




'''simple docstring'''



import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
 import tensorflow as tf

 from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments







@require_tf
class 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Optional[int]  ,				__lowerCamelCase :     Dict   ):

      """simple docstring"""
      for model_result in results.values():
         for batch_size, sequence_length in zip(model_result["bs"]  ,				model_result["ss"]   ):
            _SCREAMING_SNAKE_CASE	=				model_result["result"][batch_size][sequence_length]
            self.assertIsNotNone(__lowerCamelCase   )



   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"sshleifer/tiny-gpt2"
      _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmarkArguments(
          models=[MODEL_ID]  ,				training=__lowerCamelCase  ,				inference=__lowerCamelCase  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				eager_mode=__lowerCamelCase  ,				multi_process=__lowerCamelCase  ,				)
      _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmark(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result   )
      self.check_results_dict_not_empty(results.memory_inference_result   )



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"sgugger/tiny-distilbert-classification"
      _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmarkArguments(
          models=[MODEL_ID]  ,				training=__lowerCamelCase  ,				inference=__lowerCamelCase  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__lowerCamelCase  ,				only_pretrain_model=__lowerCamelCase  ,				)
      _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmark(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result   )
      self.check_results_dict_not_empty(results.memory_inference_result   )



   def 							lowerCAmelCase_      (   self :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"sshleifer/tiny-gpt2"
      _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmarkArguments(
          models=[MODEL_ID]  ,				training=__lowerCamelCase  ,				inference=__lowerCamelCase  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__lowerCamelCase  ,				)
      _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmark(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result   )
      self.check_results_dict_not_empty(results.memory_inference_result   )



   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"sshleifer/tiny-gpt2"
      _SCREAMING_SNAKE_CASE	=				AutoConfig.from_pretrained(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmarkArguments(
          models=[MODEL_ID]  ,				training=__lowerCamelCase  ,				inference=__lowerCamelCase  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				eager_mode=__lowerCamelCase  ,				multi_process=__lowerCamelCase  ,				)
      _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmark(__lowerCamelCase  ,				[config]   )
      _SCREAMING_SNAKE_CASE	=				benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result   )
      self.check_results_dict_not_empty(results.memory_inference_result   )



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"sshleifer/tiny-gpt2"
      _SCREAMING_SNAKE_CASE	=				AutoConfig.from_pretrained(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmarkArguments(
          models=[MODEL_ID]  ,				training=__lowerCamelCase  ,				inference=__lowerCamelCase  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__lowerCamelCase  ,				)
      _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmark(__lowerCamelCase  ,				[config]   )
      _SCREAMING_SNAKE_CASE	=				benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result   )
      self.check_results_dict_not_empty(results.memory_inference_result   )



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"sshleifer/tiny-gpt2"
      _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmarkArguments(
          models=[MODEL_ID]  ,				training=__lowerCamelCase  ,				inference=__lowerCamelCase  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__lowerCamelCase  ,				)
      _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmark(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result   )
      self.check_results_dict_not_empty(results.memory_train_result   )



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"sshleifer/tiny-gpt2"
      _SCREAMING_SNAKE_CASE	=				AutoConfig.from_pretrained(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmarkArguments(
          models=[MODEL_ID]  ,				training=__lowerCamelCase  ,				inference=__lowerCamelCase  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__lowerCamelCase  ,				)
      _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmark(__lowerCamelCase  ,				[config]   )
      _SCREAMING_SNAKE_CASE	=				benchmark.run()
      self.check_results_dict_not_empty(results.time_train_result   )
      self.check_results_dict_not_empty(results.memory_train_result   )



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"patrickvonplaten/t5-tiny-random"
      _SCREAMING_SNAKE_CASE	=				AutoConfig.from_pretrained(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmarkArguments(
          models=[MODEL_ID]  ,				training=__lowerCamelCase  ,				inference=__lowerCamelCase  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				multi_process=__lowerCamelCase  ,				)
      _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmark(__lowerCamelCase  ,				configs=[config]   )
      _SCREAMING_SNAKE_CASE	=				benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result   )
      self.check_results_dict_not_empty(results.memory_inference_result   )



   @unittest.skipIf(is_tf_available() and len(tf.config.list_physical_devices("GPU"   )   ) == 0  ,				"Cannot do xla on CPU."   )
   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"sshleifer/tiny-gpt2"
      _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmarkArguments(
          models=[MODEL_ID]  ,				training=__lowerCamelCase  ,				inference=__lowerCamelCase  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				use_xla=__lowerCamelCase  ,				multi_process=__lowerCamelCase  ,				)
      _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmark(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				benchmark.run()
      self.check_results_dict_not_empty(results.time_inference_result   )
      self.check_results_dict_not_empty(results.memory_inference_result   )



   def 							lowerCAmelCase_      (   self :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"sshleifer/tiny-gpt2"
      with tempfile.TemporaryDirectory() as tmp_dir:
         _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmarkArguments(
             models=[MODEL_ID]  ,				inference=__lowerCamelCase  ,				save_to_csv=__lowerCamelCase  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				inference_time_csv_file=os.path.join(__lowerCamelCase  ,				"inf_time.csv"   )  ,				inference_memory_csv_file=os.path.join(__lowerCamelCase  ,				"inf_mem.csv"   )  ,				env_info_csv_file=os.path.join(__lowerCamelCase  ,				"env.csv"   )  ,				multi_process=__lowerCamelCase  ,				)
         _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmark(__lowerCamelCase   )
         benchmark.run()
         self.assertTrue(Path(os.path.join(__lowerCamelCase  ,				"inf_time.csv"   )   ).exists()   )
         self.assertTrue(Path(os.path.join(__lowerCamelCase  ,				"inf_mem.csv"   )   ).exists()   )
         self.assertTrue(Path(os.path.join(__lowerCamelCase  ,				"env.csv"   )   ).exists()   )



