


'''simple docstring'''



import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
      import tensorflow as tf
      from tensorflow.python.framework.errors_impl import ResourceExhaustedError

      from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
      import pyanvml.pyanvml as nvml

__UpperCAmelCase      :Dict			    =       logging.get_logger(__name__)




def      _a     (						_lowercase :     bool      , _lowercase :     bool  ):





     '''simple docstring'''






     def run_func(_lowercase :     Optional[Any]  ):
          @wraps(_lowercase  )
          def run_in_eager_mode(*_lowercase :     int      , **_lowercase :     Dict  ):
               return func(*_lowercase      , **_lowercase  )

          @wraps(_lowercase  )
          @tf.function(experimental_compile=_lowercase  )
          def run_in_graph_mode(*_lowercase :     int      , **_lowercase :     Dict  ):
               return func(*_lowercase      , **_lowercase  )

          if do_eager_mode is True:
               if use_xla is not False:
                    raise ValueError(
                        '''Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'''  )
               return run_in_eager_mode
          else:
               return run_in_graph_mode

     return run_func




def      _a     (						_lowercase :     int      , _lowercase :     int      , _lowercase :     int  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Dict  							=      random.Random()
     __UpperCAmelCase     :			Union[str, Any]  							=      [rng.randint(0      , vocab_size - 1  ) for i in range(batch_size * sequence_length  )]
     return tf.constant(_lowercase      , shape=(batch_size, sequence_length)      , dtype=tf.intaa  )



class      a    (		_a	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			TensorFlowBenchmarkArguments
     SCREAMING_SNAKE_CASE	:			PretrainedConfig
     SCREAMING_SNAKE_CASE	:			str							       =       "TensorFlow"

     @property
     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							List[Any]:
          return tf.__version__

     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		str							,			snake_case					:		int							,			snake_case					:		int      )						->							float:
          # initialize GPU on separate process
          __UpperCAmelCase     :			Union[str, Any]  							=      self.args.strategy
          if strategy is None:
               raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )
          __UpperCAmelCase     :			Tuple  							=      self._prepare_inference_func(snake_case							,			snake_case							,			snake_case      )
          return self._measure_speed(_inference      )

     def 			lowerCamelCase__			(    self					:		int							,			snake_case					:		str							,			snake_case					:		int							,			snake_case					:		int      )						->							float:
          __UpperCAmelCase     :			int  							=      self.args.strategy
          if strategy is None:
               raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )
          __UpperCAmelCase     :			Union[str, Any]  							=      self._prepare_train_func(snake_case							,			snake_case							,			snake_case      )
          return self._measure_speed(_train      )

     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		str							,			snake_case					:		int							,			snake_case					:		int      )						->							[Memory, Optional[MemorySummary]]:
          # initialize GPU on separate process
          if self.args.is_gpu:
               tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]							,			snake_case      )
          __UpperCAmelCase     :			str  							=      self.args.strategy
          if strategy is None:
               raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )
          __UpperCAmelCase     :			Dict  							=      self._prepare_inference_func(snake_case							,			snake_case							,			snake_case      )
          return self._measure_memory(_inference      )

     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		str							,			snake_case					:		int							,			snake_case					:		int      )						->							[Memory, Optional[MemorySummary]]:
          if self.args.is_gpu:
               tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]							,			snake_case      )
          __UpperCAmelCase     :			Optional[Any]  							=      self.args.strategy
          if strategy is None:
               raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )

          __UpperCAmelCase     :			Optional[int]  							=      self._prepare_train_func(snake_case							,			snake_case							,			snake_case      )
          return self._measure_memory(_train      )

     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		str							,			snake_case					:		int							,			snake_case					:		int      )						->							Callable[[], None]:
          __UpperCAmelCase     :			Optional[Any]  							=      self.config_dict[model_name]

          if self.args.fpaa:
               raise NotImplementedError('''Mixed precision is currently not supported.'''      )

          __UpperCAmelCase     :			Tuple  							=      (
              hasattr(snake_case							,			'''architectures'''      )
              and isinstance(config.architectures							,			snake_case      )
              and len(config.architectures      ) > 0
          )
          if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                    __UpperCAmelCase     :			str  							=      '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                    __UpperCAmelCase     :			Dict  							=      __import__('''transformers'''							,			fromlist=[model_class]      )
                    __UpperCAmelCase     :			Union[str, Any]  							=      getattr(snake_case							,			snake_case      )
                    __UpperCAmelCase     :			Union[str, Any]  							=      model_cls(snake_case      )
               except ImportError:
                    raise ImportError(
                        f'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
                        ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''      )
          else:
               __UpperCAmelCase     :			List[Any]  							=      TF_MODEL_MAPPING[config.__class__](snake_case      )

