


'''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      ,				__magic_name__      ,				)					->							Tuple:
      _a			      =							parent
      _a			      =							13
      _a			      =							7
      _a			      =							True
      _a			      =							True
      _a			      =							True
      _a			      =							True
      _a			      =							True
      _a			      =							False
      _a			      =							False
      _a			      =							False
      _a			      =							2
      _a			      =							99
      _a			      =							0
      _a			      =							32
      _a			      =							2
      _a			      =							4
      _a			      =							0.1
      _a			      =							0.1
      _a			      =							5_12
      _a			      =							16
      _a			      =							2
      _a			      =							0.0_2
      _a			      =							3
      _a			      =							4
      _a			      =							"last"
      _a			      =							True
      _a			      =							None
      _a			      =							0
 def 							__UpperCAmelCase		( self						)					->							str:
      _a			      =							ids_tensor([self.batch_size, self.seq_length]      ,				self.vocab_size						)
      _a			      =							random_attention_mask([self.batch_size, self.seq_length]      ,				dtype=tf.floataa						)

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

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

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

      _a			      =							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 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				)					->							Any:
      _a			      =							TFFlaubertModel(config=__a						)
      _a			      =							{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
      _a			      =							model(__a						)

      _a			      =							[input_ids, input_mask]
      _a			      =							model(__a						)
      self.parent.assertEqual(result.last_hidden_state.shape      ,				(self.batch_size, self.seq_length, self.hidden_size)						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				)					->							Dict:
      _a			      =							TFFlaubertWithLMHeadModel(__a						)

      _a			      =							{"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
      _a			      =							model(__a						)

      self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.seq_length, self.vocab_size)						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				)					->							List[str]:
      _a			      =							TFFlaubertForQuestionAnsweringSimple(__a						)

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

      _a			      =							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 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				)					->							Optional[Any]:
      _a			      =							TFFlaubertForSequenceClassification(__a						)

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

      _a			      =							model(__a						)

      self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.type_sequence_label_size)						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				)					->							Any:
      _a			      =							self.num_labels
      _a			      =							TFFlaubertForTokenClassification(config=__a						)
      _a			      =							{"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
      _a			      =							model(__a						)
      self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.seq_length, self.num_labels)						)
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				)					->							str:
      _a			      =							self.num_choices
      _a			      =							TFFlaubertForMultipleChoice(config=__a						)
      _a			      =							tf.tile(tf.expand_dims(__a      ,				1						)      ,				(1, self.num_choices, 1)						)
      _a			      =							tf.tile(tf.expand_dims(__a      ,				1						)      ,				(1, self.num_choices, 1)						)
      _a			      =							tf.tile(tf.expand_dims(__a      ,				1						)      ,				(1, self.num_choices, 1)						)
      _a			      =							{
          "input_ids": multiple_choice_inputs_ids,
          "attention_mask": multiple_choice_input_mask,
          "token_type_ids": multiple_choice_token_type_ids,
      }
      _a			      =							model(__a						)
      self.parent.assertEqual(result.logits.shape      ,				(self.batch_size, self.num_choices)						)




 def 							__UpperCAmelCase		( self						)					->							Dict:
      _a			      =							self.prepare_config_and_inputs()
      (
          _a  
      )			      =							config_and_inputs
      _a			      =							{
          "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     (     _UpperCamelCase  ,				_UpperCamelCase  ,				unittest.TestCase       ):
 _lowerCAmelCase 					=	(
     (
         TFFlaubertModel,
         TFFlaubertWithLMHeadModel,
         TFFlaubertForSequenceClassification,
         TFFlaubertForQuestionAnsweringSimple,
         TFFlaubertForTokenClassification,
         TFFlaubertForMultipleChoice,
     )
     if is_tf_available()
     else ()
 )
 _lowerCAmelCase 					=	(
     (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
 )  # TODO (PVP): Check other models whether language generation is also applicable
 _lowerCAmelCase 					=	(
     {
         "feature-extraction": TFFlaubertModel,
         "fill-mask": TFFlaubertWithLMHeadModel,
         "question-answering": TFFlaubertForQuestionAnsweringSimple,
         "text-classification": TFFlaubertForSequenceClassification,
         "token-classification": TFFlaubertForTokenClassification,
         "zero-shot": TFFlaubertForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 _lowerCAmelCase 					=	False
 _lowerCAmelCase 					=	False
 def 							__UpperCAmelCase		( self      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__      ,				__magic_name__						)					->							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 							__UpperCAmelCase		( self						)					->							List[str]:
      _a			      =							TFFlaubertModelTester(self						)
      _a			      =							ConfigTester(self      ,				config_class=__a      ,				emb_dim=37						)
 def 							__UpperCAmelCase		( self						)					->							List[str]:
      self.config_tester.run_common_tests()
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_model(*__a						)
 def 							__UpperCAmelCase		( self						)					->							Dict:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_lm_head(*__a						)
 def 							__UpperCAmelCase		( self						)					->							Any:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_qa(*__a						)
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_sequence_classif(*__a						)
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_for_token_classification(*__a						)
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      _a			      =							self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_flaubert_for_multiple_choice(*__a						)




 @slow
 def 							__UpperCAmelCase		( self						)					->							List[Any]:
      for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _a			      =							TFFlaubertModel.from_pretrained(__a						)
           self.assertIsNotNone(__a						)





@require_tf
@require_sentencepiece
@require_tokenizers
class  a     (     unittest.TestCase       ):
 @slow
 def 							__UpperCAmelCase		( self						)					->							Tuple:
      _a			      =							TFFlaubertModel.from_pretrained('jplu/tf-flaubert-small-cased'						)

      _a			      =							tf.convert_to_tensor(
          [[0, 1_58, 7_35, 25_92, 14_24, 67_27, 82, 1]]      ,				dtype=tf.intaa      ,				)  # "J'aime flaubert !"

      _a			      =							model(__a						)[0]
      _a			      =							tf.TensorShape((1, 8, 5_12)						)
      self.assertEqual(output.shape      ,				__a						)
      # compare the actual values for a slice.
      _a			      =							tf.convert_to_tensor(
          [
              [
                  [-1.8_7_6_8_7_7_3, -1.5_6_6_5_5_5, 0.2_7_0_7_2_4_1_8],
                  [-1.6_9_2_0_0_3_8, -0.5_8_7_3_5_0_5, 1.9_3_2_9_5_9_9],
                  [-2.9_5_6_3_9_8_5, -1.6_9_9_3_8_3_5, 1.7_9_7_2_0_5_2],
              ]
          ]      ,				dtype=tf.floataa      ,				)

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





'''simple docstring'''






class  a     :
 def __init__( self      ,				__magic_name__						)					->							Optional[int]:
      _a			      =							n
      _a			      =							[None] * self.n
      _a			      =							0  # index of the first element
      _a			      =							0
      _a			      =							0
 def __len__( self						)					->							int:
      return self.size
 def 							__UpperCAmelCase		( self						)					->							bool:
      return self.size == 0
 def 							__UpperCAmelCase		( self						)					->							Optional[Any]:
      return False if self.is_empty() else self.array[self.front]
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Optional[Any]:
      if self.size >= self.n:
           raise Exception('QUEUE IS FULL'						)

      _a			      =							data
      _a			      =							(self.rear + 1) % self.n
      self.size += 1
      return self




 def 							__UpperCAmelCase		( self						)					->							Any:
      if self.size == 0:
           raise Exception('UNDERFLOW'						)

      _a			      =							self.array[self.front]
      _a			      =							None
      _a			      =							(self.front + 1) % self.n
      self.size -= 1
      return temp

