

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase   :      List[str]     =     {
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase   :      Optional[int]     =     ['''DebertaTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase   :      Dict     =     [
        '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''DebertaForMaskedLM''',
        '''DebertaForQuestionAnswering''',
        '''DebertaForSequenceClassification''',
        '''DebertaForTokenClassification''',
        '''DebertaModel''',
        '''DebertaPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase   :      Optional[int]     =     [
        '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFDebertaForMaskedLM''',
        '''TFDebertaForQuestionAnswering''',
        '''TFDebertaForSequenceClassification''',
        '''TFDebertaForTokenClassification''',
        '''TFDebertaModel''',
        '''TFDebertaPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
    from .tokenization_deberta import DebertaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_deberta_fast import DebertaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deberta import (
            DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            DebertaForMaskedLM,
            DebertaForQuestionAnswering,
            DebertaForSequenceClassification,
            DebertaForTokenClassification,
            DebertaModel,
            DebertaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_deberta import (
            TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFDebertaForMaskedLM,
            TFDebertaForQuestionAnswering,
            TFDebertaForSequenceClassification,
            TFDebertaForTokenClassification,
            TFDebertaModel,
            TFDebertaPreTrainedModel,
        )


else:
    import sys

    lowerCAmelCase   :      int     =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)










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        snake_case_   (unittest.TestCase      ):
					def  lowerCamelCase__(      self   :Any			,__snake_case   :List[Any]      )					->    Union[str, Any]:
												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(__snake_case      )
					def  lowerCamelCase__(      self   :List[str]      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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[Any]      )					->    Union[str, Any]:
												a__             =     'sgugger/tiny-distilbert-classification'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,only_pretrain_model=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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[Any]      )					->    Optional[Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,torchscript=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												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  lowerCamelCase__(      self   :int      )					->    str:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,fpaa=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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]      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												# set architectures equal to `None`
												a__             =     None
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,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  lowerCamelCase__(      self   :Dict      )					->    int:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												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  lowerCamelCase__(      self   :int      )					->    List[str]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,fpaa=__snake_case			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case      )
												a__             =     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   :str      )					->    Union[str, Any]:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,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  lowerCamelCase__(      self   :List[Any]      )					->    Any:
												a__             =     'sshleifer/tinier_bart'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,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  lowerCamelCase__(      self   :Tuple      )					->    Dict:
												a__             =     'sshleifer/tiny-gpt2'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,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  lowerCamelCase__(      self   :Union[str, Any]      )					->    Optional[int]:
												a__             =     'sshleifer/tinier_bart'
												a__             =     AutoConfig.from_pretrained(__snake_case      )
												a__             =     PyTorchBenchmarkArguments(
												    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,multi_process=__snake_case			,)
												a__             =     PyTorchBenchmark(__snake_case			,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  lowerCamelCase__(      self   :Optional[int]      )					->    List[Any]:
												a__             =     'sshleifer/tiny-gpt2'
												with tempfile.TemporaryDirectory() as tmp_dir:
																			a__             =     PyTorchBenchmarkArguments(
																			    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,save_to_csv=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,inference_time_csv_file=os.path.join(__snake_case			,'inf_time.csv'      )			,train_memory_csv_file=os.path.join(__snake_case			,'train_mem.csv'      )			,inference_memory_csv_file=os.path.join(__snake_case			,'inf_mem.csv'      )			,train_time_csv_file=os.path.join(__snake_case			,'train_time.csv'      )			,env_info_csv_file=os.path.join(__snake_case			,'env.csv'      )			,multi_process=__snake_case			,)
																			a__             =     PyTorchBenchmark(__snake_case      )
																			benchmark.run()
																			self.assertTrue(Path(os.path.join(__snake_case			,'inf_time.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'train_time.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'inf_mem.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'train_mem.csv'      )      ).exists()      )
																			self.assertTrue(Path(os.path.join(__snake_case			,'env.csv'      )      ).exists()      )



					def  lowerCamelCase__(      self   :Optional[int]      )					->    List[str]:
												a__             =     'sshleifer/tiny-gpt2'

												def _check_summary_is_not_empty(__snake_case   :List[str]      ):
																			self.assertTrue(hasattr(__snake_case			,'sequential'      )      )
																			self.assertTrue(hasattr(__snake_case			,'cumulative'      )      )
																			self.assertTrue(hasattr(__snake_case			,'current'      )      )
																			self.assertTrue(hasattr(__snake_case			,'total'      )      )

												with tempfile.TemporaryDirectory() as tmp_dir:
																			a__             =     PyTorchBenchmarkArguments(
																			    models=[MODEL_ID]			,training=__snake_case			,inference=__snake_case			,sequence_lengths=[8]			,batch_sizes=[1]			,log_filename=os.path.join(__snake_case			,'log.txt'      )			,log_print=__snake_case			,trace_memory_line_by_line=__snake_case			,multi_process=__snake_case			,)
																			a__             =     PyTorchBenchmark(__snake_case      )
																			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(__snake_case			,'log.txt'      )      ).exists()      )




