

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    ):
							def    lowerCAmelCase					(		self		:      Tuple       ,SCREAMING_SNAKE_CASE__		:      int):
								for model_result in results.values():
									for batch_size, sequence_length in zip(model_result['bs']       ,model_result['ss']):
										__lowerCamelCase    :    Optional[int] 	=			model_result['result'][batch_size][sequence_length]
										self.assertIsNotNone(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    Optional[int] 	=			'sshleifer/tiny-gpt2'
								__lowerCamelCase    :    Tuple 	=			PyTorchBenchmarkArguments(
								    models=[MODEL_ID]       ,training=SCREAMING_SNAKE_CASE__       ,inference=SCREAMING_SNAKE_CASE__       ,sequence_lengths=[8]       ,batch_sizes=[1]       ,multi_process=SCREAMING_SNAKE_CASE__       ,)
								__lowerCamelCase    :    List[Any] 	=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[Any] 	=			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]):
								__lowerCamelCase    :    int 	=			'sgugger/tiny-distilbert-classification'
								__lowerCamelCase    :    str 	=			PyTorchBenchmarkArguments(
								    models=[MODEL_ID]       ,training=SCREAMING_SNAKE_CASE__       ,inference=SCREAMING_SNAKE_CASE__       ,sequence_lengths=[8]       ,batch_sizes=[1]       ,multi_process=SCREAMING_SNAKE_CASE__       ,only_pretrain_model=SCREAMING_SNAKE_CASE__       ,)
								__lowerCamelCase    :    Optional[Any] 	=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[Any] 	=			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):
								__lowerCamelCase    :    int 	=			'sshleifer/tiny-gpt2'
								__lowerCamelCase    :    str 	=			PyTorchBenchmarkArguments(
								    models=[MODEL_ID]       ,training=SCREAMING_SNAKE_CASE__       ,inference=SCREAMING_SNAKE_CASE__       ,torchscript=SCREAMING_SNAKE_CASE__       ,sequence_lengths=[8]       ,batch_sizes=[1]       ,multi_process=SCREAMING_SNAKE_CASE__       ,)
								__lowerCamelCase    :    Optional[int] 	=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    str 	=			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		:      List[str]):
								__lowerCamelCase    :    Union[str, Any] 	=			'sshleifer/tiny-gpt2'
								__lowerCamelCase    :    List[str] 	=			PyTorchBenchmarkArguments(
								    models=[MODEL_ID]       ,training=SCREAMING_SNAKE_CASE__       ,inference=SCREAMING_SNAKE_CASE__       ,fpaa=SCREAMING_SNAKE_CASE__       ,sequence_lengths=[8]       ,batch_sizes=[1]       ,multi_process=SCREAMING_SNAKE_CASE__       ,)
								__lowerCamelCase    :    Union[str, Any] 	=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    List[Any] 	=			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):
								__lowerCamelCase    :    int 	=			'sshleifer/tiny-gpt2'
								__lowerCamelCase    :    str 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
								# set architectures equal to `None`
								__lowerCamelCase    :    Tuple 	=			None
								__lowerCamelCase    :    Any 	=			PyTorchBenchmarkArguments(
								    models=[MODEL_ID]       ,training=SCREAMING_SNAKE_CASE__       ,inference=SCREAMING_SNAKE_CASE__       ,sequence_lengths=[8]       ,batch_sizes=[1]       ,multi_process=SCREAMING_SNAKE_CASE__       ,)
								__lowerCamelCase    :    Tuple 	=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,configs=[config])
								__lowerCamelCase    :    Optional[int] 	=			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):
								__lowerCamelCase    :    List[str] 	=			'sshleifer/tiny-gpt2'
								__lowerCamelCase    :    str 	=			PyTorchBenchmarkArguments(
								    models=[MODEL_ID]       ,training=SCREAMING_SNAKE_CASE__       ,inference=SCREAMING_SNAKE_CASE__       ,sequence_lengths=[8]       ,batch_sizes=[1]       ,multi_process=SCREAMING_SNAKE_CASE__       ,)
								__lowerCamelCase    :    Dict 	=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Tuple 	=			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		:      Union[str, Any]):
								__lowerCamelCase    :    Optional[Any] 	=			'sshleifer/tiny-gpt2'
								__lowerCamelCase    :    Optional[Any] 	=			PyTorchBenchmarkArguments(
								    models=[MODEL_ID]       ,training=SCREAMING_SNAKE_CASE__       ,inference=SCREAMING_SNAKE_CASE__       ,sequence_lengths=[8]       ,batch_sizes=[1]       ,fpaa=SCREAMING_SNAKE_CASE__       ,multi_process=SCREAMING_SNAKE_CASE__       ,)
								__lowerCamelCase    :    Optional[Any] 	=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			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):
								__lowerCamelCase    :    Any 	=			'sshleifer/tiny-gpt2'
								__lowerCamelCase    :    List[str] 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			PyTorchBenchmarkArguments(
								    models=[MODEL_ID]       ,training=SCREAMING_SNAKE_CASE__       ,inference=SCREAMING_SNAKE_CASE__       ,sequence_lengths=[8]       ,batch_sizes=[1]       ,multi_process=SCREAMING_SNAKE_CASE__       ,)
								__lowerCamelCase    :    List[Any] 	=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,configs=[config])
								__lowerCamelCase    :    Dict 	=			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]):
								__lowerCamelCase    :    Tuple 	=			'sshleifer/tinier_bart'
								__lowerCamelCase    :    Union[str, Any] 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Dict 	=			PyTorchBenchmarkArguments(
								    models=[MODEL_ID]       ,training=SCREAMING_SNAKE_CASE__       ,inference=SCREAMING_SNAKE_CASE__       ,sequence_lengths=[8]       ,batch_sizes=[1]       ,multi_process=SCREAMING_SNAKE_CASE__       ,)
								__lowerCamelCase    :    Dict 	=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,configs=[config])
								__lowerCamelCase    :    Optional[Any] 	=			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		:      Any):
								__lowerCamelCase    :    Any 	=			'sshleifer/tiny-gpt2'
								__lowerCamelCase    :    Tuple 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Any 	=			PyTorchBenchmarkArguments(
								    models=[MODEL_ID]       ,training=SCREAMING_SNAKE_CASE__       ,inference=SCREAMING_SNAKE_CASE__       ,sequence_lengths=[8]       ,batch_sizes=[1]       ,multi_process=SCREAMING_SNAKE_CASE__       ,)
								__lowerCamelCase    :    Dict 	=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,configs=[config])
								__lowerCamelCase    :    str 	=			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		:      Dict):
								__lowerCamelCase    :    int 	=			'sshleifer/tinier_bart'
								__lowerCamelCase    :    Optional[Any] 	=			AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			PyTorchBenchmarkArguments(
								    models=[MODEL_ID]       ,training=SCREAMING_SNAKE_CASE__       ,inference=SCREAMING_SNAKE_CASE__       ,sequence_lengths=[8]       ,batch_sizes=[1]       ,multi_process=SCREAMING_SNAKE_CASE__       ,)
								__lowerCamelCase    :    Optional[int] 	=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__       ,configs=[config])
								__lowerCamelCase    :    Any 	=			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):
								__lowerCamelCase    :    int 	=			'sshleifer/tiny-gpt2'
								with tempfile.TemporaryDirectory() as tmp_dir:
									__lowerCamelCase    :    List[Any] 	=			PyTorchBenchmarkArguments(
									    models=[MODEL_ID]       ,training=SCREAMING_SNAKE_CASE__       ,inference=SCREAMING_SNAKE_CASE__       ,save_to_csv=SCREAMING_SNAKE_CASE__       ,sequence_lengths=[8]       ,batch_sizes=[1]       ,inference_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,'inf_time.csv')       ,train_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,'train_mem.csv')       ,inference_memory_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,'inf_mem.csv')       ,train_time_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,'train_time.csv')       ,env_info_csv_file=os.path.join(SCREAMING_SNAKE_CASE__       ,'env.csv')       ,multi_process=SCREAMING_SNAKE_CASE__       ,)
									__lowerCamelCase    :    int 	=			PyTorchBenchmark(SCREAMING_SNAKE_CASE__)
									benchmark.run()
									self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,'inf_time.csv')).exists())
									self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,'train_time.csv')).exists())
									self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,'inf_mem.csv')).exists())
									self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,'train_mem.csv')).exists())
									self.assertTrue(Path(os.path.join(SCREAMING_SNAKE_CASE__       ,'env.csv')).exists())


