


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        UpperCamelCase__   (	unittest.TestCase   ):

					def   UpperCAmelCase__       (     self       :   Any							,					UpperCamelCase__       :   Optional[int]       ):






										'''simple docstring'''






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

					def   UpperCAmelCase__       (     self       :   Optional[Any]       ):






										'''simple docstring'''






										lowercase_  							=			"""sshleifer/tiny-gpt2"""
										lowercase_  							=			TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]							,					training=UpperCamelCase__							,					inference=UpperCamelCase__							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					eager_mode=UpperCamelCase__							,					multi_process=UpperCamelCase__							,					)
										lowercase_  							=			TensorFlowBenchmark(UpperCamelCase__       )
										lowercase_  							=			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       ):






										'''simple docstring'''






										lowercase_  							=			"""sgugger/tiny-distilbert-classification"""
										lowercase_  							=			TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]							,					training=UpperCamelCase__							,					inference=UpperCamelCase__							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					multi_process=UpperCamelCase__							,					only_pretrain_model=UpperCamelCase__							,					)
										lowercase_  							=			TensorFlowBenchmark(UpperCamelCase__       )
										lowercase_  							=			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[Any]       ):






										'''simple docstring'''






										lowercase_  							=			"""sshleifer/tiny-gpt2"""
										lowercase_  							=			TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]							,					training=UpperCamelCase__							,					inference=UpperCamelCase__							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					multi_process=UpperCamelCase__							,					)
										lowercase_  							=			TensorFlowBenchmark(UpperCamelCase__       )
										lowercase_  							=			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       :   int       ):






										'''simple docstring'''






										lowercase_  							=			"""sshleifer/tiny-gpt2"""
										lowercase_  							=			AutoConfig.from_pretrained(UpperCamelCase__       )
										lowercase_  							=			TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]							,					training=UpperCamelCase__							,					inference=UpperCamelCase__							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					eager_mode=UpperCamelCase__							,					multi_process=UpperCamelCase__							,					)
										lowercase_  							=			TensorFlowBenchmark(UpperCamelCase__							,					[config]       )
										lowercase_  							=			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]       ):






										'''simple docstring'''






										lowercase_  							=			"""sshleifer/tiny-gpt2"""
										lowercase_  							=			AutoConfig.from_pretrained(UpperCamelCase__       )
										lowercase_  							=			TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]							,					training=UpperCamelCase__							,					inference=UpperCamelCase__							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					multi_process=UpperCamelCase__							,					)
										lowercase_  							=			TensorFlowBenchmark(UpperCamelCase__							,					[config]       )
										lowercase_  							=			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]       ):






										'''simple docstring'''






										lowercase_  							=			"""sshleifer/tiny-gpt2"""
										lowercase_  							=			TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]							,					training=UpperCamelCase__							,					inference=UpperCamelCase__							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					multi_process=UpperCamelCase__							,					)
										lowercase_  							=			TensorFlowBenchmark(UpperCamelCase__       )
										lowercase_  							=			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       :   Any       ):






										'''simple docstring'''






										lowercase_  							=			"""sshleifer/tiny-gpt2"""
										lowercase_  							=			AutoConfig.from_pretrained(UpperCamelCase__       )
										lowercase_  							=			TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]							,					training=UpperCamelCase__							,					inference=UpperCamelCase__							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					multi_process=UpperCamelCase__							,					)
										lowercase_  							=			TensorFlowBenchmark(UpperCamelCase__							,					[config]       )
										lowercase_  							=			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]       ):






										'''simple docstring'''






										lowercase_  							=			"""patrickvonplaten/t5-tiny-random"""
										lowercase_  							=			AutoConfig.from_pretrained(UpperCamelCase__       )
										lowercase_  							=			TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]							,					training=UpperCamelCase__							,					inference=UpperCamelCase__							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					multi_process=UpperCamelCase__							,					)
										lowercase_  							=			TensorFlowBenchmark(UpperCamelCase__							,					configs=[config]       )
										lowercase_  							=			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       :   Any       ):






