"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, 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 numpy as np
    import tensorflow as tf

    from transformers import (
        TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaubertConfig,
        TFFlaubertForMultipleChoice,
        TFFlaubertForQuestionAnsweringSimple,
        TFFlaubertForSequenceClassification,
        TFFlaubertForTokenClassification,
        TFFlaubertModel,
        TFFlaubertWithLMHeadModel,
    )


class        a				:


    def __init__(					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[Any]   ,				):
      _UpperCAmelCase						 =      parent
      _UpperCAmelCase						 =      13
      _UpperCAmelCase						 =      7
      _UpperCAmelCase						 =      True
      _UpperCAmelCase						 =      True
      _UpperCAmelCase						 =      True
      _UpperCAmelCase						 =      True
      _UpperCAmelCase						 =      True
      _UpperCAmelCase						 =      False
      _UpperCAmelCase						 =      False
      _UpperCAmelCase						 =      False
      _UpperCAmelCase						 =      2
      _UpperCAmelCase						 =      99
      _UpperCAmelCase						 =      0
      _UpperCAmelCase						 =      32
      _UpperCAmelCase						 =      2
      _UpperCAmelCase						 =      4
      _UpperCAmelCase						 =      0.1
      _UpperCAmelCase						 =      0.1
      _UpperCAmelCase						 =      512
      _UpperCAmelCase						 =      16
      _UpperCAmelCase						 =      2
      _UpperCAmelCase						 =      0.02
      _UpperCAmelCase						 =      3
      _UpperCAmelCase						 =      4
      _UpperCAmelCase						 =      """last"""
      _UpperCAmelCase						 =      True
      _UpperCAmelCase						 =      None
      _UpperCAmelCase						 =      0


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size					)
      _UpperCAmelCase						 =      random_attention_mask([self.batch_size, self.seq_length]   ,				dtype=tf.floataa					)

      _UpperCAmelCase						 =      None
      if self.use_input_lengths:
        _UpperCAmelCase						 =      (
            ids_tensor([self.batch_size]   ,				vocab_size=2					) + self.seq_length - 2
        )  # small variation of seq_length

      _UpperCAmelCase						 =      None
      if self.use_token_type_ids:
        _UpperCAmelCase						 =      ids_tensor([self.batch_size, self.seq_length]   ,				self.n_langs					)

      _UpperCAmelCase						 =      None
      _UpperCAmelCase						 =      None
      _UpperCAmelCase						 =      None
      if self.use_labels:
        _UpperCAmelCase						 =      ids_tensor([self.batch_size]   ,				self.type_sequence_label_size					)
        _UpperCAmelCase						 =      ids_tensor([self.batch_size, self.seq_length]   ,				self.num_labels					)
        _UpperCAmelCase						 =      ids_tensor([self.batch_size]   ,				2   ,				dtype=tf.floataa					)
        _UpperCAmelCase						 =      ids_tensor([self.batch_size]   ,				self.num_choices					)

      _UpperCAmelCase						 =      FlaubertConfig(
          vocab_size=self.vocab_size   ,				n_special=self.n_special   ,				emb_dim=self.hidden_size   ,				n_layers=self.num_hidden_layers   ,				n_heads=self.num_attention_heads   ,				dropout=self.hidden_dropout_prob   ,				attention_dropout=self.attention_probs_dropout_prob   ,				gelu_activation=self.gelu_activation   ,				sinusoidal_embeddings=self.sinusoidal_embeddings   ,				asm=self.asm   ,				causal=self.causal   ,				n_langs=self.n_langs   ,				max_position_embeddings=self.max_position_embeddings   ,				initializer_range=self.initializer_range   ,				summary_type=self.summary_type   ,				use_proj=self.use_proj   ,				bos_token_id=self.bos_token_id   ,				)

      return (
          config,
          input_ids,
          token_type_ids,
          input_lengths,
          sequence_labels,
          token_labels,
          is_impossible_labels,
          choice_labels,
          input_mask,
      )


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  Tuple   ,				):
      _UpperCAmelCase						 =      TFFlaubertModel(config=__lowerCAmelCase					)
      _UpperCAmelCase						 =      {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
      _UpperCAmelCase						 =      model(__lowerCAmelCase					)

