




"""simple docstring"""

import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
   from ... import PreTrainedTokenizerBase, TensorType

a         =      logging.get_logger(__name__)





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					int				  =     '''vision-encoder-decoder'''
 UpperCAmelCase   :					List[Any]				  =     True

 def __init__(			self	:	List[str]   ,							**_UpperCAmelCase	:	Tuple		):
       super().__init__(**_UpperCAmelCase		)
       if "encoder" not in kwargs or "decoder" not in kwargs:
             raise ValueError(
                 F'''A configuraton of type {self.model_type} cannot be instantiated because '''
                 F'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''		)

       _A        =       kwargs.pop('encoder'		)
       _A        =       encoder_config.pop('model_type'		)
       _A        =       kwargs.pop('decoder'		)
       _A        =       decoder_config.pop('model_type'		)

       _A        =       AutoConfig.for_model(_UpperCAmelCase   ,							**_UpperCAmelCase		)
       _A        =       AutoConfig.for_model(_UpperCAmelCase   ,							**_UpperCAmelCase		)
       _A        =       True

 @classmethod
 def       lowerCAmelCase_							(			cls	:	Tuple   ,							_UpperCAmelCase	:	PretrainedConfig   ,							_UpperCAmelCase	:	PretrainedConfig   ,							**_UpperCAmelCase	:	List[str]		):
       logger.info('Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'		)
       _A        =       True
       _A        =       True

       return cls(encoder=encoder_config.to_dict()   ,							decoder=decoder_config.to_dict()   ,							**_UpperCAmelCase		)



 def       lowerCAmelCase_							(			self	:	Any		):
       _A        =       copy.deepcopy(self.__dict__		)
       _A        =       self.encoder.to_dict()
       _A        =       self.decoder.to_dict()
       _A        =       self.__class__.model_type
       return output





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Dict				  =     version.parse('''1.11'''							)

 @property
 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       return OrderedDict(
           [
               ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
           ]		)

 @property
 def       lowerCAmelCase_							(			self	:	Any		):
       return 1E-4



 @property
 def       lowerCAmelCase_							(			self	:	Tuple		):
       return OrderedDict({'last_hidden_state': {0: 'batch', 1: 'encoder_sequence'}}		)





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''

 @property
 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       _A        =       OrderedDict()
       _A        =       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
       _A        =       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
       _A        =       {0: 'batch', 1: 'encoder_sequence'}

       return common_inputs



 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	"PreTrainedTokenizerBase"   ,							_UpperCAmelCase	:	int = -1   ,							_UpperCAmelCase	:	int = -1   ,							_UpperCAmelCase	:	bool = False   ,							_UpperCAmelCase	:	Optional["TensorType"] = None   ,							):
       import torch

       _A        =       OrderedDict()

       _A        =       super().generate_dummy_inputs(
           _UpperCAmelCase   ,							batch_size=_UpperCAmelCase   ,							seq_length=_UpperCAmelCase   ,							is_pair=_UpperCAmelCase   ,							framework=_UpperCAmelCase		)

       _A       ,   _A        =       dummy_input['input_ids'].shape
       _A        =       (batch, encoder_sequence, self._config.encoder_hidden_size)
       _A        =       dummy_input.pop('input_ids'		)
       _A        =       dummy_input.pop('attention_mask'		)
       _A        =       torch.zeros(_UpperCAmelCase		)

       return common_inputs





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''

 @property
 def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
       pass

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	PretrainedConfig		):
       return VisionEncoderDecoderEncoderOnnxConfig(_UpperCAmelCase		)



 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	PretrainedConfig   ,							_UpperCAmelCase	:	PretrainedConfig   ,							_UpperCAmelCase	:	str = "default"		):
       _A        =       encoder_config.hidden_size
       return VisionEncoderDecoderDecoderOnnxConfig(_UpperCAmelCase   ,							_UpperCAmelCase		)







"""simple docstring"""

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         =      logging.get_logger(__name__)






def 						_snake_case					( _snake_case							: bool    ,  _snake_case							: bool							)   -> Tuple:






      '''simple docstring'''


      def run_func(_snake_case							: Any							):
            @wraps(_snake_case							)
            def run_in_eager_mode(*_snake_case							: List[str]    ,  **_snake_case							: Tuple							):
                  return func(*_snake_case    ,  **_snake_case							)

