





'''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      snake_case     :




 """simple docstring"""



 def __init__(       self       :							Dict							,	__A       :							Tuple							,	):
        __UpperCamelCase       =  parent
        __UpperCamelCase       =  1_3
        __UpperCamelCase       =  7
        __UpperCamelCase       =  True
        __UpperCamelCase       =  True
        __UpperCamelCase       =  True
        __UpperCamelCase       =  True
        __UpperCamelCase       =  True
        __UpperCamelCase       =  False
        __UpperCamelCase       =  False
        __UpperCamelCase       =  False
        __UpperCamelCase       =  2
        __UpperCamelCase       =  9_9
        __UpperCamelCase       =  0
        __UpperCamelCase       =  3_2
        __UpperCamelCase       =  2
        __UpperCamelCase       =  4
        __UpperCamelCase       =  0.1
        __UpperCamelCase       =  0.1
        __UpperCamelCase       =  5_1_2
        __UpperCamelCase       =  1_6
        __UpperCamelCase       =  2
        __UpperCamelCase       =  0.02
        __UpperCamelCase       =  3
        __UpperCamelCase       =  4
        __UpperCamelCase       =  'last'
        __UpperCamelCase       =  True
        __UpperCamelCase       =  None
        __UpperCamelCase       =  0






 def        _lowerCamelCase					(       self       :							List[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       :							Any							,	__A       :							Dict							,	__A       :							int							,	__A       :							Tuple							,	__A       :							Optional[int]							,	__A       :							Optional[int]							,	__A       :							Tuple							,	__A       :							List[str]							,	__A       :							Tuple							,	__A       :							Optional[int]							,	):
        __UpperCamelCase       =  TFFlaubertModel(config=__A	)
        __UpperCamelCase       =  {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
        __UpperCamelCase       =  model(__A	)

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






 def        _lowerCamelCase					(       self       :							str							,	__A       :							Any							,	__A       :							str							,	__A       :							List[str]							,	__A       :							Tuple							,	__A       :							List[str]							,	__A       :							Any							,	__A       :							str							,	__A       :							Dict							,	__A       :							int							,	):
        __UpperCamelCase       =  TFFlaubertWithLMHeadModel(__A	)

        __UpperCamelCase       =  {'input_ids': input_ids, 'lengths': input_lengths, 'langs': token_type_ids}
        __UpperCamelCase       =  model(__A	)

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






 def        _lowerCamelCase					(       self       :							Union[str, Any]							,	__A       :							int							,	__A       :							Tuple							,	__A       :							Dict							,	__A       :							int							,	__A       :							Tuple							,	__A       :							int							,	__A       :							Tuple							,	__A       :							Union[str, Any]							,	__A       :							Any							,	):
        __UpperCamelCase       =  TFFlaubertForQuestionAnsweringSimple(__A	)

        __UpperCamelCase       =  {'input_ids': input_ids, 'lengths': input_lengths}

        __UpperCamelCase       =  model(__A	)

        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       :							Dict							,	__A       :							List[str]							,	__A       :							List[Any]							,	__A       :							List[Any]							,	__A       :							Union[str, Any]							,	__A       :							str							,	__A       :							str							,	__A       :							List[str]							,	__A       :							Union[str, Any]							,	__A       :							List[Any]							,	):
        __UpperCamelCase       =  TFFlaubertForSequenceClassification(__A	)

        __UpperCamelCase       =  {'input_ids': input_ids, 'lengths': input_lengths}

        __UpperCamelCase       =  model(__A	)

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






 def        _lowerCamelCase					(       self       :							List[str]							,	__A       :							List[str]							,	__A       :							Dict							,	__A       :							Union[str, Any]							,	__A       :							List[Any]							,	__A       :							List[Any]							,	__A       :							str							,	__A       :							int							,	__A       :							int							,	__A       :							Union[str, Any]							,	):
        __UpperCamelCase       =  self.num_labels
        __UpperCamelCase       =  TFFlaubertForTokenClassification(config=__A	)
        __UpperCamelCase       =  {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
        __UpperCamelCase       =  model(__A	)
        self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.seq_length, self.num_labels)	)






