
"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TFDebertaForMaskedLM,
        TFDebertaForQuestionAnswering,
        TFDebertaForSequenceClassification,
        TFDebertaForTokenClassification,
        TFDebertaModel,
    )






class   _UpperCAmelCase					:
    def __init__(     self    ,     snake_case_    ,     snake_case_=13    ,     snake_case_=7    ,     snake_case_=True    ,     snake_case_=True    ,     snake_case_=True    ,     snake_case_=True    ,     snake_case_=99    ,     snake_case_=32    ,     snake_case_=2    ,     snake_case_=4    ,     snake_case_=37    ,     snake_case_="gelu"    ,     snake_case_=0.1    ,     snake_case_=0.1    ,     snake_case_=5_12    ,     snake_case_=16    ,     snake_case_=2    ,     snake_case_=0.02    ,     snake_case_=3    ,     snake_case_=4    ,     snake_case_=None    ,     ):
      _snake_case  :    Tuple            = parent
      _snake_case  :    Any            = 13
      _snake_case  :    Union[str, Any]            = 7
      _snake_case  :    List[str]            = True
      _snake_case  :    List[Any]            = True
      _snake_case  :    Union[str, Any]            = True
      _snake_case  :    str            = True
      _snake_case  :    int            = 99
      _snake_case  :    str            = 32
      _snake_case  :    Dict            = 2
      _snake_case  :    str            = 4
      _snake_case  :    List[str]            = 37
      _snake_case  :    int            = "gelu"
      _snake_case  :    Any            = 0.1
      _snake_case  :    Union[str, Any]            = 0.1
      _snake_case  :    List[str]            = 5_12
      _snake_case  :    List[Any]            = 16
      _snake_case  :    Optional[Any]            = False
      _snake_case  :    List[str]            = -1
      _snake_case  :    Dict            = True
      _snake_case  :    int            = 2
      _snake_case  :    Dict            = 0.02
      _snake_case  :    int            = 3
      _snake_case  :    Dict            = 4
      _snake_case  :    List[Any]            = None
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[int]            = ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size  )

      _snake_case  :    Optional[int]            = None
      if self.use_input_mask:
        _snake_case  :    Optional[int]            = random_attention_mask([self.batch_size, self.seq_length]  )

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

      _snake_case  :    str            = None
      _snake_case  :    Any            = None
      _snake_case  :    Any            = None
      if self.use_labels:
        _snake_case  :    str            = ids_tensor([self.batch_size]    ,     self.type_sequence_label_size  )
        _snake_case  :    Optional[int]            = ids_tensor([self.batch_size, self.seq_length]    ,     self.num_labels  )

      _snake_case  :    List[str]            = DebertaConfig(
          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    ,     relative_attention=self.relative_attention    ,     max_relative_positions=self.max_relative_positions    ,     position_biased_input=self.position_biased_input    ,     initializer_range=self.initializer_range    ,     return_dict=snake_case_    ,     )

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    Optional[Any]            = TFDebertaModel(config=snake_case_  )
      _snake_case  :    Dict            = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

      _snake_case  :    Any            = [input_ids, input_mask]
      _snake_case  :    Tuple            = model(snake_case_  )

      _snake_case  :    Any            = model(snake_case_  )

      self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, self.seq_length, self.hidden_size)  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    List[Any]            = TFDebertaForMaskedLM(config=snake_case_  )
      _snake_case  :    str            = {
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }
      _snake_case  :    int            = model(snake_case_  )
      self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.vocab_size)  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    Optional[int]            = self.num_labels
      _snake_case  :    str            = TFDebertaForSequenceClassification(config=snake_case_  )
      _snake_case  :    Optional[int]            = {
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }

      _snake_case  :    Optional[Any]            = model(snake_case_  )
      self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_labels)  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    str            = self.num_labels
      _snake_case  :    List[str]            = TFDebertaForTokenClassification(config=snake_case_  )
      _snake_case  :    Dict            = {
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }
      _snake_case  :    Tuple            = model(snake_case_  )
      self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.num_labels)  )
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    List[str]            = TFDebertaForQuestionAnswering(config=snake_case_  )
      _snake_case  :    Optional[int]            = {
          "input_ids": input_ids,
          "attention_mask": input_mask,
          "token_type_ids": token_type_ids,
      }

