
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_				(  A     ):




      '''simple docstring'''







      a		:Tuple						 =      42
      a		:Union[str, Any]						 =      42
      a		:int						 =      'PyTorch'







      @property
      def lowercase__		( self):
            return torch.__version__







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      self._prepare_inference_func(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_)
            return self._measure_speed(_inference)







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      self._prepare_inference_func(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_)
            return self._measure_memory(_inference)







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      self._prepare_train_func(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_)
            return self._measure_speed(_train)







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      self._prepare_train_func(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_)
            return self._measure_memory(_train)







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      self.config_dict[model_name]

            if self.args.torchscript:
                  lowerCAmelCase_						=      True

            lowerCAmelCase_						=      (
                hasattr(SCREAMING_SNAKE_CASE_					,					'''architectures''')
                and isinstance(config.architectures					,					SCREAMING_SNAKE_CASE_)
                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(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_)
                        lowerCAmelCase_						=      model_cls(SCREAMING_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:
                  lowerCAmelCase_						=      MODEL_MAPPING[config.__class__](SCREAMING_SNAKE_CASE_)

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

            # encoder-decoder has vocab size saved differently
            lowerCAmelCase_						=      config.vocab_size if hasattr(SCREAMING_SNAKE_CASE_					,					'''vocab_size''') else config.encoder.vocab_size
            lowerCAmelCase_						=      torch.randint(SCREAMING_SNAKE_CASE_					,					(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(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_)
            else:
                  lowerCAmelCase_						=      model

            def encoder_decoder_forward():
                  with torch.no_grad():
                        lowerCAmelCase_						=      inference_model(SCREAMING_SNAKE_CASE_					,					decoder_input_ids=SCREAMING_SNAKE_CASE_)
                  return outputs

            def encoder_forward():
                  with torch.no_grad():
                        lowerCAmelCase_						=      inference_model(SCREAMING_SNAKE_CASE_)
                  return outputs

            lowerCAmelCase_						=      encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
            return _forward







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase):
            lowerCAmelCase_						=      self.config_dict[model_name]

            lowerCAmelCase_						=      (
                hasattr(SCREAMING_SNAKE_CASE_					,					'''architectures''')
                and isinstance(config.architectures					,					SCREAMING_SNAKE_CASE_)
                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(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_)
                        lowerCAmelCase_						=      model_cls(SCREAMING_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:
                  lowerCAmelCase_						=      MODEL_WITH_LM_HEAD_MAPPING[config.__class__](SCREAMING_SNAKE_CASE_)

            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(SCREAMING_SNAKE_CASE_					,					'''vocab_size''') else config.encoder.vocab_size
            lowerCAmelCase_						=      torch.randint(SCREAMING_SNAKE_CASE_					,					(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(SCREAMING_SNAKE_CASE_					,					labels=SCREAMING_SNAKE_CASE_)[0]
                  loss.backward()
                  return loss

            def compute_loss_and_backprob_encoder_decoder():
                  lowerCAmelCase_						=      train_model(SCREAMING_SNAKE_CASE_					,					decoder_input_ids=SCREAMING_SNAKE_CASE_					,					labels=SCREAMING_SNAKE_CASE_)[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 lowercase__		( self					,					_UpperCAmelCase):
            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(
                            SCREAMING_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
                  lowerCAmelCase_						=      timeit.repeat(
                      SCREAMING_SNAKE_CASE_					,					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(SCREAMING_SNAKE_CASE_) / 10.0
            except RuntimeError as e:
                  self.print_fn(f'Doesn\'t fit on GPU. {e}')
                  return "N/A"







      def lowercase__		( self					,					_UpperCAmelCase):
            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(SCREAMING_SNAKE_CASE_)
                              lowerCAmelCase_						=      meminfo.used
                              lowerCAmelCase_						=      Memory(SCREAMING_SNAKE_CASE_)
                              # shutdown nvml
                              nvml.nvmlShutdown()
                  else:
                        # cpu
                        lowerCAmelCase_						=      measure_peak_memory_cpu(SCREAMING_SNAKE_CASE_)
                        lowerCAmelCase_						=      Memory(SCREAMING_SNAKE_CASE_) if isinstance(SCREAMING_SNAKE_CASE_					,					SCREAMING_SNAKE_CASE_) else memory_bytes

                  if self.args.trace_memory_line_by_line:
                        lowerCAmelCase_						=      stop_memory_tracing(SCREAMING_SNAKE_CASE_)
                  else:
                        lowerCAmelCase_						=      None

