




'''simple docstring'''


import unittest
from dataclasses import dataclass

import pytest

from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class 						a_				(			UpperCAmelCase__						):
	lowercase_       :     str			  =      ComputeEnvironment.AMAZON_SAGEMAKER
	lowercase_       :     str			  =      True
	lowercase_       :     Union[str, Any]			  =      '''ml.p3.2xlarge'''
	lowercase_       :     Dict			  =      '''accelerate_sagemaker_execution_role'''
	lowercase_       :     Dict			  =      '''hf-sm'''
	lowercase_       :     int			  =      '''us-east-1'''
	lowercase_       :     List[str]			  =      1
	lowercase_       :     Dict			  =      '''accelerate-sagemaker-1'''
	lowercase_       :     Optional[Any]			  =      '''1.6'''
	lowercase_       :     Optional[Any]			  =      '''4.4'''
	lowercase_       :     Dict			  =      '''train.py'''
	lowercase_       :     str			  =      [
	    '''--model_name_or_path''',
	    '''bert''',
	    '''--do_train''',
	    '''False''',
	    '''--epochs''',
	    '''3''',
	    '''--learning_rate''',
	    '''5e-5''',
	    '''--max_steps''',
	    '''50.5''',
	]
	lowercase_       :     List[Any]			  =      [
	    '''--model_name_or_path''',
	    '''bert''',
	    '''--do_train''',
	    '''--do_test''',
	    '''False''',
	    '''--do_predict''',
	    '''--epochs''',
	    '''3''',
	    '''--learning_rate''',
	    '''5e-5''',
	    '''--max_steps''',
	    '''50.5''',
	]





class 						a_				(			unittest.TestCase						):




	def   lowercase__   (       self     :  List[Any]      ):
							# If no defaults are changed, `to_kwargs` returns an empty dict.
							__snake_case						 =   _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args      )
							assert isinstance(converted_args['model_name_or_path']       ,		__lowerCAmelCase      )
							assert isinstance(converted_args['do_train']       ,		__lowerCAmelCase      )
							assert isinstance(converted_args['epochs']       ,		__lowerCAmelCase      )
							assert isinstance(converted_args['learning_rate']       ,		__lowerCAmelCase      )
							assert isinstance(converted_args['max_steps']       ,		__lowerCAmelCase      )

							with pytest.raises(__lowerCAmelCase      ):
													_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args      )







'''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_				:




	def __init__(       self     :  Tuple       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  Union[str, Any]=1_3       ,		__lowerCAmelCase     :  int=7       ,		__lowerCAmelCase     :  int=True       ,		__lowerCAmelCase     :  str=True       ,		__lowerCAmelCase     :  str=True       ,		__lowerCAmelCase     :  Optional[Any]=True       ,		__lowerCAmelCase     :  List[Any]=9_9       ,		__lowerCAmelCase     :  List[str]=3_2       ,		__lowerCAmelCase     :  List[Any]=2       ,		__lowerCAmelCase     :  Any=4       ,		__lowerCAmelCase     :  Any=3_7       ,		__lowerCAmelCase     :  Union[str, Any]="gelu"       ,		__lowerCAmelCase     :  Any=0.1       ,		__lowerCAmelCase     :  int=0.1       ,		__lowerCAmelCase     :  Optional[int]=5_1_2       ,		__lowerCAmelCase     :  Optional[Any]=1_6       ,		__lowerCAmelCase     :  Optional[Any]=2       ,		__lowerCAmelCase     :  Dict=0.02       ,		__lowerCAmelCase     :  Dict=False       ,		__lowerCAmelCase     :  int=True       ,		__lowerCAmelCase     :  Tuple="None"       ,		__lowerCAmelCase     :  Optional[int]=3       ,		__lowerCAmelCase     :  Optional[int]=4       ,		__lowerCAmelCase     :  Union[str, Any]=None       ,		):
							__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     :  Dict      ):
							__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=__lowerCAmelCase       ,		)

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




	def   lowercase__   (       self     :  Union[str, Any]       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Union[str, Any]      ):
							__snake_case						 =   TFDebertaVaModel(config=__lowerCAmelCase      )
							__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(__lowerCAmelCase      )