      _snake_case  :    Optional[Any]            = model(snake_case_  )
      self.parent.assertEqual(result.start_logits.shape    ,     (self.batch_size, self.seq_length)  )
      self.parent.assertEqual(result.end_logits.shape    ,     (self.batch_size, self.seq_length)  )


    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Any            = self.prepare_config_and_inputs()
      (
          (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       (
          _snake_case
      )      ,       
      )  :    Union[str, Any]            = config_and_inputs
      _snake_case  :    Dict            = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
      return config, inputs_dict






@require_tf
class   _UpperCAmelCase					(       _snake_case		,       _snake_case		,       unittest.TestCase):
    __lowercase				:			str	=       (
        (
            TFDebertaModel,
            TFDebertaForMaskedLM,
            TFDebertaForQuestionAnswering,
            TFDebertaForSequenceClassification,
            TFDebertaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    __lowercase				:			List[str]	=       (
        {
            """feature-extraction""": TFDebertaModel,
            """fill-mask""": TFDebertaForMaskedLM,
            """question-answering""": TFDebertaForQuestionAnswering,
            """text-classification""": TFDebertaForSequenceClassification,
            """token-classification""": TFDebertaForTokenClassification,
            """zero-shot""": TFDebertaForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )

    __lowercase				:			Tuple	=       False
    __lowercase				:			str	=       False
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    str            = TFDebertaModelTester(self  )
      _snake_case  :    int            = ConfigTester(self    ,     config_class=snake_case_    ,     hidden_size=37  )
    def        lowerCamelCase__						(     self  ):
      self.config_tester.run_common_tests()
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Any            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Any            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*snake_case_  )
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    int            = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*snake_case_  )


    @slow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = TFDebertaModel.from_pretrained("kamalkraj/deberta-base"  )
      self.assertIsNotNone(snake_case_  )






@require_tf
class   _UpperCAmelCase					(       unittest.TestCase):
    @unittest.skip(reason="Model not available yet"  )
    def        lowerCamelCase__						(     self  ):
      pass


    @slow
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    int            = TFDebertaModel.from_pretrained("kamalkraj/deberta-base"  )
      _snake_case  :    List[str]            = tf.constant([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  )
      _snake_case  :    Optional[Any]            = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]  )
      _snake_case  :    List[Any]            = model(snake_case_    ,     attention_mask=snake_case_  )[0]

      _snake_case  :    str            = tf.constant(
          [
              [
                  [-0.59855896, -0.80552566, -0.8462135],
                  [1.4484025, -0.93483794, -0.80593085],
                  [0.3122741, 0.00316059, -1.4131377],
              ]
          ]  )
      tf.debugging.assert_near(output[:, 1:4, 1:4]    ,     snake_case_    ,     atol=1E-4  )



"""simple docstring"""
def 					a__ ( a :					str		):

  """simple docstring"""







  _snake_case  :    Tuple            = word.upper()
  _snake_case  :    List[Any]            = 0
  _snake_case  :    Any            = {}
  _snake_case  :    List[str]            = []

  for letter in word:
    if letter not in letter_nums:
      _snake_case  :    Optional[int]            = str(a		)
      next_num += 1
    word_pattern.append(letter_nums[letter]		)
  return ".".join(a		)


if __name__ == "__main__":
    import pprint
    import time

    _a   :      str       =				time.time()
    with open("""dictionary.txt""") as in_file:
        _a   :      Optional[int]       =				in_file.read().splitlines()

    _a   :      dict       =				{}
    for word in word_list:
        _a   :      Optional[Any]       =				get_word_pattern(word)
        if pattern in all_patterns:
            all_patterns[pattern].append(word)
        else:
            _a   :      List[Any]       =				[word]

    with open("""word_patterns.txt""", """w""") as out_file:
        out_file.write(pprint.pformat(all_patterns))

    _a   :      List[Any]       =				round(time.time() - start_time, 2)
    print(f'Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.')
    # Done!  9,581 word patterns found in 0.58 seconds.

