




import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__					    =    get_tests_dir("""fixtures/test_sentencepiece.model""")


if is_torch_available():
					from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

UpperCamelCase__					    =    256047
UpperCamelCase__					    =    256145
@require_sentencepiece
@require_tokenizers
class     a__					(      snake_case__				,       unittest.TestCase   ):
						_a							:					List[Any]      =					NllbTokenizer
						_a							:					Any      =					NllbTokenizerFast
						_a							:					Tuple      =					True
						_a							:					Tuple      =					True
						_a							:					Any      =					{}






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											super().setUp()

											# We have a SentencePiece fixture for testing
											__lowerCAmelCase            = NllbTokenizer(_A	,    keep_accents=_A    )
											tokenizer.save_pretrained(self.tmpdirname    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = NllbTokenizer(_A	,    keep_accents=_A    )

											__lowerCAmelCase            = tokenizer.tokenize("This is a test"    )
											self.assertListEqual(_A	,    ["▁This", "▁is", "▁a", "▁t", "est"]    )

											self.assertListEqual(
											    tokenizer.convert_tokens_to_ids(_A    )	,    [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]	,    )

											__lowerCAmelCase            = tokenizer.tokenize("I was born in 92000, and this is falsé."    )
											self.assertListEqual(
											    _A	,    [
											        SPIECE_UNDERLINE + "I",
											        SPIECE_UNDERLINE + "was",
											        SPIECE_UNDERLINE + "b",
											        "or",
											        "n",
											        SPIECE_UNDERLINE + "in",
											        SPIECE_UNDERLINE + "",
											        "9",
											        "2",
											        "0",
											        "0",
											        "0",
											        ",",
											        SPIECE_UNDERLINE + "and",
											        SPIECE_UNDERLINE + "this",
											        SPIECE_UNDERLINE + "is",
											        SPIECE_UNDERLINE + "f",
											        "al",
											        "s",
											        "é",
											        ".",
											    ]	,    )
											__lowerCAmelCase            = tokenizer.convert_tokens_to_ids(_A    )
											self.assertListEqual(
											    _A	,    [
											        value + tokenizer.fairseq_offset
											        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
											    ]	,    )

											__lowerCAmelCase            = tokenizer.convert_ids_to_tokens(_A    )
											self.assertListEqual(
											    _A	,    [
											        SPIECE_UNDERLINE + "I",
											        SPIECE_UNDERLINE + "was",
											        SPIECE_UNDERLINE + "b",
											        "or",
											        "n",
											        SPIECE_UNDERLINE + "in",
											        SPIECE_UNDERLINE + "",
											        "<unk>",
											        "2",
											        "0",
											        "0",
											        "0",
											        ",",
											        SPIECE_UNDERLINE + "and",
											        SPIECE_UNDERLINE + "this",
											        SPIECE_UNDERLINE + "is",
											        SPIECE_UNDERLINE + "f",
											        "al",
											        "s",
											        "<unk>",
											        ".",
											    ]	,    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
																					__lowerCAmelCase            = self.rust_tokenizer_class.from_pretrained(_A	,    **_A    )
																					__lowerCAmelCase            = self.tokenizer_class.from_pretrained(_A	,    **_A    )

																					__lowerCAmelCase            = tempfile.mkdtemp()

																					__lowerCAmelCase            = tokenizer_r.save_pretrained(_A    )
																					__lowerCAmelCase            = tokenizer_p.save_pretrained(_A    )

																					# Checks it save with the same files + the tokenizer.json file for the fast one
																					self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files    )    )
																					__lowerCAmelCase            = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f    )
																					self.assertSequenceEqual(_A	,    _A    )

																					# Checks everything loads correctly in the same way
																					__lowerCAmelCase            = tokenizer_r.from_pretrained(_A    )
																					__lowerCAmelCase            = tokenizer_p.from_pretrained(_A    )

																					# Check special tokens are set accordingly on Rust and Python
																					for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(_A	,    _A    )    )

																					shutil.rmtree(_A    )

																					# Save tokenizer rust, legacy_format=True
																					__lowerCAmelCase            = tempfile.mkdtemp()

																					__lowerCAmelCase            = tokenizer_r.save_pretrained(_A	,    legacy_format=_A    )
																					__lowerCAmelCase            = tokenizer_p.save_pretrained(_A    )

