





"""simple docstring"""





from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
       import tensorflow as tf


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

@dataclass
class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      [
          'no_inference',
          'no_cuda',
          'no_tpu',
          'no_speed',
          'no_memory',
          'no_env_print',
          'no_multi_process',
      ]





      def __init__(       self      ,						**lowercase )		->     Any:
            for deprecated_arg in self.deprecated_args:
                  if deprecated_arg in kwargs:
                        lowerCAmelCase  						=       deprecated_arg[3:]
                        lowerCAmelCase  						=       not kwargs.pop(lowercase )
                        logger.warning(
                            f'{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'
                            f' {positive_arg}={kwargs[positive_arg]}' )
            lowerCAmelCase  						=       kwargs.pop("""tpu_name"""      ,						self.tpu_name )
            lowerCAmelCase  						=       kwargs.pop("""device_idx"""      ,						self.device_idx )
            lowerCAmelCase  						=       kwargs.pop("""eager_mode"""      ,						self.eager_mode )
            lowerCAmelCase  						=       kwargs.pop("""use_xla"""      ,						self.use_xla )
            super().__init__(**lowercase )

      _SCREAMING_SNAKE_CASE					=      field(
          default=_UpperCAmelCase     ,  metadata={'help': 'Name of TPU'}     ,  )
      _SCREAMING_SNAKE_CASE					=      field(
          default=0     ,  metadata={'help': 'CPU / GPU device index. Defaults to 0.'}     ,  )
      _SCREAMING_SNAKE_CASE					=      field(default=_UpperCAmelCase     ,  metadata={'help': 'Benchmark models in eager model.'}					)
      _SCREAMING_SNAKE_CASE					=      field(
          default=_UpperCAmelCase     ,  metadata={
              'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
          }     ,  )





      @cached_property
      def     _snake_case			(       self )		->     Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
            requires_backends(self      ,						["""tf"""] )
            lowerCAmelCase  						=       None
            if self.tpu:
                  try:
                        if self.tpu_name:
                              lowerCAmelCase  						=       tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name )
                        else:
                              lowerCAmelCase  						=       tf.distribute.cluster_resolver.TPUClusterResolver()
                  except ValueError:
                        lowerCAmelCase  						=       None
            return tpu





      @cached_property
      def     _snake_case			(       self )		->     Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
            requires_backends(self      ,						["""tf"""] )
            if self.is_tpu:
                  tf.config.experimental_connect_to_cluster(self._setup_tpu )
                  tf.tpu.experimental.initialize_tpu_system(self._setup_tpu )

                  lowerCAmelCase  						=       tf.distribute.TPUStrategy(self._setup_tpu )
            else:
                  # currently no multi gpu is allowed
                  if self.is_gpu:
                        # TODO: Currently only single GPU is supported
                        tf.config.set_visible_devices(self.gpu_list[self.device_idx]      ,						"""GPU""" )
                        lowerCAmelCase  						=       tf.distribute.OneDeviceStrategy(device=f'/gpu:{self.device_idx}' )
                  else:
                        tf.config.set_visible_devices([]      ,						"""GPU""" )  # disable GPU
                        lowerCAmelCase  						=       tf.distribute.OneDeviceStrategy(device=f'/cpu:{self.device_idx}' )

            return strategy





      @property
      def     _snake_case			(       self )		->     bool:
            requires_backends(self      ,						["""tf"""] )
            return self._setup_tpu is not None





      @property
      def     _snake_case			(       self )		->     "tf.distribute.Strategy":
            requires_backends(self      ,						["""tf"""] )
            return self._setup_strategy





      @property
      def     _snake_case			(       self )		->     int:
            requires_backends(self      ,						["""tf"""] )
            return tf.config.list_physical_devices("""GPU""" )





      @property
      def     _snake_case			(       self )		->     int:
            requires_backends(self      ,						["""tf"""] )
            if self.cuda:
                  return len(self.gpu_list )
            return 0





      @property
      def     _snake_case			(       self )		->     bool:
            return self.n_gpu > 0













"""simple docstring"""





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

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


if TYPE_CHECKING:
       from ... import FeatureExtractionMixin, TensorType

SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   {
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      'imagegpt'
      _SCREAMING_SNAKE_CASE					=      ['past_key_values']
      _SCREAMING_SNAKE_CASE					=      {
          'hidden_size': 'n_embd',
          'max_position_embeddings': 'n_positions',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }





      def __init__(       self      ,						lowercase=512 + 1      ,						lowercase=32 * 32      ,						lowercase=512      ,						lowercase=24      ,						lowercase=8      ,						lowercase=None      ,						lowercase="quick_gelu"      ,						lowercase=0.1      ,						lowercase=0.1      ,						lowercase=0.1      ,						lowercase=1e-5      ,						lowercase=0.02      ,						lowercase=True      ,						lowercase=True      ,						lowercase=False      ,						lowercase=False      ,						lowercase=False      ,						**lowercase      ,						)		->     Any:
            lowerCAmelCase  						=       vocab_size
            lowerCAmelCase  						=       n_positions
            lowerCAmelCase  						=       n_embd
            lowerCAmelCase  						=       n_layer
            lowerCAmelCase  						=       n_head
            lowerCAmelCase  						=       n_inner
            lowerCAmelCase  						=       activation_function
            lowerCAmelCase  						=       resid_pdrop
            lowerCAmelCase  						=       embd_pdrop
            lowerCAmelCase  						=       attn_pdrop
            lowerCAmelCase  						=       layer_norm_epsilon
            lowerCAmelCase  						=       initializer_range
            lowerCAmelCase  						=       scale_attn_weights
            lowerCAmelCase  						=       use_cache
            lowerCAmelCase  						=       scale_attn_by_inverse_layer_idx
            lowerCAmelCase  						=       reorder_and_upcast_attn
            lowerCAmelCase  						=       tie_word_embeddings

            super().__init__(tie_word_embeddings=lowercase      ,						**lowercase )







class 		lowercase					(					_UpperCAmelCase					):





      @property
      def     _snake_case			(       self )		->     Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ("""input_ids""", {0: """batch""", 1: """sequence"""}),
                ] )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = 1      ,						lowercase = -1      ,						lowercase = False      ,						lowercase = None      ,						lowercase = 3      ,						lowercase = 32      ,						lowercase = 32      ,						)		->     Mapping[str, Any]:
            lowerCAmelCase  						=       self._generate_dummy_images(lowercase      ,						lowercase      ,						lowercase      ,						lowercase )
            lowerCAmelCase  						=       dict(preprocessor(images=lowercase      ,						return_tensors=lowercase ) )

            return inputs






