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


UpperCAmelCase      		=					logging.get_logger(__name__)




class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						PyTorchBenchmarkArguments
 _lowerCamelCase    :						PretrainedConfig
 _lowerCamelCase    :						str					     =			"PyTorch"

 @property
 def        __snake_case       (      self							:		Any	)			->				Any:
        return torch.__version__

 def        __snake_case       (      self							:		Dict							,      lowerCamelCase							:		str							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int	)			->				float:
        _UpperCamelCase  :							Tuple	    =	self._prepare_inference_func(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)
        return self._measure_speed(_inference	)

 def        __snake_case       (      self							:		int							,      lowerCamelCase							:		str							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int	)			->				[Memory, Optional[MemorySummary]]:
        _UpperCamelCase  :							Dict	    =	self._prepare_inference_func(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)
        return self._measure_memory(_inference	)

 def        __snake_case       (      self							:		Union[str, Any]							,      lowerCamelCase							:		str							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int	)			->				float:
        _UpperCamelCase  :							Optional[Any]	    =	self._prepare_train_func(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)
        return self._measure_speed(_train	)

 def        __snake_case       (      self							:		List[Any]							,      lowerCamelCase							:		str							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int	)			->				[Memory, Optional[MemorySummary]]:
        _UpperCamelCase  :							Union[str, Any]	    =	self._prepare_train_func(lowerCamelCase							,      lowerCamelCase							,      lowerCamelCase	)
        return self._measure_memory(_train	)

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		str							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int	)			->				Callable[[], None]:
        _UpperCamelCase  :							List[str]	    =	self.config_dict[model_name]

        if self.args.torchscript:
               _UpperCamelCase  :							int	    =	True

        _UpperCamelCase  :							int	    =	(
            hasattr(lowerCamelCase							,      'architectures'	)
            and isinstance(config.architectures							,      lowerCamelCase	)
            and len(config.architectures	) > 0
        )
        if not self.args.only_pretrain_model and has_model_class_in_config:
               try:
                      _UpperCamelCase  :							List[str]	    =	config.architectures[0]
                      _UpperCamelCase  :							Union[str, Any]	    =	__import__('transformers'							,      fromlist=[model_class]	)
                      _UpperCamelCase  :							Dict	    =	getattr(lowerCamelCase							,      lowerCamelCase	)
                      _UpperCamelCase  :							List[str]	    =	model_cls(lowerCamelCase	)
               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:
               _UpperCamelCase  :							List[str]	    =	MODEL_MAPPING[config.__class__](lowerCamelCase	)

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

        # encoder-decoder has vocab size saved differently
        _UpperCamelCase  :							Any	    =	config.vocab_size if hasattr(lowerCamelCase							,      'vocab_size'	) else config.encoder.vocab_size
        _UpperCamelCase  :							List[str]	    =	torch.randint(lowerCamelCase							,      (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():
                      _UpperCamelCase  :							str	    =	torch.jit.trace(lowerCamelCase							,      lowerCamelCase	)
        else:
               _UpperCamelCase  :							Union[str, Any]	    =	model

        def encoder_decoder_forward():
               with torch.no_grad():
                      _UpperCamelCase  :							Tuple	    =	inference_model(lowerCamelCase							,      decoder_input_ids=lowerCamelCase	)
               return outputs

        def encoder_forward():
               with torch.no_grad():
                      _UpperCamelCase  :							Tuple	    =	inference_model(lowerCamelCase	)
               return outputs

        _UpperCamelCase  :							int	    =	encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
        return _forward

 def        __snake_case       (      self							:		Tuple							,      lowerCamelCase							:		str							,      lowerCamelCase							:		int							,      lowerCamelCase							:		int	)			->				Callable[[], None]:
        _UpperCamelCase  :							int	    =	self.config_dict[model_name]

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

        if self.args.torchscript:
               raise NotImplementedError('Training for torchscript is currently not implemented'	)
        else:
               _UpperCamelCase  :							List[str]	    =	model

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

        # encoder-decoder has vocab size saved differently
        _UpperCamelCase  :							Optional[Any]	    =	config.vocab_size if hasattr(lowerCamelCase							,      'vocab_size'	) else config.encoder.vocab_size
        _UpperCamelCase  :							Union[str, Any]	    =	torch.randint(lowerCamelCase							,      (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():
               _UpperCamelCase  :							List[str]	    =	train_model(lowerCamelCase							,      labels=lowerCamelCase	)[0]
               loss.backward()
               return loss

        def compute_loss_and_backprob_encoder_decoder():
               _UpperCamelCase  :							List[Any]	    =	train_model(lowerCamelCase							,      decoder_input_ids=lowerCamelCase							,      labels=lowerCamelCase	)[0]
               loss.backward()
               return loss

        _UpperCamelCase  :							Optional[int]	    =	(
            compute_loss_and_backprob_encoder_decoder
            if config.is_encoder_decoder
            else compute_loss_and_backprob_encoder
        )
        return _train

 def        __snake_case       (      self							:		Any							,      lowerCamelCase							:		List[Any]	)			->				float:
        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(
                          lowerCamelCase							,      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
               _UpperCamelCase  :							int	    =	timeit.repeat(
                   lowerCamelCase							,      repeat=self.args.repeat							,      number=1_0							,      )

