


"""simple docstring"""


import unittest

import numpy as np

from transformers import AlbertConfig, 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.albert.modeling_flax_albert import (
       FlaxAlbertForMaskedLM,
       FlaxAlbertForMultipleChoice,
       FlaxAlbertForPreTraining,
       FlaxAlbertForQuestionAnswering,
       FlaxAlbertForSequenceClassification,
       FlaxAlbertForTokenClassification,
       FlaxAlbertModel,
   )

class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



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


   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       Union[str, Any]				=						ids_tensor([self.batch_size, self.seq_length] ,     self.vocab_size						)

    lowercase__       :       Dict				=						None
    if self.use_attention_mask:
     lowercase__       :       Any				=						random_attention_mask([self.batch_size, self.seq_length]						)

    lowercase__       :       str				=						None
    if self.use_token_type_ids:
     lowercase__       :       List[str]				=						ids_tensor([self.batch_size, self.seq_length] ,     self.type_vocab_size						)

    lowercase__       :       Union[str, Any]				=						AlbertConfig(
        vocab_size=self.vocab_size ,     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 ,     max_position_embeddings=self.max_position_embeddings ,     type_vocab_size=self.type_vocab_size ,     is_decoder=lowerCamelCase__ ,     initializer_range=self.initializer_range ,     )

    return config, input_ids, token_type_ids, attention_mask







   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       Any				=						self.prepare_config_and_inputs()
    lowercase__	,       lowercase__	,       lowercase__	,       lowercase__       :       Tuple				=						config_and_inputs
    lowercase__       :       Union[str, Any]				=						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
    return config, inputs_dict

@require_flax
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    (
       (
           FlaxAlbertModel,
           FlaxAlbertForPreTraining,
           FlaxAlbertForMaskedLM,
           FlaxAlbertForMultipleChoice,
           FlaxAlbertForQuestionAnswering,
           FlaxAlbertForSequenceClassification,
           FlaxAlbertForTokenClassification,
           FlaxAlbertForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )


   def 					UpperCAmelCase__(						self						)      ->							List[Any]:
    lowercase__       :       Tuple				=						FlaxAlbertModelTester(self						)







   @slow
   def 					UpperCAmelCase__(						self						)      ->							Any:
    for model_class_name in self.all_model_classes:
     lowercase__       :       Dict				=						model_class_name.from_pretrained("""albert-base-v2"""						)
     lowercase__       :       Optional[int]				=						model(np.ones((1, 1)						)						)
     self.assertIsNotNone(lowerCamelCase__						)

@require_flax
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @slow
   def 					UpperCAmelCase__(						self						)      ->							Union[str, Any]:
    lowercase__       :       List[Any]				=						FlaxAlbertModel.from_pretrained("""albert-base-v2"""						)
    lowercase__       :       Tuple				=						np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]						)
    lowercase__       :       Dict				=						np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
    lowercase__       :       Optional[Any]				=						model(lowerCamelCase__ ,     attention_mask=lowerCamelCase__						)[0]
    lowercase__       :       List[Any]				=						(1, 11, 768)
    self.assertEqual(output.shape ,     lowerCamelCase__						)
    lowercase__       :       Tuple				=						np.array(
        [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]						)

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



"""simple docstring"""


import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       Optional[int]									=    LayoutLMTokenizer
   _a   :       Dict									=    LayoutLMTokenizerFast
   _a   :       Dict									=    True
   _a   :       Any									=    True


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    super().setUp()

    lowercase__       :       int				=						[
        """[UNK]""",
        """[CLS]""",
        """[SEP]""",
        """want""",
        """##want""",
        """##ed""",
        """wa""",
        """un""",
        """runn""",
        """##ing""",
        """,""",
        """low""",
        """lowest""",
    ]
    lowercase__       :       Dict				=						os.path.join(self.tmpdirname ,     VOCAB_FILES_NAMES["""vocab_file"""]						)
    with open(self.vocab_file ,     """w""" ,     encoding="""utf-8"""						) as vocab_writer:
     vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]						)						)


   def 					UpperCAmelCase__(						self ,     **lowerCamelCase__						)      ->							Optional[int]:
    return LayoutLMTokenizer.from_pretrained(self.tmpdirname ,     **lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							List[Any]:
    lowercase__       :       List[str]				=						"""UNwant\u00E9d,running"""
    lowercase__       :       Optional[int]				=						"""unwanted, running"""
    return input_text, output_text


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       str				=						self.tokenizer_class(self.vocab_file						)

    lowercase__       :       Optional[Any]				=						tokenizer.tokenize("""UNwant\u00E9d,running"""						)
    self.assertListEqual(lowerCamelCase__ ,     ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""]						)
    self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__						) ,     [7, 4, 5, 10, 8, 9]						)







   def 					UpperCAmelCase__(						self						)      ->							Any:
    pass