


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 					__magic_name__		(   unittest.TestCase):


     def __init__(					self							:   Optional[int]							,		lowerCamelCase__							:   Dict							,		lowerCamelCase__							:   Any=100							,		lowerCamelCase__							:   str=13							,		lowerCamelCase__							:   Any=30							,		lowerCamelCase__							:   Union[str, Any]=2							,		lowerCamelCase__							:   Optional[int]=3							,		lowerCamelCase__							:   str=True							,		lowerCamelCase__							:   Dict=True							,		lowerCamelCase__							:   Tuple=32							,		lowerCamelCase__							:   Optional[Any]=5							,		lowerCamelCase__							:   int=4							,		lowerCamelCase__							:   Dict=37							,		lowerCamelCase__							:   List[Any]="gelu"							,		lowerCamelCase__							:   Optional[int]=0.1							,		lowerCamelCase__							:   List[str]=0.1							,		lowerCamelCase__							:   Optional[int]=10							,		lowerCamelCase__							:   List[Any]=0.02							,		lowerCamelCase__							:   List[str]=3							,		)     ->					List[Any]:





      '''simple docstring'''






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

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


     def    UpperCAmelCase__		(					self							:   List[Any]	)     ->					List[str]:





      '''simple docstring'''






      UpperCamelCase__    :			Optional[Any]      				=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

      UpperCamelCase__    :			Dict      				=      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=SCREAMING_SNAKE_CASE__							,		initializer_range=self.initializer_range							,		)

      return config, pixel_values, labels


     def    UpperCAmelCase__		(					self							:   Tuple							,		lowerCamelCase__							:   int							,		lowerCamelCase__							:   str							,		lowerCamelCase__							:   str	)     ->					str:





      '''simple docstring'''






      UpperCamelCase__    :			List[str]      				=      FlaxBeitModel(config=SCREAMING_SNAKE_CASE__	)
      UpperCamelCase__    :			Dict      				=      model(SCREAMING_SNAKE_CASE__	)
      self.parent.assertEqual(result.last_hidden_state.shape							,		(self.batch_size, self.seq_length, self.hidden_size)	)


     def    UpperCAmelCase__		(					self							:   List[str]							,		lowerCamelCase__							:   str							,		lowerCamelCase__							:   Any							,		lowerCamelCase__							:   int	)     ->					Tuple:





      '''simple docstring'''






      UpperCamelCase__    :			str      				=      FlaxBeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE__	)
      UpperCamelCase__    :			int      				=      model(SCREAMING_SNAKE_CASE__	)
      self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.seq_length - 1, self.vocab_size)	)


     def    UpperCAmelCase__		(					self							:   Union[str, Any]							,		lowerCamelCase__							:   Optional[Any]							,		lowerCamelCase__							:   int							,		lowerCamelCase__							:   Dict	)     ->					Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__    :			Tuple      				=      self.type_sequence_label_size
      UpperCamelCase__    :			Union[str, Any]      				=      FlaxBeitForImageClassification(config=SCREAMING_SNAKE_CASE__	)
      UpperCamelCase__    :			Union[str, Any]      				=      model(SCREAMING_SNAKE_CASE__	)
      self.parent.assertEqual(result.logits.shape							,		(self.batch_size, self.type_sequence_label_size)	)

      # test greyscale images
      UpperCamelCase__    :			str      				=      1
      UpperCamelCase__    :			Optional[int]      				=      FlaxBeitForImageClassification(SCREAMING_SNAKE_CASE__	)

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


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





      '''simple docstring'''






      UpperCamelCase__    :			str      				=      self.prepare_config_and_inputs()
      (
          (
          UpperCamelCase__       
      )				, (
          UpperCamelCase__       
      )				, (
          UpperCamelCase__       
      )				,        
      )    :			Any      				=      config_and_inputs
      UpperCamelCase__    :			List[str]      				=      {'''pixel_values''': pixel_values}
      return config, inputs_dict



@require_flax
class 					__magic_name__		(   UpperCAmelCase__    , unittest.TestCase):
     A:						List[Any]								=					(
         (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
     )


     def    UpperCAmelCase__		(					self							:   Tuple	)     ->					None:





      '''simple docstring'''






      UpperCamelCase__    :			Optional[Any]      				=      FlaxBeitModelTester(self	)
      UpperCamelCase__    :			Union[str, Any]      				=      ConfigTester(self							,		config_class=SCREAMING_SNAKE_CASE__							,		has_text_modality=SCREAMING_SNAKE_CASE__							,		hidden_size=37	)


     def    UpperCAmelCase__		(					self							:   Any	)     ->					int:





      '''simple docstring'''






      self.config_tester.run_common_tests()


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





      '''simple docstring'''






