[str]    						=      FlaxBeitForImageClassification(snake_case_						)

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




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







@require_flax
class     UpperCAmelCase_       (       _a		,		unittest.TestCase	):
    """simple docstring"""







    lowercase					=		(
        (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
    )




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    str    						=      FlaxBeitModelTester(self						)
          snake_case__  :    Tuple    						=      ConfigTester(self ,     config_class=snake_case_ ,     has_text_modality=snake_case_ ,     hidden_size=37						)




    def lowerCamelCase       (    self					:     Union[str, Any]						):
          self.config_tester.run_common_tests()




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

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

                snake_case__  :    Union[str, Any]    						=      ["""pixel_values"""]
                self.assertListEqual(arg_names[:1] ,     snake_case_						)




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__						,				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__						):
                      snake_case__  :    str    						=      self._prepare_for_class(snake_case_ ,     snake_case_						)
                      snake_case__  :    Optional[Any]    						=      model_class(snake_case_						)

                      @jax.jit
                      def model_jitted(snake_case_					:     List[Any] ,     **snake_case_					:     Tuple						):
                            return model(pixel_values=snake_case_ ,     **snake_case_						)

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

                      with self.subTest("""JIT Disabled"""						):
                            with jax.disable_jit():
                                  snake_case__  :    Tuple    						=      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					:     List[Any]						):
          snake_case__  :    List[str]    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case_						)




    def lowerCamelCase       (    self					:     Optional[int]						):
          snake_case__  :    List[Any]    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*snake_case_						)




    def lowerCamelCase       (    self					:     Any						):
          snake_case__  :    Dict    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case_						)




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





def 						__snake_case(				)		-> Optional[Any]:
      snake_case__  :    Optional[Any]    						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
      return image







@require_vision
@require_flax
class     UpperCAmelCase_       (       unittest.TestCase	):
    """simple docstring"""







    @cached_property
    def lowerCamelCase       (    self					:     Optional[int]						):
          return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""						) if is_vision_available() else None




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

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

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

          # forward pass
          snake_case__  :    Dict    						=      model(pixel_values=snake_case_ ,     bool_masked_pos=snake_case_						)
          snake_case__  :    List[str]    						=      outputs.logits

          # verify the logits
          snake_case__  :    Dict    						=      (1, 196, 8_192)
          self.assertEqual(logits.shape ,     snake_case_						)

          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] ,     snake_case_ ,     atol=1E-2						)						)




    @slow
    def lowerCamelCase       (    self					:     Tuple						):
          snake_case__  :    List[Any]    						=      FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""						)

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

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

          # verify the logits
          snake_case__  :    str    						=      (1, 1_000)
          self.assertEqual(logits.shape ,     snake_case_						)

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

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

          snake_case__  :    Union[str, Any]    						=      281
          self.assertEqual(logits.argmax(-1						).item() ,     snake_case_						)




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

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

          # forward pass
          snake_case__  :    Optional[int]    						=      model(**snake_case_						)
          snake_case__  :    int    						=      outputs.logits

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

          snake_case__  :    Optional[int]    						=      np.array([1.6881, -0.2787, 0.5901]						)

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

          snake_case__  :    Optional[int]    						=      2_396
          self.assertEqual(logits.argmax(-1						).item() ,     snake_case_						)





'''simple docstring'''





import os
import time

import pytest

from datasets.utils.filelock import FileLock, Timeout





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    int    						=      FileLock(str(tmpdir / """foo.lock"""		)		)
      snake_case__  :    Dict    						=      FileLock(str(tmpdir / """foo.lock"""		)		)
      snake_case__  :    List[str]    						=      0.01
      with locka.acquire():
            with pytest.raises(_lowerCAmelCase		):
                  snake_case__  :    str    						=      time.time()
                  locka.acquire(_lowerCAmelCase		)
                  assert time.time() - _start > timeout





def 						__snake_case(				_lowerCAmelCase		)		-> Tuple:
      snake_case__  :    Dict    						=      """a""" * 1_000 + """.lock"""
      snake_case__  :    int    						=      FileLock(str(tmpdir / filename		)		)
      assert locka._lock_file.endswith(""".lock"""		)
      assert not locka._lock_file.endswith(_lowerCAmelCase		)
      assert len(os.path.basename(locka._lock_file		)		) <= 255
      snake_case__  :    Dict    						=      FileLock(tmpdir / filename		)
      with locka.acquire():
            with pytest.raises(_lowerCAmelCase		):
                  locka.acquire(0		)
