"""simple docstring"""



from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 (
           TFDebertaVaForMaskedLM,
           TFDebertaVaForQuestionAnswering,
           TFDebertaVaForSequenceClassification,
           TFDebertaVaForTokenClassification,
           TFDebertaVaModel,
       )



class      _A	:




    """simple docstring"""




    def __init__(		self							:     Any	,      A_							:     Any	,      A_							:     List[Any]=13	,      A_							:     Dict=7	,      A_							:     Union[str, Any]=True	,      A_							:     List[str]=True	,      A_							:     int=True	,      A_							:     Any=True	,      A_							:     List[str]=99	,      A_							:     List[str]=32	,      A_							:     Tuple=2	,      A_							:     List[str]=4	,      A_							:     Dict=37	,      A_							:     Optional[int]="gelu"	,      A_							:     Dict=0.1	,      A_							:     Optional[int]=0.1	,      A_							:     Union[str, Any]=512	,      A_							:     Optional[Any]=16	,      A_							:     Any=2	,      A_							:     Optional[Any]=0.02	,      A_							:     Tuple=False	,      A_							:     Dict=True	,      A_							:     Union[str, Any]="None"	,      A_							:     List[Any]=3	,      A_							:     List[Any]=4	,      A_							:     List[str]=None	,      )    ->       Optional[int]:
         __snake_case				=							parent
         __snake_case				=							batch_size
         __snake_case				=							seq_length
         __snake_case				=							is_training
         __snake_case				=							use_input_mask
         __snake_case				=							use_token_type_ids
         __snake_case				=							use_labels
         __snake_case				=							vocab_size
         __snake_case				=							hidden_size
         __snake_case				=							num_hidden_layers
         __snake_case				=							num_attention_heads
         __snake_case				=							intermediate_size
         __snake_case				=							hidden_act
         __snake_case				=							hidden_dropout_prob
         __snake_case				=							attention_probs_dropout_prob
         __snake_case				=							max_position_embeddings
         __snake_case				=							type_vocab_size
         __snake_case				=							type_sequence_label_size
         __snake_case				=							initializer_range
         __snake_case				=							num_labels
         __snake_case				=							num_choices
         __snake_case				=							relative_attention
         __snake_case				=							position_biased_input
         __snake_case				=							pos_att_type
         __snake_case				=							scope
    def  lowercase					(		self							:     Optional[int]    )    ->       List[Any]:
         __snake_case				=							ids_tensor([self.batch_size, self.seq_length]	,      self.vocab_size    )

         __snake_case				=							None
         if self.use_input_mask:
              __snake_case				=							random_attention_mask([self.batch_size, self.seq_length]    )

         __snake_case				=							None
         if self.use_token_type_ids:
              __snake_case				=							ids_tensor([self.batch_size, self.seq_length]	,      self.type_vocab_size    )

         __snake_case				=							None
         __snake_case				=							None
         __snake_case				=							None
         if self.use_labels:
              __snake_case				=							ids_tensor([self.batch_size]	,      self.type_sequence_label_size    )
              __snake_case				=							ids_tensor([self.batch_size, self.seq_length]	,      self.num_labels    )

         __snake_case				=							DebertaVaConfig(
             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	,      relative_attention=self.relative_attention	,      position_biased_input=self.position_biased_input	,      initializer_range=self.initializer_range	,      return_dict=A_	,      )

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
    def  lowercase					(		self							:     Optional[int]	,      A_							:     Tuple	,      A_							:     List[Any]	,      A_							:     List[Any]	,      A_							:     Dict	,      A_							:     Tuple	,      A_							:     Union[str, Any]	,      A_							:     str    )    ->       Tuple:
         __snake_case				=							TFDebertaVaModel(config=A_    )
         __snake_case				=							{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

         __snake_case				=							[input_ids, input_mask]
         __snake_case				=							model(A_    )

         __snake_case				=							model(A_    )

         self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)    )
    def  lowercase					(		self							:     Optional[int]	,      A_							:     int	,      A_							:     Optional[Any]	,      A_							:     Any	,      A_							:     List[Any]	,      A_							:     Dict	,      A_							:     List[Any]	,      A_							:     Union[str, Any]    )    ->       Dict:
         __snake_case				=							TFDebertaVaForMaskedLM(config=A_    )
         __snake_case				=							{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.vocab_size)    )
    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     Optional[int]	,      A_							:     List[str]	,      A_							:     Any	,      A_							:     List[Any]	,      A_							:     Union[str, Any]	,      A_							:     int	,      A_							:     Union[str, Any]    )    ->       List[str]:
         __snake_case				=							self.num_labels
         __snake_case				=							TFDebertaVaForSequenceClassification(config=A_    )
         __snake_case				=							{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.num_labels)    )
    def  lowercase					(		self							:     Union[str, Any]	,      A_							:     Optional[int]	,      A_							:     int	,      A_							:     int	,      A_							:     str	,      A_							:     Union[str, Any]	,      A_							:     Union[str, Any]	,      A_							:     List[Any]    )    ->       Dict:
         __snake_case				=							self.num_labels
         __snake_case				=							TFDebertaVaForTokenClassification(config=A_    )
         __snake_case				=							{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }
         __snake_case				=							model(A_    )
         self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.seq_length, self.num_labels)    )
    def  lowercase					(		self							:     Optional[int]	,      A_							:     Union[str, Any]	,      A_							:     List[Any]	,      A_							:     int	,      A_							:     Optional[int]	,      A_							:     Optional[Any]	,      A_							:     Optional[Any]	,      A_							:     Optional[int]    )    ->       Optional[int]:
         __snake_case				=							TFDebertaVaForQuestionAnswering(config=A_    )
         __snake_case				=							{
             '''input_ids''': input_ids,
             '''attention_mask''': input_mask,
             '''token_type_ids''': token_type_ids,
         }

