



from __future__ import annotations

import unittest

from transformers import DebertaConfig, 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 (
	    TFDebertaForMaskedLM,
	    TFDebertaForQuestionAnswering,
	    TFDebertaForSequenceClassification,
	    TFDebertaForTokenClassification,
	    TFDebertaModel,
	)

class 	__snake_case :







							'''simple docstring'''






							def __init__(							self					,					a_					,					a_=13					,					a_=7					,					a_=True					,					a_=True					,					a_=True					,					a_=True					,					a_=99					,					a_=32					,					a_=2					,					a_=4					,					a_=37					,					a_="gelu"					,					a_=0.1					,					a_=0.1					,					a_=512					,					a_=16					,					a_=2					,					a_=0.02					,					a_=3					,					a_=4					,					a_=None					,					):
											a__			 =						parent
											a__			 =						13
											a__			 =						7
											a__			 =						True
											a__			 =						True
											a__			 =						True
											a__			 =						True
											a__			 =						99
											a__			 =						32
											a__			 =						2
											a__			 =						4
											a__			 =						37
											a__			 =						"""gelu"""
											a__			 =						0.1
											a__			 =						0.1
											a__			 =						512
											a__			 =						16
											a__			 =						False
											a__			 =						-1
											a__			 =						True
											a__			 =						2
											a__			 =						0.02
											a__			 =						3
											a__			 =						4
											a__			 =						None


							def _a (							self						):
											a__			 =						ids_tensor([self.batch_size, self.seq_length]					,					self.vocab_size						)

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

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

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

											a__			 =						DebertaConfig(
											    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					,					max_relative_positions=self.max_relative_positions					,					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 _a (							self					,					a_					,					a_					,					a_					,					a_					,					a_					,					a_					,					a_						):
											a__			 =						TFDebertaModel(config=a_						)
											a__			 =						{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

											a__			 =						[input_ids, input_mask]
											a__			 =						model(a_						)

											a__			 =						model(a_						)

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


							def _a (							self					,					a_					,					a_					,					a_					,					a_					,					a_					,					a_					,					a_						):
											a__			 =						TFDebertaForMaskedLM(config=a_						)
											a__			 =						{
											    """input_ids""": input_ids,
											    """attention_mask""": input_mask,
											    """token_type_ids""": token_type_ids,
											}
											a__			 =						model(a_						)
											self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.seq_length, self.vocab_size)						)


							def _a (							self					,					a_					,					a_					,					a_					,					a_					,					a_					,					a_					,					a_						):
											a__			 =						self.num_labels
											a__			 =						TFDebertaForSequenceClassification(config=a_						)
											a__			 =						{
											    """input_ids""": input_ids,
											    """attention_mask""": input_mask,
											    """token_type_ids""": token_type_ids,
											}

											a__			 =						model(a_						)
											self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.num_labels)						)


							def _a (							self					,					a_					,					a_					,					a_					,					a_					,					a_					,					a_					,					a_						):
											a__			 =						self.num_labels
											a__			 =						TFDebertaForTokenClassification(config=a_						)
											a__			 =						{
											    """input_ids""": input_ids,
											    """attention_mask""": input_mask,
											    """token_type_ids""": token_type_ids,
											}
											a__			 =						model(a_						)
											self.parent.assertEqual(result.logits.shape					,					(self.batch_size, self.seq_length, self.num_labels)						)


							def _a (							self					,					a_					,					a_					,					a_					,					a_					,					a_					,					a_					,					a_						):
											a__			 =						TFDebertaForQuestionAnswering(config=a_						)
											a__			 =						{
											    """input_ids""": input_ids,
											    """attention_mask""": input_mask,
											    """token_type_ids""": token_type_ids,
											}

											a__			 =						model(a_						)
											self.parent.assertEqual(result.start_logits.shape					,					(self.batch_size, self.seq_length)						)
											self.parent.assertEqual(result.end_logits.shape					,					(self.batch_size, self.seq_length)						)







