


import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
	import tensorflow as tf
	from tensorflow.python.framework.errors_impl import ResourceExhaustedError

	from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
	import pyanvml.pyanvml as nvml

a : Optional[int]		=				logging.get_logger(__name__)


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			def run_func(_UpperCamelCase  ):
						@wraps(_UpperCamelCase  )
						def run_in_eager_mode(*_UpperCamelCase       ,			**_UpperCamelCase  ):
									return func(*_UpperCamelCase       ,			**_UpperCamelCase  )

						@wraps(_UpperCamelCase  )
						@tf.function(experimental_compile=_UpperCamelCase  )
						def run_in_graph_mode(*_UpperCamelCase       ,			**_UpperCamelCase  ):
									return func(*_UpperCamelCase       ,			**_UpperCamelCase  )

						if do_eager_mode is True:
									if use_xla is not False:
												raise ValueError(
												    '''Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'''  )
									return run_in_eager_mode
						else:
									return run_in_graph_mode

			return run_func


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   random.Random()
			__lowercase 					=   [rng.randint(0       ,			vocab_size - 1  ) for i in range(batch_size * sequence_length  )]
			return tf.constant(_UpperCamelCase       ,			shape=(batch_size, sequence_length)       ,			dtype=tf.intaa  )





class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				42
					__UpperCAmelCase            =				42
					__UpperCAmelCase            =				"TensorFlow"






					@property
					def A      (				self						)   ->			str:







								'''simple docstring'''

								return tf.__version__






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			float:







								'''simple docstring'''

								__lowercase 					=   self.args.strategy
								if strategy is None:
											raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''						)
								__lowercase 					=   self._prepare_inference_func(snake_case_ ,       snake_case_ ,       snake_case_						)
								return self._measure_speed(_inference						)






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			float:







								'''simple docstring'''

								__lowercase 					=   self.args.strategy
								if strategy is None:
											raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''						)
								__lowercase 					=   self._prepare_train_func(snake_case_ ,       snake_case_ ,       snake_case_						)
								return self._measure_speed(_train						)






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			[Memory, Optional[MemorySummary]]:







								'''simple docstring'''

								if self.args.is_gpu:
											tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] ,       snake_case_						)
								__lowercase 					=   self.args.strategy
								if strategy is None:
											raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''						)
								__lowercase 					=   self._prepare_inference_func(snake_case_ ,       snake_case_ ,       snake_case_						)
								return self._measure_memory(_inference						)






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			[Memory, Optional[MemorySummary]]:







								'''simple docstring'''

								if self.args.is_gpu:
											tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] ,       snake_case_						)
								__lowercase 					=   self.args.strategy
								if strategy is None:
											raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''						)

								__lowercase 					=   self._prepare_train_func(snake_case_ ,       snake_case_ ,       snake_case_						)
								return self._measure_memory(_train						)






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			Callable[[], None]:







								'''simple docstring'''

								__lowercase 					=   self.config_dict[model_name]

								if self.args.fpaa:
											raise NotImplementedError('''Mixed precision is currently not supported.'''						)

								__lowercase 					=   (
								    hasattr(snake_case_ ,       '''architectures'''						)
								    and isinstance(config.architectures ,       snake_case_						)
								    and len(config.architectures						) > 0
								)
								if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
														__lowercase 					=   '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
														__lowercase 					=   __import__('''transformers''' ,       fromlist=[model_class]						)
														__lowercase 					=   getattr(snake_case_ ,       snake_case_						)
														__lowercase 					=   model_cls(snake_case_						)
											except ImportError:
														raise ImportError(
														    F'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
														    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''						)
								else:
											__lowercase 					=   TF_MODEL_MAPPING[config.__class__](snake_case_						)

								# encoder-decoder has vocab size saved differently
								__lowercase 					=   config.vocab_size if hasattr(snake_case_ ,       '''vocab_size'''						) else config.encoder.vocab_size
								__lowercase 					=   random_input_ids(snake_case_ ,       snake_case_ ,       snake_case_						)

								@run_with_tf_optimizations(self.args.eager_mode ,       self.args.use_xla						)
								def encoder_decoder_forward():
											return model(snake_case_ ,       decoder_input_ids=snake_case_ ,       training=snake_case_						)

								@run_with_tf_optimizations(self.args.eager_mode ,       self.args.use_xla						)
								def encoder_forward():
											return model(snake_case_ ,       training=snake_case_						)

								__lowercase 					=   encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

								return _inference






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_						)   ->			Callable[[], None]:







								'''simple docstring'''

