





'''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


lowerCAmelCase_					:    Optional[Any]      	=			logging.get_logger(__name__)
@dataclass
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =[
				    'no_inference',
				    'no_cuda',
				    'no_tpu',
				    'no_speed',
				    'no_memory',
				    'no_env_print',
				    'no_multi_process',
				]




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

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




				@cached_property
				def   UpperCamelCase__   (  self     :					List[str]					):
								requires_backends(self					,				["tf"]					)
								_a								= None
								if self.tpu:
												try:
																if self.tpu_name:
																				_a								= tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name					)
																else:
																				_a								= tf.distribute.cluster_resolver.TPUClusterResolver()
												except ValueError:
																_a								= None
								return tpu




				@cached_property
				def   UpperCamelCase__   (  self     :					Optional[int]					):
								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					)

												_a								= 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"					)
																_a								= tf.distribute.OneDeviceStrategy(device=f'/gpu:{self.device_idx}'					)
												else:
																tf.config.set_visible_devices([]					,				"GPU"					)  # disable GPU
																_a								= tf.distribute.OneDeviceStrategy(device=f'/cpu:{self.device_idx}'					)

								return strategy




				@property
				def   UpperCamelCase__   (  self     :					List[Any]					):
								requires_backends(self					,				["tf"]					)
								return self._setup_tpu is not None




				@property
				def   UpperCamelCase__   (  self     :					str					):
								requires_backends(self					,				["tf"]					)
								return self._setup_strategy




				@property
				def   UpperCamelCase__   (  self     :					Tuple					):
								requires_backends(self					,				["tf"]					)
								return tf.config.list_physical_devices("GPU"					)




				@property
				def   UpperCamelCase__   (  self     :					Tuple					):
								requires_backends(self					,				["tf"]					)
								if self.cuda:
												return len(self.gpu_list					)
								return 0




				@property
				def   UpperCamelCase__   (  self     :					Tuple					):
								return self.n_gpu > 0







'''simple docstring'''







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


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

lowerCAmelCase_					:    List[str]      	=			{
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            ='trocr'
				__a            =['past_key_values']
				__a            ={
				    'num_attention_heads': 'decoder_attention_heads',
				    'hidden_size': 'd_model',
				    'num_hidden_layers': 'decoder_layers',
				}




				def __init__(  self     :					Optional[int]					,				__a     :					Any=5_02_65					,				__a     :					Optional[int]=10_24					,				__a     :					List[Any]=12					,				__a     :					str=16					,				__a     :					int=40_96					,				__a     :					Optional[Any]="gelu"					,				__a     :					Union[str, Any]=5_12					,				__a     :					Dict=0.1					,				__a     :					List[str]=0.0					,				__a     :					Union[str, Any]=0.0					,				__a     :					Any=2					,				__a     :					Union[str, Any]=0.02					,				__a     :					Any=0.0					,				__a     :					List[str]=True					,				__a     :					Optional[Any]=False					,				__a     :					Union[str, Any]=True					,				__a     :					Optional[Any]=True					,				__a     :					Any=1					,				__a     :					List[Any]=0					,				__a     :					Any=2					,				**__a     :					Optional[Any]					,				):
								_a								= vocab_size
								_a								= d_model
								_a								= decoder_layers
								_a								= decoder_attention_heads
								_a								= decoder_ffn_dim
								_a								= activation_function
								_a								= max_position_embeddings
								_a								= dropout
								_a								= attention_dropout
								_a								= activation_dropout
								_a								= init_std
								_a								= decoder_layerdrop
								_a								= use_cache
								_a								= scale_embedding
								_a								= use_learned_position_embeddings
								_a								= layernorm_embedding

								super().__init__(
								    pad_token_id=__a					,				bos_token_id=__a					,				eos_token_id=__a					,				decoder_start_token_id=__a					,				**__a					,				)