										'''simple docstring'''






										lowercase_  							=			"""sshleifer/tiny-gpt2"""
										lowercase_  							=			TensorFlowBenchmarkArguments(
										    models=[MODEL_ID]							,					training=UpperCamelCase__							,					inference=UpperCamelCase__							,					sequence_lengths=[8]							,					batch_sizes=[1]							,					use_xla=UpperCamelCase__							,					multi_process=UpperCamelCase__							,					)
										lowercase_  							=			TensorFlowBenchmark(UpperCamelCase__       )
										lowercase_  							=			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]       ):






										'''simple docstring'''






										lowercase_  							=			"""sshleifer/tiny-gpt2"""
										with tempfile.TemporaryDirectory() as tmp_dir:
															lowercase_  							=			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__							,					)
															lowercase_  							=			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   UpperCAmelCase__       (     self       :   List[str]       ):






										'''simple docstring'''






										lowercase_  							=			"""sshleifer/tiny-gpt2"""

										def _check_summary_is_not_empty(UpperCamelCase__       :   str       ):
															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:
															lowercase_  							=			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__							,					)
															lowercase_  							=			TensorFlowBenchmark(UpperCamelCase__       )
															lowercase_  							=			benchmark.run()
															_check_summary_is_not_empty(result.inference_summary       )
															self.assertTrue(Path(os.path.join(UpperCamelCase__							,					"""log.txt"""       )       ).exists()       )






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin







class        UpperCamelCase__   (	__magic_name__							,				__magic_name__							,				__magic_name__   ):

					@register_to_config
					def __init__(     self       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   float							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   int							,					UpperCamelCase__       :   str							,					UpperCamelCase__       :   bool = False							,					):






										'''simple docstring'''






										super().__init__()

										lowercase_  							=			nn.Embedding(UpperCamelCase__							,					UpperCamelCase__       )

										lowercase_  							=			nn.Embedding(UpperCamelCase__							,					UpperCamelCase__       )
										lowercase_  							=			False

										lowercase_  							=			nn.Dropout(p=UpperCamelCase__       )

										lowercase_  							=			TaConfig(
										    vocab_size=UpperCamelCase__							,					d_model=UpperCamelCase__							,					num_heads=UpperCamelCase__							,					d_kv=UpperCamelCase__							,					d_ff=UpperCamelCase__							,					dropout_rate=UpperCamelCase__							,					feed_forward_proj=UpperCamelCase__							,					is_decoder=UpperCamelCase__							,					is_encoder_decoder=UpperCamelCase__							,					)

										lowercase_  							=			nn.ModuleList()
										for lyr_num in range(UpperCamelCase__       ):
															lowercase_  							=			TaBlock(UpperCamelCase__       )
															self.encoders.append(UpperCamelCase__       )

										lowercase_  							=			TaLayerNorm(UpperCamelCase__       )
										lowercase_  							=			nn.Dropout(p=UpperCamelCase__       )







					def   UpperCAmelCase__       (     self       :   Optional[Any]							,					UpperCamelCase__       :   Optional[int]							,					UpperCamelCase__       :   str       ):






										'''simple docstring'''






										lowercase_  							=			self.token_embedder(UpperCamelCase__       )

										lowercase_  							=			encoder_input_tokens.shape[1]
										lowercase_  							=			torch.arange(UpperCamelCase__							,					device=encoder_input_tokens.device       )
										x += self.position_encoding(UpperCamelCase__       )

										lowercase_  							=			self.dropout_pre(UpperCamelCase__       )

										# inverted the attention mask
										lowercase_  							=			encoder_input_tokens.size()
										lowercase_  							=			self.get_extended_attention_mask(UpperCamelCase__							,					UpperCamelCase__       )

										for lyr in self.encoders:
															lowercase_  							=			lyr(UpperCamelCase__							,					UpperCamelCase__       )[0]
										lowercase_  							=			self.layer_norm(UpperCamelCase__       )

										return self.dropout_post(UpperCamelCase__       ), encoder_inputs_mask


