import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
     import torch

     from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
     import pyanvml.pyanvml as nvml


snake_case__						      =			logging.get_logger(__name__)



class 			UpperCamelCase					(			__lowercase			):

     '''simple docstring'''

     A_					    =	42
     A_					    =	42
     A_					    =	"PyTorch"



     @property
     def 		UpperCamelCase_    (       self    )			->					Union[str, Any]:






           """simple docstring"""
           return torch.__version__



     def 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_    )			->					float:






           """simple docstring"""
           _lowerCamelCase           =					self._prepare_inference_func(A_					,					A_					,					A_    )
           return self._measure_speed(_inference    )



     def 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_    )			->					[Memory, Optional[MemorySummary]]:






           """simple docstring"""
           _lowerCamelCase           =					self._prepare_inference_func(A_					,					A_					,					A_    )
           return self._measure_memory(_inference    )



     def 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_    )			->					float:






           """simple docstring"""
           _lowerCamelCase           =					self._prepare_train_func(A_					,					A_					,					A_    )
           return self._measure_speed(_train    )



     def 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_    )			->					[Memory, Optional[MemorySummary]]:






           """simple docstring"""
           _lowerCamelCase           =					self._prepare_train_func(A_					,					A_					,					A_    )
           return self._measure_memory(_train    )



     def 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_    )			->					Callable[[], None]:






           """simple docstring"""
           _lowerCamelCase           =					self.config_dict[model_name]

           if self.args.torchscript:
                 _lowerCamelCase           =					True

           _lowerCamelCase           =					(
               hasattr(A_					,					'''architectures'''    )
               and isinstance(config.architectures					,					A_    )
               and len(config.architectures    ) > 0
           )
           if not self.args.only_pretrain_model and has_model_class_in_config:
                 try:
                       _lowerCamelCase           =					config.architectures[0]
                       _lowerCamelCase           =					__import__('''transformers'''					,					fromlist=[model_class]    )
                       _lowerCamelCase           =					getattr(A_					,					A_    )
                       _lowerCamelCase           =					model_cls(A_    )
                 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:
                 _lowerCamelCase           =					MODEL_MAPPING[config.__class__](A_    )

           model.eval()
           model.to(self.args.device    )

           # encoder-decoder has vocab size saved differently
           _lowerCamelCase           =					config.vocab_size if hasattr(A_					,					'''vocab_size'''    ) else config.encoder.vocab_size
           _lowerCamelCase           =					torch.randint(A_					,					(batch_size, sequence_length)					,					dtype=torch.long					,					device=self.args.device    )

           if self.args.fpaa:
                 logger.info('''Running training in Mixed Precision...'''    )
                 if not self.args.is_gpu:
                       raise ValueError('''Mixed precision is possible only for GPU.'''    )
                 # amp seems to have memory leaks so that memory usage
                 # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
                 model.half()

           if self.args.torchscript:
                 with torch.no_grad():
                       _lowerCamelCase           =					torch.jit.trace(A_					,					A_    )
           else:
                 _lowerCamelCase           =					model

           def encoder_decoder_forward():
                 with torch.no_grad():
                       _lowerCamelCase           =					inference_model(A_					,					decoder_input_ids=A_    )
                 return outputs

           def encoder_forward():
                 with torch.no_grad():
                       _lowerCamelCase           =					inference_model(A_    )
                 return outputs

           _lowerCamelCase           =					encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
           return _forward



     def 		UpperCamelCase_    (       self					,					A_					,					A_					,					A_    )			->					Callable[[], None]:






           """simple docstring"""
           _lowerCamelCase           =					self.config_dict[model_name]

           _lowerCamelCase           =					(
               hasattr(A_					,					'''architectures'''    )
               and isinstance(config.architectures					,					A_    )
               and len(config.architectures    ) > 0
           )
           if not self.args.only_pretrain_model and has_model_class_in_config:
                 try:
                       _lowerCamelCase           =					config.architectures[0]
                       _lowerCamelCase           =					__import__('''transformers'''					,					fromlist=[model_class]    )
                       _lowerCamelCase           =					getattr(A_					,					A_    )
                       _lowerCamelCase           =					model_cls(A_    )
                 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:
                 _lowerCamelCase           =					MODEL_WITH_LM_HEAD_MAPPING[config.__class__](A_    )

           if self.args.torchscript:
                 raise NotImplementedError('''Training for torchscript is currently not implemented'''    )
           else:
                 _lowerCamelCase           =					model

           model.train()
           model.to(self.args.device    )