							__snake_case						 =   model(__lowerCAmelCase      )

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




	def   lowercase__   (       self     :  Optional[Any]       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  int       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  List[Any]      ):
							__snake_case						 =   TFDebertaVaForMaskedLM(config=__lowerCAmelCase      )
							__snake_case						 =   {
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'token_type_ids': token_type_ids,
							}
							__snake_case						 =   model(__lowerCAmelCase      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.vocab_size)      )




	def   lowercase__   (       self     :  Optional[int]       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  List[Any]      ):
							__snake_case						 =   self.num_labels
							__snake_case						 =   TFDebertaVaForSequenceClassification(config=__lowerCAmelCase      )
							__snake_case						 =   {
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'token_type_ids': token_type_ids,
							}

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




	def   lowercase__   (       self     :  Tuple       ,		__lowerCAmelCase     :  Dict       ,		__lowerCAmelCase     :  str       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  List[str]       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  int      ):
							__snake_case						 =   self.num_labels
							__snake_case						 =   TFDebertaVaForTokenClassification(config=__lowerCAmelCase      )
							__snake_case						 =   {
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'token_type_ids': token_type_ids,
							}
							__snake_case						 =   model(__lowerCAmelCase      )
							self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.seq_length, self.num_labels)      )




	def   lowercase__   (       self     :  int       ,		__lowerCAmelCase     :  Optional[Any]       ,		__lowerCAmelCase     :  List[Any]       ,		__lowerCAmelCase     :  Any       ,		__lowerCAmelCase     :  Tuple       ,		__lowerCAmelCase     :  Union[str, Any]       ,		__lowerCAmelCase     :  Optional[int]       ,		__lowerCAmelCase     :  List[Any]      ):
							__snake_case						 =   TFDebertaVaForQuestionAnswering(config=__lowerCAmelCase      )
							__snake_case						 =   {
							    'input_ids': input_ids,
							    'attention_mask': input_mask,
							    'token_type_ids': token_type_ids,
							}

							__snake_case						 =   model(__lowerCAmelCase      )
							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     :  Union[str, 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						):
	lowercase_       :     List[str]			  =      (
	    (
	        TFDebertaVaModel,
	        TFDebertaVaForMaskedLM,
	        TFDebertaVaForQuestionAnswering,
	        TFDebertaVaForSequenceClassification,
	        TFDebertaVaForTokenClassification,
	    )
	    if is_tf_available()
	    else ()
	)
	lowercase_       :     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 {}
	)

	lowercase_       :     int			  =      False
	lowercase_       :     List[Any]			  =      False




	def   lowercase__   (       self     :  Any      ):
							__snake_case						 =   TFDebertaVaModelTester(self      )
							__snake_case						 =   ConfigTester(self       ,		config_class=__lowerCAmelCase       ,		hidden_size=3_7      )




	def   lowercase__   (       self     :  Dict      ):
							self.config_tester.run_common_tests()




	def   lowercase__   (       self     :  Optional[int]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Union[str, Any]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  List[Any]      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Any      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase      )




	def   lowercase__   (       self     :  Dict      ):
							__snake_case						 =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase      )




	@slow
	def   lowercase__   (       self     :  List[str]      ):
							__snake_case						 =   TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'      )
							self.assertIsNotNone(__lowerCAmelCase      )





@require_tf
class 						a_				(			unittest.TestCase						):




	@unittest.skip(reason='Model not available yet'      )
	def   lowercase__   (       self     :  List[str]      ):
							pass




	@slow
	def   lowercase__   (       self     :  int      ):
							__snake_case						 =   TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'      )
							__snake_case						 =   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]]      )
							__snake_case						 =   tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
							__snake_case						 =   model(__lowerCAmelCase       ,		attention_mask=__lowerCAmelCase      )[0]

							__snake_case						 =   tf.constant(
							    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]      )
							tf.debugging.assert_near(output[:, 1:4, 1:4]       ,		__lowerCAmelCase       ,		atol=1E-4      )

