





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 					__lowerCAmelCase       :






			def __init__(self					,							__magic_name__					,							)     ->   Union[str, Any]:


							'''simple docstring'''



							snake_case_						:    Tuple				=  parent
							snake_case_						:    Tuple				=  13
							snake_case_						:    List[str]				=  7
							snake_case_						:    Union[str, Any]				=  True
							snake_case_						:    Tuple				=  True
							snake_case_						:    Union[str, Any]				=  True
							snake_case_						:    Union[str, Any]				=  True
							snake_case_						:    Dict				=  True
							snake_case_						:    Optional[int]				=  False
							snake_case_						:    str				=  False
							snake_case_						:    Tuple				=  False
							snake_case_						:    Optional[int]				=  2
							snake_case_						:    Union[str, Any]				=  99
							snake_case_						:    Any				=  0
							snake_case_						:    List[Any]				=  32
							snake_case_						:    Tuple				=  2
							snake_case_						:    Optional[int]				=  4
							snake_case_						:    Optional[Any]				=  0.1
							snake_case_						:    Tuple				=  0.1
							snake_case_						:    Optional[int]				=  512
							snake_case_						:    Any				=  16
							snake_case_						:    Union[str, Any]				=  2
							snake_case_						:    Dict				=  0.02
							snake_case_						:    List[Any]				=  3
							snake_case_						:    int				=  4
							snake_case_						:    int				=  '''last'''
							snake_case_						:    Optional[int]				=  True
							snake_case_						:    List[str]				=  None
							snake_case_						:    str				=  0






			def 			lowerCamelCase  (self    )     ->   str:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  ids_tensor([self.batch_size, self.seq_length]					,							self.vocab_size    )
							snake_case_						:    str				=  random_attention_mask([self.batch_size, self.seq_length]					,							dtype=tf.floataa    )

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

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

							snake_case_						:    Union[str, Any]				=  None
							snake_case_						:    Optional[int]				=  None
							snake_case_						:    Dict				=  None
							if self.use_labels:
											snake_case_						:    Optional[int]				=  ids_tensor([self.batch_size]					,							self.type_sequence_label_size    )
											snake_case_						:    List[Any]				=  ids_tensor([self.batch_size, self.seq_length]					,							self.num_labels    )
											snake_case_						:    Dict				=  ids_tensor([self.batch_size]					,							2					,							dtype=tf.floataa    )
											snake_case_						:    Optional[Any]				=  ids_tensor([self.batch_size]					,							self.num_choices    )

							snake_case_						:    List[Any]				=  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					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							)     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Union[str, Any]				=  TFFlaubertModel(config=__magic_name__    )
							snake_case_						:    str				=  {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
							snake_case_						:    Any				=  model(__magic_name__    )

							snake_case_						:    str				=  [input_ids, input_mask]
							snake_case_						:    str				=  model(__magic_name__    )
							self.parent.assertEqual(result.last_hidden_state.shape					,							(self.batch_size, self.seq_length, self.hidden_size)    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							)     ->   List[str]:


							'''simple docstring'''



							snake_case_						:    Any				=  TFFlaubertWithLMHeadModel(__magic_name__    )

							snake_case_						:    int				=  {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
							snake_case_						:    Optional[int]				=  model(__magic_name__    )

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






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							)     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    List[str]				=  TFFlaubertForQuestionAnsweringSimple(__magic_name__    )

							snake_case_						:    int				=  {'''input_ids''': input_ids, '''lengths''': input_lengths}

							snake_case_						:    str				=  model(__magic_name__    )

							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					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							)     ->   List[str]:


							'''simple docstring'''



							snake_case_						:    Dict				=  TFFlaubertForSequenceClassification(__magic_name__    )

							snake_case_						:    Union[str, Any]				=  {'''input_ids''': input_ids, '''lengths''': input_lengths}

							snake_case_						:    str				=  model(__magic_name__    )

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






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							)     ->   int:


							'''simple docstring'''



							snake_case_						:    Any				=  self.num_labels
							snake_case_						:    List[str]				=  TFFlaubertForTokenClassification(config=__magic_name__    )
							snake_case_						:    Union[str, Any]				=  {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
							snake_case_						:    Any				=  model(__magic_name__    )
							self.parent.assertEqual(result.logits.shape					,							(self.batch_size, self.seq_length, self.num_labels)    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							)     ->   List[str]:


							'''simple docstring'''



							snake_case_						:    Union[str, Any]				=  self.num_choices
							snake_case_						:    Tuple				=  TFFlaubertForMultipleChoice(config=__magic_name__    )
							snake_case_						:    List[Any]				=  tf.tile(tf.expand_dims(__magic_name__					,							1    )					,							(1, self.num_choices, 1)    )
							snake_case_						:    Any				=  tf.tile(tf.expand_dims(__magic_name__					,							1    )					,							(1, self.num_choices, 1)    )
							snake_case_						:    List[Any]				=  tf.tile(tf.expand_dims(__magic_name__					,							1    )					,							(1, self.num_choices, 1)    )
							snake_case_						:    Dict				=  {
							    '''input_ids''': multiple_choice_inputs_ids,
							    '''attention_mask''': multiple_choice_input_mask,
							    '''token_type_ids''': multiple_choice_token_type_ids,
							}
							snake_case_						:    int				=  model(__magic_name__    )
							self.parent.assertEqual(result.logits.shape					,							(self.batch_size, self.num_choices)    )






			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    List[str]				=  self.prepare_config_and_inputs()
							(
							    (
							    snake_case_
							)			,							(
							    snake_case_
							)			,							(
							    snake_case_
							)			,							(
							    snake_case_
							)			,							(
							    snake_case_
							)			,							(
							    snake_case_
							)			,							(
							    snake_case_
							)			,							(
							    snake_case_
							)			,							(
							    snake_case_
							)			,							
							)						:    int				=  config_and_inputs
							snake_case_						:    Union[str, Any]				=  {
							    '''input_ids''': input_ids,
							    '''token_type_ids''': token_type_ids,
							    '''langs''': token_type_ids,
							    '''lengths''': input_lengths,
							}
							return config, inputs_dict




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






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   Union[str, Any]:


							'''simple docstring'''



							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    )     ->   str:


							'''simple docstring'''



							snake_case_						:    int				=  TFFlaubertModelTester(self    )
							snake_case_						:    Optional[Any]				=  ConfigTester(self					,							config_class=__magic_name__					,							emb_dim=37    )






			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							self.config_tester.run_common_tests()






			def 			lowerCamelCase  (self    )     ->   str:


							'''simple docstring'''



							snake_case_						:    Optional[Any]				=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_model(*__magic_name__    )






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


							'''simple docstring'''



							snake_case_						:    int				=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_lm_head(*__magic_name__    )






			def 			lowerCamelCase  (self    )     ->   Dict:


							'''simple docstring'''



							snake_case_						:    Dict				=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_qa(*__magic_name__    )






			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_sequence_classif(*__magic_name__    )






			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    int				=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_for_token_classification(*__magic_name__    )






			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_flaubert_for_multiple_choice(*__magic_name__    )






			@slow
			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											snake_case_						:    List[Any]				=  TFFlaubertModel.from_pretrained(__magic_name__    )
											self.assertIsNotNone(__magic_name__    )




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






			@slow
			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							snake_case_						:    List[Any]				=  TFFlaubertModel.from_pretrained('''jplu/tf-flaubert-small-cased'''    )

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

							snake_case_						:    Tuple				=  model(__magic_name__    )[0]
							snake_case_						:    str				=  tf.TensorShape((1, 8, 512)    )
							self.assertEqual(output.shape					,							__magic_name__    )
							# compare the actual values for a slice.
							snake_case_						:    List[str]				=  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    )    )







from __future__ import annotations






def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase )		->						tuple[float, list[float]]:






				"""simple docstring"""


				snake_case_						:    Dict				=  list(range(len(_UpperCamelCase ) ) )
				snake_case_						:    Dict				=  [v / w for v, w in zip(_UpperCamelCase ,				_UpperCamelCase )]
				index.sort(key=lambda _UpperCamelCase : ratio[i] ,				reverse=_UpperCamelCase )

				snake_case_						:    float				=  0
				snake_case_						:    list[float]				=  [0] * len(_UpperCamelCase )
				for i in index:
								if weight[i] <= capacity:
												snake_case_						:    Dict				=  1
												max_value += value[i]
												capacity -= weight[i]
								else:
												snake_case_						:    Union[str, Any]				=  capacity / weight[i]
												max_value += value[i] * capacity / weight[i]
												break

				return max_value, fractions


if __name__ == "__main__":
	import doctest

	doctest.testmod()
