






'''simple docstring'''



from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, 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 tensorflow as tf

      from transformers import (
          TFRoFormerForCausalLM,
          TFRoFormerForMaskedLM,
          TFRoFormerForMultipleChoice,
          TFRoFormerForQuestionAnswering,
          TFRoFormerForSequenceClassification,
          TFRoFormerForTokenClassification,
          TFRoFormerModel,
      )
      from transformers.models.roformer.modeling_tf_roformer import (
          TFRoFormerSelfAttention,
          TFRoFormerSinusoidalPositionalEmbedding,
      )
class UpperCAmelCase_					:




      """simple docstring"""




      def __init__( self			:		Optional[Any]     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Union[str, Any]=13     ,      UpperCAmelCase			:		int=7     ,      UpperCAmelCase			:		Union[str, Any]=True     ,      UpperCAmelCase			:		int=True     ,      UpperCAmelCase			:		Any=True     ,      UpperCAmelCase			:		int=True     ,      UpperCAmelCase			:		Any=99     ,      UpperCAmelCase			:		List[Any]=32     ,      UpperCAmelCase			:		str=2     ,      UpperCAmelCase			:		str=4     ,      UpperCAmelCase			:		List[Any]=37     ,      UpperCAmelCase			:		str="gelu"     ,      UpperCAmelCase			:		List[Any]=0.1     ,      UpperCAmelCase			:		int=0.1     ,      UpperCAmelCase			:		Dict=512     ,      UpperCAmelCase			:		List[Any]=16     ,      UpperCAmelCase			:		List[str]=2     ,      UpperCAmelCase			:		Any=0.0_2     ,      UpperCAmelCase			:		List[str]=3     ,      UpperCAmelCase			:		Dict=4     ,      UpperCAmelCase			:		Optional[int]=None     ,      )  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =parent
       lowercase					:   Tuple				  =13
       lowercase					:   Any				  =7
       lowercase					:   Union[str, Any]				  =True
       lowercase					:   Any				  =True
       lowercase					:   Optional[int]				  =True
       lowercase					:   List[str]				  =True
       lowercase					:   Tuple				  =99
       lowercase					:   str				  =32
       lowercase					:   Union[str, Any]				  =2
       lowercase					:   Dict				  =4
       lowercase					:   Union[str, Any]				  =37
       lowercase					:   Union[str, Any]				  ='''gelu'''
       lowercase					:   Any				  =0.1
       lowercase					:   Dict				  =0.1
       lowercase					:   Dict				  =512
       lowercase					:   List[str]				  =16
       lowercase					:   Dict				  =2
       lowercase					:   int				  =0.0_2
       lowercase					:   List[Any]				  =3
       lowercase					:   List[str]				  =4
       lowercase					:   Optional[Any]				  =None






      def 				A__      ( self			:		Union[str, Any]		)  ->  int:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =ids_tensor([self.batch_size, self.seq_length]     ,      self.vocab_size		)

       lowercase					:   str				  =None
       if self.use_input_mask:
        lowercase					:   int				  =random_attention_mask([self.batch_size, self.seq_length]		)

       lowercase					:   Any				  =None
       if self.use_token_type_ids:
        lowercase					:   List[str]				  =ids_tensor([self.batch_size, self.seq_length]     ,      self.type_vocab_size		)

       lowercase					:   List[Any]				  =None
       lowercase					:   List[str]				  =None
       lowercase					:   List[str]				  =None
       if self.use_labels:
        lowercase					:   Optional[Any]				  =ids_tensor([self.batch_size]     ,      self.type_sequence_label_size		)
        lowercase					:   List[Any]				  =ids_tensor([self.batch_size, self.seq_length]     ,      self.num_labels		)
        lowercase					:   Any				  =ids_tensor([self.batch_size]     ,      self.num_choices		)

       lowercase					:   List[Any]				  =RoFormerConfig(
           vocab_size=self.vocab_size     ,      hidden_size=self.hidden_size     ,      num_hidden_layers=self.num_hidden_layers     ,      num_attention_heads=self.num_attention_heads     ,      intermediate_size=self.intermediate_size     ,      hidden_act=self.hidden_act     ,      hidden_dropout_prob=self.hidden_dropout_prob     ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,      max_position_embeddings=self.max_position_embeddings     ,      type_vocab_size=self.type_vocab_size     ,      initializer_range=self.initializer_range     ,      return_dict=UpperCAmelCase     ,      )

