



"""simple docstring"""
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     lowerCamelCase  :






					'''simple docstring'''





					def __init__(	self:							Optional[int]						,					snake_case:							List[str]						,					snake_case:							Any=13						,					snake_case:							int=7						,					snake_case:							List[str]=True						,					snake_case:							Union[str, Any]=True						,					snake_case:							Tuple=True						,					snake_case:							List[Any]=True						,					snake_case:							Any=99						,					snake_case:							List[str]=32						,					snake_case:							List[str]=2						,					snake_case:							Optional[Any]=4						,					snake_case:							Tuple=37						,					snake_case:							Optional[Any]="gelu"						,					snake_case:							List[str]=0.1						,					snake_case:							Optional[Any]=0.1						,					snake_case:							Tuple=512						,					snake_case:							Optional[int]=16						,					snake_case:							Optional[Any]=2						,					snake_case:							Optional[Any]=0.0_2						,					snake_case:							Optional[int]=3						,					snake_case:							Optional[Any]=4						,					snake_case:							Any=None						,					)       ->     List[Any]:
											snake_case_			:Optional[int]						=      parent
											snake_case_			:List[Any]						=      13
											snake_case_			:Dict						=      7
											snake_case_			:Optional[int]						=      True
											snake_case_			:int						=      True
											snake_case_			:Union[str, Any]						=      True
											snake_case_			:List[Any]						=      True
											snake_case_			:Union[str, Any]						=      99
											snake_case_			:Union[str, Any]						=      32
											snake_case_			:Any						=      2
											snake_case_			:List[Any]						=      4
											snake_case_			:Any						=      37
											snake_case_			:Optional[Any]						=      """gelu"""
											snake_case_			:Any						=      0.1
											snake_case_			:str						=      0.1
											snake_case_			:Any						=      512
											snake_case_			:int						=      16
											snake_case_			:Dict						=      False
											snake_case_			:List[str]						=      -1
											snake_case_			:Union[str, Any]						=      True
											snake_case_			:int						=      2
											snake_case_			:Optional[int]						=      0.0_2
											snake_case_			:Optional[int]						=      3
											snake_case_			:str						=      4
											snake_case_			:Union[str, Any]						=      None






					def        lowerCAmelCase_    (	self:							Optional[int]						)       ->     int:
											snake_case_			:Union[str, Any]						=      ids_tensor([self.batch_size, self.seq_length]						,					self.vocab_size						)

											snake_case_			:List[Any]						=      None
											if self.use_input_mask:
																	snake_case_			:Any						=      random_attention_mask([self.batch_size, self.seq_length]						)

											snake_case_			:Optional[int]						=      None
											if self.use_token_type_ids:
																	snake_case_			:Union[str, Any]						=      ids_tensor([self.batch_size, self.seq_length]						,					self.type_vocab_size						)

											snake_case_			:Union[str, Any]						=      None
											snake_case_			:str						=      None
											snake_case_			:str						=      None
											if self.use_labels:
																	snake_case_			:Dict						=      ids_tensor([self.batch_size]						,					self.type_sequence_label_size						)
																	snake_case_			:Any						=      ids_tensor([self.batch_size, self.seq_length]						,					self.num_labels						)

											snake_case_			:Tuple						=      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=snake_case						,					)

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






					def        lowerCAmelCase_    (	self:							Union[str, Any]						,					snake_case:							Dict						,					snake_case:							Optional[int]						,					snake_case:							List[str]						,					snake_case:							Tuple						,					snake_case:							List[str]						,					snake_case:							List[Any]						,					snake_case:							Optional[Any]						)       ->     int:
											snake_case_			:Tuple						=      TFDebertaModel(config=snake_case						)
											snake_case_			:str						=      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

											snake_case_			:List[Any]						=      [input_ids, input_mask]
											snake_case_			:int						=      model(snake_case						)

											snake_case_			:Union[str, Any]						=      model(snake_case						)

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






					def        lowerCAmelCase_    (	self:							Any						,					snake_case:							int						,					snake_case:							Tuple						,					snake_case:							Any						,					snake_case:							Any						,					snake_case:							List[str]						,					snake_case:							List[Any]						,					snake_case:							Union[str, Any]						)       ->     Optional[int]:
											snake_case_			:int						=      TFDebertaForMaskedLM(config=snake_case						)
											snake_case_			:List[Any]						=      {
											    """input_ids""": input_ids,
											    """attention_mask""": input_mask,
											    """token_type_ids""": token_type_ids,
											}
											snake_case_			:Tuple						=      model(snake_case						)
											self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.vocab_size)						)






					def        lowerCAmelCase_    (	self:							Any						,					snake_case:							List[Any]						,					snake_case:							Any						,					snake_case:							Any						,					snake_case:							Optional[int]						,					snake_case:							Optional[int]						,					snake_case:							Union[str, Any]						,					snake_case:							Tuple						)       ->     List[str]:
											snake_case_			:int						=      self.num_labels
											snake_case_			:Any						=      TFDebertaForSequenceClassification(config=snake_case						)
											snake_case_			:Optional[Any]						=      {
											    """input_ids""": input_ids,
											    """attention_mask""": input_mask,
											    """token_type_ids""": token_type_ids,
											}

