






import functools
import operator

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


__UpperCAmelCase							:   str							=  logging.get_logger(__name__)

__UpperCAmelCase							:   List[Any]							=  {
    'microsoft/wavlm-base': 'https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json',
    # See all WavLM models at https://huggingface.co/models?filter=wavlm
}


class 					_snake_case						(			lowerCamelCase__ ):
	_A          =   'wavlm'






	def __init__(		self					,UpperCamelCase=32					,UpperCamelCase=768					,UpperCamelCase=12					,UpperCamelCase=12					,UpperCamelCase=3_072					,UpperCamelCase="gelu"					,UpperCamelCase=0.1					,UpperCamelCase=0.1					,UpperCamelCase=0.1					,UpperCamelCase=0.0					,UpperCamelCase=0.1					,UpperCamelCase=0.1					,UpperCamelCase=0.02					,UpperCamelCase=1E-5					,UpperCamelCase="group"					,UpperCamelCase="gelu"					,UpperCamelCase=(512, 512, 512, 512, 512, 512, 512)					,UpperCamelCase=(5, 2, 2, 2, 2, 2, 2)					,UpperCamelCase=(10, 3, 3, 3, 3, 2, 2)					,UpperCamelCase=False					,UpperCamelCase=128					,UpperCamelCase=16					,UpperCamelCase=320					,UpperCamelCase=800					,UpperCamelCase=False					,UpperCamelCase=True					,UpperCamelCase=0.05					,UpperCamelCase=10					,UpperCamelCase=2					,UpperCamelCase=0.0					,UpperCamelCase=10					,UpperCamelCase=320					,UpperCamelCase=2					,UpperCamelCase=0.1					,UpperCamelCase=100					,UpperCamelCase=256					,UpperCamelCase=256					,UpperCamelCase=0.1					,UpperCamelCase="mean"					,UpperCamelCase=False					,UpperCamelCase=False					,UpperCamelCase=256					,UpperCamelCase=(512, 512, 512, 512, 1_500)					,UpperCamelCase=(5, 3, 3, 1, 1)					,UpperCamelCase=(1, 2, 3, 1, 1)					,UpperCamelCase=512					,UpperCamelCase=80					,UpperCamelCase=0					,UpperCamelCase=1					,UpperCamelCase=2					,UpperCamelCase=False					,UpperCamelCase=3					,UpperCamelCase=2					,UpperCamelCase=3					,UpperCamelCase=None					,**UpperCamelCase					,)						->    Tuple:
					super().__init__(**__lowerCamelCase					,pad_token_id=__lowerCamelCase					,bos_token_id=__lowerCamelCase					,eos_token_id=__lowerCamelCase       )
					snake_case__      :Tuple         =   hidden_size
					snake_case__      :Dict         =   feat_extract_norm
					snake_case__      :List[Any]         =   feat_extract_activation
					snake_case__      :Union[str, Any]         =   list(__lowerCamelCase       )
					snake_case__      :Union[str, Any]         =   list(__lowerCamelCase       )
					snake_case__      :Tuple         =   list(__lowerCamelCase       )
					snake_case__      :int         =   conv_bias
					snake_case__      :Optional[int]         =   num_buckets
					snake_case__      :Dict         =   max_bucket_distance
					snake_case__      :Optional[Any]         =   num_conv_pos_embeddings
					snake_case__      :Dict         =   num_conv_pos_embedding_groups
					snake_case__      :Union[str, Any]         =   len(self.conv_dim       )
					snake_case__      :List[str]         =   num_hidden_layers
					snake_case__      :Union[str, Any]         =   intermediate_size
					snake_case__      :Optional[Any]         =   hidden_act
					snake_case__      :List[Any]         =   num_attention_heads
					snake_case__      :Tuple         =   hidden_dropout
					snake_case__      :List[Any]         =   attention_dropout
					snake_case__      :List[str]         =   activation_dropout
					snake_case__      :Any         =   feat_proj_dropout
					snake_case__      :int         =   final_dropout
					snake_case__      :Optional[Any]         =   layerdrop
					snake_case__      :Dict         =   layer_norm_eps
					snake_case__      :Optional[int]         =   initializer_range
					snake_case__      :int         =   num_ctc_classes
					snake_case__      :Tuple         =   vocab_size
					snake_case__      :Any         =   do_stable_layer_norm
					snake_case__      :List[str]         =   use_weighted_layer_sum
					snake_case__      :Any         =   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
					snake_case__      :Union[str, Any]         =   apply_spec_augment
					snake_case__      :List[Any]         =   mask_time_prob
					snake_case__      :int         =   mask_time_length
					snake_case__      :Union[str, Any]         =   mask_time_min_masks
					snake_case__      :List[Any]         =   mask_feature_prob
					snake_case__      :List[Any]         =   mask_feature_length

					# parameters for pretraining with codevector quantized representations
					snake_case__      :List[str]         =   num_codevectors_per_group
					snake_case__      :str         =   num_codevector_groups
					snake_case__      :Dict         =   contrastive_logits_temperature
					snake_case__      :Any         =   num_negatives
					snake_case__      :List[str]         =   codevector_dim
					snake_case__      :str         =   proj_codevector_dim
					snake_case__      :Union[str, Any]         =   diversity_loss_weight

					# ctc loss
					snake_case__      :Any         =   ctc_loss_reduction
					snake_case__      :Dict         =   ctc_zero_infinity

					# adapter
					snake_case__      :int         =   add_adapter
					snake_case__      :Dict         =   adapter_kernel_size
					snake_case__      :Dict         =   adapter_stride
					snake_case__      :Dict         =   num_adapter_layers
					snake_case__      :str         =   output_hidden_size or hidden_size

					# SequenceClassification-specific parameter. Feel free to ignore for other classes.
					snake_case__      :Tuple         =   classifier_proj_size

					# XVector-specific parameters. Feel free to ignore for other classes.
					snake_case__      :Union[str, Any]         =   list(__lowerCamelCase       )
					snake_case__      :int         =   list(__lowerCamelCase       )
					snake_case__      :List[Any]         =   list(__lowerCamelCase       )
					snake_case__      :int         =   xvector_output_dim






	@property
	def    lowerCAmelCase_							(		self       )						->    List[str]:
					return functools.reduce(operator.mul					,self.conv_stride					,1       )









import pytest


__UpperCAmelCase							:   int							=  "__dummy_dataset1__"

__UpperCAmelCase							:   int							=  "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n                \"ner_tags\": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            \"O\",\n                            \"B-PER\",\n                            \"I-PER\",\n                            \"B-ORG\",\n                            \"I-ORG\",\n                            \"B-LOC\",\n                            \"I-LOC\",\n                        ]\n                    )\n                ),\n                \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n                \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n"





@pytest.fixture
def 					lowercase_ (    )     -> Optional[Any]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_NAME





@pytest.fixture
def 					lowercase_ (    )     -> Optional[int]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_CODE





@pytest.fixture
def 					lowercase_ (    __snake_case     :							Optional[int]	,	__snake_case     :							List[Any]	,	__snake_case     :							Any      )     -> Dict:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   dataset_loading_script_name
				snake_case__      :Optional[Any]         =   tmp_path / "datasets" / script_name
				script_dir.mkdir(parents=__snake_case      )
				snake_case__      :List[Any]         =   script_dir / F'{script_name}.py'
				with open(__snake_case	,	"w"      ) as f:
								f.write(__snake_case      )
				return str(__snake_case      )