								__lowercase 					=   self.config_dict[model_name]

								if self.args.eager_mode is not False:
											raise ValueError('''Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.'''						)

								if self.args.fpaa:
											raise NotImplementedError('''Mixed precision is currently not supported.'''						)

								__lowercase 					=   (
								    hasattr(snake_case_ ,       '''architectures'''						)
								    and isinstance(config.architectures ,       snake_case_						)
								    and len(config.architectures						) > 0
								)
								if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
														__lowercase 					=   '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
														__lowercase 					=   __import__('''transformers''' ,       fromlist=[model_class]						)
														__lowercase 					=   getattr(snake_case_ ,       snake_case_						)
														__lowercase 					=   model_cls(snake_case_						)
											except ImportError:
														raise ImportError(
														    F'{model_class} does not exist. If you just want to test the pretrained model, you might want to'
														    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''						)
								else:
											__lowercase 					=   TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](snake_case_						)

								# encoder-decoder has vocab size saved differently
								__lowercase 					=   config.vocab_size if hasattr(snake_case_ ,       '''vocab_size'''						) else config.encoder.vocab_size
								__lowercase 					=   random_input_ids(snake_case_ ,       snake_case_ ,       snake_case_						)

								@run_with_tf_optimizations(self.args.eager_mode ,       self.args.use_xla						)
								def encoder_decoder_train():
											__lowercase 					=   model(snake_case_ ,       decoder_input_ids=snake_case_ ,       labels=snake_case_ ,       training=snake_case_						)[0]
											__lowercase 					=   tf.gradients(snake_case_ ,       model.trainable_variables						)
											return gradients

								@run_with_tf_optimizations(self.args.eager_mode ,       self.args.use_xla						)
								def encoder_train():
											__lowercase 					=   model(snake_case_ ,       labels=snake_case_ ,       training=snake_case_						)[0]
											__lowercase 					=   tf.gradients(snake_case_ ,       model.trainable_variables						)
											return gradients

								__lowercase 					=   encoder_decoder_train if config.is_encoder_decoder else encoder_train

								return _train






					def A      (				self ,       snake_case_						)   ->			float:







								'''simple docstring'''

								with self.args.strategy.scope():
											try:
														if self.args.is_tpu or self.args.use_xla:
																	# run additional 10 times to stabilize compilation for tpu
																	logger.info('''Do inference on TPU. Running model 5 times to stabilize compilation'''						)
																	timeit.repeat(snake_case_ ,       repeat=1 ,       number=5						)

														# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
														__lowercase 					=   timeit.repeat(
														    snake_case_ ,       repeat=self.args.repeat ,       number=1_0 ,       )

														return min(snake_case_						) / 1_0.0
											except ResourceExhaustedError as e:
														self.print_fn(F'Doesn\'t fit on GPU. {e}'						)






					def A      (				self ,       snake_case_						)   ->			[Memory, MemorySummary]:







								'''simple docstring'''

								logger.info(
								    '''Note that TensorFlow allocates more memory than '''
								    '''it might need to speed up computation. '''
								    '''The memory reported here corresponds to the memory '''
								    '''reported by `nvidia-smi`, which can vary depending '''
								    '''on total available memory on the GPU that is used.'''						)
								with self.args.strategy.scope():
											try:
														if self.args.trace_memory_line_by_line:
																	if not self.args.eager_mode:
																				raise ValueError(
																				    '''`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'''
																				    ''' consumption line by line.'''						)
																	__lowercase 					=   start_memory_tracing('''transformers'''						)

														if self.args.is_tpu:
																	# tpu
																	raise NotImplementedError(
																	    '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking'''
																	    ''' with `args.memory=False`'''						)
														elif self.args.is_gpu:
																	# gpu
																	if not is_pyanvml_available():
																				logger.warning(
																				    '''py3nvml not installed, we won\'t log GPU memory usage. '''
																				    '''Install py3nvml (pip install py3nvml) to log information about GPU.'''						)
																				__lowercase 					=   '''N/A'''
																	else:
																				logger.info(
																				    '''Measuring total GPU usage on GPU device. Make sure to not have additional processes'''
																				    ''' running on the same GPU.'''						)
																				# init nvml
																				nvml.nvmlInit()
																				func()
																				__lowercase 					=   nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx						)
																				__lowercase 					=   nvml.nvmlDeviceGetMemoryInfo(snake_case_						)
																				__lowercase 					=   meminfo.used
																				__lowercase 					=   Memory(snake_case_						)
																				# shutdown nvml
																				nvml.nvmlShutdown()
														else:
																	# cpu
																	if self.args.trace_memory_line_by_line:
																				logger.info(
																				    '''When enabling line by line tracing, the max peak memory for CPU is inaccurate in'''
																				    ''' TensorFlow.'''						)
																				__lowercase 					=   None
																	else:
																				__lowercase 					=   measure_peak_memory_cpu(snake_case_						)
																				__lowercase 					=   Memory(snake_case_						) if isinstance(snake_case_ ,       snake_case_						) else memory_bytes
														if self.args.trace_memory_line_by_line:
																	__lowercase 					=   stop_memory_tracing(snake_case_						)
																	if memory is None:
																				__lowercase 					=   summary.total
														else:
																	__lowercase 					=   None

