




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       a__	( unittest.TestCase				):







     def __init__(     self  ,		A  ,		A=100  ,		A=13  ,		A=30  ,		A=2  ,		A=3  ,		A=True  ,		A=True  ,		A=32  ,		A=5  ,		A=4  ,		A=37  ,		A="gelu"  ,		A=0.1  ,		A=0.1  ,		A=10  ,		A=0.0_2  ,		A=3  ,		)  ->   Any:



          '''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       lowerCAmelCase_    (     self			)  ->   List[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=A  ,		initializer_range=self.initializer_range  ,		)

          return config, pixel_values, labels







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A			)  ->   List[Any]:



          '''simple docstring'''



          a     =				FlaxBeitModel(config=A			)
          a     =				model(A			)
          self.parent.assertEqual(result.last_hidden_state.shape  ,		(self.batch_size, self.seq_length, self.hidden_size)			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A			)  ->   str:



          '''simple docstring'''



          a     =				FlaxBeitForMaskedImageModeling(config=A			)
          a     =				model(A			)
          self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.seq_length - 1, self.vocab_size)			)







     def       lowerCAmelCase_    (     self  ,		A  ,		A  ,		A			)  ->   Dict:



          '''simple docstring'''



          a     =				self.type_sequence_label_size
          a     =				FlaxBeitForImageClassification(config=A			)
          a     =				model(A			)
          self.parent.assertEqual(result.logits.shape  ,		(self.batch_size, self.type_sequence_label_size)			)

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

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







     def       lowerCAmelCase_    (     self			)  ->   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       a__	( UpperCamelCase__       , unittest.TestCase				):
     a		:      Optional[int]		 =     (
         (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
     )







     def       lowerCAmelCase_    (     self			)  ->   None:



          '''simple docstring'''



          a     =				FlaxBeitModelTester(self			)
          a     =				ConfigTester(self  ,		config_class=A  ,		has_text_modality=A  ,		hidden_size=37			)







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          self.config_tester.run_common_tests()







     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          a  ,				a     =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               a     =				model_class(A			)
               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]  ,		A			)







     def       lowerCAmelCase_    (     self			)  ->   Tuple:



          '''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(A  ,		A			)
                    a     =				model_class(A			)

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

                    with self.subTest("JIT Enabled"			):
                         a     =				model_jitted(**A			).to_tuple()

                    with self.subTest("JIT Disabled"			):
                         with jax.disable_jit():
                              a     =				model_jitted(**A			).to_tuple()

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







     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A			)







     def       lowerCAmelCase_    (     self			)  ->   Optional[Any]:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*A			)







     def       lowerCAmelCase_    (     self			)  ->   Union[str, Any]:



          '''simple docstring'''



          a     =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*A			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   Optional[int]:



          '''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, 224, 224)			)			)
               self.assertIsNotNone(A			)







def  SCREAMING_SNAKE_CASE							(       )      ->					Any:
     a     =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
     return image





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







     @cached_property
     def       lowerCAmelCase_    (     self			)  ->   Any:



          '''simple docstring'''



          return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"			) if is_vision_available() else None







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



          '''simple docstring'''



          a     =				FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"			)

          a     =				self.default_image_processor
          a     =				prepare_img()
          a     =				image_processor(images=A  ,		return_tensors="np"			).pixel_values

          # prepare bool_masked_pos
          a     =				np.ones((1, 196)  ,		dtype=A			)

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

          # verify the logits
          a     =				(1, 196, 8192)
          self.assertEqual(logits.shape  ,		A			)

          a     =				np.array(
              [[-3.2_4_3_7, 0.5_0_7_2, -13.9174], [-3.2_4_5_6, 0.4_9_4_8, -13.9401], [-3.2_0_3_3, 0.5_1_2_1, -13.8550]]			)

          self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]  ,		A  ,		atol=1e-2			)			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   str:



          '''simple docstring'''



          a     =				FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"			)

          a     =				self.default_image_processor
          a     =				prepare_img()
          a     =				image_processor(images=A  ,		return_tensors="np"			)

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

          # verify the logits
          a     =				(1, 1000)
          self.assertEqual(logits.shape  ,		A			)

          a     =				np.array([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]			)