           # encoder-decoder has vocab size saved differently
           _lowerCamelCase           =					config.vocab_size if hasattr(A_					,					'''vocab_size'''    ) else config.encoder.vocab_size
           _lowerCamelCase           =					torch.randint(A_					,					(batch_size, sequence_length)					,					dtype=torch.long					,					device=self.args.device    )

           if self.args.fpaa:
                 logger.info('''Running training in Mixed Precision...'''    )
                 if not self.args.is_gpu:
                       raise ValueError('''Mixed precision is possible only for GPU.'''    )

                 # amp seems to have memory leaks so that memory usage
                 # is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
                 model.half()

           def compute_loss_and_backprob_encoder():
                 _lowerCamelCase           =					train_model(A_					,					labels=A_    )[0]
                 loss.backward()
                 return loss

           def compute_loss_and_backprob_encoder_decoder():
                 _lowerCamelCase           =					train_model(A_					,					decoder_input_ids=A_					,					labels=A_    )[0]
                 loss.backward()
                 return loss

           _lowerCamelCase           =					(
               compute_loss_and_backprob_encoder_decoder
               if config.is_encoder_decoder
               else compute_loss_and_backprob_encoder
           )
           return _train



     def 		UpperCamelCase_    (       self					,					A_    )			->					float:






           """simple docstring"""
           try:
                 if self.args.is_tpu or self.args.torchscript:
                       # run additional 10 times to stabilize compilation for tpu and torchscript
                       logger.info('''Do inference on TPU or torchscript. Running model 5 times to stabilize compilation'''    )
                       timeit.repeat(
                           A_					,					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
                 _lowerCamelCase           =					timeit.repeat(
                     A_					,					repeat=self.args.repeat					,					number=10					,					)

                 if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
                       import torch_xla.debug.metrics as met

                       self.print_fn(met.metrics_report()    )

                 return min(A_    ) / 10.0
           except RuntimeError as e:
                 self.print_fn(F'Doesn\'t fit on GPU. {e}'    )
                 return "N/A"





     def 		UpperCamelCase_    (       self					,					A_    )			->					[Memory, MemorySummary]:






           """simple docstring"""
           try:
                 if self.args.trace_memory_line_by_line:
                       _lowerCamelCase           =					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'''
                           ''' `--no-memory` or `args.memory=False`'''    )
                 elif self.args.is_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.'''    )
                             _lowerCamelCase           =					'''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()
                             _lowerCamelCase           =					nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx    )
                             _lowerCamelCase           =					nvml.nvmlDeviceGetMemoryInfo(A_    )
                             _lowerCamelCase           =					meminfo.used
                             _lowerCamelCase           =					Memory(A_    )
                             # shutdown nvml
                             nvml.nvmlShutdown()
                 else:
                       # cpu
                       _lowerCamelCase           =					measure_peak_memory_cpu(A_    )
                       _lowerCamelCase           =					Memory(A_    ) if isinstance(A_					,					A_    ) else memory_bytes

                 if self.args.trace_memory_line_by_line:
                       _lowerCamelCase           =					stop_memory_tracing(A_    )
                 else:
                       _lowerCamelCase           =					None

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






'''simple docstring'''







import argparse
import json
import re
import string
import sys
from collections import Counter