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






      def 				A__      ( self			:		List[str]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		List[Any]		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   List[Any]				  =TFRoFormerModel(config=UpperCAmelCase		)
       lowercase					:   Optional[int]				  ={'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

       lowercase					:   Tuple				  =[input_ids, input_mask]
       lowercase					:   str				  =model(UpperCAmelCase		)

       lowercase					:   Dict				  =model(UpperCAmelCase		)

       self.parent.assertEqual(result.last_hidden_state.shape     ,      (self.batch_size, self.seq_length, self.hidden_size)		)






      def 				A__      ( self			:		Tuple     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		List[str]		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Dict				  =True
       lowercase					:   List[Any]				  =TFRoFormerForCausalLM(config=UpperCAmelCase		)
       lowercase					:   Union[str, Any]				  ={
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }
       lowercase					:   Optional[Any]				  =model(UpperCAmelCase		)['''logits''']
       self.parent.assertListEqual(
           list(prediction_scores.numpy().shape		)     ,      [self.batch_size, self.seq_length, self.vocab_size]		)






      def 				A__      ( self			:		List[Any]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		Tuple		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   List[Any]				  =TFRoFormerForMaskedLM(config=UpperCAmelCase		)
       lowercase					:   List[str]				  ={
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }
       lowercase					:   Dict				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.seq_length, self.vocab_size)		)






      def 				A__      ( self			:		Tuple     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		Tuple     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		int		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =self.num_labels
       lowercase					:   Optional[int]				  =TFRoFormerForSequenceClassification(config=UpperCAmelCase		)
       lowercase					:   Optional[int]				  ={
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }

       lowercase					:   Optional[Any]				  =model(UpperCAmelCase		)
       self.parent.assertEqual(result.logits.shape     ,      (self.batch_size, self.num_labels)		)






      def 				A__      ( self			:		Optional[Any]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Tuple		)  ->  Union[str, Any]:


       '''simple docstring'''

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






      def 				A__      ( self			:		Optional[int]     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		Any     ,      UpperCAmelCase			:		List[str]     ,      UpperCAmelCase			:		int		)  ->  Optional[int]:


       '''simple docstring'''

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






      def 				A__      ( self			:		int     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Dict     ,      UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		Union[str, Any]     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		str		)  ->  Any:


       '''simple docstring'''

       lowercase					:   Tuple				  =TFRoFormerForQuestionAnswering(config=UpperCAmelCase		)
       lowercase					:   List[str]				  ={
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }

       lowercase					:   List[str]				  =model(UpperCAmelCase		)
       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 				A__      ( self			:		List[Any]		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =self.prepare_config_and_inputs()
       (
           (
           lowercase
       )  ,		(
           lowercase
       )  ,		(
           lowercase
       )  ,		(
           lowercase
       )  ,		(
           lowercase
       )  ,		(
           lowercase
       )  ,		(
           lowercase
       )  ,		
       )					:   Optional[int]				  =config_and_inputs
       lowercase					:   str				  ={'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict





@require_tf
class UpperCAmelCase_					(    __A				,       __A				,       unittest.TestCase				):




      """simple docstring"""




