

"""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   _A  (  UpperCamelCase_					:	bool,      UpperCamelCase_					:	bool)     ->						Optional[int]:
   '''simple docstring'''


   def run_func(UpperCamelCase_					:	int):
      @wraps(UpperCamelCase_)
      def run_in_eager_mode(*UpperCamelCase_					:	Union[str, Any],      **UpperCamelCase_					:	Optional[Any]):
         return func(*UpperCamelCase_,      **UpperCamelCase_)

      @wraps(UpperCamelCase_)
      @tf.function(experimental_compile=UpperCamelCase_)
      def run_in_graph_mode(*UpperCamelCase_					:	int,      **UpperCamelCase_					:	Union[str, Any]):
         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   _A  (  UpperCamelCase_					:	int,      UpperCamelCase_					:	int,      UpperCamelCase_					:	int)     ->						["tf.Tensor"]:
   '''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  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : TensorFlowBenchmarkArguments
 __UpperCAmelCase     : PretrainedConfig
 __UpperCAmelCase     : str       		=     "TensorFlow"


 @property
 def  _lowercase			(      self							:      Union[str, Any]     ):
    return tf.__version__


 def  _lowercase			(      self							:      Optional[int], UpperCAmelCase__							:      str, UpperCAmelCase__							:      int, UpperCAmelCase__							:      int     ):
    # initialize GPU on separate process
    __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(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__     )
    return self._measure_speed(_inference     )


 def  _lowercase			(      self							:      int, UpperCAmelCase__							:      str, UpperCAmelCase__							:      int, UpperCAmelCase__							:      int     ):
    __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(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__     )
    return self._measure_speed(_train     )


 def  _lowercase			(      self							:      Any, 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__     )
    __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(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__     )
    return self._measure_memory(_inference     )


 def  _lowercase			(      self							:      Optional[Any], 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__     )
    __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(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__     )
    return self._measure_memory(_train     )


 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      str, UpperCAmelCase__							:      int, UpperCAmelCase__							:      int     ):
    __lowercase								=						self.config_dict[model_name]

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

    __lowercase								=						(
        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:
          __lowercase								=						"TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
          __lowercase								=						__import__("transformers", fromlist=[model_class]     )
          __lowercase								=						getattr(UpperCAmelCase__, UpperCAmelCase__     )
          __lowercase								=						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:
       __lowercase								=						TF_MODEL_MAPPING[config.__class__](UpperCAmelCase__     )

    # encoder-decoder has vocab size saved differently
    __lowercase								=						config.vocab_size if hasattr(UpperCAmelCase__, "vocab_size"     ) else config.encoder.vocab_size
    __lowercase								=						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__     )

    __lowercase								=						encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

    return _inference


 def  _lowercase			(      self							:      Any, UpperCAmelCase__							:      str, UpperCAmelCase__							:      int, UpperCAmelCase__							:      int     ):
    __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(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:
          __lowercase								=						"TF" + config.architectures[0]  # prepend 'TF' for tensorflow model
          __lowercase								=						__import__("transformers", fromlist=[model_class]     )
          __lowercase								=						getattr(UpperCAmelCase__, UpperCAmelCase__     )
          __lowercase								=						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:
       __lowercase								=						TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCAmelCase__     )

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

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

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

    __lowercase								=						encoder_decoder_train if config.is_encoder_decoder else encoder_train

    return _train


 def  _lowercase			(      self							:      Optional[int], UpperCAmelCase__							:      List[Any]     ):
    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
          __lowercase								=						timeit.repeat(
              UpperCAmelCase__, repeat=self.args.repeat, number=1_0, )

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



 def  _lowercase			(      self							:      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."     )
             __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(UpperCAmelCase__     )
                __lowercase								=						meminfo.used
                __lowercase								=						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."     )
                __lowercase								=						None
             else:
                __lowercase								=						measure_peak_memory_cpu(UpperCAmelCase__     )
                __lowercase								=						Memory(UpperCAmelCase__     ) if isinstance(UpperCAmelCase__, UpperCAmelCase__     ) else memory_bytes
          if self.args.trace_memory_line_by_line:
             __lowercase								=						stop_memory_tracing(UpperCAmelCase__     )
             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







"""simple docstring"""





import argparse
import os
import re


_a							=							'src/transformers/models/auto'


# re pattern that matches mapping introductions:
#    SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
_a							=							re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]+\s+)=\s+OrderedDict')
# re pattern that matches identifiers in mappings
_a							=							re.compile(r'\s*\(\s*"(\S[^"]+)"')




def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	bool = False)     ->						Optional[Any]:
   '''simple docstring'''


   with open(UpperCamelCase_,      "r",      encoding="utf-8") as f:
      __lowercase								=						f.read()

   __lowercase								=						content.split("\n")
   __lowercase								=						[]
   __lowercase								=						0
   while line_idx < len(UpperCamelCase_):
      if _re_intro_mapping.search(lines[line_idx]) is not None:
         __lowercase								=						len(re.search(r"^(\s*)\S",      lines[line_idx]).groups()[0]) + 8
         # Start of a new mapping!
         while not lines[line_idx].startswith(" " * indent + "("):
            new_lines.append(lines[line_idx])
            line_idx += 1

         __lowercase								=						[]
         while lines[line_idx].strip() != "]":
            # Blocks either fit in one line or not
            if lines[line_idx].strip() == "(":
               __lowercase								=						line_idx
               while not lines[line_idx].startswith(" " * indent + ")"):
                  line_idx += 1
               blocks.append("\n".join(lines[start_idx : line_idx + 1]))
            else:
               blocks.append(lines[line_idx])
            line_idx += 1

         # Sort blocks by their identifiers
         __lowercase								=						sorted(UpperCamelCase_,      key=lambda UpperCamelCase_: _re_identifier.search(UpperCamelCase_).groups()[0])
         new_lines += blocks
      else:
         new_lines.append(lines[line_idx])
         line_idx += 1

   if overwrite:
      with open(UpperCamelCase_,      "w",      encoding="utf-8") as f:
         f.write("\n".join(UpperCamelCase_))
   elif "\n".join(UpperCamelCase_) != content:
      return True




def   _A  (  UpperCamelCase_					:	bool = False)     ->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						[os.path.join(UpperCamelCase_,      UpperCamelCase_) for f in os.listdir(UpperCamelCase_) if f.endswith(".py")]
   __lowercase								=						[sort_auto_mapping(UpperCamelCase_,      overwrite=UpperCamelCase_) for fname in fnames]

   if not overwrite and any(UpperCamelCase_):
      __lowercase								=						[f for f, d in zip(UpperCamelCase_,      UpperCamelCase_) if d]
      raise ValueError(
          F"""The following files have auto mappings that need sorting: {", ".join(UpperCamelCase_)}. Run `make style` to fix"""
          " this.")


if __name__ == "__main__":
   _a							=							argparse.ArgumentParser()
   parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
   _a							=							parser.parse_args()

   sort_all_auto_mappings(not args.check_only)