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Optional[Any] )			->						str:

						def remove_articles(SCREAMING_SNAKE_CASE__   :      Union[str, Any] ):
												return re.sub(R'''\b(a|an|the)\b''',      ''' ''',      SCREAMING_SNAKE_CASE__ )

						def white_space_fix(SCREAMING_SNAKE_CASE__   :      Dict ):
												return " ".join(text.split() )

						def remove_punc(SCREAMING_SNAKE_CASE__   :      Union[str, Any] ):
												UpperCAmelCase_      :   int      =	set(string.punctuation )
												return "".join(ch for ch in text if ch not in exclude )

						def lower(SCREAMING_SNAKE_CASE__   :      str ):
												return text.lower()

						return white_space_fix(remove_articles(remove_punc(lower(SCREAMING_SNAKE_CASE__ ) ) ) )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Union[str, Any],      SCREAMING_SNAKE_CASE__   :      Union[str, Any] )			->						Tuple:
						UpperCAmelCase_      :   List[Any]      =	normalize_answer(SCREAMING_SNAKE_CASE__ ).split()
						UpperCAmelCase_      :   Optional[Any]      =	normalize_answer(SCREAMING_SNAKE_CASE__ ).split()
						UpperCAmelCase_      :   Any      =	Counter(SCREAMING_SNAKE_CASE__ ) & Counter(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Tuple      =	sum(common.values() )
						if num_same == 0:
												return 0
						UpperCAmelCase_      :   Union[str, Any]      =	1.0 * num_same / len(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   Dict      =	1.0 * num_same / len(SCREAMING_SNAKE_CASE__ )
						UpperCAmelCase_      :   str      =	(2 * precision * recall) / (precision + recall)
						return fa





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      int,      SCREAMING_SNAKE_CASE__   :      int )			->						Any:
						return normalize_answer(SCREAMING_SNAKE_CASE__ ) == normalize_answer(SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      Union[str, Any],      SCREAMING_SNAKE_CASE__   :      Any,      SCREAMING_SNAKE_CASE__   :      Dict )			->						Optional[int]:
						UpperCAmelCase_      :   Dict      =	[]
						for ground_truth in ground_truths:
												UpperCAmelCase_      :   str      =	metric_fn(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
												scores_for_ground_truths.append(SCREAMING_SNAKE_CASE__ )
						return max(SCREAMING_SNAKE_CASE__ )





def 							lowerCamelCase_   (	SCREAMING_SNAKE_CASE__   :      List[str],      SCREAMING_SNAKE_CASE__   :      Optional[Any] )			->						Any:
						UpperCAmelCase_      :   str      =	0
						for article in dataset:
												for paragraph in article["paragraphs"]:
																		for qa in paragraph["qas"]:
																								total += 1
																								if qa["id"] not in predictions:
																														UpperCAmelCase_      :   Union[str, Any]      =	'''Unanswered question ''' + qa['''id'''] + ''' will receive score 0.'''
																														print(SCREAMING_SNAKE_CASE__,      file=sys.stderr )
																														continue
																								UpperCAmelCase_      :   Optional[Any]      =	[x['''text'''] for x in qa['''answers''']]
																								UpperCAmelCase_      :   int      =	predictions[qa['''id''']]
																								exact_match += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )
																								fa += metric_max_over_ground_truths(SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__,      SCREAMING_SNAKE_CASE__ )

						UpperCAmelCase_      :   Any      =	1_00.0 * exact_match / total
						UpperCAmelCase_      :   Optional[int]      =	1_00.0 * fa / total

						return {"exact_match": exact_match, "f1": fa}


if __name__ == "__main__":
					snake_case_  :							Any            =	"1.1"
					snake_case_  :							Any            =	argparse.ArgumentParser(description="Evaluation for SQuAD " + expected_version)
					parser.add_argument("dataset_file", help="Dataset file")
					parser.add_argument("prediction_file", help="Prediction File")
					snake_case_  :							int            =	parser.parse_args()
					with open(args.dataset_file) as dataset_file:
										snake_case_  :							List[Any]            =	json.load(dataset_file)
										if dataset_json["version"] != expected_version:
															print(
															    "Evaluation expects v-" + expected_version + ", but got dataset with v-" + dataset_json["version"],
															    file=sys.stderr,
															)
										snake_case_  :							Tuple            =	dataset_json["data"]
					with open(args.prediction_file) as prediction_file:
										snake_case_  :							Tuple            =	json.load(prediction_file)
					print(json.dumps(evaluate(dataset, predictions)))