      _UpperCAmelCase						 =      [input_ids, input_mask]
      _UpperCAmelCase						 =      model(__lowerCAmelCase					)
      self.parent.assertEqual(result.last_hidden_state.shape   ,				(self.batch_size, self.seq_length, self.hidden_size)					)


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Optional[int]   ,				):
      _UpperCAmelCase						 =      TFFlaubertWithLMHeadModel(__lowerCAmelCase					)

      _UpperCAmelCase						 =      {"""input_ids""": input_ids, """lengths""": input_lengths, """langs""": token_type_ids}
      _UpperCAmelCase						 =      model(__lowerCAmelCase					)

      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)					)


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Dict   ,				):
      _UpperCAmelCase						 =      TFFlaubertForQuestionAnsweringSimple(__lowerCAmelCase					)

      _UpperCAmelCase						 =      {"""input_ids""": input_ids, """lengths""": input_lengths}

      _UpperCAmelCase						 =      model(__lowerCAmelCase					)

      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	:  Optional[Any]   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Optional[int]   ,				):
      _UpperCAmelCase						 =      TFFlaubertForSequenceClassification(__lowerCAmelCase					)

      _UpperCAmelCase						 =      {"""input_ids""": input_ids, """lengths""": input_lengths}

      _UpperCAmelCase						 =      model(__lowerCAmelCase					)

      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.type_sequence_label_size)					)


    def      lowerCAmelCase_  (					self	:  List[str]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  List[str]   ,				):
      _UpperCAmelCase						 =      self.num_labels
      _UpperCAmelCase						 =      TFFlaubertForTokenClassification(config=__lowerCAmelCase					)
      _UpperCAmelCase						 =      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
      _UpperCAmelCase						 =      model(__lowerCAmelCase					)
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.num_labels)					)


    def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  Dict   ,				):
      _UpperCAmelCase						 =      self.num_choices
      _UpperCAmelCase						 =      TFFlaubertForMultipleChoice(config=__lowerCAmelCase					)
      _UpperCAmelCase						 =      tf.tile(tf.expand_dims(__lowerCAmelCase   ,				1					)   ,				(1, self.num_choices, 1)					)
      _UpperCAmelCase						 =      tf.tile(tf.expand_dims(__lowerCAmelCase   ,				1					)   ,				(1, self.num_choices, 1)					)
      _UpperCAmelCase						 =      tf.tile(tf.expand_dims(__lowerCAmelCase   ,				1					)   ,				(1, self.num_choices, 1)					)
      _UpperCAmelCase						 =      {
          """input_ids""": multiple_choice_inputs_ids,
          """attention_mask""": multiple_choice_input_mask,
          """token_type_ids""": multiple_choice_token_type_ids,
      }
      _UpperCAmelCase						 =      model(__lowerCAmelCase					)
      self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_choices)					)


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      self.prepare_config_and_inputs()
      (
          (
          _UpperCAmelCase
      ) ,				(
          _UpperCAmelCase
      ) ,				(
          _UpperCAmelCase
      ) ,				(
          _UpperCAmelCase
      ) ,				(
          _UpperCAmelCase
      ) ,				(
          _UpperCAmelCase
      ) ,				(
          _UpperCAmelCase
      ) ,				(
          _UpperCAmelCase
      ) ,				(
          _UpperCAmelCase
      ) ,				
      )						 =      config_and_inputs
      _UpperCAmelCase						 =      {
          """input_ids""": input_ids,
          """token_type_ids""": token_type_ids,
          """langs""": token_type_ids,
          """lengths""": input_lengths,
      }
      return config, inputs_dict



