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

from ..utils import logging


UpperCamelCase_ 						=						logging.get_logger(__name__)






def 		_UpperCAmelCase (						UpperCamelCase:  str=None      ,				UpperCamelCase:  Union[str, Any]=None	):






	"""simple docstring"""


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







@dataclass
class    a		:
							lowercase_		:    Optional[Any] 				=	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'
							        )
							    }   ,						)

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

							lowercase_		:    str 				=	list_field(
							    default=[8, 32, 128, 512]   ,						metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'}   ,						)

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







							def    UpperCAmelCase__       (    self	:	Dict			):
								"""simple docstring"""







								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    UpperCAmelCase__       (    self	:	str			):
								"""simple docstring"""







								return json.dumps(dataclasses.asdict(self			)   ,					indent=2			)







							@property
							def    UpperCAmelCase__       (    self	:	Tuple			):
								"""simple docstring"""







								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    UpperCAmelCase__       (    self	:	List[str]			):
								"""simple docstring"""







								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








'''simple docstring'''
from ....configuration_utils import PretrainedConfig
from ....utils import logging


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

lowerCAmelCase					:    Dict           = {
    'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json',
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""mctct"""







				def __init__(       self						,				A_=8065						,				A_=1536						,				A_=36						,				A_=6144						,				A_=4						,				A_=384						,				A_=920						,				A_=1e-5						,				A_=0.3						,				A_="relu"						,				A_=0.02						,				A_=0.3						,				A_=0.3						,				A_=1						,				A_=0						,				A_=2						,				A_=1						,				A_=0.3						,				A_=1						,				A_=(7,)						,				A_=(3,)						,				A_=80						,				A_=1						,				A_=None						,				A_="sum"						,				A_=False						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__(**A_						,				pad_token_id=A_						,				bos_token_id=A_						,				eos_token_id=A_     )
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     hidden_size
											UpperCamelCase   			=     num_hidden_layers
											UpperCamelCase   			=     intermediate_size
											UpperCamelCase   			=     num_attention_heads
											UpperCamelCase   			=     attention_head_dim
											UpperCamelCase   			=     max_position_embeddings
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     layerdrop
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     hidden_dropout_prob
											UpperCamelCase   			=     attention_probs_dropout_prob
											UpperCamelCase   			=     pad_token_id
											UpperCamelCase   			=     bos_token_id
											UpperCamelCase   			=     eos_token_id
											UpperCamelCase   			=     conv_glu_dim
											UpperCamelCase   			=     conv_dropout
											UpperCamelCase   			=     num_conv_layers
											UpperCamelCase   			=     input_feat_per_channel
											UpperCamelCase   			=     input_channels
											UpperCamelCase   			=     conv_channels
											UpperCamelCase   			=     ctc_loss_reduction
											UpperCamelCase   			=     ctc_zero_infinity

											# prevents config testing fail with exporting to json
											UpperCamelCase   			=     list(A_     )
											UpperCamelCase   			=     list(A_     )

											if len(self.conv_kernel     ) != self.num_conv_layers:
																		raise ValueError(
																		    'Configuration for convolutional module is incorrect. '
																		    'It is required that `len(config.conv_kernel)` == `config.num_conv_layers` '
																		    F'''but is `len(config.conv_kernel) = {len(self.conv_kernel     )}`, '''
																		    F'''`config.num_conv_layers = {self.num_conv_layers}`.'''     )




