
import functools
import operator

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


__a         =   logging.get_logger(__name__)

__a         =   {
    'facebook/wav2vec2-base-960h': 'https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json',
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Optional[int]       	=      'wav2vec2'
	def __init__(			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[str]=3_2			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=7_6_8			,				SCREAMING_SNAKE_CASE_    :     int=1_2			,				SCREAMING_SNAKE_CASE_    :     Any=1_2			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=3_0_7_2			,				SCREAMING_SNAKE_CASE_    :     Dict="gelu"			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=0.1			,				SCREAMING_SNAKE_CASE_    :     str=0.1			,				SCREAMING_SNAKE_CASE_    :     List[Any]=0.1			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=0.0			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=0.0			,				SCREAMING_SNAKE_CASE_    :     Any=0.1			,				SCREAMING_SNAKE_CASE_    :     str=0.1			,				SCREAMING_SNAKE_CASE_    :     Any=0.02			,				SCREAMING_SNAKE_CASE_    :     Dict=1e-5			,				SCREAMING_SNAKE_CASE_    :     List[Any]="group"			,				SCREAMING_SNAKE_CASE_    :     List[str]="gelu"			,				SCREAMING_SNAKE_CASE_    :     List[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)			,				SCREAMING_SNAKE_CASE_    :     Any=(5, 2, 2, 2, 2, 2, 2)			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=(1_0, 3, 3, 3, 3, 2, 2)			,				SCREAMING_SNAKE_CASE_    :     List[str]=False			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=1_2_8			,				SCREAMING_SNAKE_CASE_    :     str=1_6			,				SCREAMING_SNAKE_CASE_    :     Any=False			,				SCREAMING_SNAKE_CASE_    :     int=True			,				SCREAMING_SNAKE_CASE_    :     str=0.05			,				SCREAMING_SNAKE_CASE_    :     Dict=1_0			,				SCREAMING_SNAKE_CASE_    :     Tuple=2			,				SCREAMING_SNAKE_CASE_    :     List[str]=0.0			,				SCREAMING_SNAKE_CASE_    :     List[str]=1_0			,				SCREAMING_SNAKE_CASE_    :     List[Any]=0			,				SCREAMING_SNAKE_CASE_    :     Dict=3_2_0			,				SCREAMING_SNAKE_CASE_    :     Any=2			,				SCREAMING_SNAKE_CASE_    :     Tuple=0.1			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=1_0_0			,				SCREAMING_SNAKE_CASE_    :     List[Any]=2_5_6			,				SCREAMING_SNAKE_CASE_    :     int=2_5_6			,				SCREAMING_SNAKE_CASE_    :     str=0.1			,				SCREAMING_SNAKE_CASE_    :     List[str]="sum"			,				SCREAMING_SNAKE_CASE_    :     Tuple=False			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=False			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=2_5_6			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0)			,				SCREAMING_SNAKE_CASE_    :     List[Any]=(5, 3, 3, 1, 1)			,				SCREAMING_SNAKE_CASE_    :     List[Any]=(1, 2, 3, 1, 1)			,				SCREAMING_SNAKE_CASE_    :     Tuple=5_1_2			,				SCREAMING_SNAKE_CASE_    :     Dict=0			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=1			,				SCREAMING_SNAKE_CASE_    :     int=2			,				SCREAMING_SNAKE_CASE_    :     Dict=False			,				SCREAMING_SNAKE_CASE_    :     int=3			,				SCREAMING_SNAKE_CASE_    :     List[Any]=2			,				SCREAMING_SNAKE_CASE_    :     str=3			,				SCREAMING_SNAKE_CASE_    :     Any=None			,				SCREAMING_SNAKE_CASE_    :     int=None			,				**SCREAMING_SNAKE_CASE_    :     Optional[Any]			,				)			->	Any:
					super().__init__(**SCREAMING_SNAKE_CASE_			,				pad_token_id=SCREAMING_SNAKE_CASE_			,				bos_token_id=SCREAMING_SNAKE_CASE_			,				eos_token_id=SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   hidden_size
					lowercase_	    =   feat_extract_norm
					lowercase_	    =   feat_extract_activation
					lowercase_	    =   list(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   list(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   list(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   conv_bias
					lowercase_	    =   num_conv_pos_embeddings
					lowercase_	    =   num_conv_pos_embedding_groups
					lowercase_	    =   len(self.conv_dim			)
					lowercase_	    =   num_hidden_layers
					lowercase_	    =   intermediate_size
					lowercase_	    =   hidden_act
					lowercase_	    =   num_attention_heads
					lowercase_	    =   hidden_dropout
					lowercase_	    =   attention_dropout
					lowercase_	    =   activation_dropout
					lowercase_	    =   feat_proj_dropout
					lowercase_	    =   final_dropout
					lowercase_	    =   layerdrop
					lowercase_	    =   layer_norm_eps
					lowercase_	    =   initializer_range
					lowercase_	    =   vocab_size
					lowercase_	    =   do_stable_layer_norm
					lowercase_	    =   use_weighted_layer_sum

					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
					lowercase_	    =   apply_spec_augment
					lowercase_	    =   mask_time_prob
					lowercase_	    =   mask_time_length
					lowercase_	    =   mask_time_min_masks
					lowercase_	    =   mask_feature_prob
					lowercase_	    =   mask_feature_length
					lowercase_	    =   mask_feature_min_masks