																					# Checks it save with the same files
																					self.assertSequenceEqual(_A	,    _A    )

																					# Checks everything loads correctly in the same way
																					__lowerCAmelCase            = tokenizer_r.from_pretrained(_A    )
																					__lowerCAmelCase            = tokenizer_p.from_pretrained(_A    )

																					# Check special tokens are set accordingly on Rust and Python
																					for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(_A	,    _A    )    )

																					shutil.rmtree(_A    )

																					# Save tokenizer rust, legacy_format=False
																					__lowerCAmelCase            = tempfile.mkdtemp()

																					__lowerCAmelCase            = tokenizer_r.save_pretrained(_A	,    legacy_format=_A    )
																					__lowerCAmelCase            = tokenizer_p.save_pretrained(_A    )

																					# Checks it saved the tokenizer.json file
																					self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files    )    )

																					# Checks everything loads correctly in the same way
																					__lowerCAmelCase            = tokenizer_r.from_pretrained(_A    )
																					__lowerCAmelCase            = tokenizer_p.from_pretrained(_A    )

																					# Check special tokens are set accordingly on Rust and Python
																					for key in tokenizer_pp.special_tokens_map:
																										self.assertTrue(hasattr(_A	,    _A    )    )

																					shutil.rmtree(_A    )






						@require_torch
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											if not self.test_seqaseq:
																return

											__lowerCAmelCase            = self.get_tokenizers()
											for tokenizer in tokenizers:
																with self.subTest(f"""{tokenizer.__class__.__name__}"""    ):
																					# Longer text that will definitely require truncation.
																					__lowerCAmelCase            = [
																					    " UN Chief Says There Is No Military Solution in Syria",
																					    " Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
																					    " Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
																					    " will only worsen the violence and misery for millions of people.",
																					]
																					__lowerCAmelCase            = [
																					    "Şeful ONU declară că nu există o soluţie militară în Siria",
																					    "Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
																					    " Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"
																					    " că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
																					]
																					try:
																										__lowerCAmelCase            = tokenizer.prepare_seqaseq_batch(
																										    src_texts=_A	,    tgt_texts=_A	,    max_length=3	,    max_target_length=1_0	,    return_tensors="pt"	,    src_lang="eng_Latn"	,    tgt_lang="ron_Latn"	,    )
																					except NotImplementedError:
																										return
																					self.assertEqual(batch.input_ids.shape[1]	,    3    )
																					self.assertEqual(batch.labels.shape[1]	,    1_0    )
																					# max_target_length will default to max_length if not specified
																					__lowerCAmelCase            = tokenizer.prepare_seqaseq_batch(
																					    _A	,    tgt_texts=_A	,    max_length=3	,    return_tensors="pt"    )
																					self.assertEqual(batch.input_ids.shape[1]	,    3    )
																					self.assertEqual(batch.labels.shape[1]	,    3    )

																					__lowerCAmelCase            = tokenizer.prepare_seqaseq_batch(
																					    src_texts=_A	,    max_length=3	,    max_target_length=1_0	,    return_tensors="pt"    )
																					self.assertEqual(batch_encoder_only.input_ids.shape[1]	,    3    )
																					self.assertEqual(batch_encoder_only.attention_mask.shape[1]	,    3    )
																					self.assertNotIn("decoder_input_ids"	,    _A    )






						@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."    )
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											pass






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""    ):
																					__lowerCAmelCase            = [AddedToken("<special>"	,    lstrip=_A    )]

																					__lowerCAmelCase            = self.rust_tokenizer_class.from_pretrained(
																					    _A	,    additional_special_tokens=_A	,    **_A    )
																					__lowerCAmelCase            = tokenizer_r.encode("Hey this is a <special> token"    )

																					__lowerCAmelCase            = tokenizer_r.encode("<special>"	,    add_special_tokens=_A    )[0]

																					self.assertTrue(special_token_id in r_output    )

																					if self.test_slow_tokenizer:
																										__lowerCAmelCase            = self.rust_tokenizer_class.from_pretrained(
																										    _A	,    additional_special_tokens=_A	,    **_A	,    )
																										__lowerCAmelCase            = self.tokenizer_class.from_pretrained(
																										    _A	,    additional_special_tokens=_A	,    **_A    )

