



"""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 	_a       (    unittest.TestCase):
			"""simple docstring"""



			def __init__(						self   :			Tuple ,      __UpperCamelCase   :			Optional[int] ,      __UpperCamelCase   :			str=1_0_0 ,      __UpperCamelCase   :			str=1_3 ,      __UpperCamelCase   :			Dict=3_0 ,      __UpperCamelCase   :			Dict=2 ,      __UpperCamelCase   :			int=3 ,      __UpperCamelCase   :			Any=True ,      __UpperCamelCase   :			Tuple=True ,      __UpperCamelCase   :			Union[str, Any]=3_2 ,      __UpperCamelCase   :			List[str]=5 ,      __UpperCamelCase   :			Any=4 ,      __UpperCamelCase   :			List[str]=3_7 ,      __UpperCamelCase   :			Any="gelu" ,      __UpperCamelCase   :			int=0.1 ,      __UpperCamelCase   :			Any=0.1 ,      __UpperCamelCase   :			Optional[int]=1_0 ,      __UpperCamelCase   :			Union[str, Any]=0.0_2 ,      __UpperCamelCase   :			Tuple=3 ,      )->Dict:
							_UpperCAmelCase							=			parent
							_UpperCAmelCase							=			vocab_size
							_UpperCAmelCase							=			batch_size
							_UpperCAmelCase							=			image_size
							_UpperCAmelCase							=			patch_size
							_UpperCAmelCase							=			num_channels
							_UpperCAmelCase							=			is_training
							_UpperCAmelCase							=			use_labels
							_UpperCAmelCase							=			hidden_size
							_UpperCAmelCase							=			num_hidden_layers
							_UpperCAmelCase							=			num_attention_heads
							_UpperCAmelCase							=			intermediate_size
							_UpperCAmelCase							=			hidden_act
							_UpperCAmelCase							=			hidden_dropout_prob
							_UpperCAmelCase							=			attention_probs_dropout_prob
							_UpperCAmelCase							=			type_sequence_label_size
							_UpperCAmelCase							=			initializer_range

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



			def        lowercase__  (						self   :			List[Any]      )->List[str]:
							_UpperCAmelCase							=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

							_UpperCAmelCase							=			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=UpperCAmelCase__ ,      initializer_range=self.initializer_range ,      )

							return config, pixel_values, labels



			def        lowercase__  (						self   :			List[Any] ,      __UpperCamelCase   :			Optional[int] ,      __UpperCamelCase   :			Optional[Any] ,      __UpperCamelCase   :			int      )->str:
							_UpperCAmelCase							=			FlaxBeitModel(config=UpperCAmelCase__      )
							_UpperCAmelCase							=			model(UpperCAmelCase__      )
							self.parent.assertEqual(result.last_hidden_state.shape ,      (self.batch_size, self.seq_length, self.hidden_size)      )



			def        lowercase__  (						self   :			str ,      __UpperCamelCase   :			Optional[Any] ,      __UpperCamelCase   :			Optional[Any] ,      __UpperCamelCase   :			Optional[int]      )->Any:
							_UpperCAmelCase							=			FlaxBeitForMaskedImageModeling(config=UpperCAmelCase__      )
							_UpperCAmelCase							=			model(UpperCAmelCase__      )
							self.parent.assertEqual(result.logits.shape ,      (self.batch_size, self.seq_length - 1, self.vocab_size)      )



			def        lowercase__  (						self   :			str ,      __UpperCamelCase   :			Dict ,      __UpperCamelCase   :			Dict ,      __UpperCamelCase   :			str      )->Optional[int]:
							_UpperCAmelCase							=			self.type_sequence_label_size
							_UpperCAmelCase							=			FlaxBeitForImageClassification(config=UpperCAmelCase__      )
							_UpperCAmelCase							=			model(UpperCAmelCase__      )
							self.parent.assertEqual(result.logits.shape ,      (self.batch_size, self.type_sequence_label_size)      )

							# test greyscale images
							_UpperCAmelCase							=			1
							_UpperCAmelCase							=			FlaxBeitForImageClassification(UpperCAmelCase__      )

							_UpperCAmelCase							=			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
							_UpperCAmelCase							=			model(UpperCAmelCase__      )



			def        lowercase__  (						self   :			Any      )->str:
							_UpperCAmelCase							=			self.prepare_config_and_inputs()
							(
							    (
							    _UpperCAmelCase  
							)						,  (
							    _UpperCAmelCase  
							)						,  (
							    _UpperCAmelCase  
							)						,    
							)							=			config_and_inputs
							_UpperCAmelCase							=			{'''pixel_values''': pixel_values}
							return config, inputs_dict



@require_flax
class 	_a       (    __lowercase	,							unittest.TestCase):
			"""simple docstring"""
			UpperCamelCase__						=		(
			    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
			)



			def        lowercase__  (						self   :			Any      )->None:
							_UpperCAmelCase							=			FlaxBeitModelTester(self      )
							_UpperCAmelCase							=			ConfigTester(self ,      config_class=UpperCAmelCase__ ,      has_text_modality=UpperCAmelCase__ ,      hidden_size=3_7      )



			def        lowercase__  (						self   :			List[Any]      )->List[Any]:
							self.config_tester.run_common_tests()



			def        lowercase__  (						self   :			Optional[int]      )->Tuple:
							_UpperCAmelCase						,  _UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs_for_common()