          # encoder-decoder has vocab size saved differently
          __UpperCAmelCase     :			Dict  							=      config.vocab_size if hasattr(snake_case							,			'''vocab_size'''      ) else config.encoder.vocab_size
          __UpperCAmelCase     :			Dict  							=      random_input_ids(snake_case							,			snake_case							,			snake_case      )

          @run_with_tf_optimizations(self.args.eager_mode							,			self.args.use_xla      )
          def encoder_decoder_forward():
               return model(snake_case							,			decoder_input_ids=snake_case							,			training=snake_case      )

          @run_with_tf_optimizations(self.args.eager_mode							,			self.args.use_xla      )
          def encoder_forward():
               return model(snake_case							,			training=snake_case      )

          __UpperCAmelCase     :			Dict  							=      encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

          return _inference

     def 			lowerCamelCase__			(    self					:		Optional[Any]							,			snake_case					:		str							,			snake_case					:		int							,			snake_case					:		int      )						->							Callable[[], None]:
          __UpperCAmelCase     :			Union[str, Any]  							=      self.config_dict[model_name]

          if self.args.eager_mode is not False:
               raise ValueError('''Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.'''      )

          if self.args.fpaa:
               raise NotImplementedError('''Mixed precision is currently not supported.'''      )

          __UpperCAmelCase     :			List[Any]  							=      (
              hasattr(snake_case							,			'''architectures'''      )
              and isinstance(config.architectures							,			snake_case      )
              and len(config.architectures      ) > 0
          )
          if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                    __UpperCAmelCase     :			List[Any]  							=      '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                    __UpperCAmelCase     :			Any  							=      __import__('''transformers'''							,			fromlist=[model_class]      )
                    __UpperCAmelCase     :			Optional[int]  							=      getattr(snake_case							,			snake_case      )
                    __UpperCAmelCase     :			Dict  							=      model_cls(snake_case      )
               except ImportError:
                    raise ImportError(
                        f'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
                        ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''      )
          else:
               __UpperCAmelCase     :			List[Any]  							=      TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](snake_case      )

          # encoder-decoder has vocab size saved differently
          __UpperCAmelCase     :			List[str]  							=      config.vocab_size if hasattr(snake_case							,			'''vocab_size'''      ) else config.encoder.vocab_size
          __UpperCAmelCase     :			Optional[Any]  							=      random_input_ids(snake_case							,			snake_case							,			snake_case      )

          @run_with_tf_optimizations(self.args.eager_mode							,			self.args.use_xla      )
          def encoder_decoder_train():
               __UpperCAmelCase     :			Dict  							=      model(snake_case							,			decoder_input_ids=snake_case							,			labels=snake_case							,			training=snake_case      )[0]
               __UpperCAmelCase     :			Optional[Any]  							=      tf.gradients(snake_case							,			model.trainable_variables      )
               return gradients

          @run_with_tf_optimizations(self.args.eager_mode							,			self.args.use_xla      )
          def encoder_train():
               __UpperCAmelCase     :			Tuple  							=      model(snake_case							,			labels=snake_case							,			training=snake_case      )[0]
               __UpperCAmelCase     :			str  							=      tf.gradients(snake_case							,			model.trainable_variables      )
               return gradients

          __UpperCAmelCase     :			List[str]  							=      encoder_decoder_train if config.is_encoder_decoder else encoder_train

          return _train

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		Optional[int]      )						->							float:
          with self.args.strategy.scope():
               try:
                    if self.args.is_tpu or self.args.use_xla:
                         # run additional 10 times to stabilize compilation for tpu
                         logger.info('''Do inference on TPU. Running model 5 times to stabilize compilation'''      )
                         timeit.repeat(snake_case							,			repeat=1							,			number=5      )

                    # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
                    __UpperCAmelCase     :			Optional[int]  							=      timeit.repeat(
                        snake_case							,			repeat=self.args.repeat							,			number=10							,			)

                    return min(snake_case      ) / 10.0
               except ResourceExhaustedError as e:
                    self.print_fn(f'Doesn\'t fit on GPU. {e}'      )






