




"""simple docstring"""

import inspect
import json
import os
import tempfile
import unittest

from transformers.models.speech_to_text_a import SpeechaTextaTokenizer
from transformers.models.speech_to_text_a.tokenization_speech_to_text_a import VOCAB_FILES_NAMES

from ...test_tokenization_common import TokenizerTesterMixin
class      lowerCamelCase__      (					lowerCamelCase_			,					unittest.TestCase     ):
			a__	: List[str]        =       SpeechaTextaTokenizer
			a__	: Optional[Any]        =       False


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					super().setUp()

					snake_case						:  Optional[int]        =			"<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab".split(" "							)
					snake_case						:  Tuple        =			["he re</w> 123", "here a 1456"]
					snake_case						:  List[str]        =			dict(zip(SCREAMING_SNAKE_CASE			,	range(len(SCREAMING_SNAKE_CASE							)							)							)							)

					snake_case						:  int        =			{"pad_token": "<pad>", "unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}

					snake_case						:  Any        =			tempfile.mkdtemp()
					snake_case						:  Dict        =			os.path.join(self.tmpdirname			,	VOCAB_FILES_NAMES["vocab_file"]							)
					snake_case						:  List[Any]        =			os.path.join(self.tmpdirname			,	VOCAB_FILES_NAMES["merges_file"]							)

					with open(self.vocab_file			,	"w"			,	encoding="utf-8"							) as fp:
							fp.write(json.dumps(SCREAMING_SNAKE_CASE							) + "\n"							)

					with open(self.merges_file			,	"w"							) as fp:
							fp.write("\n".join(SCREAMING_SNAKE_CASE							)							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Any        =			list(self.get_tokenizer().get_vocab().keys()							)

					self.assertEqual(vocab_keys[0]			,	"<s>"							)
					self.assertEqual(vocab_keys[1]			,	"<pad>"							)
					self.assertEqual(vocab_keys[-1]			,	"vocab"							)
					self.assertEqual(len(SCREAMING_SNAKE_CASE							)			,	14							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					self.assertEqual(self.get_tokenizer().vocab_size			,	14							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Any        =			SpeechaTextaTokenizer.from_pretrained(self.tmpdirname							)

					# make sure @@ is correctly concatenated
					snake_case						:  Optional[int]        =			[4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
					snake_case						:  List[str]        =			tokenizer.decode(SCREAMING_SNAKE_CASE							)

					self.assertTrue(output_string == "herecouple words ofthe"							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			SpeechaTextaTokenizer.from_pretrained(self.tmpdirname							)

					with tempfile.TemporaryDirectory() as tmp_dirname:
							tokenizer.save_pretrained(SCREAMING_SNAKE_CASE							)
							os.remove(os.path.join(SCREAMING_SNAKE_CASE			,	"merges.txt"							)							)

							# load tokenizer without merges file should not throw an error
							snake_case						:  Optional[int]        =			SpeechaTextaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE							)

					with tempfile.TemporaryDirectory() as tmp_dirname:
							# save tokenizer and load again
							tokenizer.save_pretrained(SCREAMING_SNAKE_CASE							)
							snake_case						:  Optional[Any]        =			SpeechaTextaTokenizer.from_pretrained(SCREAMING_SNAKE_CASE							)

					self.assertIsNotNone(SCREAMING_SNAKE_CASE							)



			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					if not self.test_slow_tokenizer:
							return

					snake_case						:  List[str]        =			inspect.signature(self.tokenizer_class.__init__							)
					snake_case						:  Dict        =			self.get_tokenizer()

					for parameter_name, parameter in signature.parameters.items():
							if parameter.default != inspect.Parameter.empty and parameter_name != "merges_file":
									self.assertIn(SCREAMING_SNAKE_CASE			,	tokenizer.init_kwargs							)







"""simple docstring"""

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


__A							=     logging.get_logger(__name__)

__A							=     {
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}
class      lowerCamelCase__      (					lowerCamelCase_     ):
			a__	: Optional[int]        =       """vilt"""


			def __init__(       self			,	SCREAMING_SNAKE_CASE=30_522			,	SCREAMING_SNAKE_CASE=2			,	SCREAMING_SNAKE_CASE=2			,	SCREAMING_SNAKE_CASE=40			,	SCREAMING_SNAKE_CASE=768			,	SCREAMING_SNAKE_CASE=12			,	SCREAMING_SNAKE_CASE=12			,	SCREAMING_SNAKE_CASE=3_072			,	SCREAMING_SNAKE_CASE="gelu"			,	SCREAMING_SNAKE_CASE=0.0			,	SCREAMING_SNAKE_CASE=0.0			,	SCREAMING_SNAKE_CASE=0.02			,	SCREAMING_SNAKE_CASE=1E-12			,	SCREAMING_SNAKE_CASE=384			,	SCREAMING_SNAKE_CASE=32			,	SCREAMING_SNAKE_CASE=3			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=-1			,	SCREAMING_SNAKE_CASE=False			,	SCREAMING_SNAKE_CASE=-1			,	**SCREAMING_SNAKE_CASE			,	):



					"""simple docstring"""







					super().__init__(tie_word_embeddings=SCREAMING_SNAKE_CASE			,	**SCREAMING_SNAKE_CASE							)

					snake_case						:  str        =			vocab_size
					snake_case						:  Optional[Any]        =			type_vocab_size
					snake_case						:  List[str]        =			modality_type_vocab_size
					snake_case						:  Optional[Any]        =			max_position_embeddings

					snake_case						:  Tuple        =			hidden_size
					snake_case						:  List[str]        =			num_hidden_layers
					snake_case						:  int        =			num_attention_heads
					snake_case						:  Optional[int]        =			intermediate_size
					snake_case						:  List[Any]        =			hidden_act
					snake_case						:  List[str]        =			hidden_dropout_prob
					snake_case						:  Union[str, Any]        =			attention_probs_dropout_prob
					snake_case						:  Any        =			initializer_range
					snake_case						:  List[Any]        =			layer_norm_eps

					snake_case						:  Any        =			image_size
					snake_case						:  str        =			patch_size
					snake_case						:  Optional[int]        =			num_channels
					snake_case						:  List[str]        =			qkv_bias
					snake_case						:  Optional[int]        =			max_image_length
					snake_case						:  Any        =			num_images

