import functools
import operator

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


lowercase									=				logging.get_logger(__name__)

lowercase									=				{
    "microsoft/unispeech-large-1500h-cv": (
        "https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}





class 	__lowercase    (			__a       ):




				'''simple docstring'''


				_A      :		Optional[int]   						=			'''unispeech'''





				def __init__(		self    :		Optional[Any]      ,							_a    :		Any=32      ,							_a    :		Any=768      ,							_a    :		List[Any]=12      ,							_a    :		Union[str, Any]=12      ,							_a    :		str=3_072      ,							_a    :		int="gelu"      ,							_a    :		str=0.1      ,							_a    :		Optional[Any]=0.1      ,							_a    :		Optional[int]=0.1      ,							_a    :		Tuple=0.0      ,							_a    :		Any=0.0      ,							_a    :		Union[str, Any]=0.1      ,							_a    :		str=0.1      ,							_a    :		Optional[Any]=0.02      ,							_a    :		List[str]=1E-5      ,							_a    :		str="group"      ,							_a    :		List[str]="gelu"      ,							_a    :		Union[str, Any]=(512, 512, 512, 512, 512, 512, 512)      ,							_a    :		Any=(5, 2, 2, 2, 2, 2, 2)      ,							_a    :		Tuple=(10, 3, 3, 3, 3, 2, 2)      ,							_a    :		List[Any]=False      ,							_a    :		int=128      ,							_a    :		str=16      ,							_a    :		Optional[Any]=False      ,							_a    :		List[str]=True      ,							_a    :		Union[str, Any]=0.05      ,							_a    :		Union[str, Any]=10      ,							_a    :		Tuple=2      ,							_a    :		Dict=0.0      ,							_a    :		List[Any]=10      ,							_a    :		Tuple=0      ,							_a    :		str=320      ,							_a    :		str=2      ,							_a    :		List[Any]=0.1      ,							_a    :		str=100      ,							_a    :		List[Any]=256      ,							_a    :		Tuple=256      ,							_a    :		Optional[int]=0.1      ,							_a    :		List[str]="mean"      ,							_a    :		Union[str, Any]=False      ,							_a    :		List[Any]=False      ,							_a    :		Tuple=256      ,							_a    :		Optional[int]=80      ,							_a    :		Any=0      ,							_a    :		Tuple=1      ,							_a    :		str=2      ,							_a    :		Any=0.5      ,							**_a    :		Dict      ,							):
								super().__init__(**lowerCAmelCase__      ,							pad_token_id=lowerCAmelCase__      ,							bos_token_id=lowerCAmelCase__      ,							eos_token_id=lowerCAmelCase__				)
								UpperCamelCase__					=				hidden_size
								UpperCamelCase__					=				feat_extract_norm
								UpperCamelCase__					=				feat_extract_activation
								UpperCamelCase__					=				list(lowerCAmelCase__				)
								UpperCamelCase__					=				list(lowerCAmelCase__				)
								UpperCamelCase__					=				list(lowerCAmelCase__				)
								UpperCamelCase__					=				conv_bias
								UpperCamelCase__					=				num_conv_pos_embeddings
								UpperCamelCase__					=				num_conv_pos_embedding_groups
								UpperCamelCase__					=				len(self.conv_dim				)
								UpperCamelCase__					=				num_hidden_layers
								UpperCamelCase__					=				intermediate_size
								UpperCamelCase__					=				hidden_act
								UpperCamelCase__					=				num_attention_heads
								UpperCamelCase__					=				hidden_dropout
								UpperCamelCase__					=				attention_dropout
								UpperCamelCase__					=				activation_dropout
								UpperCamelCase__					=				feat_proj_dropout
								UpperCamelCase__					=				final_dropout
								UpperCamelCase__					=				layerdrop
								UpperCamelCase__					=				layer_norm_eps
								UpperCamelCase__					=				initializer_range
								UpperCamelCase__					=				num_ctc_classes
								UpperCamelCase__					=				vocab_size
								UpperCamelCase__					=				do_stable_layer_norm
								UpperCamelCase__					=				use_weighted_layer_sum
								UpperCamelCase__					=				classifier_proj_size

								if (
								    (len(self.conv_stride				) != self.num_feat_extract_layers)
								    or (len(self.conv_kernel				) != self.num_feat_extract_layers)
								    or (len(self.conv_dim				) != self.num_feat_extract_layers)
								):
												raise ValueError(
												    '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
												    ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
												    F""" {len(self.conv_dim				)}`, `len(config.conv_stride) = {len(self.conv_stride				)}`,"""
												    F""" `len(config.conv_kernel) = {len(self.conv_kernel				)}`."""				)

								# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
								UpperCamelCase__					=				apply_spec_augment
								UpperCamelCase__					=				mask_time_prob
								UpperCamelCase__					=				mask_time_length
								UpperCamelCase__					=				mask_time_min_masks
								UpperCamelCase__					=				mask_feature_prob
								UpperCamelCase__					=				mask_feature_length
								UpperCamelCase__					=				mask_feature_min_masks

								# parameters for pretraining with codevector quantized representations
								UpperCamelCase__					=				num_codevectors_per_group
								UpperCamelCase__					=				num_codevector_groups
								UpperCamelCase__					=				contrastive_logits_temperature
								UpperCamelCase__					=				feat_quantizer_dropout
								UpperCamelCase__					=				num_negatives
								UpperCamelCase__					=				codevector_dim
								UpperCamelCase__					=				proj_codevector_dim
								UpperCamelCase__					=				diversity_loss_weight