@require_tf
class        a				(		lowerCAmelCase_	,							lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  Union[str, Any]     							=							(
        (
            TFFlaubertModel,
            TFFlaubertWithLMHeadModel,
            TFFlaubertForSequenceClassification,
            TFFlaubertForQuestionAnsweringSimple,
            TFFlaubertForTokenClassification,
            TFFlaubertForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    _snake_case     :  Union[str, Any]     							=							(
        (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Check other models whether language generation is also applicable
    _snake_case     :  Optional[Any]     							=							(
        {
            'feature-extraction': TFFlaubertModel,
            'fill-mask': TFFlaubertWithLMHeadModel,
            'question-answering': TFFlaubertForQuestionAnsweringSimple,
            'text-classification': TFFlaubertForSequenceClassification,
            'token-classification': TFFlaubertForTokenClassification,
            'zero-shot': TFFlaubertForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    _snake_case     :  Any     							=							False
    _snake_case     :  int     							=							False


    def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  int					):
      if (
          pipeline_test_casse_name == "QAPipelineTests"
          and tokenizer_name is not None
          and not tokenizer_name.endswith("""Fast"""					)
      ):
        # `QAPipelineTests` fails for a few models when the slower tokenizer are used.
        # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
        # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
        return True

      return False


    def      lowerCAmelCase_  (					self	:  int					):
      _UpperCAmelCase						 =      TFFlaubertModelTester(self					)
      _UpperCAmelCase						 =      ConfigTester(self   ,				config_class=__lowerCAmelCase   ,				emb_dim=37					)


    def      lowerCAmelCase_  (					self	:  Dict					):
      self.config_tester.run_common_tests()


    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_model(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_lm_head(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  str					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_qa(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  str					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_sequence_classif(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_for_token_classification(*__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Tuple					):
      _UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_for_multiple_choice(*__lowerCAmelCase					)


    @slow
    def      lowerCAmelCase_  (					self	:  Dict					):
      for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        _UpperCAmelCase						 =      TFFlaubertModel.from_pretrained(__lowerCAmelCase					)
        self.assertIsNotNone(__lowerCAmelCase					)



@require_tf
@require_sentencepiece
@require_tokenizers
class        a				(		unittest.TestCase   ):


    @slow
    def      lowerCAmelCase_  (					self	:  Optional[Any]					):
      _UpperCAmelCase						 =      TFFlaubertModel.from_pretrained("""jplu/tf-flaubert-small-cased"""					)

      _UpperCAmelCase						 =      tf.convert_to_tensor(
          [[0, 158, 735, 2592, 1424, 6727, 82, 1]]   ,				dtype=tf.intaa   ,				)  # "J'aime flaubert !"

      _UpperCAmelCase						 =      model(__lowerCAmelCase					)[0]
      _UpperCAmelCase						 =      tf.TensorShape((1, 8, 512)					)
      self.assertEqual(output.shape   ,				__lowerCAmelCase					)
      # compare the actual values for a slice.
      _UpperCAmelCase						 =      tf.convert_to_tensor(
          [
              [
                  [-1.8_768_773, -1.566_555, 0.27_072_418],
                  [-1.6_920_038, -0.5_873_505, 1.9_329_599],
                  [-2.9_563_985, -1.6_993_835, 1.7_972_052],
              ]
          ]   ,				dtype=tf.floataa   ,				)

      self.assertTrue(np.allclose(output[:, :3, :3].numpy()   ,				expected_slice.numpy()   ,				atol=1e-4					)					)

"""simple docstring"""


import copy
import re


class        a				:
    _snake_case     :  Any     							=							'hp'
    _snake_case     :  Union[str, Any]     							=							{}
    _snake_case     :  Optional[Any]     							=							None


    @classmethod
    def      lowerCAmelCase_  (					cls	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  str					):
      _UpperCAmelCase						 =      prefix
      _UpperCAmelCase						 =      defaults
      cls.build_naming_info()


    @staticmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Optional[Any]					):
      if len(__lowerCAmelCase					) == 0:
        return ""
      _UpperCAmelCase						 =      None
      if any(char.isdigit() for char in word					):
        raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number'''					)
      if word in info["short_word"]:
        return info["short_word"][word]
      for prefix_len in range(1   ,				len(__lowerCAmelCase					) + 1					):
        _UpperCAmelCase						 =      word[:prefix_len]
        if prefix in info["reverse_short_word"]:
          continue
        else:
          _UpperCAmelCase						 =      prefix
          break

      if short_word is None:
        # Paranoid fallback
        def int_to_alphabetic(__lowerCAmelCase	:  List[Any]					):
          _UpperCAmelCase						 =      """"""
          while integer != 0:
            _UpperCAmelCase						 =      chr(ord("""A"""					) + integer % 10					) + s
            integer //= 10
          return s

