'''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 				_lowercase     (				unittest.TestCase			):


 '''simple docstring'''






 def __init__(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	Dict      ,       SCREAMING_SNAKE_CASE__   :	Any=1_00      ,       SCREAMING_SNAKE_CASE__   :	str=13      ,       SCREAMING_SNAKE_CASE__   :	Any=30      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=2      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=3      ,       SCREAMING_SNAKE_CASE__   :	str=True      ,       SCREAMING_SNAKE_CASE__   :	Dict=True      ,       SCREAMING_SNAKE_CASE__   :	Tuple=32      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=5      ,       SCREAMING_SNAKE_CASE__   :	int=4      ,       SCREAMING_SNAKE_CASE__   :	Dict=37      ,       SCREAMING_SNAKE_CASE__   :	List[Any]="gelu"      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=0.1      ,       SCREAMING_SNAKE_CASE__   :	List[str]=0.1      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=10      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=0.0_2      ,       SCREAMING_SNAKE_CASE__   :	List[str]=3      ,       )  ->    List[Any]:
  __lowerCAmelCase											=						parent
  __lowerCAmelCase											=						vocab_size
  __lowerCAmelCase											=						batch_size
  __lowerCAmelCase											=						image_size
  __lowerCAmelCase											=						patch_size
  __lowerCAmelCase											=						num_channels
  __lowerCAmelCase											=						is_training
  __lowerCAmelCase											=						use_labels
  __lowerCAmelCase											=						hidden_size
  __lowerCAmelCase											=						num_hidden_layers
  __lowerCAmelCase											=						num_attention_heads
  __lowerCAmelCase											=						intermediate_size
  __lowerCAmelCase											=						hidden_act
  __lowerCAmelCase											=						hidden_dropout_prob
  __lowerCAmelCase											=						attention_probs_dropout_prob
  __lowerCAmelCase											=						type_sequence_label_size
  __lowerCAmelCase											=						initializer_range

  # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
  __lowerCAmelCase											=						(image_size // patch_size) ** 2
  __lowerCAmelCase											=						num_patches + 1
 def a					(      self   :	List[Any]							)  ->    List[str]:
  __lowerCAmelCase											=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

  __lowerCAmelCase											=						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 a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	str      ,       SCREAMING_SNAKE_CASE__   :	str							)  ->    str:
  __lowerCAmelCase											=						FlaxBeitModel(config=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__							)
  self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)							)
 def a					(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	str      ,       SCREAMING_SNAKE_CASE__   :	Any      ,       SCREAMING_SNAKE_CASE__   :	int							)  ->    Tuple:
  __lowerCAmelCase											=						FlaxBeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__							)
  self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.seq_length - 1, self.vocab_size)							)
 def a					(      self   :	Union[str, Any]      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]      ,       SCREAMING_SNAKE_CASE__   :	int      ,       SCREAMING_SNAKE_CASE__   :	Dict							)  ->    Optional[Any]:
  __lowerCAmelCase											=						self.type_sequence_label_size
  __lowerCAmelCase											=						FlaxBeitForImageClassification(config=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__							)
  self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.type_sequence_label_size)							)

  # test greyscale images
  __lowerCAmelCase											=						1
  __lowerCAmelCase											=						FlaxBeitForImageClassification(SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
  __lowerCAmelCase											=						model(SCREAMING_SNAKE_CASE__							)





 def a					(      self   :	List[str]							)  ->    Union[str, Any]:
  __lowerCAmelCase											=						self.prepare_config_and_inputs()
  (
      (
      __lowerCAmelCase
  )							,       (
      __lowerCAmelCase
  )							,       (
      __lowerCAmelCase
  )							,       
  )											=						config_and_inputs
  __lowerCAmelCase											=						{"""pixel_values""": pixel_values}
  return config, inputs_dict







@require_flax
class 				_lowercase     (				UpperCAmelCase__  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       List[Any]      	=					(
     (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
 )
 def a					(      self   :	Tuple							)  ->    None:
  __lowerCAmelCase											=						FlaxBeitModelTester(self							)
  __lowerCAmelCase											=						ConfigTester(self      ,       config_class=SCREAMING_SNAKE_CASE__      ,       has_text_modality=SCREAMING_SNAKE_CASE__      ,       hidden_size=37							)
 def a					(      self   :	Any							)  ->    int:
  self.config_tester.run_common_tests()
 def a					(      self   :	Union[str, Any]							)  ->    Dict:
  __lowerCAmelCase							,       __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs_for_common()

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

   __lowerCAmelCase											=						["""pixel_values"""]
   self.assertListEqual(arg_names[:1]      ,       SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	str							)  ->    str:
  __lowerCAmelCase							,       __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   with self.subTest(model_class.__name__							):
    __lowerCAmelCase											=						self._prepare_for_class(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							)
    __lowerCAmelCase											=						model_class(SCREAMING_SNAKE_CASE__							)

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

    with self.subTest("""JIT Enabled"""							):
     __lowerCAmelCase											=						model_jitted(**SCREAMING_SNAKE_CASE__							).to_tuple()

    with self.subTest("""JIT Disabled"""							):
     with jax.disable_jit():
      __lowerCAmelCase											=						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 a					(      self   :	Dict							)  ->    Tuple:
  __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	int							)  ->    List[str]:
  __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	Optional[Any]							)  ->    List[str]:
  __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__							)





 @slow
 def a					(      self   :	Optional[Any]							)  ->    int:
  for model_class_name in self.all_model_classes:
   __lowerCAmelCase											=						model_class_name.from_pretrained("""microsoft/beit-base-patch16-224"""							)
   __lowerCAmelCase											=						model(np.ones((1, 3, 2_24, 2_24)							)							)
   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)







def      UpperCamelCase_						(					)					->     Optional[Any]:






 '''simple docstring'''

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







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


 '''simple docstring'''






 @cached_property
 def a					(      self   :	Union[str, Any]							)  ->    Optional[int]:
  return BeitImageProcessor.from_pretrained("""microsoft/beit-base-patch16-224"""							) if is_vision_available() else None
 @slow
 def a					(      self   :	Optional[int]							)  ->    Dict:
  __lowerCAmelCase											=						FlaxBeitForMaskedImageModeling.from_pretrained("""microsoft/beit-base-patch16-224-pt22k"""							)

  __lowerCAmelCase											=						self.default_image_processor
  __lowerCAmelCase											=						prepare_img()
  __lowerCAmelCase											=						image_processor(images=SCREAMING_SNAKE_CASE__      ,       return_tensors="""np"""							).pixel_values

  # prepare bool_masked_pos
  __lowerCAmelCase											=						np.ones((1, 1_96)      ,       dtype=SCREAMING_SNAKE_CASE__							)

  # forward pass
  __lowerCAmelCase											=						model(pixel_values=SCREAMING_SNAKE_CASE__      ,       bool_masked_pos=SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						outputs.logits

  # verify the logits
  __lowerCAmelCase											=						(1, 1_96, 81_92)
  self.assertEqual(logits.shape      ,       SCREAMING_SNAKE_CASE__							)

  __lowerCAmelCase											=						np.array(
      [[-3.2_4_3_7, 0.5_0_7_2, -1_3.9_1_7_4], [-3.2_4_5_6, 0.4_9_4_8, -1_3.9_4_0_1], [-3.2_0_3_3, 0.5_1_2_1, -1_3.8_5_5_0]]							)

  self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]      ,       SCREAMING_SNAKE_CASE__      ,       atol=1e-2							)							)
 @slow
 def a					(      self   :	Tuple							)  ->    Any:
  __lowerCAmelCase											=						FlaxBeitForImageClassification.from_pretrained("""microsoft/beit-base-patch16-224"""							)

  __lowerCAmelCase											=						self.default_image_processor
  __lowerCAmelCase											=						prepare_img()
  __lowerCAmelCase											=						image_processor(images=SCREAMING_SNAKE_CASE__      ,       return_tensors="""np"""							)

  # forward pass
  __lowerCAmelCase											=						model(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						outputs.logits

  # verify the logits
  __lowerCAmelCase											=						(1, 10_00)
  self.assertEqual(logits.shape      ,       SCREAMING_SNAKE_CASE__							)

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

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

  __lowerCAmelCase											=						2_81
  self.assertEqual(logits.argmax(-1							).item()      ,       SCREAMING_SNAKE_CASE__							)





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

  __lowerCAmelCase											=						self.default_image_processor
  __lowerCAmelCase											=						prepare_img()
  __lowerCAmelCase											=						image_processor(images=SCREAMING_SNAKE_CASE__      ,       return_tensors="""np"""							)

  # forward pass
  __lowerCAmelCase											=						model(**SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						outputs.logits

  # verify the logits
  __lowerCAmelCase											=						(1, 2_18_41)
  self.assertEqual(logits.shape      ,       SCREAMING_SNAKE_CASE__							)

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

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

  __lowerCAmelCase											=						23_96
  self.assertEqual(logits.argmax(-1							).item()      ,       SCREAMING_SNAKE_CASE__							)







'''simple docstring'''

import itertools
from dataclasses import dataclass
from typing import Optional

import pandas as pd
import pyarrow as pa

import datasets
from datasets.table import table_cast







@dataclass
class 				_lowercase     (				datasets.BuilderConfig			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Optional[datasets.Features]      	=					None







class 				_lowercase     (				datasets.ArrowBasedBuilder			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					PandasConfig
 def a					(      self   :	Union[str, Any]							)  ->    int:
  return datasets.DatasetInfo(features=self.config.features							)
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]							)  ->    str:
  if not self.config.data_files:
   raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
  __lowerCAmelCase											=						dl_manager.download_and_extract(self.config.data_files							)
  if isinstance(SCREAMING_SNAKE_CASE__      ,       (str, list, tuple)							):
   __lowerCAmelCase											=						data_files
   if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase											=						[files]
   # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
   __lowerCAmelCase											=						[dl_manager.iter_files(SCREAMING_SNAKE_CASE__							) for file in files]
   return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,       gen_kwargs={"""files""": files}							)]
  __lowerCAmelCase											=						[]
  for split_name, files in data_files.items():
   if isinstance(SCREAMING_SNAKE_CASE__      ,       SCREAMING_SNAKE_CASE__							):
    __lowerCAmelCase											=						[files]
   # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
   __lowerCAmelCase											=						[dl_manager.iter_files(SCREAMING_SNAKE_CASE__							) for file in files]
   splits.append(datasets.SplitGenerator(name=SCREAMING_SNAKE_CASE__      ,       gen_kwargs={"""files""": files}							)							)
  return splits
 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	pa.Table							)  ->    pa.Table:
  if self.config.features is not None:
   # more expensive cast to support nested features with keys in a different order
   # allows str <-> int/float or str to Audio for example
   __lowerCAmelCase											=						table_cast(SCREAMING_SNAKE_CASE__      ,       self.config.features.arrow_schema							)
  return pa_table





 def a					(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]							)  ->    List[Any]:
  for i, file in enumerate(itertools.chain.from_iterable(SCREAMING_SNAKE_CASE__							)							):
   with open(SCREAMING_SNAKE_CASE__      ,       """rb"""							) as f:
    __lowerCAmelCase											=						pa.Table.from_pandas(pd.read_pickle(SCREAMING_SNAKE_CASE__							)							)
    yield i, self._cast_table(SCREAMING_SNAKE_CASE__							)