							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    Optional[int] 	=			'sshleifer/tiny-gpt2'

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

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







from typing import TYPE_CHECKING

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


a		={
    """configuration_roformer""": ["""ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RoFormerConfig""", """RoFormerOnnxConfig"""],
    """tokenization_roformer""": ["""RoFormerTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=["""RoFormerTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """RoFormerForCausalLM""",
					    """RoFormerForMaskedLM""",
					    """RoFormerForMultipleChoice""",
					    """RoFormerForQuestionAnswering""",
					    """RoFormerForSequenceClassification""",
					    """RoFormerForTokenClassification""",
					    """RoFormerLayer""",
					    """RoFormerModel""",
					    """RoFormerPreTrainedModel""",
					    """load_tf_weights_in_roformer""",
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFRoFormerForCausalLM""",
					    """TFRoFormerForMaskedLM""",
					    """TFRoFormerForMultipleChoice""",
					    """TFRoFormerForQuestionAnswering""",
					    """TFRoFormerForSequenceClassification""",
					    """TFRoFormerForTokenClassification""",
					    """TFRoFormerLayer""",
					    """TFRoFormerModel""",
					    """TFRoFormerPreTrainedModel""",
					]


try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a		=[
					    """FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FlaxRoFormerForMaskedLM""",
					    """FlaxRoFormerForMultipleChoice""",
					    """FlaxRoFormerForQuestionAnswering""",
					    """FlaxRoFormerForSequenceClassification""",
					    """FlaxRoFormerForTokenClassification""",
					    """FlaxRoFormerModel""",
					    """FlaxRoFormerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
					from .tokenization_roformer import RoFormerTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_roformer_fast import RoFormerTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_roformer import (
										    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    RoFormerForCausalLM,
										    RoFormerForMaskedLM,
										    RoFormerForMultipleChoice,
										    RoFormerForQuestionAnswering,
										    RoFormerForSequenceClassification,
										    RoFormerForTokenClassification,
										    RoFormerLayer,
										    RoFormerModel,
										    RoFormerPreTrainedModel,
										    load_tf_weights_in_roformer,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_roformer import (
										    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFRoFormerForCausalLM,
										    TFRoFormerForMaskedLM,
										    TFRoFormerForMultipleChoice,
										    TFRoFormerForQuestionAnswering,
										    TFRoFormerForSequenceClassification,
										    TFRoFormerForTokenClassification,
										    TFRoFormerLayer,
										    TFRoFormerModel,
										    TFRoFormerPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_roformer import (
										    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaxRoFormerForMaskedLM,
										    FlaxRoFormerForMultipleChoice,
										    FlaxRoFormerForQuestionAnswering,
										    FlaxRoFormerForSequenceClassification,
										    FlaxRoFormerForTokenClassification,
										    FlaxRoFormerModel,
										    FlaxRoFormerPreTrainedModel,
										)


else:
					import sys

					a		=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