               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(lowerCamelCase	) / 1_0.0
        except RuntimeError as e:
               self.print_fn(f"Doesn't fit on GPU. {e}"	)
               return "N/A"

 def        __snake_case       (      self							:		int							,      lowerCamelCase							:		Callable[[], None]	)			->				[Memory, MemorySummary]:
        try:
               if self.args.trace_memory_line_by_line:
                      _UpperCamelCase  :							List[str]	    =	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.'	)
                             _UpperCamelCase  :							int	    =	'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()
                             _UpperCamelCase  :							List[str]	    =	nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx	)
                             _UpperCamelCase  :							List[Any]	    =	nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase	)
                             _UpperCamelCase  :							Dict	    =	meminfo.used
                             _UpperCamelCase  :							str	    =	Memory(lowerCamelCase	)
                             # shutdown nvml
                             nvml.nvmlShutdown()
               else:
                      # cpu
                      _UpperCamelCase  :							str	    =	measure_peak_memory_cpu(lowerCamelCase	)
                      _UpperCamelCase  :							List[str]	    =	Memory(lowerCamelCase	) if isinstance(lowerCamelCase							,      lowerCamelCase	) else memory_bytes

               if self.args.trace_memory_line_by_line:
                      _UpperCamelCase  :							Optional[int]	    =	stop_memory_tracing(lowerCamelCase	)
               else:
                      _UpperCamelCase  :							int	    =	None

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







"""simple docstring"""





from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case__		:  Optional[Any]     					=					logging.get_logger(__name__)

snake_case__		:  str     					=					{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}



class 		snake_case_(							a__      ):
     __UpperCamelCase	=       '''prophetnet'''
     __UpperCamelCase	=       ['''past_key_values''']
     __UpperCamelCase	=       {
         '''num_attention_heads''': '''num_encoder_attention_heads''',
     }






     def __init__(						self	:       Union[str, Any]       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[Union[str, Callable]] = "gelu"       ,					UpperCamelCase_	:       Optional[int] = 3_0_5_2_2       ,					UpperCamelCase_	:       Optional[int] = 1_0_2_4       ,					UpperCamelCase_	:       Optional[int] = 4_0_9_6       ,					UpperCamelCase_	:       Optional[int] = 1_2       ,					UpperCamelCase_	:       Optional[int] = 1_6       ,					UpperCamelCase_	:       Optional[int] = 4_0_9_6       ,					UpperCamelCase_	:       Optional[int] = 1_2       ,					UpperCamelCase_	:       Optional[int] = 1_6       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[float] = 0.1       ,					UpperCamelCase_	:       Optional[int] = 5_1_2       ,					UpperCamelCase_	:       Optional[float] = 0.02       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[int] = 0       ,					UpperCamelCase_	:       Optional[int] = 2       ,					UpperCamelCase_	:       Optional[int] = 3_2       ,					UpperCamelCase_	:       Optional[int] = 1_2_8       ,					UpperCamelCase_	:       Optional[bool] = False       ,					UpperCamelCase_	:       Optional[float] = 0.0       ,					UpperCamelCase_	:       Optional[bool] = True       ,					UpperCamelCase_	:       Optional[int] = 0       ,					UpperCamelCase_	:       Optional[int] = 1       ,					UpperCamelCase_	:       Optional[int] = 2       ,					**UpperCamelCase_	:       List[Any]       ,					):
           lowerCAmelCase       :						Union[str, Any]							= vocab_size
           lowerCAmelCase       :						Any							= hidden_size
           lowerCAmelCase       :						Tuple							= encoder_ffn_dim
           lowerCAmelCase       :						str							= num_encoder_layers
           lowerCAmelCase       :						int							= num_encoder_attention_heads
           lowerCAmelCase       :						Optional[Any]							= decoder_ffn_dim
           lowerCAmelCase       :						Union[str, Any]							= num_decoder_layers
           lowerCAmelCase       :						Union[str, Any]							= num_decoder_attention_heads
           lowerCAmelCase       :						List[Any]							= max_position_embeddings
           lowerCAmelCase       :						Dict							= init_std  # Normal(0, this parameter)
           lowerCAmelCase       :						Optional[int]							= activation_function

           # parameters for prophetnet
           lowerCAmelCase       :						Tuple							= ngram
           lowerCAmelCase       :						List[Any]							= num_buckets
           lowerCAmelCase       :						Optional[Any]							= relative_max_distance
           lowerCAmelCase       :						Dict							= disable_ngram_loss
           lowerCAmelCase       :						Optional[int]							= eps

           # 3 Types of Dropout
           lowerCAmelCase       :						Any							= attention_dropout
           lowerCAmelCase       :						List[Any]							= activation_dropout
           lowerCAmelCase       :						Tuple							= dropout

           lowerCAmelCase       :						Optional[int]							= use_cache

           super().__init__(
               pad_token_id=UpperCamelCase_       ,					bos_token_id=UpperCamelCase_       ,					eos_token_id=UpperCamelCase_       ,					is_encoder_decoder=UpperCamelCase_       ,					add_cross_attention=UpperCamelCase_       ,					decoder_start_token_id=UpperCamelCase_       ,					**UpperCamelCase_       ,					)






     @property
     def   lowerCamelCase__  (						self	:       str					):
           return self.num_encoder_layers + self.num_decoder_layers






     @num_hidden_layers.setter
     def   lowerCamelCase__  (						self	:       Optional[Any]       ,					UpperCamelCase_	:       int					):
           raise NotImplementedError(
               '''This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'''
               ''' `num_decoder_layers`.'''					)