     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		Callable[[], None]      )						->							[Memory, MemorySummary]:
          logger.info(
              '''Note that TensorFlow allocates more memory than '''
              '''it might need to speed up computation. '''
              '''The memory reported here corresponds to the memory '''
              '''reported by `nvidia-smi`, which can vary depending '''
              '''on total available memory on the GPU that is used.'''      )
          with self.args.strategy.scope():
               try:
                    if self.args.trace_memory_line_by_line:
                         if not self.args.eager_mode:
                              raise ValueError(
                                  '''`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'''
                                  ''' consumption line by line.'''      )
                         __UpperCAmelCase     :			int  							=      start_memory_tracing('''transformers'''      )

                    if self.args.is_tpu:
                         # tpu
                         raise NotImplementedError(
                             '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'''
                             ''' with `args.memory=False`'''      )
                    elif self.args.is_gpu:
                         # gpu
                         if not is_pyanvml_available():
                              logger.warning(
                                  '''py3nvml not installed, we won\'t log GPU memory usage. '''
                                  '''Install py3nvml (pip install py3nvml) to log information about GPU.'''      )
                              __UpperCAmelCase     :			Dict  							=      '''N/A'''
                         else:
                              logger.info(
                                  '''Measuring total GPU usage on GPU device. Make sure to not have additional processes'''
                                  ''' running on the same GPU.'''      )
                              # init nvml
                              nvml.nvmlInit()
                              func()
                              __UpperCAmelCase     :			Tuple  							=      nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx      )
                              __UpperCAmelCase     :			int  							=      nvml.nvmlDeviceGetMemoryInfo(snake_case      )
                              __UpperCAmelCase     :			Optional[Any]  							=      meminfo.used
                              __UpperCAmelCase     :			int  							=      Memory(snake_case      )
                              # shutdown nvml
                              nvml.nvmlShutdown()
                    else:
                         # cpu
                         if self.args.trace_memory_line_by_line:
                              logger.info(
                                  '''When enabling line by line tracing, the max peak memory for CPU is inaccurate in'''
                                  ''' TensorFlow.'''      )
                              __UpperCAmelCase     :			int  							=      None
                         else:
                              __UpperCAmelCase     :			Optional[Any]  							=      measure_peak_memory_cpu(snake_case      )
                              __UpperCAmelCase     :			List[str]  							=      Memory(snake_case      ) if isinstance(snake_case							,			snake_case      ) else memory_bytes
                    if self.args.trace_memory_line_by_line:
                         __UpperCAmelCase     :			Tuple  							=      stop_memory_tracing(snake_case      )
                         if memory is None:
                              __UpperCAmelCase     :			str  							=      summary.total
                    else:
                         __UpperCAmelCase     :			Optional[int]  							=      None

                    return memory, summary
               except ResourceExhaustedError as e:
                    self.print_fn(f'Doesn\'t fit on GPU. {e}'      )
                    return "N/A", None



'''simple docstring'''



import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import (
          DebertaForMaskedLM,
          DebertaForQuestionAnswering,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaModel,
      )
      from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST



class      a    (		_a	):


     """simple docstring"""






     def __init__(    self					:		Optional[Any]							,			snake_case					:		Dict							,			snake_case					:		Dict=13							,			snake_case					:		str=7							,			snake_case					:		Dict=True							,			snake_case					:		Any=True							,			snake_case					:		Optional[Any]=True							,			snake_case					:		Optional[Any]=True							,			snake_case					:		List[str]=99							,			snake_case					:		str=32							,			snake_case					:		Any=5							,			snake_case					:		List[str]=4							,			snake_case					:		List[str]=37							,			snake_case					:		int="gelu"							,			snake_case					:		int=0.1							,			snake_case					:		int=0.1							,			snake_case					:		Union[str, Any]=512							,			snake_case					:		int=16							,			snake_case					:		Optional[Any]=2							,			snake_case					:		List[Any]=0.02							,			snake_case					:		Any=False							,			snake_case					:		int=True							,			snake_case					:		Union[str, Any]="None"							,			snake_case					:		str=3							,			snake_case					:		Union[str, Any]=4							,			snake_case					:		Any=None							,			)						->							List[Any]:
          __UpperCAmelCase     :			List[str]  							=      parent
          __UpperCAmelCase     :			Dict  							=      batch_size
          __UpperCAmelCase     :			Any  							=      seq_length
          __UpperCAmelCase     :			List[Any]  							=      is_training
          __UpperCAmelCase     :			List[str]  							=      use_input_mask
          __UpperCAmelCase     :			Union[str, Any]  							=      use_token_type_ids
          __UpperCAmelCase     :			Any  							=      use_labels
          __UpperCAmelCase     :			Any  							=      vocab_size
          __UpperCAmelCase     :			Dict  							=      hidden_size
          __UpperCAmelCase     :			List[str]  							=      num_hidden_layers
          __UpperCAmelCase     :			Union[str, Any]  							=      num_attention_heads
          __UpperCAmelCase     :			List[str]  							=      intermediate_size
          __UpperCAmelCase     :			str  							=      hidden_act
          __UpperCAmelCase     :			Union[str, Any]  							=      hidden_dropout_prob
          __UpperCAmelCase     :			int  							=      attention_probs_dropout_prob
          __UpperCAmelCase     :			Tuple  							=      max_position_embeddings
          __UpperCAmelCase     :			Any  							=      type_vocab_size
          __UpperCAmelCase     :			Tuple  							=      type_sequence_label_size
          __UpperCAmelCase     :			Union[str, Any]  							=      initializer_range
          __UpperCAmelCase     :			Dict  							=      num_labels
          __UpperCAmelCase     :			Any  							=      num_choices
          __UpperCAmelCase     :			Any  							=      relative_attention
          __UpperCAmelCase     :			Dict  							=      position_biased_input
          __UpperCAmelCase     :			Optional[int]  							=      pos_att_type
          __UpperCAmelCase     :			Dict  							=      scope