											snake_case_			:List[Any]						=      model(snake_case						)
											self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.num_labels)						)






					def        lowerCAmelCase_    (	self:							Union[str, Any]						,					snake_case:							Union[str, Any]						,					snake_case:							Dict						,					snake_case:							List[str]						,					snake_case:							Optional[int]						,					snake_case:							int						,					snake_case:							str						,					snake_case:							Union[str, Any]						)       ->     Any:
											snake_case_			:Optional[Any]						=      self.num_labels
											snake_case_			:Dict						=      TFDebertaForTokenClassification(config=snake_case						)
											snake_case_			:Any						=      {
											    """input_ids""": input_ids,
											    """attention_mask""": input_mask,
											    """token_type_ids""": token_type_ids,
											}
											snake_case_			:Union[str, Any]						=      model(snake_case						)
											self.parent.assertEqual(result.logits.shape						,					(self.batch_size, self.seq_length, self.num_labels)						)






					def        lowerCAmelCase_    (	self:							List[Any]						,					snake_case:							List[Any]						,					snake_case:							Union[str, Any]						,					snake_case:							str						,					snake_case:							Optional[int]						,					snake_case:							int						,					snake_case:							str						,					snake_case:							str						)       ->     Optional[Any]:
											snake_case_			:List[str]						=      TFDebertaForQuestionAnswering(config=snake_case						)
											snake_case_			:Union[str, Any]						=      {
											    """input_ids""": input_ids,
											    """attention_mask""": input_mask,
											    """token_type_ids""": token_type_ids,
											}

											snake_case_			:int						=      model(snake_case						)
											self.parent.assertEqual(result.start_logits.shape						,					(self.batch_size, self.seq_length)						)
											self.parent.assertEqual(result.end_logits.shape						,					(self.batch_size, self.seq_length)						)






					def        lowerCAmelCase_    (	self:							int						)       ->     str:
											snake_case_			:Optional[int]						=      self.prepare_config_and_inputs()
											(
											    (
											    snake_case_
											),	(
											    snake_case_
											),	(
											    snake_case_
											),	(
											    snake_case_
											),	(
											    snake_case_
											),	(
											    snake_case_
											),	(
											    snake_case_
											),	
											)			:str						=      config_and_inputs
											snake_case_			:Dict						=      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
											return config, inputs_dict

@require_tf
class     lowerCamelCase  (      _lowerCAmelCase   ,						_lowerCAmelCase   ,						unittest.TestCase    ):






					'''simple docstring'''





					_A		:      Any						=					(
					    (
					        TFDebertaModel,
					        TFDebertaForMaskedLM,
					        TFDebertaForQuestionAnswering,
					        TFDebertaForSequenceClassification,
					        TFDebertaForTokenClassification,
					    )
					    if is_tf_available()
					    else ()
					)
					_A		:      int						=					(
					    {
					        """feature-extraction""": TFDebertaModel,
					        """fill-mask""": TFDebertaForMaskedLM,
					        """question-answering""": TFDebertaForQuestionAnswering,
					        """text-classification""": TFDebertaForSequenceClassification,
					        """token-classification""": TFDebertaForTokenClassification,
					        """zero-shot""": TFDebertaForSequenceClassification,
					    }
					    if is_tf_available()
					    else {}
					)

					_A		:      str						=					False
					_A		:      Any						=					False






					def        lowerCAmelCase_    (	self:							Union[str, Any]						)       ->     Tuple:
											snake_case_			:int						=      TFDebertaModelTester(self						)
											snake_case_			:str						=      ConfigTester(self						,					config_class=snake_case						,					hidden_size=37						)






					def        lowerCAmelCase_    (	self:							Tuple						)       ->     Any:
											self.config_tester.run_common_tests()






					def        lowerCAmelCase_    (	self:							Union[str, Any]						)       ->     Optional[int]:
											snake_case_			:Optional[Any]						=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case						)






					def        lowerCAmelCase_    (	self:							Union[str, Any]						)       ->     Tuple:
											snake_case_			:Optional[Any]						=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_lm(*snake_case						)






					def        lowerCAmelCase_    (	self:							Any						)       ->     List[Any]:
											snake_case_			:Any						=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_question_answering(*snake_case						)






					def        lowerCAmelCase_    (	self:							Optional[int]						)       ->     Dict:
											snake_case_			:str						=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_sequence_classification(*snake_case						)






					def        lowerCAmelCase_    (	self:							int						)       ->     Tuple:
											snake_case_			:Dict						=      self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_token_classification(*snake_case						)






					@slow
					def        lowerCAmelCase_    (	self:							int						)       ->     Dict:
											snake_case_			:Tuple						=      TFDebertaModel.from_pretrained("""kamalkraj/deberta-base"""						)
											self.assertIsNotNone(snake_case						)

@require_tf
class     lowerCamelCase  (      unittest.TestCase    ):






					'''simple docstring'''