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

											_UpperCAmelCase							=			['''pixel_values''']
											self.assertListEqual(arg_names[:1] ,      UpperCAmelCase__      )



			def        lowercase__  (						self   :			Any      )->Union[str, Any]:
							_UpperCAmelCase						,  _UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs_for_common()

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

															@jax.jit
															def model_jitted(__UpperCamelCase   :			Dict ,      **__UpperCamelCase   :			Optional[int]      ):
																			return model(pixel_values=UpperCAmelCase__ ,      **UpperCAmelCase__      )

															with self.subTest('''JIT Enabled'''      ):
																			_UpperCAmelCase							=			model_jitted(**UpperCAmelCase__      ).to_tuple()

															with self.subTest('''JIT Disabled'''      ):
																			with jax.disable_jit():
																							_UpperCAmelCase							=			model_jitted(**UpperCAmelCase__      ).to_tuple()

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



			def        lowercase__  (						self   :			Dict      )->Optional[Any]:
							_UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCAmelCase__      )



			def        lowercase__  (						self   :			Optional[int]      )->str:
							_UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__      )



			def        lowercase__  (						self   :			List[str]      )->Dict:
							_UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__      )



			@slow
			def        lowercase__  (						self   :			str      )->Optional[Any]:
							for model_class_name in self.all_model_classes:
											_UpperCAmelCase							=			model_class_name.from_pretrained('''microsoft/beit-base-patch16-224'''      )
											_UpperCAmelCase							=			model(np.ones((1, 3, 2_2_4, 2_2_4)      )      )
											self.assertIsNotNone(UpperCAmelCase__      )







def        lowercase		(					):






				'''simple docstring'''



				_UpperCAmelCase							=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
				return image
@require_vision
@require_flax
class 	_a       (    unittest.TestCase):
			"""simple docstring"""



			@cached_property
			def        lowercase__  (						self   :			str      )->str:
							return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''      ) if is_vision_available() else None



			@slow
			def        lowercase__  (						self   :			Dict      )->int:
							_UpperCAmelCase							=			FlaxBeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''      )

							_UpperCAmelCase							=			self.default_image_processor
							_UpperCAmelCase							=			prepare_img()
							_UpperCAmelCase							=			image_processor(images=UpperCAmelCase__ ,      return_tensors='''np'''      ).pixel_values

							# prepare bool_masked_pos
							_UpperCAmelCase							=			np.ones((1, 1_9_6) ,      dtype=UpperCAmelCase__      )

							# forward pass
							_UpperCAmelCase							=			model(pixel_values=UpperCAmelCase__ ,      bool_masked_pos=UpperCAmelCase__      )
							_UpperCAmelCase							=			outputs.logits

							# verify the logits
							_UpperCAmelCase							=			(1, 1_9_6, 8_1_9_2)
							self.assertEqual(logits.shape ,      UpperCAmelCase__      )

							_UpperCAmelCase							=			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] ,      UpperCAmelCase__ ,      atol=1e-2      )      )



			@slow
			def        lowercase__  (						self   :			Any      )->List[str]:
							_UpperCAmelCase							=			FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''      )

							_UpperCAmelCase							=			self.default_image_processor
							_UpperCAmelCase							=			prepare_img()
							_UpperCAmelCase							=			image_processor(images=UpperCAmelCase__ ,      return_tensors='''np'''      )

							# forward pass
							_UpperCAmelCase							=			model(**UpperCAmelCase__      )
							_UpperCAmelCase							=			outputs.logits

							# verify the logits
							_UpperCAmelCase							=			(1, 1_0_0_0)
							self.assertEqual(logits.shape ,      UpperCAmelCase__      )

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

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

							_UpperCAmelCase							=			2_8_1
							self.assertEqual(logits.argmax(-1      ).item() ,      UpperCAmelCase__      )



			@slow
			def        lowercase__  (						self   :			int      )->str:
							_UpperCAmelCase							=			FlaxBeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''      )

							_UpperCAmelCase							=			self.default_image_processor
							_UpperCAmelCase							=			prepare_img()
							_UpperCAmelCase							=			image_processor(images=UpperCAmelCase__ ,      return_tensors='''np'''      )

							# forward pass
							_UpperCAmelCase							=			model(**UpperCAmelCase__      )
							_UpperCAmelCase							=			outputs.logits

							# verify the logits
							_UpperCAmelCase							=			(1, 2_1_8_4_1)
							self.assertEqual(logits.shape ,      UpperCAmelCase__      )

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

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

							_UpperCAmelCase							=			2_3_9_6
							self.assertEqual(logits.argmax(-1      ).item() ,      UpperCAmelCase__      )







'''simple docstring'''




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 	a_					( lowerCamelCase	:     Dict				):
   lowerCAmelCase     =			{}
   lowerCAmelCase     =			tokenizer(example['content']	, truncation=lowerCamelCase				)['input_ids']
   lowerCAmelCase     =			len(example['content']				) / len(output['input_ids']				)
   return output


__snake_case    						=HfArgumentParser(PretokenizationArguments)
__snake_case    						=parser.parse_args()
if args.num_workers is None:
       __snake_case    						=multiprocessing.cpu_count()
__snake_case    						=AutoTokenizer.from_pretrained(args.tokenizer_dir)

__snake_case    						=time.time()
__snake_case    						=load_dataset(args.dataset_name, split="""train""")
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

__snake_case    						=time.time()
__snake_case    						=ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

__snake_case    						=time.time()
ds.push_to_hub(args.tokenized_data_repo)




print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')