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






@require_tf
class      _A	(						_UpperCAmelCase							, _UpperCAmelCase							, unittest.TestCase					):




    """simple docstring"""




    UpperCamelCase_	:					Dict							=					(
        (
            TFDebertaVaModel,
            TFDebertaVaForMaskedLM,
            TFDebertaVaForQuestionAnswering,
            TFDebertaVaForSequenceClassification,
            TFDebertaVaForTokenClassification,
        )
        if is_tf_available()
        else ()
    )
    UpperCamelCase_	:					Optional[Any]							=					(
        {
            '''feature-extraction''': TFDebertaVaModel,
            '''fill-mask''': TFDebertaVaForMaskedLM,
            '''question-answering''': TFDebertaVaForQuestionAnswering,
            '''text-classification''': TFDebertaVaForSequenceClassification,
            '''token-classification''': TFDebertaVaForTokenClassification,
            '''zero-shot''': TFDebertaVaForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )

    UpperCamelCase_	:					Optional[int]							=					False
    UpperCamelCase_	:					Union[str, Any]							=					False
    def  lowercase					(		self							:     int    )    ->       List[str]:
         __snake_case				=							TFDebertaVaModelTester(self    )
         __snake_case				=							ConfigTester(self	,      config_class=A_	,      hidden_size=37    )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Optional[int]:
         self.config_tester.run_common_tests()
    def  lowercase					(		self							:     Any    )    ->       Dict:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*A_    )
    def  lowercase					(		self							:     str    )    ->       Dict:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_masked_lm(*A_    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Dict:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*A_    )
    def  lowercase					(		self							:     str    )    ->       Optional[int]:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_sequence_classification(*A_    )
    def  lowercase					(		self							:     List[Any]    )    ->       List[str]:
         __snake_case				=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*A_    )







    @slow
    def  lowercase					(		self							:     Optional[int]    )    ->       Any:
         __snake_case				=							TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''    )
         self.assertIsNotNone(A_    )






@require_tf
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    @unittest.skip(reason='''Model not available yet'''    )
    def  lowercase					(		self							:     int    )    ->       int:
         pass







    @slow
    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         __snake_case				=							TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''    )
         __snake_case				=							tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]    )
         __snake_case				=							tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
         __snake_case				=							model(A_	,      attention_mask=A_    )[0]

         __snake_case				=							tf.constant(
             [[[0.23_56, 0.19_48, 0.03_69], [-0.10_63, 0.35_86, -0.51_52], [-0.63_99, -0.02_59, -0.25_25]]]    )
         tf.debugging.assert_near(output[:, 1:4, 1:4]	,      A_	,      atol=1E-4    )
"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
       from PIL import Image

       from transformers import CLIPSegProcessor, ViTImageProcessor



@require_vision
class      _A	(						unittest.TestCase					):




    """simple docstring"""




    def  lowercase					(		self							:     Optional[int]    )    ->       List[str]:
         __snake_case				=							tempfile.mkdtemp()

         # fmt: off
         __snake_case				=							['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
         # fmt: on
         __snake_case				=							dict(zip(A_	,      range(len(A_    )    )    )    )
         __snake_case				=							['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
         __snake_case				=							{'''unk_token''': '''<unk>'''}

         __snake_case				=							os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES['''vocab_file''']    )
         __snake_case				=							os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES['''merges_file''']    )
         with open(self.vocab_file	,      '''w'''	,      encoding='''utf-8'''    ) as fp:
              fp.write(json.dumps(A_    ) + '''\n'''    )
         with open(self.merges_file	,      '''w'''	,      encoding='''utf-8'''    ) as fp:
              fp.write('''\n'''.join(A_    )    )

