


'''simple docstring'''

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

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


if TYPE_CHECKING:
     from ...onnx.config import PatchingSpec
     from ...tokenization_utils_base import PreTrainedTokenizerBase


a_      			=				logging.get_logger(__name__)

a_      			=				{
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}





class   snake_case					(					__SCREAMING_SNAKE_CASE    ):





  """simple docstring"""




  _lowerCamelCase						=						"longformer"






  def __init__(					self    ,						UpperCamelCase = 512    ,						UpperCamelCase = 2    ,						UpperCamelCase = 1    ,						UpperCamelCase = 0    ,						UpperCamelCase = 2    ,						UpperCamelCase = 3_0522    ,						UpperCamelCase = 768    ,						UpperCamelCase = 12    ,						UpperCamelCase = 12    ,						UpperCamelCase = 3072    ,						UpperCamelCase = "gelu"    ,						UpperCamelCase = 0.1    ,						UpperCamelCase = 0.1    ,						UpperCamelCase = 512    ,						UpperCamelCase = 2    ,						UpperCamelCase = 0.02    ,						UpperCamelCase = 1e-12    ,						UpperCamelCase = False    ,						**UpperCamelCase    ,						):







   """simple docstring"""
   super().__init__(pad_token_id=__snake_case    ,						**__snake_case  )

   lowerCamelCase_  				=						attention_window
   lowerCamelCase_  				=						sep_token_id
   lowerCamelCase_  				=						bos_token_id
   lowerCamelCase_  				=						eos_token_id
   lowerCamelCase_  				=						vocab_size
   lowerCamelCase_  				=						hidden_size
   lowerCamelCase_  				=						num_hidden_layers
   lowerCamelCase_  				=						num_attention_heads
   lowerCamelCase_  				=						hidden_act
   lowerCamelCase_  				=						intermediate_size
   lowerCamelCase_  				=						hidden_dropout_prob
   lowerCamelCase_  				=						attention_probs_dropout_prob
   lowerCamelCase_  				=						max_position_embeddings
   lowerCamelCase_  				=						type_vocab_size
   lowerCamelCase_  				=						initializer_range
   lowerCamelCase_  				=						layer_norm_eps
   lowerCamelCase_  				=						onnx_export





class   snake_case					(					__SCREAMING_SNAKE_CASE    ):





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase    ,						UpperCamelCase = "default"    ,						UpperCamelCase = None  ):







   """simple docstring"""
   super().__init__(__snake_case    ,						__snake_case    ,						__snake_case  )
   lowerCamelCase_  				=						True






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   if self.task == "multiple-choice":
    lowerCamelCase_  				=						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
   else:
    lowerCamelCase_  				=						{0: '''batch''', 1: '''sequence'''}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
           ("global_attention_mask", dynamic_axis),
       ]  )






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						super().outputs
   if self.task == "default":
    lowerCamelCase_  				=						{0: '''batch'''}
   return outputs






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return 1e-4






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   # needs to be >= 14 to support tril operator
   return max(super().default_onnx_opset    ,						14  )







  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase = -1    ,						UpperCamelCase = -1    ,						UpperCamelCase = False    ,						UpperCamelCase = None    ,						):







   """simple docstring"""
   lowerCamelCase_  				=						super().generate_dummy_inputs(
       preprocessor=__snake_case    ,						batch_size=__snake_case    ,						seq_length=__snake_case    ,						is_pair=__snake_case    ,						framework=__snake_case  )
   import torch

   # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
   # makes the export fail randomly
   lowerCamelCase_  				=						torch.zeros_like(inputs["input_ids"]  )
   # make every second token global
   lowerCamelCase_  				=						1

   return inputs







'''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   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def 						snake_case	(					self    ,						UpperCamelCase  ):







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






  def 						snake_case	(					self  ):







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






  def 						snake_case	(					self  ):







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






  def 						snake_case	(					self  ):







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






  def 						snake_case	(					self  ):







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






  def 						snake_case	(					self  ):







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






  def 						snake_case	(					self  ):







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






  def 						snake_case	(					self  ):







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






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"patrickvonplaten/t5-tiny-random"
   lowerCamelCase_  				=						AutoConfig.from_pretrained(UpperCamelCase  )
   lowerCamelCase_  				=						TensorFlowBenchmarkArguments(
       models=[MODEL_ID]    ,						training=UpperCamelCase    ,						inference=UpperCamelCase    ,						sequence_lengths=[8]    ,						batch_sizes=[1]    ,						multi_process=UpperCamelCase    ,						)
   lowerCamelCase_  				=						TensorFlowBenchmark(UpperCamelCase    ,						configs=[config]  )
   lowerCamelCase_  				=						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 						snake_case	(					self  ):







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






  def 						snake_case	(					self  ):







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







  def 						snake_case	(					self  ):







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

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

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



