

from collections import OrderedDict
from typing import Mapping

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


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {
    "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
    "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json",
    "junnyu/roformer_chinese_char_small": (
        "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"
    ),
    "junnyu/roformer_chinese_char_base": (
        "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"
    ),
    "junnyu/roformer_small_discriminator": (
        "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"
    ),
    "junnyu/roformer_small_generator": (
        "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}


class 			A						(							_UpperCAmelCase			):







				"""simple docstring"""





				lowerCamelCase		     =							'roformer'


				def __init__(				self			:	Union[str, Any],lowercase_			:	List[Any]=5_0_0_0_0,lowercase_			:	Dict=None,lowercase_			:	Union[str, Any]=7_6_8,lowercase_			:	Any=1_2,lowercase_			:	Tuple=1_2,lowercase_			:	Optional[int]=3_0_7_2,lowercase_			:	Optional[int]="gelu",lowercase_			:	Any=0.1,lowercase_			:	Dict=0.1,lowercase_			:	List[Any]=1_5_3_6,lowercase_			:	Optional[int]=2,lowercase_			:	List[Any]=0.02,lowercase_			:	List[str]=1E-12,lowercase_			:	Optional[int]=0,lowercase_			:	Any=False,lowercase_			:	Dict=True,**lowercase_			:	Tuple,)->   Dict:





								'''simple docstring'''







								super().__init__(pad_token_id=lowercase_,**lowercase_		)

								A__   				=       vocab_size
								A__   				=       hidden_size if embedding_size is None else embedding_size
								A__   				=       hidden_size
								A__   				=       num_hidden_layers
								A__   				=       num_attention_heads
								A__   				=       hidden_act
								A__   				=       intermediate_size
								A__   				=       hidden_dropout_prob
								A__   				=       attention_probs_dropout_prob
								A__   				=       max_position_embeddings
								A__   				=       type_vocab_size
								A__   				=       initializer_range
								A__   				=       layer_norm_eps
								A__   				=       rotary_value
								A__   				=       use_cache




class 			A						(							_UpperCAmelCase			):







				"""simple docstring"""





				@property
				def 						snake_case__					(				self			:	List[str]		)->   Mapping[str, Mapping[int, str]]:





								'''simple docstring'''







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




import os
import tempfile
import unittest
from pathlib import Path

from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device


if is_torch_available():
     from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments


@require_torch
class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	List[Any],lowercase_			:	str		)->   List[Any]:





        '''simple docstring'''







        for model_result in results.values():
            for batch_size, sequence_length in zip(model_result['bs'],model_result['ss']		):
                A__   				=       model_result['result'][batch_size][sequence_length]
                self.assertIsNotNone(lowercase_		)


    def 						snake_case__					(				self			:	Dict		)->   List[str]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       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			:	Dict		)->   List[str]:





        '''simple docstring'''







        A__   				=       'sgugger/tiny-distilbert-classification'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,only_pretrain_model=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       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			:	List[Any]		)->   Any:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,torchscript=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_inference_result		)
        self.check_results_dict_not_empty(results.memory_inference_result		)


    @unittest.skipIf(torch_device == 'cpu','Cant do half precision'		)
    def 						snake_case__					(				self			:	Any		)->   Dict:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,fpaa=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       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			:	Any		)->   Optional[Any]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        # set architectures equal to `None`
        A__   				=       None
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       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			:	Union[str, Any]		)->   int:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       benchmark.run()
        self.check_results_dict_not_empty(results.time_train_result		)
        self.check_results_dict_not_empty(results.memory_train_result		)


    @unittest.skipIf(torch_device == 'cpu','Can\'t do half precision'		)
    def 						snake_case__					(				self			:	List[Any]		)->   Dict:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],fpaa=lowercase_,multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_		)
        A__   				=       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			:	int		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       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			:	List[Any]		)->   Any:





        '''simple docstring'''







        A__   				=       'sshleifer/tinier_bart'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       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			:	List[str]		)->   List[str]:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       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			:	int		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       'sshleifer/tinier_bart'
        A__   				=       AutoConfig.from_pretrained(lowercase_		)
        A__   				=       PyTorchBenchmarkArguments(
            models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],multi_process=lowercase_,)
        A__   				=       PyTorchBenchmark(lowercase_,configs=[config]		)
        A__   				=       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			:	Optional[Any]		)->   Tuple:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'
        with tempfile.TemporaryDirectory() as tmp_dir:
            A__   				=       PyTorchBenchmarkArguments(
                models=[MODEL_ID],training=lowercase_,inference=lowercase_,save_to_csv=lowercase_,sequence_lengths=[8],batch_sizes=[1],inference_time_csv_file=os.path.join(lowercase_,'inf_time.csv'		),train_memory_csv_file=os.path.join(lowercase_,'train_mem.csv'		),inference_memory_csv_file=os.path.join(lowercase_,'inf_mem.csv'		),train_time_csv_file=os.path.join(lowercase_,'train_time.csv'		),env_info_csv_file=os.path.join(lowercase_,'env.csv'		),multi_process=lowercase_,)
            A__   				=       PyTorchBenchmark(lowercase_		)
            benchmark.run()
            self.assertTrue(Path(os.path.join(lowercase_,'inf_time.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'train_time.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'inf_mem.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'train_mem.csv'		)		).exists()		)
            self.assertTrue(Path(os.path.join(lowercase_,'env.csv'		)		).exists()		)


    def 						snake_case__					(				self			:	Tuple		)->   str:





        '''simple docstring'''







        A__   				=       'sshleifer/tiny-gpt2'

        def _check_summary_is_not_empty(lowercase_			:	Optional[Any]		):
            self.assertTrue(hasattr(lowercase_,'sequential'		)		)
            self.assertTrue(hasattr(lowercase_,'cumulative'		)		)
            self.assertTrue(hasattr(lowercase_,'current'		)		)
            self.assertTrue(hasattr(lowercase_,'total'		)		)

        with tempfile.TemporaryDirectory() as tmp_dir:
            A__   				=       PyTorchBenchmarkArguments(
                models=[MODEL_ID],training=lowercase_,inference=lowercase_,sequence_lengths=[8],batch_sizes=[1],log_filename=os.path.join(lowercase_,'log.txt'		),log_print=lowercase_,trace_memory_line_by_line=lowercase_,multi_process=lowercase_,)
            A__   				=       PyTorchBenchmark(lowercase_		)
            A__   				=       benchmark.run()
            _check_summary_is_not_empty(result.inference_summary		)
            _check_summary_is_not_empty(result.train_summary		)
            self.assertTrue(Path(os.path.join(lowercase_,'log.txt'		)		).exists()		)