																										__lowerCAmelCase            = tokenizer_p.encode("Hey this is a <special> token"    )

																										__lowerCAmelCase            = tokenizer_cr.encode("Hey this is a <special> token"    )

																										self.assertEqual(_A	,    _A    )
																										self.assertEqual(_A	,    _A    )
																										self.assertTrue(special_token_id in p_output    )
																										self.assertTrue(special_token_id in cr_output    )






@require_torch
@require_sentencepiece
@require_tokenizers
class     a__					(      unittest.TestCase   ):
						_a							:					Optional[int]      =					"""facebook/nllb-200-distilled-600M"""
						_a							:					Any      =					[
						    """ UN Chief Says There Is No Military Solution in Syria""",
						    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.""",
						]
						_a							:					Optional[Any]      =					[
						    """Şeful ONU declară că nu există o soluţie militară în Siria""",
						    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"""
						    """ pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"""
						    """ face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
						]
						_a							:					Any      =					[
						    2_5_6_0_4_7,
						    1_6_2_9_7,
						    1_3_4_4_0_8,
						    8_1_6_5,
						    2_4_8_0_6_6,
						    1_4_7_3_4,
						    9_5_0,
						    1_1_3_5,
						    1_0_5_7_2_1,
						    3_5_7_3,
						    8_3,
						    2_7_3_5_2,
						    1_0_8,
						    4_9_4_8_6,
						    2,
						]






						@classmethod
						def        __SCREAMING_SNAKE_CASE(     cls    ):




											"""simple docstring"""



											__lowerCAmelCase            = NllbTokenizer.from_pretrained(
											    cls.checkpoint_name	,    src_lang="eng_Latn"	,    tgt_lang="ron_Latn"    )
											__lowerCAmelCase            = 1
											return cls






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]	,    2_5_6_0_0_1    )
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]	,    2_5_6_0_0_2    )
											self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]	,    2_5_6_0_5_7    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.tokenizer.batch_encode_plus(self.src_text    ).input_ids[0]
											self.assertListEqual(self.expected_src_tokens	,    _A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											self.assertIn(_A	,    self.tokenizer.all_special_ids    )
											# fmt: off
											__lowerCAmelCase            = [RO_CODE, 4_2_5_4, 9_8_0_6_8, 1_1_2_9_2_3, 3_9_0_7_2, 3_9_0_9, 7_1_3, 1_0_2_7_6_7, 2_6, 1_7_3_1_4, 3_5_6_4_2, 1_4_6_8_3, 3_3_1_1_8, 2_0_2_2, 6_6_9_8_7, 2, 2_5_6_0_4_7]
											# fmt: on

											__lowerCAmelCase            = self.tokenizer.decode(_A	,    skip_special_tokens=_A    )
											__lowerCAmelCase            = self.tokenizer.decode(generated_ids[1:]	,    skip_special_tokens=_A    )
											self.assertEqual(_A	,    _A    )
											self.assertNotIn(self.tokenizer.eos_token	,    _A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = ["this is gunna be a long sentence " * 2_0]
											assert isinstance(src_text[0]	,    _A    )
											__lowerCAmelCase            = 1_0
											__lowerCAmelCase            = self.tokenizer(_A	,    max_length=_A	,    truncation=_A    ).input_ids[0]
											self.assertEqual(ids[-1]	,    2    )
											self.assertEqual(ids[0]	,    _A    )
											self.assertEqual(len(_A    )	,    _A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]    )	,    [2_5_6_2_0_3, 3]    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = tempfile.mkdtemp()
											__lowerCAmelCase            = self.tokenizer.fairseq_tokens_to_ids
											self.tokenizer.save_pretrained(_A    )
											__lowerCAmelCase            = NllbTokenizer.from_pretrained(_A    )
											self.assertDictEqual(new_tok.fairseq_tokens_to_ids	,    _A    )






