




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    lowerCamelCase    (		unittest.TestCase						):






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   str  )						->   List[Any]:
								for model_result in results.values():
													for batch_size, sequence_length in zip(model_result['''bs''']       ,						model_result['''ss''']  ):
																		__magic_name__							:	Union[str, Any]    							=							model_result['''result'''][batch_size][sequence_length]
																		self.assertIsNotNone(lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   str  )						->   str:
								__magic_name__							:	Dict    							=							'''sshleifer/tiny-gpt2'''
								__magic_name__							:	Optional[int]    							=							TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]       ,						training=lowerCamelCase_       ,						inference=lowerCamelCase_       ,						sequence_lengths=[8]       ,						batch_sizes=[1]       ,						eager_mode=lowerCamelCase_       ,						multi_process=lowerCamelCase_       ,						)
								__magic_name__							:	Union[str, Any]    							=							TensorFlowBenchmark(lowerCamelCase_  )
								__magic_name__							:	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 							UpperCAmelCase__       ( self			:   List[str]  )						->   Tuple:
								__magic_name__							:	List[str]    							=							'''sgugger/tiny-distilbert-classification'''
								__magic_name__							:	Union[str, Any]    							=							TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]       ,						training=lowerCamelCase_       ,						inference=lowerCamelCase_       ,						sequence_lengths=[8]       ,						batch_sizes=[1]       ,						multi_process=lowerCamelCase_       ,						only_pretrain_model=lowerCamelCase_       ,						)
								__magic_name__							:	Optional[int]    							=							TensorFlowBenchmark(lowerCamelCase_  )
								__magic_name__							:	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 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Any:
								__magic_name__							:	Optional[Any]    							=							'''sshleifer/tiny-gpt2'''
								__magic_name__							:	List[str]    							=							TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]       ,						training=lowerCamelCase_       ,						inference=lowerCamelCase_       ,						sequence_lengths=[8]       ,						batch_sizes=[1]       ,						multi_process=lowerCamelCase_       ,						)
								__magic_name__							:	Union[str, Any]    							=							TensorFlowBenchmark(lowerCamelCase_  )
								__magic_name__							:	List[str]    							=							benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result  )
								self.check_results_dict_not_empty(results.memory_inference_result  )

			def 							UpperCAmelCase__       ( self			:   Optional[Any]  )						->   Tuple:
								__magic_name__							:	Union[str, Any]    							=							'''sshleifer/tiny-gpt2'''
								__magic_name__							:	Any    							=							AutoConfig.from_pretrained(lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]       ,						training=lowerCamelCase_       ,						inference=lowerCamelCase_       ,						sequence_lengths=[8]       ,						batch_sizes=[1]       ,						eager_mode=lowerCamelCase_       ,						multi_process=lowerCamelCase_       ,						)
								__magic_name__							:	Any    							=							TensorFlowBenchmark(lowerCamelCase_       ,						[config]  )
								__magic_name__							:	Dict    							=							benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result  )
								self.check_results_dict_not_empty(results.memory_inference_result  )

			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Tuple:
								__magic_name__							:	Dict    							=							'''sshleifer/tiny-gpt2'''
								__magic_name__							:	Optional[int]    							=							AutoConfig.from_pretrained(lowerCamelCase_  )
								__magic_name__							:	Dict    							=							TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]       ,						training=lowerCamelCase_       ,						inference=lowerCamelCase_       ,						sequence_lengths=[8]       ,						batch_sizes=[1]       ,						multi_process=lowerCamelCase_       ,						)
								__magic_name__							:	Any    							=							TensorFlowBenchmark(lowerCamelCase_       ,						[config]  )
								__magic_name__							:	Any    							=							benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result  )
								self.check_results_dict_not_empty(results.memory_inference_result  )

			def 							UpperCAmelCase__       ( self			:   Any  )						->   str:
								__magic_name__							:	Any    							=							'''sshleifer/tiny-gpt2'''
								__magic_name__							:	Dict    							=							TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]       ,						training=lowerCamelCase_       ,						inference=lowerCamelCase_       ,						sequence_lengths=[8]       ,						batch_sizes=[1]       ,						multi_process=lowerCamelCase_       ,						)
								__magic_name__							:	Dict    							=							TensorFlowBenchmark(lowerCamelCase_  )
								__magic_name__							:	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 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   int:
								__magic_name__							:	Union[str, Any]    							=							'''sshleifer/tiny-gpt2'''
								__magic_name__							:	Any    							=							AutoConfig.from_pretrained(lowerCamelCase_  )
								__magic_name__							:	Optional[Any]    							=							TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]       ,						training=lowerCamelCase_       ,						inference=lowerCamelCase_       ,						sequence_lengths=[8]       ,						batch_sizes=[1]       ,						multi_process=lowerCamelCase_       ,						)
								__magic_name__							:	int    							=							TensorFlowBenchmark(lowerCamelCase_       ,						[config]  )
								__magic_name__							:	Optional[Any]    							=							benchmark.run()
								self.check_results_dict_not_empty(results.time_train_result  )
								self.check_results_dict_not_empty(results.memory_train_result  )