      UpperCamelCase_								=			(
          (
              TFRoFormerModel,
              TFRoFormerForCausalLM,
              TFRoFormerForMaskedLM,
              TFRoFormerForQuestionAnswering,
              TFRoFormerForSequenceClassification,
              TFRoFormerForTokenClassification,
              TFRoFormerForMultipleChoice,
          )
          if is_tf_available()
          else ()
      )
      UpperCamelCase_								=			(
          {
              '''feature-extraction''': TFRoFormerModel,
              '''fill-mask''': TFRoFormerForMaskedLM,
              '''question-answering''': TFRoFormerForQuestionAnswering,
              '''text-classification''': TFRoFormerForSequenceClassification,
              '''text-generation''': TFRoFormerForCausalLM,
              '''token-classification''': TFRoFormerForTokenClassification,
              '''zero-shot''': TFRoFormerForSequenceClassification,
          }
          if is_tf_available()
          else {}
      )

      UpperCamelCase_								=			False
      UpperCamelCase_								=			False






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		str     ,      UpperCAmelCase			:		List[Any]     ,      UpperCAmelCase			:		int     ,      UpperCAmelCase			:		Optional[int]     ,      UpperCAmelCase			:		str		)  ->  Tuple:


       '''simple docstring'''

       if pipeline_test_casse_name == "TextGenerationPipelineTests":
        return True

       return False






      def 				A__      ( self			:		str		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   List[Any]				  =TFRoFormerModelTester(self		)
       lowercase					:   Union[str, Any]				  =ConfigTester(self     ,      config_class=UpperCAmelCase     ,      hidden_size=37		)






      def 				A__      ( self			:		Optional[Any]		)  ->  Union[str, Any]:


       '''simple docstring'''

       self.config_tester.run_common_tests()






      def 				A__      ( self			:		List[str]		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   str				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCAmelCase		)






      def 				A__      ( self			:		List[Any]		)  ->  Optional[Any]:


       '''simple docstring'''

       lowercase					:   Tuple				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase		)






      def 				A__      ( self			:		str		)  ->  Optional[int]:


       '''simple docstring'''

       lowercase					:   Any				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_lm_head(*UpperCAmelCase		)






      def 				A__      ( self			:		int		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   List[Any]				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase		)






      def 				A__      ( self			:		Dict		)  ->  List[str]:


       '''simple docstring'''

       lowercase					:   Dict				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase		)






      def 				A__      ( self			:		Dict		)  ->  Any:


       '''simple docstring'''

       lowercase					:   int				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase		)






      def 				A__      ( self			:		List[Any]		)  ->  Tuple:


       '''simple docstring'''

       lowercase					:   str				  =self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase		)






      @slow
      def 				A__      ( self			:		str		)  ->  str:


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''		)
       self.assertIsNotNone(UpperCAmelCase		)





@require_tf
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      @slow
      def 				A__      ( self			:		Union[str, Any]		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   Any				  =TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''		)
       lowercase					:   Optional[Any]				  =tf.constant([[0, 1, 2, 3, 4, 5]]		)
       lowercase					:   List[str]				  =model(UpperCAmelCase		)[0]

       # TODO Replace vocab size
       lowercase					:   Tuple				  =5_0000

       lowercase					:   List[str]				  =[1, 6, vocab_size]
       self.assertEqual(output.shape     ,      UpperCAmelCase		)

       print(output[:, :3, :3]		)

       # TODO Replace values below with what was printed above.
       lowercase					:   Dict				  =tf.constant(
           [
               [
                   [-0.1_2_0_5_3_3_4_1, -1.0_2_6_4_9_0_1, 0.2_9_2_2_1_9_4_6],
                   [-1.5_1_3_3_7_8_3, 0.1_9_7_4_3_3, 0.1_5_1_9_0_6_0_7],
                   [-5.0_1_3_5_4_0_3, -3.9_0_0_2_5_6, -0.8_4_0_3_8_7_6_4],
               ]
           ]		)
       tf.debugging.assert_near(output[:, :3, :3]     ,      UpperCAmelCase     ,      atol=1e-4		)





@require_tf
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      UpperCamelCase_								=			1e-4






      def 				A__      ( self			:		int		)  ->  List[Any]:


       '''simple docstring'''

       lowercase					:   Union[str, Any]				  =tf.constant([[4, 10]]		)
       lowercase					:   List[Any]				  =TFRoFormerSinusoidalPositionalEmbedding(num_positions=6     ,      embedding_dim=6		)

       lowercase					:   Any				  =emba(input_ids.shape		)
       lowercase					:   List[str]				  =tf.constant(
           [[0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 1.0_0_0_0, 1.0_0_0_0, 1.0_0_0_0], [0.8_4_1_5, 0.0_4_6_4, 0.0_0_2_2, 0.5_4_0_3, 0.9_9_8_9, 1.0_0_0_0]]		)

       tf.debugging.assert_near(UpperCAmelCase     ,      UpperCAmelCase     ,      atol=self.tolerance		)






      def 				A__      ( self			:		Optional[Any]		)  ->  int:


       '''simple docstring'''

       lowercase					:   Optional[Any]				  =tf.constant(
           [
               [0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0, 0.0_0_0_0],
               [0.8_4_1_5, 0.8_2_1_9, 0.8_0_2_0, 0.7_8_1_9, 0.7_6_1_7],
               [0.9_0_9_3, 0.9_3_6_4, 0.9_5_8_1, 0.9_7_4_9, 0.9_8_7_0],
           ]		)
       lowercase					:   Tuple				  =TFRoFormerSinusoidalPositionalEmbedding(num_positions=512     ,      embedding_dim=512		)
       emba([2, 16, 512]		)
       lowercase					:   str				  =emba.weight[:3, :5]

       tf.debugging.assert_near(UpperCAmelCase     ,      UpperCAmelCase     ,      atol=self.tolerance		)





@require_tf
class UpperCAmelCase_					(    unittest.TestCase				):




      """simple docstring"""




      UpperCamelCase_								=			1e-4






      def 				A__      ( self			:		Dict		)  ->  Dict:


       '''simple docstring'''

       lowercase					:   str				  =tf.reshape(tf.range(2 * 12 * 16 * 64     ,      dtype=tf.floataa		)     ,      shape=(2, 12, 16, 64)		) / 100

       lowercase					:   Any				  =-tf.reshape(tf.range(2 * 12 * 16 * 64     ,      dtype=tf.floataa		)     ,      shape=(2, 12, 16, 64)		) / 100

       lowercase					:   Any				  =TFRoFormerSinusoidalPositionalEmbedding(num_positions=32     ,      embedding_dim=64		)
       lowercase					:   Optional[Any]				  =embed_positions([2, 16, 768]		)[None, None, :, :]

       lowercase  ,		lowercase					:   Optional[int]				  =TFRoFormerSelfAttention.apply_rotary_position_embeddings(
           UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase		)