      UpperCamelCase__				, UpperCamelCase__    :			Any      				=      self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCamelCase__    :			List[Any]      				=      ['''pixel_values''']
       self.assertListEqual(arg_names[:1]							,		SCREAMING_SNAKE_CASE__	)


     def    UpperCAmelCase__		(					self							:   str	)     ->					str:





      '''simple docstring'''






      UpperCamelCase__				, UpperCamelCase__    :			str      				=      self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__	):
        UpperCamelCase__    :			Optional[int]      				=      self._prepare_for_class(SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__	)
        UpperCamelCase__    :			int      				=      model_class(SCREAMING_SNAKE_CASE__	)

        @jax.jit
        def model_jitted(lowerCamelCase__							:   int							,		**lowerCamelCase__							:   Tuple	):
         return model(pixel_values=SCREAMING_SNAKE_CASE__							,		**SCREAMING_SNAKE_CASE__	)

        with self.subTest('''JIT Enabled'''	):
         UpperCamelCase__    :			Optional[Any]      				=      model_jitted(**SCREAMING_SNAKE_CASE__	).to_tuple()

        with self.subTest('''JIT Disabled'''	):
         with jax.disable_jit():
          UpperCamelCase__    :			Dict      				=      model_jitted(**SCREAMING_SNAKE_CASE__	).to_tuple()

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


     def    UpperCAmelCase__		(					self							:   Dict	)     ->					Tuple:





      '''simple docstring'''






      UpperCamelCase__    :			str      				=      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__	)


     def    UpperCAmelCase__		(					self							:   int	)     ->					List[str]:





      '''simple docstring'''






      UpperCamelCase__    :			List[Any]      				=      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__	)


     def    UpperCAmelCase__		(					self							:   Optional[Any]	)     ->					List[str]:





      '''simple docstring'''






      UpperCamelCase__    :			int      				=      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__	)


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





      '''simple docstring'''






      for model_class_name in self.all_model_classes:
       UpperCamelCase__    :			str      				=      model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''	)
       UpperCamelCase__    :			Optional[int]      				=      model(np.ones((1, 3, 224, 224)	)	)
       self.assertIsNotNone(SCREAMING_SNAKE_CASE__	)



def 	_a     ( ):







 """simple docstring"""



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






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


     @cached_property
     def    UpperCAmelCase__		(					self							:   Union[str, Any]	)     ->					Optional[int]:





      '''simple docstring'''






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


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





      '''simple docstring'''






      UpperCamelCase__    :			Dict      				=      FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''	)

      UpperCamelCase__    :			Any      				=      self.default_image_processor
      UpperCamelCase__    :			Dict      				=      prepare_img()
      UpperCamelCase__    :			str      				=      image_processor(images=SCREAMING_SNAKE_CASE__							,		return_tensors='''np'''	).pixel_values

      # prepare bool_masked_pos
      UpperCamelCase__    :			Tuple      				=      np.ones((1, 196)							,		dtype=SCREAMING_SNAKE_CASE__	)

      # forward pass
      UpperCamelCase__    :			Dict      				=      model(pixel_values=SCREAMING_SNAKE_CASE__							,		bool_masked_pos=SCREAMING_SNAKE_CASE__	)
      UpperCamelCase__    :			int      				=      outputs.logits

      # verify the logits
      UpperCamelCase__    :			Optional[int]      				=      (1, 196, 8192)
      self.assertEqual(logits.shape							,		SCREAMING_SNAKE_CASE__	)

      UpperCamelCase__    :			Union[str, 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]							,		SCREAMING_SNAKE_CASE__							,		atol=1E-2	)	)


     @slow
     def    UpperCAmelCase__		(					self							:   Tuple	)     ->					Any:





      '''simple docstring'''






      UpperCamelCase__    :			List[Any]      				=      FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''	)

      UpperCamelCase__    :			Optional[int]      				=      self.default_image_processor
      UpperCamelCase__    :			List[Any]      				=      prepare_img()
      UpperCamelCase__    :			List[str]      				=      image_processor(images=SCREAMING_SNAKE_CASE__							,		return_tensors='''np'''	)

      # forward pass
      UpperCamelCase__    :			Dict      				=      model(**SCREAMING_SNAKE_CASE__	)
      UpperCamelCase__    :			Dict      				=      outputs.logits

      # verify the logits
      UpperCamelCase__    :			List[str]      				=      (1, 1000)
      self.assertEqual(logits.shape							,		SCREAMING_SNAKE_CASE__	)

      UpperCamelCase__    :			Union[str, Any]      				=      np.array([-1.2385, -1.0987, -1.0108]	)

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

      UpperCamelCase__    :			Tuple      				=      281
      self.assertEqual(logits.argmax(-1	).item()							,		SCREAMING_SNAKE_CASE__	)