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Optional[Any]:
								__magic_name__							:	Optional[Any]    							=							'''patrickvonplaten/t5-tiny-random'''
								__magic_name__							:	str    							=							AutoConfig.from_pretrained(lowerCamelCase_  )
								__magic_name__							:	Any    							=							TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]       ,						training=lowerCamelCase_       ,						inference=lowerCamelCase_       ,						sequence_lengths=[8]       ,						batch_sizes=[1]       ,						multi_process=lowerCamelCase_       ,						)
								__magic_name__							:	Optional[int]    							=							TensorFlowBenchmark(lowerCamelCase_       ,						configs=[config]  )
								__magic_name__							:	List[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(is_tf_available() and len(tf.config.list_physical_devices('''GPU'''  )  ) == 0       ,						'''Cannot do xla on CPU.'''  )
			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   List[Any]:
								__magic_name__							:	Dict    							=							'''sshleifer/tiny-gpt2'''
								__magic_name__							:	Tuple    							=							TensorFlowBenchmarkArguments(
								    models=[MODEL_ID]       ,						training=lowerCamelCase_       ,						inference=lowerCamelCase_       ,						sequence_lengths=[8]       ,						batch_sizes=[1]       ,						use_xla=lowerCamelCase_       ,						multi_process=lowerCamelCase_       ,						)
								__magic_name__							:	List[str]    							=							TensorFlowBenchmark(lowerCamelCase_  )
								__magic_name__							:	int    							=							benchmark.run()
								self.check_results_dict_not_empty(results.time_inference_result  )
								self.check_results_dict_not_empty(results.memory_inference_result  )

			def 							UpperCAmelCase__       ( self			:   Dict  )						->   Optional[int]:
								__magic_name__							:	Tuple    							=							'''sshleifer/tiny-gpt2'''
								with tempfile.TemporaryDirectory() as tmp_dir:
													__magic_name__							:	Optional[int]    							=							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_       ,						)
													__magic_name__							:	List[str]    							=							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 							UpperCAmelCase__       ( self			:   str  )						->   int:
								__magic_name__							:	Optional[Any]    							=							'''sshleifer/tiny-gpt2'''

								def _check_summary_is_not_empty(lowerCamelCase_			:   Optional[Any]  ):
													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:
													__magic_name__							:	Dict    							=							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_       ,						)
													__magic_name__							:	Optional[Any]    							=							TensorFlowBenchmark(lowerCamelCase_  )
													__magic_name__							:	List[str]    							=							benchmark.run()
													_check_summary_is_not_empty(result.inference_summary  )
													self.assertTrue(Path(os.path.join(lowerCamelCase_       ,						'''log.txt'''  )  ).exists()  )









from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput


@dataclass
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   List[Any]       ,						lowerCamelCase_			:   Optional[Any]=1       ,						lowerCamelCase_			:   Any=0       ,						lowerCamelCase_			:   Dict=2       ,						lowerCamelCase_			:   Dict=512       ,						lowerCamelCase_			:   int="cls"       ,						lowerCamelCase_			:   Dict=False       ,						lowerCamelCase_			:   List[Any]=True       ,						**lowerCamelCase_			:   Optional[Any]       ,						)						->   Optional[int]:
								super().__init__(pad_token_id=lowerCamelCase_       ,						bos_token_id=lowerCamelCase_       ,						eos_token_id=lowerCamelCase_       ,						**lowerCamelCase_  )
								__magic_name__							:	Dict    							=							project_dim
								__magic_name__							:	int    							=							pooler_fn
								__magic_name__							:	str    							=							learn_encoder
								__magic_name__							:	Optional[int]    							=							use_attention_mask






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=[R'''pooler''', R'''logit_scale''']
			UpperCamelCase__								=[R'''position_ids''', R'''predictions.decoder.bias''']
			UpperCamelCase__								='''roberta'''
			UpperCamelCase__								=RobertaSeriesConfig

			def __init__( self			:   str       ,						lowerCamelCase_			:   Optional[Any]  )						->   Optional[Any]:
								super().__init__(lowerCamelCase_  )
								__magic_name__							:	Tuple    							=							XLMRobertaModel(lowerCamelCase_  )
								__magic_name__							:	Any    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
								__magic_name__							:	Optional[int]    							=							getattr(lowerCamelCase_       ,						'''has_pre_transformation'''       ,						lowerCamelCase_  )
								if self.has_pre_transformation:
													__magic_name__							:	List[Any]    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
													__magic_name__							:	Union[str, Any]    							=							nn.LayerNorm(config.hidden_size       ,						eps=config.layer_norm_eps  )
								self.post_init()


			def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						)						->   List[Any]:
								__magic_name__							:	Optional[int]    							=							return_dict if return_dict is not None else self.config.use_return_dict

								__magic_name__							:	Any    							=							self.base_model(
								    input_ids=lowerCamelCase_       ,						attention_mask=lowerCamelCase_       ,						token_type_ids=lowerCamelCase_       ,						position_ids=lowerCamelCase_       ,						head_mask=lowerCamelCase_       ,						inputs_embeds=lowerCamelCase_       ,						encoder_hidden_states=lowerCamelCase_       ,						encoder_attention_mask=lowerCamelCase_       ,						output_attentions=lowerCamelCase_       ,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,						return_dict=lowerCamelCase_       ,						)

								if self.has_pre_transformation:
													__magic_name__							:	Any    							=							outputs['''hidden_states'''][-2]
													__magic_name__							:	Dict    							=							self.pre_LN(lowerCamelCase_  )
													__magic_name__							:	int    							=							self.transformation_pre(lowerCamelCase_  )

													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)
								else:
													__magic_name__							:	Union[str, Any]    							=							self.transformation(outputs.last_hidden_state  )
													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)