						@require_torch
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.tokenizer(
											    self.src_text	,    text_target=self.tgt_text	,    padding=_A	,    truncation=_A	,    max_length=len(self.expected_src_tokens    )	,    return_tensors="pt"	,    )
											__lowerCAmelCase            = shift_tokens_right(
											    batch["labels"]	,    self.tokenizer.pad_token_id	,    self.tokenizer.lang_code_to_id["ron_Latn"]    )

											self.assertIsInstance(_A	,    _A    )

											self.assertEqual((2, 1_5)	,    batch.input_ids.shape    )
											self.assertEqual((2, 1_5)	,    batch.attention_mask.shape    )
											__lowerCAmelCase            = batch.input_ids.tolist()[0]
											self.assertListEqual(self.expected_src_tokens	,    _A    )
											self.assertEqual(_A	,    batch.decoder_input_ids[0, 0]    )  # EOS
											# Test that special tokens are reset
											self.assertEqual(self.tokenizer.prefix_tokens	,    [EN_CODE]    )
											self.assertEqual(self.tokenizer.suffix_tokens	,    [self.tokenizer.eos_token_id]    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.tokenizer(self.src_text	,    padding=_A	,    truncation=_A	,    max_length=3	,    return_tensors="pt"    )
											__lowerCAmelCase            = self.tokenizer(
											    text_target=self.tgt_text	,    padding=_A	,    truncation=_A	,    max_length=1_0	,    return_tensors="pt"    )
											__lowerCAmelCase            = targets["input_ids"]
											__lowerCAmelCase            = shift_tokens_right(
											    _A	,    self.tokenizer.pad_token_id	,    decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]	,    )

											self.assertEqual(batch.input_ids.shape[1]	,    3    )
											self.assertEqual(batch.decoder_input_ids.shape[1]	,    1_0    )






						@require_torch
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.tokenizer._build_translation_inputs(
											    "A test"	,    return_tensors="pt"	,    src_lang="eng_Latn"	,    tgt_lang="fra_Latn"    )

											self.assertEqual(
											    nested_simplify(_A    )	,    {
											        # A, test, EOS, en_XX
											        "input_ids": [[2_5_6_0_4_7, 7_0, 7_3_5_6, 2]],
											        "attention_mask": [[1, 1, 1, 1]],
											        # ar_AR
											        "forced_bos_token_id": 2_5_6_0_5_7,
											    }	,    )






						@require_torch
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = True
											__lowerCAmelCase            = self.tokenizer(
											    "UN Chief says there is no military solution in Syria"	,    src_lang="eng_Latn"	,    tgt_lang="fra_Latn"    )
											self.assertEqual(
											    inputs.input_ids	,    [1_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2, 2_5_6_0_4_7]    )

											__lowerCAmelCase            = False
											__lowerCAmelCase            = self.tokenizer(
											    "UN Chief says there is no military solution in Syria"	,    src_lang="eng_Latn"	,    tgt_lang="fra_Latn"    )
											self.assertEqual(
											    inputs.input_ids	,    [2_5_6_0_4_7, 1_6_2_9_7, 1_3_4_4_0_8, 2_5_6_5_3, 6_3_7_0, 2_4_8, 2_5_4, 1_0_3_9_2_9, 9_4_9_9_5, 1_0_8, 4_9_4_8_6, 2]    )










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__					:






						def __init__(     self	,    _A	,    _A=1_3	,    _A=7	,    _A=True	,    _A=True	,    _A=True	,    _A=True	,    _A=9_9	,    _A=3_2	,    _A=2	,    _A=4	,    _A=3_7	,    _A="gelu"	,    _A=0.1	,    _A=0.1	,    _A=5_1_2	,    _A=1_6	,    _A=2	,    _A=0.02	,    _A=False	,    _A=True	,    _A="None"	,    _A=3	,    _A=4	,    _A=None	,    ):




											"""simple docstring"""



											__lowerCAmelCase            = parent
											__lowerCAmelCase            = batch_size
											__lowerCAmelCase            = seq_length
											__lowerCAmelCase            = is_training
											__lowerCAmelCase            = use_input_mask
											__lowerCAmelCase            = use_token_type_ids
											__lowerCAmelCase            = use_labels
											__lowerCAmelCase            = vocab_size
											__lowerCAmelCase            = hidden_size
											__lowerCAmelCase            = num_hidden_layers
											__lowerCAmelCase            = num_attention_heads
											__lowerCAmelCase            = intermediate_size
											__lowerCAmelCase            = hidden_act
											__lowerCAmelCase            = hidden_dropout_prob
											__lowerCAmelCase            = attention_probs_dropout_prob
											__lowerCAmelCase            = max_position_embeddings
											__lowerCAmelCase            = type_vocab_size
											__lowerCAmelCase            = type_sequence_label_size
											__lowerCAmelCase            = initializer_range
											__lowerCAmelCase            = num_labels
											__lowerCAmelCase            = num_choices
											__lowerCAmelCase            = relative_attention
											__lowerCAmelCase            = position_biased_input
											__lowerCAmelCase            = pos_att_type
											__lowerCAmelCase            = scope