     @slow
     def    UpperCAmelCase__		(					self							:   List[Any]	)     ->					List[str]:





      '''simple docstring'''






      UpperCamelCase__    :			Optional[int]      				=      FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''	)

      UpperCamelCase__    :			List[Any]      				=      self.default_image_processor
      UpperCamelCase__    :			List[str]      				=      prepare_img()
      UpperCamelCase__    :			List[Any]      				=      image_processor(images=SCREAMING_SNAKE_CASE__							,		return_tensors='''np'''	)

      # forward pass
      UpperCamelCase__    :			Dict      				=      model(**SCREAMING_SNAKE_CASE__	)
      UpperCamelCase__    :			Optional[int]      				=      outputs.logits

      # verify the logits
      UpperCamelCase__    :			List[str]      				=      (1, 21841)
      self.assertEqual(logits.shape							,		SCREAMING_SNAKE_CASE__	)

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

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

      UpperCamelCase__    :			int      				=      2396
      self.assertEqual(logits.argmax(-1	).item()							,		SCREAMING_SNAKE_CASE__	)



'''simple docstring'''

import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType







class 				_lowercase     :


 '''simple docstring'''






 def __init__(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	List[str]=2      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=3      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=64      ,       SCREAMING_SNAKE_CASE__   :	int=None							)  ->    Optional[int]:
  __lowerCAmelCase											=						np.random.default_rng(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						length
  __lowerCAmelCase											=						rng.normal(size=(length,)							).astype(np.floataa							)
  __lowerCAmelCase											=						a * self.x + b + rng.normal(scale=0.1      ,       size=(length,)							).astype(np.floataa							)
 def __len__(      self   :	Union[str, Any]							)  ->    Optional[Any]:
  return self.length





 def __getitem__(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	Dict							)  ->    Optional[int]:
  return {"x": self.x[i], "y": self.y[i]}







class 				_lowercase     (				torch.nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Tuple=0      ,       SCREAMING_SNAKE_CASE__   :	Tuple=False							)  ->    Any:
  super().__init__()
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
  __lowerCAmelCase											=						True





 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=None							)  ->    str:
  if self.first_batch:
   print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""							)
   __lowerCAmelCase											=						False
  return x * self.a[0] + self.b[0]







class 				_lowercase     (				torch.nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=False							)  ->    Optional[Any]:
  super().__init__()
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__							).float()							)
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__							).float()							)
  __lowerCAmelCase											=						True





 def a					(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Any=None							)  ->    int:
  if self.first_batch:
   print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""							)
   __lowerCAmelCase											=						False
  return x * self.a + self.b







def      UpperCamelCase_						(					snake_case_       :       List[str]						,		snake_case_       :       int = 16      )					->     int:






 '''simple docstring'''

 from datasets import load_dataset
 from transformers import AutoTokenizer

 __lowerCAmelCase											=						AutoTokenizer.from_pretrained("""bert-base-cased"""      )
 __lowerCAmelCase											=						{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
 __lowerCAmelCase											=						load_dataset("""csv"""						,		data_files=snake_case_      )
 __lowerCAmelCase											=						datasets["""train"""].unique("""label"""      )

 __lowerCAmelCase											=						{v: i for i, v in enumerate(snake_case_      )}

 def tokenize_function(snake_case_       :       Optional[int]      ):
  # max_length=None => use the model max length (it's actually the default)
  __lowerCAmelCase											=						tokenizer(
      examples["""sentence1"""]						,		examples["""sentence2"""]						,		truncation=snake_case_						,		max_length=snake_case_						,		padding="""max_length"""      )
  if "label" in examples:
   __lowerCAmelCase											=						[label_to_id[l] for l in examples["""label"""]]
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 __lowerCAmelCase											=						datasets.map(
     snake_case_						,		batched=snake_case_						,		remove_columns=["""sentence1""", """sentence2""", """label"""]						,		)

 def collate_fn(snake_case_       :       List[Any]      ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  if accelerator.distributed_type == DistributedType.TPU:
   return tokenizer.pad(snake_case_						,		padding="""max_length"""						,		max_length=1_28						,		return_tensors="""pt"""      )
  return tokenizer.pad(snake_case_						,		padding="""longest"""						,		return_tensors="""pt"""      )

 # Instantiate dataloaders.
 __lowerCAmelCase											=						DataLoader(tokenized_datasets["""train"""]						,		shuffle=snake_case_						,		collate_fn=snake_case_						,		batch_size=2      )
 __lowerCAmelCase											=						DataLoader(tokenized_datasets["""validation"""]						,		shuffle=snake_case_						,		collate_fn=snake_case_						,		batch_size=1      )

 return train_dataloader, eval_dataloader