     def 			lowerCamelCase__			(    self					:		Dict      )						->							Optional[int]:
          __UpperCAmelCase     :			int  							=      ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size      )

          __UpperCAmelCase     :			Optional[Any]  							=      None
          if self.use_input_mask:
               __UpperCAmelCase     :			str  							=      ids_tensor([self.batch_size, self.seq_length]							,			vocab_size=2      )

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

          __UpperCAmelCase     :			List[str]  							=      None
          __UpperCAmelCase     :			int  							=      None
          __UpperCAmelCase     :			str  							=      None
          if self.use_labels:
               __UpperCAmelCase     :			List[str]  							=      ids_tensor([self.batch_size]							,			self.type_sequence_label_size      )
               __UpperCAmelCase     :			Union[str, Any]  							=      ids_tensor([self.batch_size, self.seq_length]							,			self.num_labels      )
               __UpperCAmelCase     :			Optional[Any]  							=      ids_tensor([self.batch_size]							,			self.num_choices      )

          __UpperCAmelCase     :			str  							=      self.get_config()

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

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							List[str]:
          return 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							,			initializer_range=self.initializer_range							,			relative_attention=self.relative_attention							,			position_biased_input=self.position_biased_input							,			pos_att_type=self.pos_att_type							,			)

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							str:
          __UpperCAmelCase     :			Optional[int]  							=      self.get_config()
          __UpperCAmelCase     :			Dict  							=      300
          return config

     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		int      )						->							Any:
          self.parent.assertListEqual(list(result.loss.size()      )							,			[]      )

     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		Optional[int]							,			snake_case					:		Any							,			snake_case					:		List[Any]							,			snake_case					:		Any							,			snake_case					:		Union[str, Any]							,			snake_case					:		Optional[Any]							,			snake_case					:		Union[str, Any]      )						->							Optional[Any]:
          __UpperCAmelCase     :			List[str]  							=      DebertaModel(config=snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			str  							=      model(snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case      )[0]
          __UpperCAmelCase     :			Tuple  							=      model(snake_case							,			token_type_ids=snake_case      )[0]
          __UpperCAmelCase     :			Optional[int]  							=      model(snake_case      )[0]

          self.parent.assertListEqual(list(sequence_output.size()      )							,			[self.batch_size, self.seq_length, self.hidden_size]      )

     def 			lowerCamelCase__			(    self					:		Optional[int]							,			snake_case					:		int							,			snake_case					:		Tuple							,			snake_case					:		Any							,			snake_case					:		Tuple							,			snake_case					:		Union[str, Any]							,			snake_case					:		Optional[int]							,			snake_case					:		List[str]      )						->							Optional[int]:
          __UpperCAmelCase     :			Union[str, Any]  							=      DebertaForMaskedLM(config=snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			int  							=      model(snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			labels=snake_case      )

          self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.vocab_size)      )

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		Union[str, Any]							,			snake_case					:		Tuple							,			snake_case					:		Optional[Any]							,			snake_case					:		Tuple							,			snake_case					:		List[Any]							,			snake_case					:		str							,			snake_case					:		Tuple      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Tuple  							=      self.num_labels
          __UpperCAmelCase     :			List[Any]  							=      DebertaForSequenceClassification(snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			Any  							=      model(snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			labels=snake_case      )
          self.parent.assertListEqual(list(result.logits.size()      )							,			[self.batch_size, self.num_labels]      )
          self.check_loss_output(snake_case      )

