



"""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 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(					self       ,	A       ,	)							->					Optional[Any]:
         _UpperCAmelCase							:  Tuple											=    parent
         _UpperCAmelCase							:  int											=    1_3
         _UpperCAmelCase							:  Optional[int]											=    7
         _UpperCAmelCase							:  Dict											=    True
         _UpperCAmelCase							:  int											=    True
         _UpperCAmelCase							:  List[Any]											=    True
         _UpperCAmelCase							:  List[str]											=    True
         _UpperCAmelCase							:  Any											=    True
         _UpperCAmelCase							:  Optional[int]											=    False
         _UpperCAmelCase							:  List[Any]											=    False
         _UpperCAmelCase							:  int											=    False
         _UpperCAmelCase							:  List[str]											=    2
         _UpperCAmelCase							:  Tuple											=    9_9
         _UpperCAmelCase							:  List[Any]											=    0
         _UpperCAmelCase							:  int											=    3_2
         _UpperCAmelCase							:  List[str]											=    2
         _UpperCAmelCase							:  List[Any]											=    4
         _UpperCAmelCase							:  Any											=    0.1
         _UpperCAmelCase							:  Any											=    0.1
         _UpperCAmelCase							:  str											=    5_1_2
         _UpperCAmelCase							:  str											=    1_6
         _UpperCAmelCase							:  Union[str, Any]											=    2
         _UpperCAmelCase							:  int											=    0.02
         _UpperCAmelCase							:  Any											=    3
         _UpperCAmelCase							:  Dict											=    4
         _UpperCAmelCase							:  int											=    '''last'''
         _UpperCAmelCase							:  Tuple											=    True
         _UpperCAmelCase							:  Any											=    None
         _UpperCAmelCase							:  Tuple											=    0





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

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

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

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

         _UpperCAmelCase							:  Dict											=    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       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	)							->					List[str]:
         _UpperCAmelCase							:  Tuple											=    TFFlaubertModel(config=A		)
         _UpperCAmelCase							:  str											=    {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
         _UpperCAmelCase							:  int											=    model(A		)

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





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	)							->					Optional[int]:
         _UpperCAmelCase							:  Tuple											=    TFFlaubertWithLMHeadModel(A		)

         _UpperCAmelCase							:  List[str]											=    {'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
         _UpperCAmelCase							:  Dict											=    model(A		)

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





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	)							->					Any:
         _UpperCAmelCase							:  Union[str, Any]											=    TFFlaubertForQuestionAnsweringSimple(A		)

         _UpperCAmelCase							:  Any											=    {'''input_ids''': input_ids, '''lengths''': input_lengths}

         _UpperCAmelCase							:  List[str]											=    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       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	)							->					int:
         _UpperCAmelCase							:  Tuple											=    TFFlaubertForSequenceClassification(A		)

         _UpperCAmelCase							:  Any											=    {'''input_ids''': input_ids, '''lengths''': input_lengths}

         _UpperCAmelCase							:  Optional[int]											=    model(A		)

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





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	A       ,	)							->					Optional[Any]:
         _UpperCAmelCase							:  Optional[Any]											=    self.num_labels
         _UpperCAmelCase							:  Dict											=    TFFlaubertForTokenClassification(config=A		)
         _UpperCAmelCase							:  Dict											=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
         _UpperCAmelCase							:  Any											=    model(A		)
         self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.seq_length, self.num_labels)		)





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





     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         _UpperCAmelCase							:  Any											=    self.prepare_config_and_inputs()
         (
             (
             _UpperCAmelCase
         )							,				(
             _UpperCAmelCase
         )							,				(
             _UpperCAmelCase
         )							,				(
             _UpperCAmelCase
         )							,				(
             _UpperCAmelCase
         )							,				(
             _UpperCAmelCase
         )							,				(
             _UpperCAmelCase
         )							,				(
             _UpperCAmelCase
         )							,				(
             _UpperCAmelCase
         )							,				
         )							:  Any											=    config_and_inputs
         _UpperCAmelCase							:  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 							_UpperCAmelCase							( a		,a		,unittest.TestCase      ):





     '''simple docstring'''



     a__									=(
         (
             TFFlaubertModel,
             TFFlaubertWithLMHeadModel,
             TFFlaubertForSequenceClassification,
             TFFlaubertForQuestionAnsweringSimple,
             TFFlaubertForTokenClassification,
             TFFlaubertForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     a__									=(
         (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
     a__									=(
         {
             '''feature-extraction''': TFFlaubertModel,
             '''fill-mask''': TFFlaubertWithLMHeadModel,
             '''question-answering''': TFFlaubertForQuestionAnsweringSimple,
             '''text-classification''': TFFlaubertForSequenceClassification,
             '''token-classification''': TFFlaubertForTokenClassification,
             '''zero-shot''': TFFlaubertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     a__									=False
     a__									=False





     def  __lowerCAmelCase	(					self       ,	A       ,	A       ,	A       ,	A       ,	A		)							->					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		)							->					List[Any]:
         _UpperCAmelCase							:  Any											=    TFFlaubertModelTester(self		)
         _UpperCAmelCase							:  Any											=    ConfigTester(self       ,	config_class=A       ,	emb_dim=3_7		)





     def  __lowerCAmelCase	(					self		)							->					int:
         self.config_tester.run_common_tests()





     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         _UpperCAmelCase							:  List[Any]											=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_model(*A		)





     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         _UpperCAmelCase							:  List[Any]											=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_lm_head(*A		)





     def  __lowerCAmelCase	(					self		)							->					Any:
         _UpperCAmelCase							:  Optional[Any]											=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_qa(*A		)





     def  __lowerCAmelCase	(					self		)							->					Any:
         _UpperCAmelCase							:  Optional[int]											=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_sequence_classif(*A		)





     def  __lowerCAmelCase	(					self		)							->					Tuple:
         _UpperCAmelCase							:  Dict											=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_for_token_classification(*A		)





     def  __lowerCAmelCase	(					self		)							->					int:
         _UpperCAmelCase							:  int											=    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_flaubert_for_multiple_choice(*A		)





     @slow
     def  __lowerCAmelCase	(					self		)							->					Dict:
         for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _UpperCAmelCase							:  Optional[Any]											=    TFFlaubertModel.from_pretrained(A		)
             self.assertIsNotNone(A		)



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





     '''simple docstring'''



     @slow
     def  __lowerCAmelCase	(					self		)							->					str:
         _UpperCAmelCase							:  Dict											=    TFFlaubertModel.from_pretrained('''jplu/tf-flaubert-small-cased'''		)

         _UpperCAmelCase							:  Dict											=    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							:  Union[str, Any]											=    model(A		)[0]
         _UpperCAmelCase							:  str											=    tf.TensorShape((1, 8, 5_1_2)		)
         self.assertEqual(output.shape       ,	A		)
         # compare the actual values for a slice.
         _UpperCAmelCase							:  Union[str, Any]											=    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 warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


_lowerCAmelCase    :Optional[int]     =					logging.get_logger(__name__)
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     def __init__(					self       ,	*A       ,	**A		)							->					None:
         warnings.warn(
             '''The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
             ''' use DonutImageProcessor instead.'''       ,	A       ,	)
         super().__init__(*A       ,	**A		)


