




"""simple docstring"""






import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class lowerCAmelCase__			(  lowercase,			unittest.TestCase		):






			'''simple docstring'''



			lowerCamelCase__   =    OpenAIGPTTokenizer
			lowerCamelCase__   =    OpenAIGPTTokenizerFast
			lowerCamelCase__   =    True
			lowerCamelCase__   =    False
			def       A_					(						self						):
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							_lowerCamelCase  :    Union[str, Any]					=					[
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    'w</w>',
							    'r</w>',
							    't</w>',
							    'lo',
							    'low',
							    'er</w>',
							    'low</w>',
							    'lowest</w>',
							    'newer</w>',
							    'wider</w>',
							    '<unk>',
							]
							_lowerCamelCase  :    Optional[int]					=					dict(zip(lowercase						,   range(len(lowercase						)						)						)						)
							_lowerCamelCase  :    Dict					=					['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

							_lowerCamelCase  :    Optional[int]					=					os.path.join(self.tmpdirname						,   VOCAB_FILES_NAMES['vocab_file']						)
							_lowerCamelCase  :    str					=					os.path.join(self.tmpdirname						,   VOCAB_FILES_NAMES['merges_file']						)
							with open(self.vocab_file						,   'w'						) as fp:
											fp.write(json.dumps(lowercase						)						)
							with open(self.merges_file						,   'w'						) as fp:
											fp.write('\n'.join(lowercase						)						)
			def       A_					(						self						,   lowercase						):
							return "lower newer", "lower newer"
			def       A_					(						self						):
							_lowerCamelCase  :    str					=					OpenAIGPTTokenizer(self.vocab_file						,   self.merges_file						)

							_lowerCamelCase  :    Optional[Any]					=					'lower'
							_lowerCamelCase  :    str					=					['low', 'er</w>']
							_lowerCamelCase  :    Any					=					tokenizer.tokenize(lowercase						)
							self.assertListEqual(lowercase						,   lowercase						)

							_lowerCamelCase  :    Tuple					=					tokens + ['<unk>']
							_lowerCamelCase  :    Optional[Any]					=					[14, 15, 20]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase						)						,   lowercase						)
			def       A_					(						self						,   lowercase=15						):
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
															_lowerCamelCase  :    Optional[int]					=					self.rust_tokenizer_class.from_pretrained(lowercase						,   **lowercase						)

															# Simple input
															_lowerCamelCase  :    Tuple					=					'This is a simple input'
															_lowerCamelCase  :    Optional[Any]					=					['This is a simple input 1', 'This is a simple input 2']
															_lowerCamelCase  :    Any					=					('This is a simple input', 'This is a pair')
															_lowerCamelCase  :    Union[str, Any]					=					[
															    ('This is a simple input 1', 'This is a simple input 2'),
															    ('This is a simple pair 1', 'This is a simple pair 2'),
															]

															# Simple input tests
															self.assertRaises(lowercase						,   tokenizer_r.encode						,   lowercase						,   max_length=lowercase						,   padding='max_length'						)

															# Simple input
															self.assertRaises(lowercase						,   tokenizer_r.encode_plus						,   lowercase						,   max_length=lowercase						,   padding='max_length'						)

															# Simple input
															self.assertRaises(
															    lowercase						,   tokenizer_r.batch_encode_plus						,   lowercase						,   max_length=lowercase						,   padding='max_length'						,   )

															# Pair input
															self.assertRaises(lowercase						,   tokenizer_r.encode						,   lowercase						,   max_length=lowercase						,   padding='max_length'						)

															# Pair input
															self.assertRaises(lowercase						,   tokenizer_r.encode_plus						,   lowercase						,   max_length=lowercase						,   padding='max_length'						)

															# Pair input
															self.assertRaises(
															    lowercase						,   tokenizer_r.batch_encode_plus						,   lowercase						,   max_length=lowercase						,   padding='max_length'						,   )






			def       A_					(						self						):
							pass






@require_ftfy
@require_spacy
@require_tokenizers
class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			pass





"""simple docstring"""






import functools
import operator

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


lowercase__  		=		logging.get_logger(__name__)

lowercase__  		=		{
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class lowerCAmelCase__			(  lowercase		):