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

            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 						_snake_case					( _snake_case							: int    ,  _snake_case							: int    ,  _snake_case							: int							)   -> ["tf.Tensor"]:






      '''simple docstring'''


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







class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					TensorFlowBenchmarkArguments
 UpperCAmelCase   :					PretrainedConfig
 UpperCAmelCase   :					str				  =     "TensorFlow"

 @property
 def       lowerCAmelCase_							(			self	:	str		):
       return tf.__version__

 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       # initialize GPU on separate process
       _A        =       self.args.strategy
       if strategy is None:
             raise ValueError('A device strategy has to be initialized before using TensorFlow.'		)
       _A        =       self._prepare_inference_func(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
       return self._measure_speed(_inference		)

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       _A        =       self.args.strategy
       if strategy is None:
             raise ValueError('A device strategy has to be initialized before using TensorFlow.'		)
       _A        =       self._prepare_train_func(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
       return self._measure_speed(_train		)

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       # initialize GPU on separate process
       if self.args.is_gpu:
             tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]   ,							_UpperCAmelCase		)
       _A        =       self.args.strategy
       if strategy is None:
             raise ValueError('A device strategy has to be initialized before using TensorFlow.'		)
       _A        =       self._prepare_inference_func(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
       return self._measure_memory(_inference		)

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

       _A        =       self._prepare_train_func(_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase		)
       return self._measure_memory(_train		)

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       _A        =       self.config_dict[model_name]

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

       _A        =       (
           hasattr(_UpperCAmelCase   ,							'architectures'		)
           and isinstance(config.architectures   ,							_UpperCAmelCase		)
           and len(config.architectures		) > 0
       )
       if not self.args.only_pretrain_model and has_model_class_in_config:
             try:
                   _A        =       'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
                   _A        =       __import__('transformers'   ,							fromlist=[model_class]		)
                   _A        =       getattr(_UpperCAmelCase   ,							_UpperCAmelCase		)
                   _A        =       model_cls(_UpperCAmelCase		)
             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:
             _A        =       TF_MODEL_MAPPING[config.__class__](_UpperCAmelCase		)

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

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

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

       _A        =       encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

       return _inference

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	int		):
       _A        =       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.'		)

       _A        =       (
           hasattr(_UpperCAmelCase   ,							'architectures'		)
           and isinstance(config.architectures   ,							_UpperCAmelCase		)
           and len(config.architectures		) > 0
       )
       if not self.args.only_pretrain_model and has_model_class_in_config:
             try:
                   _A        =       'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
                   _A        =       __import__('transformers'   ,							fromlist=[model_class]		)
                   _A        =       getattr(_UpperCAmelCase   ,							_UpperCAmelCase		)
                   _A        =       model_cls(_UpperCAmelCase		)
             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:
             _A        =       TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_UpperCAmelCase		)

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

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

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

       _A        =       encoder_decoder_train if config.is_encoder_decoder else encoder_train

       return _train

 def       lowerCAmelCase_							(			self	:	Tuple   ,							_UpperCAmelCase	:	int		):
       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(_UpperCAmelCase   ,							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
                   _A        =       timeit.repeat(
                       _UpperCAmelCase   ,							repeat=self.args.repeat   ,							number=10   ,							)

                   return min(_UpperCAmelCase		) / 10.0
             except ResourceExhaustedError as e:
                   self.print_fn(F'''Doesn\'t fit on GPU. {e}'''		)



 def       lowerCAmelCase_							(			self	:	Optional[int]   ,							_UpperCAmelCase	:	Callable[[], None]		):
       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.'		)
                         _A        =       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.'		)
                               _A        =       '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()
                               _A        =       nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx		)
                               _A        =       nvml.nvmlDeviceGetMemoryInfo(_UpperCAmelCase		)
                               _A        =       meminfo.used
                               _A        =       Memory(_UpperCAmelCase		)
                               # 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.'		)
                               _A        =       None
                         else:
                               _A        =       measure_peak_memory_cpu(_UpperCAmelCase		)
                               _A        =       Memory(_UpperCAmelCase		) if isinstance(_UpperCAmelCase   ,							_UpperCAmelCase		) else memory_bytes
                   if self.args.trace_memory_line_by_line:
                         _A        =       stop_memory_tracing(_UpperCAmelCase		)
                         if memory is None:
                               _A        =       summary.total
                   else:
                         _A        =       None

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