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		List[str]							,			snake_case					:		Union[str, Any]							,			snake_case					:		List[str]							,			snake_case					:		Dict							,			snake_case					:		Optional[Any]							,			snake_case					:		Dict							,			snake_case					:		Optional[int]      )						->							int:
          __UpperCAmelCase     :			List[Any]  							=      self.num_labels
          __UpperCAmelCase     :			Union[str, Any]  							=      DebertaForTokenClassification(config=snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			Any  							=      model(snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			labels=snake_case      )
          self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.num_labels)      )

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		List[Any]							,			snake_case					:		Optional[Any]							,			snake_case					:		str							,			snake_case					:		Optional[int]							,			snake_case					:		int							,			snake_case					:		int							,			snake_case					:		str      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Union[str, Any]  							=      DebertaForQuestionAnswering(config=snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			Optional[int]  							=      model(
              snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			start_positions=snake_case							,			end_positions=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					:		str      )						->							int:
          __UpperCAmelCase     :			int  							=      self.prepare_config_and_inputs()
          (
              (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       
          )     :			Any  							=      config_and_inputs
          __UpperCAmelCase     :			str  							=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
          return config, inputs_dict



@require_torch
class      a    (		_a     ,  _a     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       (
         (
             DebertaModel,
             DebertaForMaskedLM,
             DebertaForSequenceClassification,
             DebertaForTokenClassification,
             DebertaForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE	:			Dict							       =       (
         {
             "feature-extraction": DebertaModel,
             "fill-mask": DebertaForMaskedLM,
             "question-answering": DebertaForQuestionAnswering,
             "text-classification": DebertaForSequenceClassification,
             "token-classification": DebertaForTokenClassification,
             "zero-shot": DebertaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )

     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       True
     SCREAMING_SNAKE_CASE	:			Dict							       =       False
     SCREAMING_SNAKE_CASE	:			List[Any]							       =       False
     SCREAMING_SNAKE_CASE	:			List[str]							       =       False
     SCREAMING_SNAKE_CASE	:			Any							       =       False

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Dict  							=      DebertaModelTester(self      )
          __UpperCAmelCase     :			List[Any]  							=      ConfigTester(self							,			config_class=snake_case							,			hidden_size=37      )

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Dict:
          self.config_tester.run_common_tests()

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Optional[Any]:
          __UpperCAmelCase     :			Any  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_model(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Dict      )						->							List[str]:
          __UpperCAmelCase     :			Tuple  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_sequence_classification(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Dict      )						->							str:
          __UpperCAmelCase     :			Any  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_masked_lm(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Any:
          __UpperCAmelCase     :			Optional[int]  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_question_answering(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Dict      )						->							Optional[Any]:
          __UpperCAmelCase     :			Any  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_token_classification(*snake_case      )






     @slow
     def 			lowerCamelCase__			(    self					:		Dict      )						->							Tuple:
          for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCAmelCase     :			str  							=      DebertaModel.from_pretrained(snake_case      )
               self.assertIsNotNone(snake_case      )



@require_torch
@require_sentencepiece
@require_tokenizers
class      a    (		unittest.TestCase	):


     """simple docstring"""






     @unittest.skip(reason='''Model not available yet'''      )
     def 			lowerCamelCase__			(    self					:		Dict      )						->							Tuple:
          pass






     @slow
     def 			lowerCamelCase__			(    self					:		int      )						->							Optional[Any]:
          __UpperCAmelCase     :			Any  							=      DebertaModel.from_pretrained('''microsoft/deberta-base'''      )

          __UpperCAmelCase     :			Any  							=      torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]      )
          __UpperCAmelCase     :			Dict  							=      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
          with torch.no_grad():
               __UpperCAmelCase     :			int  							=      model(snake_case							,			attention_mask=snake_case      )[0]
          # compare the actual values for a slice.
          __UpperCAmelCase     :			Optional[Any]  							=      torch.tensor(
              [[[-0.5_986, -0.8_055, -0.8_462], [1.4_484, -0.9_348, -0.8_059], [0.3_123, 0.0_032, -1.4_131]]]      )
          self.assertTrue(torch.allclose(output[:, 1:4, 1:4]							,			snake_case							,			atol=1E-4      )							,			f'{output[:, 1:4, 1:4]}'      )