			'''simple docstring'''



			lowerCamelCase__   =    """sew-d"""
			def __init__(						self						,   lowercase=32						,   lowercase=768						,   lowercase=12						,   lowercase=12						,   lowercase=3072						,   lowercase=2						,   lowercase=512						,   lowercase=256						,   lowercase=True						,   lowercase=True						,   lowercase=("p2c", "c2p")						,   lowercase="layer_norm"						,   lowercase="gelu_python"						,   lowercase=0.1						,   lowercase=0.1						,   lowercase=0.1						,   lowercase=0.0						,   lowercase=0.1						,   lowercase=0.02						,   lowercase=1E-7						,   lowercase=1E-5						,   lowercase="group"						,   lowercase="gelu"						,   lowercase=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)						,   lowercase=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)						,   lowercase=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)						,   lowercase=False						,   lowercase=128						,   lowercase=16						,   lowercase=True						,   lowercase=0.05						,   lowercase=10						,   lowercase=2						,   lowercase=0.0						,   lowercase=10						,   lowercase=0						,   lowercase="mean"						,   lowercase=False						,   lowercase=False						,   lowercase=256						,   lowercase=0						,   lowercase=1						,   lowercase=2						,   **lowercase						,   ):
							super().__init__(**lowercase						,   pad_token_id=lowercase						,   bos_token_id=lowercase						,   eos_token_id=lowercase						)
							_lowerCamelCase  :    List[str]					=					hidden_size
							_lowerCamelCase  :    int					=					feat_extract_norm
							_lowerCamelCase  :    Optional[Any]					=					feat_extract_activation
							_lowerCamelCase  :    Optional[Any]					=					list(lowercase						)
							_lowerCamelCase  :    Union[str, Any]					=					list(lowercase						)
							_lowerCamelCase  :    Optional[Any]					=					list(lowercase						)
							_lowerCamelCase  :    List[str]					=					conv_bias
							_lowerCamelCase  :    str					=					num_conv_pos_embeddings
							_lowerCamelCase  :    Optional[Any]					=					num_conv_pos_embedding_groups
							_lowerCamelCase  :    Tuple					=					len(self.conv_dim						)
							_lowerCamelCase  :    Optional[int]					=					num_hidden_layers
							_lowerCamelCase  :    List[str]					=					intermediate_size
							_lowerCamelCase  :    Dict					=					squeeze_factor
							_lowerCamelCase  :    Any					=					max_position_embeddings
							_lowerCamelCase  :    List[str]					=					position_buckets
							_lowerCamelCase  :    Dict					=					share_att_key
							_lowerCamelCase  :    List[str]					=					relative_attention
							_lowerCamelCase  :    Optional[Any]					=					norm_rel_ebd
							_lowerCamelCase  :    List[Any]					=					list(lowercase						)
							_lowerCamelCase  :    List[Any]					=					hidden_act
							_lowerCamelCase  :    Optional[Any]					=					num_attention_heads
							_lowerCamelCase  :    Optional[int]					=					hidden_dropout
							_lowerCamelCase  :    Optional[int]					=					attention_dropout
							_lowerCamelCase  :    Any					=					activation_dropout
							_lowerCamelCase  :    Tuple					=					feat_proj_dropout
							_lowerCamelCase  :    Any					=					final_dropout
							_lowerCamelCase  :    str					=					layer_norm_eps
							_lowerCamelCase  :    Tuple					=					feature_layer_norm_eps
							_lowerCamelCase  :    Any					=					initializer_range
							_lowerCamelCase  :    Dict					=					vocab_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)`,'
											    F'''but is `len(config.conv_dim) = {len(self.conv_dim						)}`, `len(config.conv_stride)'''
											    F'''= {len(self.conv_stride						)}`, `len(config.conv_kernel) = {len(self.conv_kernel						)}`.'''						)

							# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
							_lowerCamelCase  :    Dict					=					apply_spec_augment
							_lowerCamelCase  :    int					=					mask_time_prob
							_lowerCamelCase  :    Union[str, Any]					=					mask_time_length
							_lowerCamelCase  :    Optional[Any]					=					mask_time_min_masks
							_lowerCamelCase  :    List[Any]					=					mask_feature_prob
							_lowerCamelCase  :    Union[str, Any]					=					mask_feature_length
							_lowerCamelCase  :    List[Any]					=					mask_feature_min_masks

							# ctc loss
							_lowerCamelCase  :    List[str]					=					ctc_loss_reduction
							_lowerCamelCase  :    List[str]					=					ctc_zero_infinity

							# sequence classification
							_lowerCamelCase  :    Dict					=					use_weighted_layer_sum
							_lowerCamelCase  :    List[Any]					=					classifier_proj_size






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