         __snake_case				=							{
             '''do_resize''': True,
             '''size''': 20,
             '''do_center_crop''': True,
             '''crop_size''': 18,
             '''do_normalize''': True,
             '''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
             '''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
         }
         __snake_case				=							os.path.join(self.tmpdirname	,      A_    )
         with open(self.image_processor_file	,      '''w'''	,      encoding='''utf-8'''    ) as fp:
              json.dump(A_	,      A_    )
    def  lowercase					(		self							:     Optional[Any]	,      **A_							:     Dict    )    ->       Any:
         return CLIPTokenizer.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Optional[int]	,      **A_							:     str    )    ->       str:
         return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Any	,      **A_							:     Tuple    )    ->       Tuple:
         return ViTImageProcessor.from_pretrained(self.tmpdirname	,      **A_    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Optional[Any]:
         shutil.rmtree(self.tmpdirname    )
    def  lowercase					(		self							:     int    )    ->       Optional[Any]:
         __snake_case				=							[np.random.randint(255	,      size=(3, 30, 400)	,      dtype=np.uinta    )]

         __snake_case				=							[Image.fromarray(np.moveaxis(A_	,      0	,      -1    )    ) for x in image_inputs]

         return image_inputs
    def  lowercase					(		self							:     Optional[Any]    )    ->       Optional[Any]:
         __snake_case				=							self.get_tokenizer()
         __snake_case				=							self.get_rust_tokenizer()
         __snake_case				=							self.get_image_processor()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )
         processor_slow.save_pretrained(self.tmpdirname    )
         __snake_case				=							CLIPSegProcessor.from_pretrained(self.tmpdirname	,      use_fast=A_    )

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )
         processor_fast.save_pretrained(self.tmpdirname    )
         __snake_case				=							CLIPSegProcessor.from_pretrained(self.tmpdirname    )

         self.assertEqual(processor_slow.tokenizer.get_vocab()	,      tokenizer_slow.get_vocab()    )
         self.assertEqual(processor_fast.tokenizer.get_vocab()	,      tokenizer_fast.get_vocab()    )
         self.assertEqual(tokenizer_slow.get_vocab()	,      tokenizer_fast.get_vocab()    )
         self.assertIsInstance(processor_slow.tokenizer	,      A_    )
         self.assertIsInstance(processor_fast.tokenizer	,      A_    )

         self.assertEqual(processor_slow.image_processor.to_json_string()	,      image_processor.to_json_string()    )
         self.assertEqual(processor_fast.image_processor.to_json_string()	,      image_processor.to_json_string()    )
         self.assertIsInstance(processor_slow.image_processor	,      A_    )
         self.assertIsInstance(processor_fast.image_processor	,      A_    )
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Any:
         __snake_case				=							CLIPSegProcessor(tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()    )
         processor.save_pretrained(self.tmpdirname    )

         __snake_case				=							self.get_tokenizer(bos_token='''(BOS)'''	,      eos_token='''(EOS)'''    )
         __snake_case				=							self.get_image_processor(do_normalize=A_	,      padding_value=1.0    )

         __snake_case				=							CLIPSegProcessor.from_pretrained(
             self.tmpdirname	,      bos_token='''(BOS)'''	,      eos_token='''(EOS)'''	,      do_normalize=A_	,      padding_value=1.0    )

         self.assertEqual(processor.tokenizer.get_vocab()	,      tokenizer_add_kwargs.get_vocab()    )
         self.assertIsInstance(processor.tokenizer	,      A_    )

         self.assertEqual(processor.image_processor.to_json_string()	,      image_processor_add_kwargs.to_json_string()    )
         self.assertIsInstance(processor.image_processor	,      A_    )
    def  lowercase					(		self							:     Any    )    ->       str:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							image_processor(A_	,      return_tensors='''np'''    )
         __snake_case				=							processor(images=A_	,      return_tensors='''np'''    )

         for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()	,      input_processor[key].sum()	,      delta=1E-2    )
    def  lowercase					(		self							:     List[str]    )    ->       List[Any]:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							'''lower newer'''

         __snake_case				=							processor(text=A_    )

         __snake_case				=							tokenizer(A_    )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]	,      encoded_processor[key]    )
    def  lowercase					(		self							:     List[Any]    )    ->       str:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							'''lower newer'''
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(text=A_	,      images=A_    )

         self.assertListEqual(list(inputs.keys()    )	,      ['''input_ids''', '''attention_mask''', '''pixel_values''']    )

         # test if it raises when no input is passed
         with pytest.raises(A_    ):
              processor()
    def  lowercase					(		self							:     Union[str, Any]    )    ->       Any:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							self.prepare_image_inputs()
         __snake_case				=							self.prepare_image_inputs()

         __snake_case				=							processor(images=A_	,      visual_prompt=A_    )

         self.assertListEqual(list(inputs.keys()    )	,      ['''pixel_values''', '''conditional_pixel_values''']    )

         # test if it raises when no input is passed
         with pytest.raises(A_    ):
              processor()







    def  lowercase					(		self							:     Optional[int]    )    ->       Dict:
         __snake_case				=							self.get_image_processor()
         __snake_case				=							self.get_tokenizer()

         __snake_case				=							CLIPSegProcessor(tokenizer=A_	,      image_processor=A_    )

         __snake_case				=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __snake_case				=							processor.batch_decode(A_    )
         __snake_case				=							tokenizer.batch_decode(A_    )

         self.assertListEqual(A_	,      A_    )