   def 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				"sshleifer/tiny-gpt2"

      def _check_summary_is_not_empty(__lowerCamelCase :     int   ):
         self.assertTrue(hasattr(__lowerCamelCase  ,				"sequential"   )   )
         self.assertTrue(hasattr(__lowerCamelCase  ,				"cumulative"   )   )
         self.assertTrue(hasattr(__lowerCamelCase  ,				"current"   )   )
         self.assertTrue(hasattr(__lowerCamelCase  ,				"total"   )   )

      with tempfile.TemporaryDirectory() as tmp_dir:
         _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmarkArguments(
             models=[MODEL_ID]  ,				inference=__lowerCamelCase  ,				sequence_lengths=[8]  ,				batch_sizes=[1]  ,				log_filename=os.path.join(__lowerCamelCase  ,				"log.txt"   )  ,				log_print=__lowerCamelCase  ,				trace_memory_line_by_line=__lowerCamelCase  ,				eager_mode=__lowerCamelCase  ,				multi_process=__lowerCamelCase  ,				)
         _SCREAMING_SNAKE_CASE	=				TensorFlowBenchmark(__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				benchmark.run()
         _check_summary_is_not_empty(result.inference_summary   )
         self.assertTrue(Path(os.path.join(__lowerCamelCase  ,				"log.txt"   )   ).exists()   )







'''simple docstring'''



from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
 from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCamelCase_			=						logging.get_logger(__name__)

lowerCamelCase_			=						{
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''deberta-v2'''



   def __init__(   self :     str  ,				__lowerCamelCase :     Union[str, Any]=1_2_8_1_0_0  ,				__lowerCamelCase :     Optional[int]=1_5_3_6  ,				__lowerCamelCase :     Optional[int]=2_4  ,				__lowerCamelCase :     Optional[int]=2_4  ,				__lowerCamelCase :     Tuple=6_1_4_4  ,				__lowerCamelCase :     List[str]="gelu"  ,				__lowerCamelCase :     int=0.1  ,				__lowerCamelCase :     Optional[Any]=0.1  ,				__lowerCamelCase :     Union[str, Any]=5_1_2  ,				__lowerCamelCase :     Optional[Any]=0  ,				__lowerCamelCase :     str=0.0_2  ,				__lowerCamelCase :     int=1e-7  ,				__lowerCamelCase :     Any=False  ,				__lowerCamelCase :     Any=-1  ,				__lowerCamelCase :     Tuple=0  ,				__lowerCamelCase :     str=True  ,				__lowerCamelCase :     List[Any]=None  ,				__lowerCamelCase :     Optional[int]=0  ,				__lowerCamelCase :     Any="gelu"  ,				**__lowerCamelCase :     Union[str, Any]  ,				):

      """simple docstring"""
      super().__init__(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				hidden_size
      _SCREAMING_SNAKE_CASE	=				num_hidden_layers
      _SCREAMING_SNAKE_CASE	=				num_attention_heads
      _SCREAMING_SNAKE_CASE	=				intermediate_size
      _SCREAMING_SNAKE_CASE	=				hidden_act
      _SCREAMING_SNAKE_CASE	=				hidden_dropout_prob
      _SCREAMING_SNAKE_CASE	=				attention_probs_dropout_prob
      _SCREAMING_SNAKE_CASE	=				max_position_embeddings
      _SCREAMING_SNAKE_CASE	=				type_vocab_size
      _SCREAMING_SNAKE_CASE	=				initializer_range
      _SCREAMING_SNAKE_CASE	=				relative_attention
      _SCREAMING_SNAKE_CASE	=				max_relative_positions
      _SCREAMING_SNAKE_CASE	=				pad_token_id
      _SCREAMING_SNAKE_CASE	=				position_biased_input

      # Backwards compatibility
      if type(__lowerCamelCase   ) == str:
         _SCREAMING_SNAKE_CASE	=				[x.strip() for x in pos_att_type.lower().split("|"   )]

      _SCREAMING_SNAKE_CASE	=				pos_att_type
      _SCREAMING_SNAKE_CASE	=				vocab_size
      _SCREAMING_SNAKE_CASE	=				layer_norm_eps

      _SCREAMING_SNAKE_CASE	=				kwargs.get("pooler_hidden_size"  ,				__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				pooler_dropout
      _SCREAMING_SNAKE_CASE	=				pooler_hidden_act







class 		lowercase_   (			A       ):


   """simple docstring"""

   @property
   def 							lowerCAmelCase_      (   self :     List[Any]   ):

      """simple docstring"""
      if self.task == "multiple-choice":
         _SCREAMING_SNAKE_CASE	=				{0: "batch", 1: "choice", 2: "sequence"}
      else:
         _SCREAMING_SNAKE_CASE	=				{0: "batch", 1: "sequence"}
      if self._config.type_vocab_size > 0:
         return OrderedDict(
             [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]   )
      else:
         return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]   )



   @property
   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      return 1_2



   def 							lowerCAmelCase_      (   self :     List[str]  ,				__lowerCamelCase :     Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     int = -1  ,				__lowerCamelCase :     bool = False  ,				__lowerCamelCase :     Optional["TensorType"] = None  ,				__lowerCamelCase :     int = 3  ,				__lowerCamelCase :     int = 4_0  ,				__lowerCamelCase :     int = 4_0  ,				__lowerCamelCase :     "PreTrainedTokenizerBase" = None  ,				):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				super().generate_dummy_inputs(preprocessor=__lowerCamelCase  ,				framework=__lowerCamelCase   )
      if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
         del dummy_inputs["token_type_ids"]
      return dummy_inputs