 def        _lowerCamelCase					(       self       :							int							,	__A       :							Dict							,	__A       :							List[str]							,	__A       :							List[str]							,	__A       :							Optional[Any]							,	__A       :							Union[str, Any]							,	__A       :							Optional[int]							,	__A       :							List[str]							,	__A       :							Optional[Any]							,	__A       :							Dict							,	):
        __UpperCamelCase       =  self.num_choices
        __UpperCamelCase       =  TFFlaubertForMultipleChoice(config=__A	)
        __UpperCamelCase       =  tf.tile(tf.expand_dims(__A							,	1	)							,	(1, self.num_choices, 1)	)
        __UpperCamelCase       =  tf.tile(tf.expand_dims(__A							,	1	)							,	(1, self.num_choices, 1)	)
        __UpperCamelCase       =  tf.tile(tf.expand_dims(__A							,	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(__A	)
        self.parent.assertEqual(result.logits.shape							,	(self.batch_size, self.num_choices)	)






 def        _lowerCamelCase					(       self       :							Dict	):
        __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      snake_case     (    __lowerCamelCase			,       __lowerCamelCase			,       unittest.TestCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Any					=(
     (
         TFFlaubertModel,
         TFFlaubertWithLMHeadModel,
         TFFlaubertForSequenceClassification,
         TFFlaubertForQuestionAnsweringSimple,
         TFFlaubertForTokenClassification,
         TFFlaubertForMultipleChoice,
     )
     if is_tf_available()
     else ()
 )
 SCREAMING_SNAKE_CASE_	:					List[Any]					=(
     (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
 )  # TODO (PVP): Check other models whether language generation is also applicable
 SCREAMING_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 {}
 )
 SCREAMING_SNAKE_CASE_	:					List[str]					=False
 SCREAMING_SNAKE_CASE_	:					Optional[int]					=False






 def        _lowerCamelCase					(       self       :							str							,	__A       :							Any							,	__A       :							int							,	__A       :							Optional[int]							,	__A       :							Tuple							,	__A       :							Optional[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       :							Tuple	):
        __UpperCamelCase       =  TFFlaubertModelTester(self	)
        __UpperCamelCase       =  ConfigTester(self							,	config_class=__A							,	emb_dim=3_7	)






 def        _lowerCamelCase					(       self       :							Optional[Any]	):
        self.config_tester.run_common_tests()






 def        _lowerCamelCase					(       self       :							Dict	):
        __UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_model(*__A	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        __UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_lm_head(*__A	)






 def        _lowerCamelCase					(       self       :							List[str]	):
        __UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_qa(*__A	)






 def        _lowerCamelCase					(       self       :							str	):
        __UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_sequence_classif(*__A	)






 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_for_token_classification(*__A	)






 def        _lowerCamelCase					(       self       :							Union[str, Any]	):
        __UpperCamelCase       =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_flaubert_for_multiple_choice(*__A	)






 @slow
 def        _lowerCamelCase					(       self       :							Optional[int]	):
        for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCamelCase       =  TFFlaubertModel.from_pretrained(__A	)
               self.assertIsNotNone(__A	)






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




 """simple docstring"""



 @slow
 def        _lowerCamelCase					(       self       :							Optional[int]	):
        __UpperCamelCase       =  TFFlaubertModel.from_pretrained('jplu/tf-flaubert-small-cased'	)

        __UpperCamelCase       =  tf.convert_to_tensor(
            [[0, 1_5_8, 7_3_5, 2_5_9_2, 1_4_2_4, 6_7_2_7, 8_2, 1]]							,	dtype=tf.intaa							,	)  # "J'aime flaubert !"

        __UpperCamelCase       =  model(__A	)[0]
        __UpperCamelCase       =  tf.TensorShape((1, 8, 5_1_2)	)
        self.assertEqual(output.shape							,	__A	)
        # compare the actual values for a slice.
        __UpperCamelCase       =  tf.convert_to_tensor(
            [
                [
                    [-1.876_8773, -1.56_6555, 0.2707_2418],
                    [-1.692_0038, -0.587_3505, 1.932_9599],
                    [-2.956_3985, -1.699_3835, 1.797_2052],
                ]
            ]							,	dtype=tf.floataa							,	)

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

import random
from typing import Any






def  snake_case  (       snake_case__			:list)						->   list[Any]:
       for _ in range(len(snake_case__)):
              _A        =  random.randint(0   ,	len(snake_case__) - 1)
              _A        =  random.randint(0   ,	len(snake_case__) - 1)
              _A   ,  _A        =  data[b], data[a]
       return data


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						[0, 1, 2, 3, 4, 5, 6, 7]
    _SCREAMING_SNAKE_CASE										=						['python', 'says', 'hello', '!']
    print('Fisher-Yates Shuffle:')
    print('List', integers, strings)
    print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))