       lowercase					:   Any				  =tf.constant(
           [
               [0.0_0_0_0, 0.0_1_0_0, 0.0_2_0_0, 0.0_3_0_0, 0.0_4_0_0, 0.0_5_0_0, 0.0_6_0_0, 0.0_7_0_0],
               [-0.2_0_1_2, 0.8_8_9_7, 0.0_2_6_3, 0.9_4_0_1, 0.2_0_7_4, 0.9_4_6_3, 0.3_4_8_1, 0.9_3_4_3],
               [-1.7_0_5_7, 0.6_2_7_1, -1.2_1_4_5, 1.3_8_9_7, -0.6_3_0_3, 1.7_6_4_7, -0.1_1_7_3, 1.8_9_8_5],
               [-2.1_7_3_1, -1.6_3_9_7, -2.7_3_5_8, 0.2_8_5_4, -2.1_8_4_0, 1.7_1_8_3, -1.3_0_1_8, 2.4_8_7_1],
               [0.2_7_1_7, -3.6_1_7_3, -2.9_2_0_6, -2.1_9_8_8, -3.6_6_3_8, 0.3_8_5_8, -2.9_1_5_5, 2.2_9_8_0],
               [3.9_8_5_9, -2.1_5_8_0, -0.7_9_8_4, -4.4_9_0_4, -4.1_1_8_1, -2.0_2_5_2, -4.4_7_8_2, 1.1_2_5_3],
           ]		)
       lowercase					:   int				  =tf.constant(
           [
               [0.0_0_0_0, -0.0_1_0_0, -0.0_2_0_0, -0.0_3_0_0, -0.0_4_0_0, -0.0_5_0_0, -0.0_6_0_0, -0.0_7_0_0],
               [0.2_0_1_2, -0.8_8_9_7, -0.0_2_6_3, -0.9_4_0_1, -0.2_0_7_4, -0.9_4_6_3, -0.3_4_8_1, -0.9_3_4_3],
               [1.7_0_5_7, -0.6_2_7_1, 1.2_1_4_5, -1.3_8_9_7, 0.6_3_0_3, -1.7_6_4_7, 0.1_1_7_3, -1.8_9_8_5],
               [2.1_7_3_1, 1.6_3_9_7, 2.7_3_5_8, -0.2_8_5_4, 2.1_8_4_0, -1.7_1_8_3, 1.3_0_1_8, -2.4_8_7_1],
               [-0.2_7_1_7, 3.6_1_7_3, 2.9_2_0_6, 2.1_9_8_8, 3.6_6_3_8, -0.3_8_5_8, 2.9_1_5_5, -2.2_9_8_0],
               [-3.9_8_5_9, 2.1_5_8_0, 0.7_9_8_4, 4.4_9_0_4, 4.1_1_8_1, 2.0_2_5_2, 4.4_7_8_2, -1.1_2_5_3],
           ]		)

       tf.debugging.assert_near(query_layer[0, 0, :6, :8]     ,      UpperCAmelCase     ,      atol=self.tolerance		)
       tf.debugging.assert_near(key_layer[0, 0, :6, :8]     ,      UpperCAmelCase     ,      atol=self.tolerance		)











'''simple docstring'''



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			['''image_processor''', '''tokenizer''']
      UpperCamelCase_								=			'''LayoutLMv2ImageProcessor'''
      UpperCamelCase_								=			('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')






      def __init__( self			:		List[str]     ,      UpperCAmelCase			:		Tuple=None     ,      UpperCAmelCase			:		str=None     ,      **UpperCAmelCase			:		Dict		)  ->  Optional[int]:


       '''simple docstring'''

       if "feature_extractor" in kwargs:
        warnings.warn(
            '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
            ''' instead.'''     ,      UpperCAmelCase     ,      )
        lowercase					:   Any				  =kwargs.pop('''feature_extractor'''		)

       lowercase					:   Dict				  =image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
        raise ValueError('''You need to specify an `image_processor`.'''		)
       if tokenizer is None:
        raise ValueError('''You need to specify a `tokenizer`.'''		)

       super().__init__(UpperCAmelCase     ,      UpperCAmelCase		)






      def __call__( self			:		Any     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None     ,      UpperCAmelCase			:		Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None     ,      UpperCAmelCase			:		Union[List[List[int]], List[List[List[int]]]] = None     ,      UpperCAmelCase			:		Optional[Union[List[int], List[List[int]]]] = None     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Union[bool, str, PaddingStrategy] = False     ,      UpperCAmelCase			:		Union[bool, str, TruncationStrategy] = None     ,      UpperCAmelCase			:		Optional[int] = None     ,      UpperCAmelCase			:		int = 0     ,      UpperCAmelCase			:		Optional[int] = None     ,      UpperCAmelCase			:		Optional[bool] = None     ,      UpperCAmelCase			:		Optional[bool] = None     ,      UpperCAmelCase			:		bool = False     ,      UpperCAmelCase			:		bool = False     ,      UpperCAmelCase			:		bool = False     ,      UpperCAmelCase			:		bool = False     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Optional[Union[str, TensorType]] = None     ,      **UpperCAmelCase			:		Any     ,      )  ->  BatchEncoding:


       '''simple docstring'''

       if self.image_processor.apply_ocr and (boxes is not None):
        raise ValueError(
            '''You cannot provide bounding boxes '''
            '''if you initialized the image processor with apply_ocr set to True.'''		)

       if self.image_processor.apply_ocr and (word_labels is not None):
        raise ValueError(
            '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''		)

       if return_overflowing_tokens is True and return_offsets_mapping is False:
        raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''		)

       # first, apply the image processor
       lowercase					:   Tuple				  =self.image_processor(images=UpperCAmelCase     ,      return_tensors=UpperCAmelCase		)

       # second, apply the tokenizer
       if text is not None and self.image_processor.apply_ocr and text_pair is None:
        if isinstance(UpperCAmelCase     ,      UpperCAmelCase		):
         lowercase					:   Optional[Any]				  =[text]  # add batch dimension (as the image processor always adds a batch dimension)
        lowercase					:   List[str]				  =features['''words''']

       lowercase					:   Optional[Any]				  =self.tokenizer(
           text=text if text is not None else features['''words''']     ,      text_pair=text_pair if text_pair is not None else None     ,      boxes=boxes if boxes is not None else features['''boxes''']     ,      word_labels=UpperCAmelCase     ,      add_special_tokens=UpperCAmelCase     ,      padding=UpperCAmelCase     ,      truncation=UpperCAmelCase     ,      max_length=UpperCAmelCase     ,      stride=UpperCAmelCase     ,      pad_to_multiple_of=UpperCAmelCase     ,      return_token_type_ids=UpperCAmelCase     ,      return_attention_mask=UpperCAmelCase     ,      return_overflowing_tokens=UpperCAmelCase     ,      return_special_tokens_mask=UpperCAmelCase     ,      return_offsets_mapping=UpperCAmelCase     ,      return_length=UpperCAmelCase     ,      verbose=UpperCAmelCase     ,      return_tensors=UpperCAmelCase     ,      **UpperCAmelCase     ,      )

       # add pixel values
       lowercase					:   List[str]				  =features.pop('''pixel_values'''		)
       if return_overflowing_tokens is True:
        lowercase					:   str				  =self.get_overflowing_images(UpperCAmelCase     ,      encoded_inputs['''overflow_to_sample_mapping''']		)
       lowercase					:   Dict				  =images

       return encoded_inputs






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		Optional[Any]     ,      UpperCAmelCase			:		str		)  ->  str:


       '''simple docstring'''

       lowercase					:   str				  =[]
       for sample_idx in overflow_to_sample_mapping:
        images_with_overflow.append(images[sample_idx]		)

       if len(UpperCAmelCase		) != len(UpperCAmelCase		):
        raise ValueError(
            '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
            f' {len(UpperCAmelCase		)} and {len(UpperCAmelCase		)}'		)

       return images_with_overflow






      def 				A__      ( self			:		Union[str, Any]     ,      *UpperCAmelCase			:		Tuple     ,      **UpperCAmelCase			:		Dict		)  ->  int:


       '''simple docstring'''

       return self.tokenizer.batch_decode(*UpperCAmelCase     ,      **UpperCAmelCase		)






      def 				A__      ( self			:		Dict     ,      *UpperCAmelCase			:		Any     ,      **UpperCAmelCase			:		Dict		)  ->  str:


       '''simple docstring'''

       return self.tokenizer.decode(*UpperCAmelCase     ,      **UpperCAmelCase		)






      @property
      def 				A__      ( self			:		List[str]		)  ->  List[str]:


       '''simple docstring'''

       return ["input_ids", "bbox", "attention_mask", "image"]






      @property
      def 				A__      ( self			:		int		)  ->  List[str]:


       '''simple docstring'''

       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''     ,      UpperCAmelCase     ,      )
       return self.image_processor_class






      @property
      def 				A__      ( self			:		Dict		)  ->  List[str]:


       '''simple docstring'''

       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''     ,      UpperCAmelCase     ,      )
       return self.image_processor