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





from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCamelCase_				:							Union[str, Any]       =    logging.get_logger(__name__)

lowerCamelCase_				:							Optional[Any]       =    {
    "google/long-t5-local-base": "https://huggingface.co/google/long-t5-local-base/blob/main/config.json",
    "google/long-t5-local-large": "https://huggingface.co/google/long-t5-local-large/blob/main/config.json",
    "google/long-t5-tglobal-base": "https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json",
    "google/long-t5-tglobal-large": "https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json",
}







class  _lowerCamelCase			(lowerCamelCase					):
     lowercase__				  =			"""longt5"""
     lowercase__				  =			["""past_key_values"""]
     lowercase__				  =			{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

     def __init__(  self							,       SCREAMING_SNAKE_CASE_=32_128							,       SCREAMING_SNAKE_CASE_=512							,       SCREAMING_SNAKE_CASE_=64							,       SCREAMING_SNAKE_CASE_=2_048							,       SCREAMING_SNAKE_CASE_=6							,       SCREAMING_SNAKE_CASE_=None							,       SCREAMING_SNAKE_CASE_=8							,       SCREAMING_SNAKE_CASE_=127							,       SCREAMING_SNAKE_CASE_=16							,       SCREAMING_SNAKE_CASE_=32							,       SCREAMING_SNAKE_CASE_=128							,       SCREAMING_SNAKE_CASE_=0.1							,       SCREAMING_SNAKE_CASE_=1E-6							,       SCREAMING_SNAKE_CASE_=1.0							,       SCREAMING_SNAKE_CASE_="relu"							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_="local"							,       SCREAMING_SNAKE_CASE_=True							,       SCREAMING_SNAKE_CASE_=0							,       SCREAMING_SNAKE_CASE_=1							,       **SCREAMING_SNAKE_CASE_							,       ):
            __snake_case						       =   vocab_size
            __snake_case						       =   d_model
            __snake_case						       =   d_kv
            __snake_case						       =   d_ff
            __snake_case						       =   num_layers
            # default = symmetry
            __snake_case						       =   num_decoder_layers if num_decoder_layers is not None else self.num_layers
            __snake_case						       =   num_heads
            __snake_case						       =   local_radius
            __snake_case						       =   global_block_size
            __snake_case						       =   relative_attention_num_buckets
            __snake_case						       =   relative_attention_max_distance
            __snake_case						       =   dropout_rate
            __snake_case						       =   layer_norm_epsilon
            __snake_case						       =   initializer_factor
            __snake_case						       =   feed_forward_proj
            __snake_case						       =   encoder_attention_type
            __snake_case						       =   use_cache

            __snake_case						       =   self.feed_forward_proj.split('-'    )
            __snake_case						       =   act_info[-1]
            __snake_case						       =   act_info[0] == 'gated'

            if len(SCREAMING_SNAKE_CASE_    ) > 1 and act_info[0] != "gated" or len(SCREAMING_SNAKE_CASE_    ) > 2:
                   raise ValueError(
                       f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
                       'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                       '\'gated-gelu\' or \'relu\''    )

            # for backwards compatibility
            if feed_forward_proj == "gated-gelu":
                   __snake_case						       =   'gelu_new'

            super().__init__(
                pad_token_id=SCREAMING_SNAKE_CASE_							,       eos_token_id=SCREAMING_SNAKE_CASE_							,       is_encoder_decoder=SCREAMING_SNAKE_CASE_							,       **SCREAMING_SNAKE_CASE_							,       )







class  _lowerCamelCase			(lowerCamelCase					):

     @property
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   {
                'input_ids': {0: 'batch', 1: 'encoder_sequence'},
                'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
            }
            if self.use_past:
                   __snake_case						       =   'past_encoder_sequence + sequence'
                   __snake_case						       =   {0: 'batch'}
                   __snake_case						       =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                   __snake_case						       =   {0: 'batch', 1: 'decoder_sequence'}
                   __snake_case						       =   {0: 'batch', 1: 'decoder_sequence'}

            if self.use_past:
                   self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_							,       direction='inputs'    )

            return common_inputs

     @property
     def 						__lowerCamelCase				(  self    ):
            return 13
