


"""simple docstring"""







def _A		(		lowercase			,       lowercase			,       lowercase			,       lowercase			,       lowercase			,       lowercase			,       ):







    """simple docstring"""
    a											=list(_A						)
    a											=list(_A						)
    a											=len(_A						)
    a											=len(_A						)

    a											=[
        [0 for _ in range(len_destination_seq + 1						)] for _ in range(len_source_seq + 1						)
    ]
    a											=[
        ['''0''' for _ in range(len_destination_seq + 1						)] for _ in range(len_source_seq + 1						)
    ]

    for i in range(1			,       len_source_seq + 1						):
        a											=i * delete_cost
        a											=f'''D{source_seq[i - 1]:c}'''

    for i in range(1			,       len_destination_seq + 1						):
        a											=i * insert_cost
        a											=f'''I{destination_seq[i - 1]:c}'''

    for i in range(1			,       len_source_seq + 1						):
        for j in range(1			,       len_destination_seq + 1						):
            if source_seq[i - 1] == destination_seq[j - 1]:
                a											=costs[i - 1][j - 1] + copy_cost
                a											=f'''C{source_seq[i - 1]:c}'''
            else:
                a											=costs[i - 1][j - 1] + replace_cost
                a											=f'''R{source_seq[i - 1]:c}''' + str(destination_seq[j - 1]						)

            if costs[i - 1][j] + delete_cost < costs[i][j]:
                a											=costs[i - 1][j] + delete_cost
                a											=f'''D{source_seq[i - 1]:c}'''

            if costs[i][j - 1] + insert_cost < costs[i][j]:
                a											=costs[i][j - 1] + insert_cost
                a											=f'''I{destination_seq[j - 1]:c}'''

    return costs, ops
def _A		(		lowercase			,       lowercase			,       lowercase						):







    """simple docstring"""







    if i == 0 and j == 0:
        return []
    else:
        if ops[i][j][0] == "C" or ops[i][j][0] == "R":
            a											=assemble_transformation(_A			,       i - 1			,       j - 1						)
            seq.append(ops[i][j]						)
            return seq
        elif ops[i][j][0] == "D":
            a											=assemble_transformation(_A			,       i - 1			,       _A						)
            seq.append(ops[i][j]						)
            return seq
        else:
            a											=assemble_transformation(_A			,       _A			,       j - 1						)
            seq.append(ops[i][j]						)
            return seq


if __name__ == "__main__":
       lowerCamelCase_		,    lowerCamelCase_  :  int     		=    compute_transform_tables("""Python""", """Algorithms""", -1, 1, 2, 2)

       lowerCamelCase_  :  List[Any]     		=    len(operations)
       lowerCamelCase_  :  Tuple     		=    len(operations[0])
       lowerCamelCase_  :  List[str]     		=    assemble_transformation(operations, m - 1, n - 1)

       lowerCamelCase_  :  List[Any]     		=    list("""Python""")
       lowerCamelCase_  :  Optional[Any]     		=    0
       lowerCamelCase_  :  str     		=    0

       with open("""min_cost.txt""", """w""") as file:
              for op in sequence:
                     print("""""".join(string))

                     if op[0] == "C":
                            file.write("""%-16s""" % """Copy %c""" % op[1])
                            file.write("""\t\t\t""" + """""".join(string))
                            file.write("""\r\n""")

                            cost -= 1
                     elif op[0] == "R":
                            lowerCamelCase_  :  Union[str, Any]     		=    op[2]

                            file.write("""%-16s""" % ("""Replace %c""" % op[1] + """ with """ + str(op[2])))
                            file.write("""\t\t""" + """""".join(string))
                            file.write("""\r\n""")

                            cost += 1
                     elif op[0] == "D":
                            string.pop(i)

                            file.write("""%-16s""" % """Delete %c""" % op[1])
                            file.write("""\t\t\t""" + """""".join(string))
                            file.write("""\r\n""")

                            cost += 2
                     else:
                            string.insert(i, op[1])

