






import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.distilbert.modeling_flax_distilbert import (
	    FlaxDistilBertForMaskedLM,
	    FlaxDistilBertForMultipleChoice,
	    FlaxDistilBertForQuestionAnswering,
	    FlaxDistilBertForSequenceClassification,
	    FlaxDistilBertForTokenClassification,
	    FlaxDistilBertModel,
	)






class 						UpperCAmelCase__ (							unittest.TestCase							):







		def __init__(					self       ,A__       ,A__=13       ,A__=7       ,A__=True       ,A__=True       ,A__=True       ,A__=True       ,A__=99       ,A__=32       ,A__=5       ,A__=4       ,A__=37       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=512       ,A__=16       ,A__=2       ,A__=0.02       ,A__=4       ,):
						_A	:				List[str]   =      parent
						_A	:				int   =      batch_size
						_A	:				Optional[Any]   =      seq_length
						_A	:				Optional[Any]   =      is_training
						_A	:				Optional[Any]   =      use_attention_mask
						_A	:				Tuple   =      use_token_type_ids
						_A	:				Any   =      use_labels
						_A	:				Union[str, Any]   =      vocab_size
						_A	:				List[str]   =      hidden_size
						_A	:				Optional[int]   =      num_hidden_layers
						_A	:				int   =      num_attention_heads
						_A	:				List[str]   =      intermediate_size
						_A	:				List[str]   =      hidden_act
						_A	:				Union[str, Any]   =      hidden_dropout_prob
						_A	:				Union[str, Any]   =      attention_probs_dropout_prob
						_A	:				str   =      max_position_embeddings
						_A	:				List[Any]   =      type_vocab_size
						_A	:				str   =      type_sequence_label_size
						_A	:				List[str]   =      initializer_range
						_A	:				Optional[Any]   =      num_choices







		def  A__      (					self		):
						_A	:				int   =      ids_tensor([self.batch_size, self.seq_length]       ,self.vocab_size		)

						_A	:				Union[str, Any]   =      None
						if self.use_attention_mask:
										_A	:				str   =      random_attention_mask([self.batch_size, self.seq_length]		)

						_A	:				List[str]   =      DistilBertConfig(
						    vocab_size=self.vocab_size       ,dim=self.hidden_size       ,n_layers=self.num_hidden_layers       ,n_heads=self.num_attention_heads       ,hidden_dim=self.intermediate_size       ,hidden_act=self.hidden_act       ,dropout=self.hidden_dropout_prob       ,attention_dropout=self.attention_probs_dropout_prob       ,max_position_embeddings=self.max_position_embeddings       ,initializer_range=self.initializer_range       ,tie_weights_=A__       ,)

						return config, input_ids, attention_mask







		def  A__      (					self		):
						_A	:				str   =      self.prepare_config_and_inputs()
						_A  ,				_A  ,				_A	:				Dict   =      config_and_inputs
						_A	:				int   =      {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
						return config, inputs_dict






@require_flax
class 						UpperCAmelCase__ (							__snake_case     ,				unittest.TestCase							):
		__snake_case      :       List[Any]     =   (
		    (
		        FlaxDistilBertModel,
		        FlaxDistilBertForMaskedLM,
		        FlaxDistilBertForMultipleChoice,
		        FlaxDistilBertForQuestionAnswering,
		        FlaxDistilBertForSequenceClassification,
		        FlaxDistilBertForTokenClassification,
		        FlaxDistilBertForQuestionAnswering,
		    )
		    if is_flax_available()
		    else ()
		)







		def  A__      (					self		):
						_A	:				Tuple   =      FlaxDistilBertModelTester(self		)







		@slow
		def  A__      (					self		):
						for model_class_name in self.all_model_classes:
										_A	:				Optional[int]   =      model_class_name.from_pretrained('''distilbert-base-uncased'''		)
										_A	:				List[str]   =      model(np.ones((1, 1)		)		)
										self.assertIsNotNone(A__		)






@require_flax
class 						UpperCAmelCase__ (							unittest.TestCase							):







		@slow
		def  A__      (					self		):
						_A	:				Optional[Any]   =      FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased'''		)
						_A	:				Any   =      np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]		)
						_A	:				str   =      np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
						_A	:				Union[str, Any]   =      model(A__       ,attention_mask=A__		)[0]
						_A	:				Tuple   =      (1, 11, 768)
						self.assertEqual(output.shape       ,A__		)
						_A	:				Tuple   =      np.array([[[-0.16_39, 0.32_99, 0.16_48], [-0.17_46, 0.32_89, 0.17_10], [-0.18_84, 0.33_57, 0.18_10]]]		)

						self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]       ,A__       ,atol=1E-4		)		)











def  a__ (__lowercase							:str					,  __lowercase							:str   )		-> float:

				def get_matched_characters(__lowercase							:str					,  __lowercase							:str   ) -> str:
								_A	:				Union[str, Any]   =      []
								_A	:				Dict   =      min(len(_stra   )					,  len(_stra   )   ) // 2
								for i, l in enumerate(_stra   ):
												_A	:				Tuple   =      int(max(0					,  i - limit   )   )
												_A	:				Union[str, Any]   =      int(min(i + limit + 1					,  len(_stra   )   )   )
												if l in _stra[left:right]:
																matched.append(__lowercase   )
																_A	:				List[Any]   =      f"""{_stra[0:_stra.index(__lowercase   )]} {_stra[_stra.index(__lowercase   ) + 1:]}"""

								return "".join(__lowercase   )

				# matching characters
				_A	:				str   =      get_matched_characters(__lowercase					,  __lowercase   )
				_A	:				Optional[Any]   =      get_matched_characters(__lowercase					,  __lowercase   )
				_A	:				Any   =      len(__lowercase   )

				# transposition
				_A	:				str   =      (
				    len([(ca, ca) for ca, ca in zip(__lowercase					,  __lowercase   ) if ca != ca]   ) // 2
				)

				if not match_count:
								_A	:				Optional[int]   =      0.0
				else:
								_A	:				str   =      (
								    1
								    / 3
								    * (
								        match_count / len(__lowercase   )
								        + match_count / len(__lowercase   )
								        + (match_count - transpositions) / match_count
								    )
								)

				# common prefix up to 4 characters
				_A	:				Any   =      0
				for ca, ca in zip(stra[:4]					,  stra[:4]   ):
								if ca == ca:
												prefix_len += 1
								else:
												break

				return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(jaro_winkler('hello', 'world'))