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




@require_tf
class 	__snake_case (				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__			: Optional[Any]             =   (
							    (
							        TFDebertaModel,
							        TFDebertaForMaskedLM,
							        TFDebertaForQuestionAnswering,
							        TFDebertaForSequenceClassification,
							        TFDebertaForTokenClassification,
							    )
							    if is_tf_available()
							    else ()
							)
							UpperCamelCase__			: List[str]             =   (
							    {
							        """feature-extraction""": TFDebertaModel,
							        """fill-mask""": TFDebertaForMaskedLM,
							        """question-answering""": TFDebertaForQuestionAnswering,
							        """text-classification""": TFDebertaForSequenceClassification,
							        """token-classification""": TFDebertaForTokenClassification,
							        """zero-shot""": TFDebertaForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)

							UpperCamelCase__			: Tuple             =   False
							UpperCamelCase__			: List[str]             =   False


							def _a (							self						):
											a__			 =						TFDebertaModelTester(self						)
											a__			 =						ConfigTester(self					,					config_class=a_					,					hidden_size=37						)


							def _a (							self						):
											self.config_tester.run_common_tests()


							def _a (							self						):
											a__			 =						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*a_						)


							def _a (							self						):
											a__			 =						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*a_						)


							def _a (							self						):
											a__			 =						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*a_						)


							def _a (							self						):
											a__			 =						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*a_						)


							def _a (							self						):
											a__			 =						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*a_						)







							@slow
							def _a (							self						):
											a__			 =						TFDebertaModel.from_pretrained("""kamalkraj/deberta-base"""						)
											self.assertIsNotNone(a_						)




@require_tf
class 	__snake_case (				unittest.TestCase):







							'''simple docstring'''






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







							@slow
							def _a (							self						):
											a__			 =						TFDebertaModel.from_pretrained("""kamalkraj/deberta-base"""						)
											a__			 =						tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						)
											a__			 =						tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
											a__			 =						model(a_					,					attention_mask=a_						)[0]

											a__			 =						tf.constant(
											    [
											        [
											            [-0.59_855_896, -0.80_552_566, -0.8_462_135],
											            [1.4_484_025, -0.93_483_794, -0.80_593_085],
											            [0.3_122_741, 0.00_316_059, -1.4_131_377],
											        ]
											    ]						)
											tf.debugging.assert_near(output[:, 1:4, 1:4]					,					a_					,					atol=1E-4						)






import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 	__snake_case (				unittest.TestCase):







							'''simple docstring'''






							@cached_property
							def _a (							self						):
											return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""						)


							@cached_property
							def _a (							self						):
											return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""						)


							def _a (							self						):
											a__			 =						self.tokenizer_ab
											a__			 =						""" I am a small frog."""
											a__			 =						tok([src_text]					,					padding=a_					,					truncation=a_						)["""input_ids"""]
											a__			 =						tok.batch_decode(a_					,					skip_special_tokens=a_					,					clean_up_tokenization_spaces=a_						)[0]
											assert src_text == decoded


							def _a (							self						):
											a__			 =						self.rust_tokenizer_ab
											a__			 =						""" I am a small frog."""
											a__			 =						tok([src_text]					,					padding=a_					,					truncation=a_						)["""input_ids"""]
											a__			 =						tok.batch_decode(a_					,					skip_special_tokens=a_					,					clean_up_tokenization_spaces=a_						)[0]
											assert src_text == decoded


							def _a (							self						):
											assert self.tokenizer_ab.add_prefix_space
											assert self.tokenizer_ab([""" Sam""", """Sam"""]						).input_ids == [[5_502, 2], [5_502, 2]]







							def _a (							self						):
											assert self.rust_tokenizer_ab.add_prefix_space
											assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]						).input_ids == [[5_502, 2], [5_502, 2]]