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size    )

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

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

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

											__lowerCAmelCase            = 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        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFDebertaVaModel(config=_A    )
											__lowerCAmelCase            = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}

											__lowerCAmelCase            = [input_ids, input_mask]
											__lowerCAmelCase            = model(_A    )

											__lowerCAmelCase            = model(_A    )

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






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFDebertaVaForMaskedLM(config=_A    )
											__lowerCAmelCase            = {
											    "input_ids": input_ids,
											    "attention_mask": input_mask,
											    "token_type_ids": token_type_ids,
											}
											__lowerCAmelCase            = model(_A    )
											self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.vocab_size)    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.num_labels
											__lowerCAmelCase            = TFDebertaVaForSequenceClassification(config=_A    )
											__lowerCAmelCase            = {
											    "input_ids": input_ids,
											    "attention_mask": input_mask,
											    "token_type_ids": token_type_ids,
											}

											__lowerCAmelCase            = model(_A    )
											self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.num_labels)    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.num_labels
											__lowerCAmelCase            = TFDebertaVaForTokenClassification(config=_A    )
											__lowerCAmelCase            = {
											    "input_ids": input_ids,
											    "attention_mask": input_mask,
											    "token_type_ids": token_type_ids,
											}
											__lowerCAmelCase            = model(_A    )
											self.parent.assertEqual(result.logits.shape	,    (self.batch_size, self.seq_length, self.num_labels)    )






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFDebertaVaForQuestionAnswering(config=_A    )
											__lowerCAmelCase            = {
											    "input_ids": input_ids,
											    "attention_mask": input_mask,
											    "token_type_ids": token_type_ids,
											}

											__lowerCAmelCase            = 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        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.prepare_config_and_inputs()
											(
											    (
											    __lowerCAmelCase
											)     ,							(
											    __lowerCAmelCase
											)     ,							(
											    __lowerCAmelCase
											)     ,							(
											    __lowerCAmelCase
											)     ,							(
											    __lowerCAmelCase
											)     ,							(
											    __lowerCAmelCase
											)     ,							(
											    __lowerCAmelCase
											)     ,							
											)            = config_and_inputs
											__lowerCAmelCase            = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
											return config, inputs_dict






@require_tf
class     a__					(      snake_case__				,       snake_case__				,       unittest.TestCase   ):
						_a							:					Optional[Any]      =					(
						    (
						        TFDebertaVaModel,
						        TFDebertaVaForMaskedLM,
						        TFDebertaVaForQuestionAnswering,
						        TFDebertaVaForSequenceClassification,
						        TFDebertaVaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						_a							:					Union[str, Any]      =					(
						    {
						        """feature-extraction""": TFDebertaVaModel,
						        """fill-mask""": TFDebertaVaForMaskedLM,
						        """question-answering""": TFDebertaVaForQuestionAnswering,
						        """text-classification""": TFDebertaVaForSequenceClassification,
						        """token-classification""": TFDebertaVaForTokenClassification,
						        """zero-shot""": TFDebertaVaForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)

						_a							:					str      =					False
						_a							:					List[str]      =					False






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFDebertaVaModelTester(self    )
											__lowerCAmelCase            = ConfigTester(self	,    config_class=_A	,    hidden_size=3_7    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*_A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*_A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*_A    )






						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*_A    )






						@slow
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"    )
											self.assertIsNotNone(_A    )






@require_tf
class     a__					(      unittest.TestCase   ):






						@unittest.skip(reason="Model not available yet"    )
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											pass






						@slow
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge"    )
											__lowerCAmelCase            = tf.constant([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]    )
											__lowerCAmelCase            = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
											__lowerCAmelCase            = model(_A	,    attention_mask=_A    )[0]

											__lowerCAmelCase            = 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    )




