
"""simple docstring"""



import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
    from transformers.models.bert.modeling_flax_bert import (
        FlaxBertForMaskedLM,
        FlaxBertForMultipleChoice,
        FlaxBertForNextSentencePrediction,
        FlaxBertForPreTraining,
        FlaxBertForQuestionAnswering,
        FlaxBertForSequenceClassification,
        FlaxBertForTokenClassification,
        FlaxBertModel,
    )





class 				a			(					unittest.TestCase      ):



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



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Any     							=					ids_tensor([self.batch_size, self.seq_length]     ,	self.vocab_size      )

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

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

          UpperCAmelCase__	:      Union[str, Any]     							=					BertConfig(
              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=UpperCamelCase_     ,	initializer_range=self.initializer_range     ,	)

          return config, input_ids, token_type_ids, attention_mask



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      str     							=					self.prepare_config_and_inputs()
          UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[int]     							=					config_and_inputs
          UpperCAmelCase__	:      Optional[int]     							=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
          return config, inputs_dict



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Tuple     							=					self.prepare_config_and_inputs()
          UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__							,							UpperCAmelCase__	:      List[str]     							=					config_and_inputs

          UpperCAmelCase__	:      Any     							=					True
          UpperCAmelCase__	:      str     							=					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
          UpperCAmelCase__	:      Dict     							=					ids_tensor([self.batch_size, self.seq_length]     ,	vocab_size=2      )

          return (
              config,
              input_ids,
              attention_mask,
              encoder_hidden_states,
              encoder_attention_mask,
          )





@require_flax
class 				a			(					lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   Union[str, Any]           =     True

      UpperCamelCase							:   List[str]           =     (
          (
              FlaxBertModel,
              FlaxBertForPreTraining,
              FlaxBertForMaskedLM,
              FlaxBertForMultipleChoice,
              FlaxBertForQuestionAnswering,
              FlaxBertForNextSentencePrediction,
              FlaxBertForSequenceClassification,
              FlaxBertForTokenClassification,
              FlaxBertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					FlaxBertModelTester(self      )



      @slow
      def  __snake_case							(   self      ):
          # Only check this for base model, not necessary for all model classes.
          # This will also help speed-up tests.
          UpperCAmelCase__	:      str     							=					FlaxBertModel.from_pretrained('bert-base-cased'      )
          UpperCAmelCase__	:      List[str]     							=					model(np.ones((1, 1)      )      )
          self.assertIsNotNone(UpperCamelCase_      )



"""simple docstring"""



UpperCamelCase__          =	{
    'meter': 'm',
    'kilometer': 'km',
    'megametre': 'Mm',
    'gigametre': 'Gm',
    'terametre': 'Tm',
    'petametre': 'Pm',
    'exametre': 'Em',
    'zettametre': 'Zm',
    'yottametre': 'Ym',
}
# Exponent of the factor(meter)
UpperCamelCase__          =	{
    'm': 0,
    'km': 3,
    'Mm': 6,
    'Gm': 9,
    'Tm': 12,
    'Pm': 15,
    'Em': 18,
    'Zm': 21,
    'Ym': 24,
}
def 					lowerCamelCase   (     _snake_case    ,_snake_case    ,_snake_case ):
    UpperCAmelCase__	:      int     							=					from_type.lower().strip('s' )
    UpperCAmelCase__	:      List[Any]     							=					to_type.lower().strip('s' )

    UpperCAmelCase__	:      Optional[int]     							=					UNIT_SYMBOL.get(_snake_case    ,_snake_case )
    UpperCAmelCase__	:      Optional[Any]     							=					UNIT_SYMBOL.get(_snake_case    ,_snake_case )

    if from_sanitized not in METRIC_CONVERSION:
        UpperCAmelCase__	:      Optional[int]     							=					(
            F'''Invalid \'from_type\' value: {from_type!r}.\n'''
            F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
        )
        raise ValueError(_snake_case )
    if to_sanitized not in METRIC_CONVERSION:
        UpperCAmelCase__	:      int     							=					(
            F'''Invalid \'to_type\' value: {to_type!r}.\n'''
            F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
        )
        raise ValueError(_snake_case )
    UpperCAmelCase__	:      str     							=					METRIC_CONVERSION[from_sanitized]
    UpperCAmelCase__	:      Optional[int]     							=					METRIC_CONVERSION[to_sanitized]
    UpperCAmelCase__	:      int     							=					1

    if from_exponent > to_exponent:
        UpperCAmelCase__	:      List[str]     							=					from_exponent - to_exponent
    else:
        UpperCAmelCase__	:      int     							=					-(to_exponent - from_exponent)

    return value * pow(10    ,_snake_case )


if __name__ == "__main__":
    from doctest import testmod

    testmod()