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

					# ctc loss
					lowercase_	    =   ctc_loss_reduction
					lowercase_	    =   ctc_zero_infinity

					# adapter
					lowercase_	    =   add_adapter
					lowercase_	    =   adapter_kernel_size
					lowercase_	    =   adapter_stride
					lowercase_	    =   num_adapter_layers
					lowercase_	    =   output_hidden_size or hidden_size
					lowercase_	    =   adapter_attn_dim

					# SequenceClassification-specific parameter. Feel free to ignore for other classes.
					lowercase_	    =   classifier_proj_size

					# XVector-specific parameters. Feel free to ignore for other classes.
					lowercase_	    =   list(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   list(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   list(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   xvector_output_dim




	@property
	def 		_lowercase    (			self    :     Any			)			->	Dict:
					return functools.reduce(operator.mul			,				self.conv_stride			,				1			)








from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
 import datasets




class A    (  UpperCamelCase_	):






      @slow
      @require_torch
      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						int            =EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     ,  'prajjwal1/bert-tiny'					)
             _lowerCamelCase			:						Dict            =BertTokenizer.from_pretrained('bert-base-uncased'					)

             _lowerCamelCase			:						Union[str, Any]            =bertabert.config.encoder.vocab_size
             _lowerCamelCase			:						Dict            =tokenizer.sep_token_id
             _lowerCamelCase			:						List[Any]            =tokenizer.cls_token_id
             _lowerCamelCase			:						Optional[int]            =128

             _lowerCamelCase			:						int            =datasets.load_dataset('cnn_dailymail'     ,  '3.0.0'     ,  split='train[:1%]'					)
             _lowerCamelCase			:						int            =datasets.load_dataset('cnn_dailymail'     ,  '3.0.0'     ,  split='validation[:1%]'					)

             _lowerCamelCase			:						Optional[int]            =train_dataset.select(range(32					)					)
             _lowerCamelCase			:						Optional[int]            =val_dataset.select(range(16					)					)

             _lowerCamelCase			:						Optional[int]            =4

             def _map_to_encoder_decoder_inputs(lowercase_		:	Tuple					):
                    # Tokenizer will automatically set [BOS] <text> [EOS]
                    _lowerCamelCase			:						Optional[int]            =tokenizer(batch['article']     ,  padding='max_length'     ,  truncation=lowercase_     ,  max_length=512					)
                    _lowerCamelCase			:						List[str]            =tokenizer(batch['highlights']     ,  padding='max_length'     ,  truncation=lowercase_     ,  max_length=128					)
                    _lowerCamelCase			:						List[str]            =inputs.input_ids
                    _lowerCamelCase			:						Any            =inputs.attention_mask

                    _lowerCamelCase			:						List[str]            =outputs.input_ids
                    _lowerCamelCase			:						int            =outputs.input_ids.copy()
                    _lowerCamelCase			:						List[str]            =[
                        [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                    ]
                    _lowerCamelCase			:						Dict            =outputs.attention_mask

                    assert all(len(lowercase_					) == 512 for x in inputs.input_ids					)
                    assert all(len(lowercase_					) == 128 for x in outputs.input_ids					)

                    return batch

             def _compute_metrics(lowercase_		:	str					):
                    _lowerCamelCase			:						List[Any]            =pred.label_ids
                    _lowerCamelCase			:						List[Any]            =pred.predictions

                    # all unnecessary tokens are removed
                    _lowerCamelCase			:						str            =tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)
                    _lowerCamelCase			:						Tuple            =tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)

                    _lowerCamelCase			:						List[str]            =sum([int(pred_str[i] == label_str[i]					) for i in range(len(lowercase_					)					)]					) / len(lowercase_					)

                    return {"accuracy": accuracy}

             # map train dataset
             _lowerCamelCase			:						Tuple            =train_dataset.map(
                 _map_to_encoder_decoder_inputs     ,  batched=lowercase_     ,  batch_size=lowercase_     ,  remove_columns=['article', 'highlights']     ,  )
             train_dataset.set_format(
                 type='torch'     ,  columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,  )

             # same for validation dataset
             _lowerCamelCase			:						Tuple            =val_dataset.map(
                 _map_to_encoder_decoder_inputs     ,  batched=lowercase_     ,  batch_size=lowercase_     ,  remove_columns=['article', 'highlights']     ,  )
             val_dataset.set_format(
                 type='torch'     ,  columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,  )

             _lowerCamelCase			:						Dict            =self.get_auto_remove_tmp_dir()

             _lowerCamelCase			:						Optional[int]            =SeqaSeqTrainingArguments(
                 output_dir=lowercase_     ,  per_device_train_batch_size=lowercase_     ,  per_device_eval_batch_size=lowercase_     ,  predict_with_generate=lowercase_     ,  evaluation_strategy='steps'     ,  do_train=lowercase_     ,  do_eval=lowercase_     ,  warmup_steps=0     ,  eval_steps=2     ,  logging_steps=2     ,  )

             # instantiate trainer
             _lowerCamelCase			:						Optional[Any]            =SeqaSeqTrainer(
                 model=lowercase_     ,  args=lowercase_     ,  compute_metrics=_compute_metrics     ,  train_dataset=lowercase_     ,  eval_dataset=lowercase_     ,  tokenizer=lowercase_     ,  )

             # start training
             trainer.train()