					@unittest.skip(reason="""Model not available yet"""						)
					def        lowerCAmelCase_    (	self:							Optional[int]						)       ->     List[Any]:
											pass






					@slow
					def        lowerCAmelCase_    (	self:							Any						)       ->     List[Any]:
											snake_case_			:Optional[int]						=      TFDebertaModel.from_pretrained("""kamalkraj/deberta-base"""						)
											snake_case_			:str						=      tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						)
											snake_case_			:int						=      tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
											snake_case_			:Dict						=      model(snake_case						,					attention_mask=snake_case						)[0]

											snake_case_			:int						=      tf.constant(
											    [
											        [
											            [-0.5_9_8_5_5_8_9_6, -0.8_0_5_5_2_5_6_6, -0.8_4_6_2_1_3_5],
											            [1.4_4_8_4_0_2_5, -0.9_3_4_8_3_7_9_4, -0.8_0_5_9_3_0_8_5],
											            [0.3_1_2_2_7_4_1, 0.0_0_3_1_6_0_5_9, -1.4_1_3_1_3_7_7],
											        ]
											    ]						)
											tf.debugging.assert_near(output[:, 1:4, 1:4]						,					snake_case						,					atol=1E-4						)





"""simple docstring"""
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__a     =    logging.get_logger(__name__)

__a     =    {
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}
class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					_A		:      List[Any]						=					"""longt5"""
					_A		:      Dict						=					["""past_key_values"""]
					_A		:      int						=					{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






					def __init__(	self:							str						,					snake_case:							List[Any]=32_128						,					snake_case:							Optional[int]=512						,					snake_case:							Union[str, Any]=64						,					snake_case:							str=2_048						,					snake_case:							Any=6						,					snake_case:							Optional[Any]=None						,					snake_case:							str=8						,					snake_case:							Tuple=127						,					snake_case:							Any=16						,					snake_case:							int=32						,					snake_case:							int=128						,					snake_case:							int=0.1						,					snake_case:							Optional[int]=1E-6						,					snake_case:							List[Any]=1.0						,					snake_case:							Optional[int]="relu"						,					snake_case:							str=True						,					snake_case:							Optional[int]="local"						,					snake_case:							Any=True						,					snake_case:							Tuple=0						,					snake_case:							Any=1						,					**snake_case:							Any						,					)       ->     List[str]:
											snake_case_			:Any						=      vocab_size
											snake_case_			:List[Any]						=      d_model
											snake_case_			:Any						=      d_kv
											snake_case_			:int						=      d_ff
											snake_case_			:Tuple						=      num_layers
											# default = symmetry
											snake_case_			:str						=      num_decoder_layers if num_decoder_layers is not None else self.num_layers
											snake_case_			:Any						=      num_heads
											snake_case_			:str						=      local_radius
											snake_case_			:Optional[int]						=      global_block_size
											snake_case_			:Union[str, Any]						=      relative_attention_num_buckets
											snake_case_			:List[str]						=      relative_attention_max_distance
											snake_case_			:Dict						=      dropout_rate
											snake_case_			:str						=      layer_norm_epsilon
											snake_case_			:str						=      initializer_factor
											snake_case_			:Any						=      feed_forward_proj
											snake_case_			:List[str]						=      encoder_attention_type
											snake_case_			:List[str]						=      use_cache

											snake_case_			:Tuple						=      self.feed_forward_proj.split("""-"""						)
											snake_case_			:Any						=      act_info[-1]
											snake_case_			:Union[str, Any]						=      act_info[0] == """gated"""

											if len(snake_case						) > 1 and act_info[0] != "gated" or len(snake_case						) > 2:
																	raise ValueError(
																	    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
																	    """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
																	    """'gated-gelu' or 'relu'"""						)

											# for backwards compatibility
											if feed_forward_proj == "gated-gelu":
																	snake_case_			:Optional[int]						=      """gelu_new"""

											super().__init__(
											    pad_token_id=snake_case						,					eos_token_id=snake_case						,					is_encoder_decoder=snake_case						,					**snake_case						,					)

class     lowerCamelCase  (      _lowerCAmelCase    ):






					'''simple docstring'''





					@property
					def        lowerCAmelCase_    (	self:							Dict						)       ->     Mapping[str, Mapping[int, str]]:
											snake_case_			:Dict						=      {
											    """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
											    """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
											}
											if self.use_past:
																	snake_case_			:List[Any]						=      """past_encoder_sequence + sequence"""
																	snake_case_			:List[Any]						=      {0: """batch"""}
																	snake_case_			:List[Any]						=      {0: """batch""", 1: """past_decoder_sequence + sequence"""}
											else:
																	snake_case_			:List[str]						=      {0: """batch""", 1: """decoder_sequence"""}
																	snake_case_			:List[Any]						=      {0: """batch""", 1: """decoder_sequence"""}

											if self.use_past:
																	self.fill_with_past_key_values_(snake_case						,					direction="""inputs"""						)

											return common_inputs






					@property
					def        lowerCAmelCase_    (	self:							List[str]						)       ->     int:
											return 13