        _UpperCAmelCase						 =      0
        while True:
          _UpperCAmelCase						 =      word + """#""" + int_to_alphabetic(__lowerCAmelCase					)
          if sword in info["reverse_short_word"]:
            continue
          else:
            _UpperCAmelCase						 =      sword
            break

      _UpperCAmelCase						 =      short_word
      _UpperCAmelCase						 =      word
      return short_word


    @staticmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Optional[int]					):
      _UpperCAmelCase						 =      param_name.split("""_"""					)

      _UpperCAmelCase						 =      [TrialShortNamer.shortname_for_word(__lowerCAmelCase   ,				__lowerCAmelCase					) for word in words]

      # We try to create a separatorless short name, but if there is a collision we have to fallback
      # to a separated short name
      _UpperCAmelCase						 =      ["""""", """_"""]

      for separator in separators:
        _UpperCAmelCase						 =      separator.join(__lowerCAmelCase					)
        if shortname not in info["reverse_short_param"]:
          _UpperCAmelCase						 =      shortname
          _UpperCAmelCase						 =      param_name
          return shortname

      return param_name


    @staticmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Optional[int]					):
      _UpperCAmelCase						 =      TrialShortNamer.shortname_for_key(__lowerCAmelCase   ,				__lowerCAmelCase					)
      _UpperCAmelCase						 =      short_name
      _UpperCAmelCase						 =      param_name


    @classmethod
    def      lowerCAmelCase_  (					cls	:  Optional[Any]					):
      if cls.NAMING_INFO is not None:
        return

      _UpperCAmelCase						 =      {
          """short_word""": {},
          """reverse_short_word""": {},
          """short_param""": {},
          """reverse_short_param""": {},
      }

      _UpperCAmelCase						 =      list(cls.DEFAULTS.keys()					)

      for k in field_keys:
        cls.add_new_param_name(__lowerCAmelCase   ,				__lowerCAmelCase					)

      _UpperCAmelCase						 =      info


    @classmethod
    def      lowerCAmelCase_  (					cls	:  List[str]   ,				__lowerCAmelCase	:  int					):
      cls.build_naming_info()
      assert cls.PREFIX is not None
      _UpperCAmelCase						 =      [copy.copy(cls.PREFIX					)]

      for k, v in params.items():
        if k not in cls.DEFAULTS:
          raise Exception(f'''You should provide a default value for the param name {k} with value {v}'''					)
        if v == cls.DEFAULTS[k]:
          # The default value is not added to the name
          continue

        _UpperCAmelCase						 =      cls.NAMING_INFO["""short_param"""][k]

        if isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					):
          _UpperCAmelCase						 =      1 if v else 0

        _UpperCAmelCase						 =      """""" if isinstance(__lowerCAmelCase   ,				(int, float)					) else """-"""
        _UpperCAmelCase						 =      f'''{key}{sep}{v}'''
        name.append(__lowerCAmelCase					)

      return "_".join(__lowerCAmelCase					)


    @classmethod
    def      lowerCAmelCase_  (					cls	:  Optional[int]   ,				__lowerCAmelCase	:  Optional[Any]					):
      _UpperCAmelCase						 =      repr[len(cls.PREFIX					) + 1 :]
      if repr == "":
        _UpperCAmelCase						 =      []
      else:
        _UpperCAmelCase						 =      repr.split("""_"""					)

      _UpperCAmelCase						 =      {}

      for value in values:
        if "-" in value:
          _UpperCAmelCase ,				_UpperCAmelCase						 =      value.split("""-"""					)
        else:
          _UpperCAmelCase						 =      re.sub("""[0-9.]"""   ,				""""""   ,				__lowerCAmelCase					)
          _UpperCAmelCase						 =      float(re.sub("""[^0-9.]"""   ,				""""""   ,				__lowerCAmelCase					)					)

        _UpperCAmelCase						 =      cls.NAMING_INFO["""reverse_short_param"""][p_k]

        _UpperCAmelCase						 =      p_v

      for k in cls.DEFAULTS:
        if k not in parameters:
          _UpperCAmelCase						 =      cls.DEFAULTS[k]

      return parameters