														return memory, summary
											except ResourceExhaustedError as e:
														self.print_fn(F'Doesn\'t fit on GPU. {e}'						)
														return "N/A", None






import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class 	lowerCamelCase_       :



					'''simple docstring'''







					def __init__(				self ,       snake_case_=2 ,       snake_case_=3 ,       snake_case_=6_4 ,       snake_case_=None						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   np.random.default_rng(snake_case_						)
								__lowercase 					=   length
								__lowercase 					=   rng.normal(size=(length,)						).astype(np.floataa						)
								__lowercase 					=   a * self.x + b + rng.normal(scale=0.1 ,       size=(length,)						).astype(np.floataa						)






					def __len__(				self						)   ->			Dict:







								'''simple docstring'''

								return self.length






					def __getitem__(				self ,       snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

								return {"x": self.x[i], "y": self.y[i]}




class 	lowerCamelCase_       (				torch.nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_=0 ,       snake_case_=0 ,       snake_case_=False						)   ->			Optional[Any]:







								'''simple docstring'''

								super().__init__()
								__lowercase 					=   torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
								__lowercase 					=   torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
								__lowercase 					=   True






					def A      (				self ,       snake_case_=None						)   ->			List[Any]:







								'''simple docstring'''

								if self.first_batch:
											print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'						)
											__lowercase 					=   False
								return x * self.a[0] + self.b[0]




class 	lowerCamelCase_       (				torch.nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_=0 ,       snake_case_=0 ,       snake_case_=False						)   ->			List[str]:







								'''simple docstring'''

								super().__init__()
								__lowercase 					=   torch.nn.Parameter(torch.tensor(snake_case_						).float()						)
								__lowercase 					=   torch.nn.Parameter(torch.tensor(snake_case_						).float()						)
								__lowercase 					=   True






					def A      (				self ,       snake_case_=None						)   ->			str:







								'''simple docstring'''

								if self.first_batch:
											print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'						)
											__lowercase 					=   False
								return x * self.a + self.b




def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase = 16  ):


			'''simple docstring'''







			from datasets import load_dataset
			from transformers import AutoTokenizer

			__lowercase 					=   AutoTokenizer.from_pretrained('''bert-base-cased'''  )
			__lowercase 					=   {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
			__lowercase 					=   load_dataset('''csv'''       ,			data_files=_UpperCamelCase  )
			__lowercase 					=   datasets['''train'''].unique('''label'''  )

			__lowercase 					=   {v: i for i, v in enumerate(_UpperCamelCase  )}

			def tokenize_function(_UpperCamelCase  ):
						# max_length=None => use the model max length (it's actually the default)
						__lowercase 					=   tokenizer(
						    examples['''sentence1''']       ,			examples['''sentence2''']       ,			truncation=_UpperCamelCase       ,			max_length=_UpperCamelCase       ,			padding='''max_length'''  )
						if "label" in examples:
									__lowercase 					=   [label_to_id[l] for l in examples['''label''']]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			__lowercase 					=   datasets.map(
			    _UpperCamelCase       ,			batched=_UpperCamelCase       ,			remove_columns=['''sentence1''', '''sentence2''', '''label''']       ,			)

			def collate_fn(_UpperCamelCase  ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(_UpperCamelCase       ,			padding='''max_length'''       ,			max_length=1_28       ,			return_tensors='''pt'''  )
						return tokenizer.pad(_UpperCamelCase       ,			padding='''longest'''       ,			return_tensors='''pt'''  )

			# Instantiate dataloaders.
			__lowercase 					=   DataLoader(tokenized_datasets['''train''']       ,			shuffle=_UpperCamelCase       ,			collate_fn=_UpperCamelCase       ,			batch_size=2  )
			__lowercase 					=   DataLoader(tokenized_datasets['''validation''']       ,			shuffle=_UpperCamelCase       ,			collate_fn=_UpperCamelCase       ,			batch_size=1  )

			return train_dataloader, eval_dataloader