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

          a     =				281
          self.assertEqual(logits.argmax(-1			).item()  ,		A			)







     @slow
     def       lowerCAmelCase_    (     self			)  ->   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=A  ,		return_tensors="np"			)

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

          # verify the logits
          a     =				(1, 21841)
          self.assertEqual(logits.shape  ,		A			)

          a     =				np.array([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]			)

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

          a     =				2396
          self.assertEqual(logits.argmax(-1			).item()  ,		A			)







import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__				:      Any	   = logging.get_logger(__name__)

lowercase__				:      int	   = {
    "asapp/sew-d-tiny-100k": "https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}




class       a__	( UpperCamelCase__				):
     a		:      Optional[Any]		 =     """sew-d"""







     def __init__(     self  ,		A=32  ,		A=768  ,		A=12  ,		A=12  ,		A=3072  ,		A=2  ,		A=512  ,		A=256  ,		A=True  ,		A=True  ,		A=("p2c", "c2p")  ,		A="layer_norm"  ,		A="gelu_python"  ,		A=0.1  ,		A=0.1  ,		A=0.1  ,		A=0.0  ,		A=0.1  ,		A=0.0_2  ,		A=1e-7  ,		A=1e-5  ,		A="group"  ,		A="gelu"  ,		A=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)  ,		A=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)  ,		A=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)  ,		A=False  ,		A=128  ,		A=16  ,		A=True  ,		A=0.0_5  ,		A=10  ,		A=2  ,		A=0.0  ,		A=10  ,		A=0  ,		A="mean"  ,		A=False  ,		A=False  ,		A=256  ,		A=0  ,		A=1  ,		A=2  ,		**A  ,		)  ->   Dict:



          '''simple docstring'''



          super().__init__(**A  ,		pad_token_id=A  ,		bos_token_id=A  ,		eos_token_id=A			)
          a     =				hidden_size
          a     =				feat_extract_norm
          a     =				feat_extract_activation
          a     =				list(A			)
          a     =				list(A			)
          a     =				list(A			)
          a     =				conv_bias
          a     =				num_conv_pos_embeddings
          a     =				num_conv_pos_embedding_groups
          a     =				len(self.conv_dim			)
          a     =				num_hidden_layers
          a     =				intermediate_size
          a     =				squeeze_factor
          a     =				max_position_embeddings
          a     =				position_buckets
          a     =				share_att_key
          a     =				relative_attention
          a     =				norm_rel_ebd
          a     =				list(A			)
          a     =				hidden_act
          a     =				num_attention_heads
          a     =				hidden_dropout
          a     =				attention_dropout
          a     =				activation_dropout
          a     =				feat_proj_dropout
          a     =				final_dropout
          a     =				layer_norm_eps
          a     =				feature_layer_norm_eps
          a     =				initializer_range
          a     =				vocab_size

          if (
              (len(self.conv_stride			) != self.num_feat_extract_layers)
              or (len(self.conv_kernel			) != self.num_feat_extract_layers)
              or (len(self.conv_dim			) != self.num_feat_extract_layers)
          ):
               raise ValueError(
                   "Configuration for convolutional layers is incorrect."
                   "It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
                   F'''but is `len(config.conv_dim) = {len(self.conv_dim			)}`, `len(config.conv_stride)'''
                   F'''= {len(self.conv_stride			)}`, `len(config.conv_kernel) = {len(self.conv_kernel			)}`.'''			)

          # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
          a     =				apply_spec_augment
          a     =				mask_time_prob
          a     =				mask_time_length
          a     =				mask_time_min_masks
          a     =				mask_feature_prob
          a     =				mask_feature_length
          a     =				mask_feature_min_masks

          # ctc loss
          a     =				ctc_loss_reduction
          a     =				ctc_zero_infinity

          # sequence classification
          a     =				use_weighted_layer_sum
          a     =				classifier_proj_size







     @property
     def       lowerCAmelCase_    (     self			)  ->   List[str]:



          '''simple docstring'''



          return functools.reduce(operator.mul  ,		self.conv_stride  ,		1			)

