





"""simple docstring"""





import functools
import operator

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


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    '''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   (   UpperCAmelCase_   ):
      _SCREAMING_SNAKE_CASE							      = 'unispeech'





      def __init__(			self ,		lowercase=32 ,		lowercase=768 ,		lowercase=12 ,		lowercase=12 ,		lowercase=3_072 ,		lowercase="gelu" ,		lowercase=0.1 ,		lowercase=0.1 ,		lowercase=0.1 ,		lowercase=0.0 ,		lowercase=0.0 ,		lowercase=0.1 ,		lowercase=0.1 ,		lowercase=0.02 ,		lowercase=1e-5 ,		lowercase="group" ,		lowercase="gelu" ,		lowercase=(512, 512, 512, 512, 512, 512, 512) ,		lowercase=(5, 2, 2, 2, 2, 2, 2) ,		lowercase=(10, 3, 3, 3, 3, 2, 2) ,		lowercase=False ,		lowercase=128 ,		lowercase=16 ,		lowercase=False ,		lowercase=True ,		lowercase=0.05 ,		lowercase=10 ,		lowercase=2 ,		lowercase=0.0 ,		lowercase=10 ,		lowercase=0 ,		lowercase=320 ,		lowercase=2 ,		lowercase=0.1 ,		lowercase=100 ,		lowercase=256 ,		lowercase=256 ,		lowercase=0.1 ,		lowercase="mean" ,		lowercase=False ,		lowercase=False ,		lowercase=256 ,		lowercase=80 ,		lowercase=0 ,		lowercase=1 ,		lowercase=2 ,		lowercase=0.5 ,		**lowercase ,		)						->	List[Any]:
            super().__init__(**__lowerCamelCase ,		pad_token_id=__lowerCamelCase ,		bos_token_id=__lowerCamelCase ,		eos_token_id=__lowerCamelCase					)
            lowerCAmelCase    		=			hidden_size
            lowerCAmelCase    		=			feat_extract_norm
            lowerCAmelCase    		=			feat_extract_activation
            lowerCAmelCase    		=			list(__lowerCamelCase					)
            lowerCAmelCase    		=			list(__lowerCamelCase					)
            lowerCAmelCase    		=			list(__lowerCamelCase					)
            lowerCAmelCase    		=			conv_bias
            lowerCAmelCase    		=			num_conv_pos_embeddings
            lowerCAmelCase    		=			num_conv_pos_embedding_groups
            lowerCAmelCase    		=			len(self.conv_dim					)
            lowerCAmelCase    		=			num_hidden_layers
            lowerCAmelCase    		=			intermediate_size
            lowerCAmelCase    		=			hidden_act
            lowerCAmelCase    		=			num_attention_heads
            lowerCAmelCase    		=			hidden_dropout
            lowerCAmelCase    		=			attention_dropout
            lowerCAmelCase    		=			activation_dropout
            lowerCAmelCase    		=			feat_proj_dropout
            lowerCAmelCase    		=			final_dropout
            lowerCAmelCase    		=			layerdrop
            lowerCAmelCase    		=			layer_norm_eps
            lowerCAmelCase    		=			initializer_range
            lowerCAmelCase    		=			num_ctc_classes
            lowerCAmelCase    		=			vocab_size
            lowerCAmelCase    		=			do_stable_layer_norm
            lowerCAmelCase    		=			use_weighted_layer_sum
            lowerCAmelCase    		=			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
            lowerCAmelCase    		=			apply_spec_augment
            lowerCAmelCase    		=			mask_time_prob
            lowerCAmelCase    		=			mask_time_length
            lowerCAmelCase    		=			mask_time_min_masks
            lowerCAmelCase    		=			mask_feature_prob
            lowerCAmelCase    		=			mask_feature_length
            lowerCAmelCase    		=			mask_feature_min_masks

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

            # ctc loss
            lowerCAmelCase    		=			ctc_loss_reduction
            lowerCAmelCase    		=			ctc_zero_infinity

            # pretraining loss
            lowerCAmelCase    		=			replace_prob





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













import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   namedtuple(
    '''_TestCommandArgs''',
    [
        '''dataset''',
        '''name''',
        '''cache_dir''',
        '''data_dir''',
        '''all_configs''',
        '''save_infos''',
        '''ignore_verifications''',
        '''force_redownload''',
        '''clear_cache''',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case							)		->    Optional[int]:
		return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def  SCREAMING_SNAKE_CASE				(      snake_case							)		->    Dict:
		__lowercase											=     _TestCommandArgs(dataset=snake_case						,   all_configs=snake_case						,   save_infos=snake_case							)
		__lowercase											=     TestCommand(*snake_case							)
		test_command.run()
		__lowercase											=     os.path.join(snake_case						,   'README.md'							)
		assert os.path.exists(snake_case							)
		__lowercase											=     DatasetInfosDict.from_directory(snake_case							)
		__lowercase											=     DatasetInfosDict(
		    {
		        'default': DatasetInfo(
		            features=Features(
		                {
		                    'tokens': Sequence(Value('string'							)							),
		                    'ner_tags': Sequence(
		                        ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC']							)							),
		                    'langs': Sequence(Value('string'							)							),
		                    'spans': Sequence(Value('string'							)							),
		                }							)						,   splits=[
		                {
		                    'name': 'train',
		                    'num_bytes': 2_351_563,
		                    'num_examples': 10_000,
		                },
		                {
		                    'name': 'validation',
		                    'num_bytes': 238_418,
		                    'num_examples': 1_000,
		                },
		            ]						,   download_size=3_940_680						,   dataset_size=2_589_981						,   )
		    }							)
		assert dataset_infos.keys() == expected_dataset_infos.keys()
		for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
				__lowercase						,      __lowercase											=     getattr(dataset_infos['default']						,   snake_case							), getattr(expected_dataset_infos['default']						,   snake_case							)
				if key == "num_bytes":
						assert is_apercent_close(snake_case						,   snake_case							)
				elif key == "splits":
						assert list(snake_case							) == list(snake_case							)
						for split in result:
								assert result[split].name == expected[split].name
								assert result[split].num_examples == expected[split].num_examples
								assert is_apercent_close(result[split].num_bytes						,   expected[split].num_bytes							)
				else:
						result == expected