								# ctc loss
								UpperCamelCase__					=				ctc_loss_reduction
								UpperCamelCase__					=				ctc_zero_infinity

								# pretraining loss
								UpperCamelCase__					=				replace_prob





				@property
				def      A_							(		self    :		Optional[int]				):
								return functools.reduce(operator.mul      ,							self.conv_stride      ,							1				)





import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
						raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")

# TF training parameters
_snake_case      :							Any     	=    False
_snake_case      :							int     	=    False

def  __snake_case							(	__magic_name__						):







				'''simple docstring'''


				return TrainCommand(__magic_name__						)





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						@staticmethod
						def        SCREAMING_SNAKE_CASE(			lowerCAmelCase__							:ArgumentParser     )  ->str:
										lowercase         =      parser.add_parser("train"		,    help="CLI tool to train a model on a task."     )

										train_parser.add_argument(
										    "--train_data"		,    type=lowerCAmelCase__		,    required=lowerCAmelCase__		,    help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."		,    )
										train_parser.add_argument(
										    "--column_label"		,    type=lowerCAmelCase__		,    default=0		,    help="Column of the dataset csv file with example labels."     )
										train_parser.add_argument(
										    "--column_text"		,    type=lowerCAmelCase__		,    default=1		,    help="Column of the dataset csv file with example texts."     )
										train_parser.add_argument(
										    "--column_id"		,    type=lowerCAmelCase__		,    default=2		,    help="Column of the dataset csv file with example ids."     )
										train_parser.add_argument(
										    "--skip_first_row"		,    action="store_true"		,    help="Skip the first row of the csv file (headers)."     )

										train_parser.add_argument("--validation_data"		,    type=lowerCAmelCase__		,    default=""		,    help="path to validation dataset."     )
										train_parser.add_argument(
										    "--validation_split"		,    type=lowerCAmelCase__		,    default=0.1		,    help="if validation dataset is not provided, fraction of train dataset to use as validation dataset."		,    )

										train_parser.add_argument("--output"		,    type=lowerCAmelCase__		,    default="./"		,    help="path to saved the trained model."     )

										train_parser.add_argument(
										    "--task"		,    type=lowerCAmelCase__		,    default="text_classification"		,    help="Task to train the model on."     )
										train_parser.add_argument(
										    "--model"		,    type=lowerCAmelCase__		,    default="bert-base-uncased"		,    help="Model's name or path to stored model."     )
										train_parser.add_argument("--train_batch_size"		,    type=lowerCAmelCase__		,    default=32		,    help="Batch size for training."     )
										train_parser.add_argument("--valid_batch_size"		,    type=lowerCAmelCase__		,    default=64		,    help="Batch size for validation."     )
										train_parser.add_argument("--learning_rate"		,    type=lowerCAmelCase__		,    default=3E-5		,    help="Learning rate."     )
										train_parser.add_argument("--adam_epsilon"		,    type=lowerCAmelCase__		,    default=1E-0_8		,    help="Epsilon for Adam optimizer."     )
										train_parser.set_defaults(func=lowerCAmelCase__     )





						def __init__(			self							:Optional[int]		,    lowerCAmelCase__							:Namespace     )  ->str:
										lowercase         =      logging.get_logger("transformers-cli/training"     )

										lowercase         =      "tf" if is_tf_available() else "torch"

										os.makedirs(args.output		,    exist_ok=lowerCAmelCase__     )
										lowercase         =      args.output

										lowercase         =      args.column_label
										lowercase         =      args.column_text
										lowercase         =      args.column_id

										self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''     )
										if args.task == "text_classification":
														lowercase         =      TextClassificationPipeline.from_pretrained(args.model     )
										elif args.task == "token_classification":
														raise NotImplementedError
										elif args.task == "question_answering":
														raise NotImplementedError

										self.logger.info(F'''Loading dataset from {args.train_data}'''     )
										lowercase         =      Processor.create_from_csv(
										    args.train_data		,    column_label=args.column_label		,    column_text=args.column_text		,    column_id=args.column_id		,    skip_first_row=args.skip_first_row		,    )
										lowercase         =      None
										if args.validation_data:
														self.logger.info(F'''Loading validation dataset from {args.validation_data}'''     )
														lowercase         =      Processor.create_from_csv(
														    args.validation_data		,    column_label=args.column_label		,    column_text=args.column_text		,    column_id=args.column_id		,    skip_first_row=args.skip_first_row		,    )

										lowercase         =      args.validation_split
										lowercase         =      args.train_batch_size
										lowercase         =      args.valid_batch_size
										lowercase         =      args.learning_rate
										lowercase         =      args.adam_epsilon





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->List[str]:
										if self.framework == "tf":
														return self.run_tf()
										return self.run_torch()





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Dict:
										raise NotImplementedError





						def        SCREAMING_SNAKE_CASE(			self							:Any     )  ->str:
										self.pipeline.fit(
										    self.train_dataset		,    validation_data=self.valid_dataset		,    validation_split=self.validation_split		,    learning_rate=self.learning_rate		,    adam_epsilon=self.adam_epsilon		,    train_batch_size=self.train_batch_size		,    valid_batch_size=self.valid_batch_size		,    )

										# Save trained pipeline
										self.pipeline.save_pretrained(self.output     )
