



"""simple docstring"""




import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


_lowerCAmelCase							:   str				=				logging.get_logger(__name__)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str=None		,    SCREAMING_SNAKE_CASE__      :		str=None							)    ->  Optional[Any]:





     '''simple docstring'''

     return field(default_factory=lambda: default		,    metadata=__snake_case							)






@dataclass
class       UpperCAmelCase_    :
      __SCREAMING_SNAKE_CASE		:  List[str]						=					list_field(
          default=[]			,     metadata={
              'help': (
                  'Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'
                  ' of all available models'
              )
          }			,     )

      __SCREAMING_SNAKE_CASE		:  Dict						=					list_field(
          default=[8]			,     metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'}		)

      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					list_field(
          default=[8, 3_2, 1_2_8, 5_1_2]			,     metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'}			,     )

      __SCREAMING_SNAKE_CASE		:  List[Any]						=					field(
          default=__lowercase			,     metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'}			,     )
      __SCREAMING_SNAKE_CASE		:  Union[str, Any]						=					field(
          default=__lowercase			,     metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'}			,     )
      __SCREAMING_SNAKE_CASE		:  List[Any]						=					field(
          default=__lowercase			,     metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'}		)
      __SCREAMING_SNAKE_CASE		:  Tuple						=					field(default=__lowercase			,     metadata={'help': 'Use FP16 to accelerate inference.'}		)
      __SCREAMING_SNAKE_CASE		:  int						=					field(default=__lowercase			,     metadata={'help': 'Benchmark training of model'}		)
      __SCREAMING_SNAKE_CASE		:  List[Any]						=					field(default=__lowercase			,     metadata={'help': 'Verbose memory tracing'}		)
      __SCREAMING_SNAKE_CASE		:  int						=					field(
          default=__lowercase			,     metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'}			,     )
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					field(
          default=__lowercase			,     metadata={
              'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
          }			,     )
      __SCREAMING_SNAKE_CASE		:  Tuple						=					field(default=__lowercase			,     metadata={'help': 'Trace memory line by line'}		)
      __SCREAMING_SNAKE_CASE		:  List[str]						=					field(default=__lowercase			,     metadata={'help': 'Save result to a CSV file'}		)
      __SCREAMING_SNAKE_CASE		:  str						=					field(default=__lowercase			,     metadata={'help': 'Save all print statements in a log file'}		)
      __SCREAMING_SNAKE_CASE		:  Union[str, Any]						=					field(default=__lowercase			,     metadata={'help': 'Whether to print environment information'}		)
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					field(
          default=__lowercase			,     metadata={
              'help': (
                  'Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'
                  ' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'
                  ' for debugging / testing and on TPU.'
              )
          }			,     )
      __SCREAMING_SNAKE_CASE		:  int						=					field(
          default=f"""inference_time_{round(time()		)}.csv"""			,     metadata={'help': 'CSV filename used if saving time results to csv.'}			,     )
      __SCREAMING_SNAKE_CASE		:  List[str]						=					field(
          default=f"""inference_memory_{round(time()		)}.csv"""			,     metadata={'help': 'CSV filename used if saving memory results to csv.'}			,     )
      __SCREAMING_SNAKE_CASE		:  Any						=					field(
          default=f"""train_time_{round(time()		)}.csv"""			,     metadata={'help': 'CSV filename used if saving time results to csv for training.'}			,     )
      __SCREAMING_SNAKE_CASE		:  Tuple						=					field(
          default=f"""train_memory_{round(time()		)}.csv"""			,     metadata={'help': 'CSV filename used if saving memory results to csv for training.'}			,     )
      __SCREAMING_SNAKE_CASE		:  int						=					field(
          default=f"""env_info_{round(time()		)}.csv"""			,     metadata={'help': 'CSV filename used if saving environment information.'}			,     )
      __SCREAMING_SNAKE_CASE		:  str						=					field(
          default=f"""log_{round(time()		)}.csv"""			,     metadata={'help': 'Log filename used if print statements are saved in log.'}			,     )
      __SCREAMING_SNAKE_CASE		:  List[str]						=					field(default=3			,     metadata={'help': 'Times an experiment will be run.'}		)
      __SCREAMING_SNAKE_CASE		:  Tuple						=					field(
          default=__lowercase			,     metadata={
              'help': (
                  'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
                  ' model weights.'
              )
          }			,     )



      def 		snake_case_   (	self	:      Union[str, Any]		):
           warnings.warn(
               f'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
               " are deprecated in general and it is advised to use external Benchmarking libraries "
               " to benchmark Transformer models."	,      _A	,      )



      def 		snake_case_   (	self	:      str		):
           return json.dumps(dataclasses.asdict(self		)	,      indent=2		)



      @property
      def 		snake_case_   (	self	:      List[Any]		):
           if len(self.models		) <= 0:
                raise ValueError(
                    "Please make sure you provide at least one model name / model identifier, *e.g.* `--models"
                    " bert-base-cased` or `args.models = [\'bert-base-cased\']."		)
           return self.models



      @property
      def 		snake_case_   (	self	:      Optional[int]		):
           if not self.multi_process:
                return False
           elif self.is_tpu:
                logger.info("Multiprocessing is currently not possible on TPU."		)
                return False
           else:
                return True

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


_lowerCAmelCase       =		logging.get_logger(__name__)

_lowerCAmelCase       =		{
    "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     lowerCAmelCase_     (       __lowercase ):
							UpperCAmelCase				   =     "trocr"
							UpperCAmelCase				   =     ["past_key_values"]
							UpperCAmelCase				   =     {
							    "num_attention_heads": "decoder_attention_heads",
							    "hidden_size": "d_model",
							    "num_hidden_layers": "decoder_layers",
							}







							def __init__(   self      :     List[str] ,			_A      :     Optional[Any]=5_0265 ,			_A      :     Optional[Any]=1024 ,			_A      :     Optional[Any]=12 ,			_A      :     Any=16 ,			_A      :     Any=4096 ,			_A      :     Optional[Any]="gelu" ,			_A      :     Union[str, Any]=512 ,			_A      :     Dict=0.1 ,			_A      :     List[str]=0.0 ,			_A      :     Optional[Any]=0.0 ,			_A      :     Union[str, Any]=2 ,			_A      :     Any=0.02 ,			_A      :     List[str]=0.0 ,			_A      :     List[str]=True ,			_A      :     str=False ,			_A      :     List[str]=True ,			_A      :     Optional[Any]=True ,			_A      :     Optional[int]=1 ,			_A      :     int=0 ,			_A      :     Any=2 ,			**_A      :     Optional[int] ,			):
												_UpperCamelCase			     =     vocab_size
												_UpperCamelCase			     =     d_model
												_UpperCamelCase			     =     decoder_layers
												_UpperCamelCase			     =     decoder_attention_heads
												_UpperCamelCase			     =     decoder_ffn_dim
												_UpperCamelCase			     =     activation_function
												_UpperCamelCase			     =     max_position_embeddings
												_UpperCamelCase			     =     dropout
												_UpperCamelCase			     =     attention_dropout
												_UpperCamelCase			     =     activation_dropout
												_UpperCamelCase			     =     init_std
												_UpperCamelCase			     =     decoder_layerdrop
												_UpperCamelCase			     =     use_cache
												_UpperCamelCase			     =     scale_embedding
												_UpperCamelCase			     =     use_learned_position_embeddings
												_UpperCamelCase			     =     layernorm_embedding

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