                            file.write("""%-16s""" % """Insert %c""" % op[1])
                            file.write("""\t\t\t""" + """""".join(string))
                            file.write("""\r\n""")

                            cost += 2

                     i += 1

              print("""""".join(string))
              print("""Cost: """, cost)

              file.write("""\r\nMinimum cost: """ + str(cost))


import unittest

import numpy as np

from transformers import ElectraConfig, 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():
       from transformers.models.electra.modeling_flax_electra import (
           FlaxElectraForCausalLM,
           FlaxElectraForMaskedLM,
           FlaxElectraForMultipleChoice,
           FlaxElectraForPreTraining,
           FlaxElectraForQuestionAnswering,
           FlaxElectraForSequenceClassification,
           FlaxElectraForTokenClassification,
           FlaxElectraModel,
       )







class 		__magic_name__     (				unittest.TestCase				):

   '''simple docstring'''




   def __init__(							self,       lowercase_,       lowercase_=13,       lowercase_=7,       lowercase_=True,       lowercase_=True,       lowercase_=True,       lowercase_=True,       lowercase_=99,       lowercase_=24,       lowercase_=32,       lowercase_=5,       lowercase_=4,       lowercase_=37,       lowercase_="gelu",       lowercase_=0.1,       lowercase_=0.1,       lowercase_=512,       lowercase_=16,       lowercase_=2,       lowercase_=0.02,       lowercase_=4,       )				->       int:






     """simple docstring"""






     a__    =parent
     a__    =batch_size
     a__    =seq_length
     a__    =is_training
     a__    =use_attention_mask
     a__    =use_token_type_ids
     a__    =use_labels
     a__    =vocab_size
     a__    =hidden_size
     a__    =embedding_size
     a__    =num_hidden_layers
     a__    =num_attention_heads
     a__    =intermediate_size
     a__    =hidden_act
     a__    =hidden_dropout_prob
     a__    =attention_probs_dropout_prob
     a__    =max_position_embeddings
     a__    =type_vocab_size
     a__    =type_sequence_label_size
     a__    =initializer_range
     a__    =num_choices



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =ids_tensor([self.batch_size, self.seq_length],       self.vocab_size			)

     a__    =None
     if self.use_attention_mask:
       a__    =random_attention_mask([self.batch_size, self.seq_length]			)

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

     a__    =ElectraConfig(
         vocab_size=self.vocab_size,       hidden_size=self.hidden_size,       embedding_size=self.embedding_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,       initializer_range=self.initializer_range,       )

     return config, input_ids, token_type_ids, attention_mask



   def 					_UpperCAmelCase					(							self			)				->       List[Any]:






     """simple docstring"""






     a__    =self.prepare_config_and_inputs()
     a__, a__, a__, a__    =config_and_inputs
     a__    ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
     return config, inputs_dict







@require_flax
class 		__magic_name__     (				lowerCamelCase__						,			unittest.TestCase				):

   '''simple docstring'''




   lowerCamelCase__					:      List[str]        =  True

   lowerCamelCase__					:      Tuple        =  (
       (
           FlaxElectraModel,
           FlaxElectraForCausalLM,
           FlaxElectraForMaskedLM,
           FlaxElectraForPreTraining,
           FlaxElectraForTokenClassification,
           FlaxElectraForQuestionAnswering,
           FlaxElectraForMultipleChoice,
           FlaxElectraForSequenceClassification,
       )
       if is_flax_available()
       else ()
   )



   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






     a__    =FlaxElectraModelTester(self			)



   @slow
   def 					_UpperCAmelCase					(							self			)				->       str:






     """simple docstring"""






     for model_class_name in self.all_model_classes:
       if model_class_name == FlaxElectraForMaskedLM:
         a__    =model_class_name.from_pretrained('''google/electra-small-generator'''			)
       else:
         a__    =model_class_name.from_pretrained('''google/electra-small-discriminator'''			)
       a__    =model(np.ones((1, 1)			)			)
       self.assertIsNotNone(lowercase_			)
