




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 				__snake_case     (_a  ,		unittest.TestCase						):
				lowerCAmelCase__			=   SpeechaTextaTokenizer
				lowerCAmelCase__			=   False



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]					)     ->			Optional[int]:





								'''simple docstring'''




								super().setUp()

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

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

								_lowerCAmelCase			:			Union[str, Any]        =					tempfile.mkdtemp()
								_lowerCAmelCase			:			Tuple        =					os.path.join(self.tmpdirname						,		VOCAB_FILES_NAMES["""vocab_file"""]					)
								_lowerCAmelCase			:			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(_UpperCAmelCase					) + """\n"""					)

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



				def 					SCREAMING_SNAKE_CASE				(					self					:						int					)     ->			Any:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					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(_UpperCAmelCase					)						,		14					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			int:





								'''simple docstring'''




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



				def 					SCREAMING_SNAKE_CASE				(					self					:						str					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase			:			List[Any]        =					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

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

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



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict					)     ->			Any:





								'''simple docstring'''




								_lowerCAmelCase			:			str        =					SpeechaTextaTokenizer.from_pretrained(self.tmpdirname					)

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

												# load tokenizer without merges file should not throw an error
												_lowerCAmelCase			:			Any        =					SpeechaTextaTokenizer.from_pretrained(_UpperCAmelCase					)

								with tempfile.TemporaryDirectory() as tmp_dirname:
												# save tokenizer and load again
												tokenizer.save_pretrained(_UpperCAmelCase					)
												_lowerCAmelCase			:			int        =					SpeechaTextaTokenizer.from_pretrained(_UpperCAmelCase					)

								self.assertIsNotNone(_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[Any]					)     ->			Optional[Any]:





								'''simple docstring'''




								if not self.test_slow_tokenizer:
												return

								_lowerCAmelCase			:			Dict        =					inspect.signature(self.tokenizer_class.__init__					)
								_lowerCAmelCase			:			Union[str, Any]        =					self.get_tokenizer()

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







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCamelCase					:     Optional[int]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Optional[int]					  =  {
    "squeezebert/squeezebert-uncased": (
        "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json"
    ),
    "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json",
    "squeezebert/squeezebert-mnli-headless": (
        "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json"
    ),
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
				lowerCAmelCase__			=   "squeezebert"



				def __init__(					self					:						Optional[int]						,		_UpperCAmelCase					:						Tuple=3_0522						,		_UpperCAmelCase					:						str=768						,		_UpperCAmelCase					:						int=12						,		_UpperCAmelCase					:						Tuple=12						,		_UpperCAmelCase					:						Tuple=3072						,		_UpperCAmelCase					:						Union[str, Any]="gelu"						,		_UpperCAmelCase					:						Union[str, Any]=0.1						,		_UpperCAmelCase					:						List[Any]=0.1						,		_UpperCAmelCase					:						List[Any]=512						,		_UpperCAmelCase					:						Tuple=2						,		_UpperCAmelCase					:						Optional[Any]=0.02						,		_UpperCAmelCase					:						Any=1E-12						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=768						,		_UpperCAmelCase					:						Any=4						,		_UpperCAmelCase					:						Any=4						,		_UpperCAmelCase					:						Optional[int]=4						,		_UpperCAmelCase					:						str=1						,		_UpperCAmelCase					:						Union[str, Any]=4						,		_UpperCAmelCase					:						List[str]=4						,		**_UpperCAmelCase					:						int						,		)     ->			Any:





								'''simple docstring'''




								super().__init__(pad_token_id=_UpperCAmelCase						,		**_UpperCAmelCase					)

								_lowerCAmelCase			:			Optional[Any]        =					vocab_size
								_lowerCAmelCase			:			Optional[Any]        =					hidden_size
								_lowerCAmelCase			:			Tuple        =					num_hidden_layers
								_lowerCAmelCase			:			int        =					num_attention_heads
								_lowerCAmelCase			:			List[Any]        =					hidden_act
								_lowerCAmelCase			:			List[Any]        =					intermediate_size
								_lowerCAmelCase			:			List[str]        =					hidden_dropout_prob
								_lowerCAmelCase			:			Any        =					attention_probs_dropout_prob
								_lowerCAmelCase			:			str        =					max_position_embeddings
								_lowerCAmelCase			:			int        =					type_vocab_size
								_lowerCAmelCase			:			str        =					initializer_range
								_lowerCAmelCase			:			int        =					layer_norm_eps
								_lowerCAmelCase			:			int        =					embedding_size
								_lowerCAmelCase			:			Optional[int]        =					q_groups
								_lowerCAmelCase			:			List[Any]        =					k_groups
								_lowerCAmelCase			:			List[Any]        =					v_groups
								_lowerCAmelCase			:			Dict        =					post_attention_groups
								_lowerCAmelCase			:			Optional[Any]        =					intermediate_groups
								_lowerCAmelCase			:			str        =					output_groups






class 				__snake_case     (_a						):



				@property
				def 					SCREAMING_SNAKE_CASE				(					self					:						Union[str, Any]					)     ->			Mapping[str, Mapping[int, str]]:





								'''simple docstring'''




								if self.task == "multiple-choice":
												_lowerCAmelCase			:			Optional[int]        =					{0: """batch""", 1: """choice""", 2: """sequence"""}
								else:
												_lowerCAmelCase			:			int        =					{0: """batch""", 1: """sequence"""}
								return OrderedDict(
								    [
								        ("""input_ids""", dynamic_axis),
								        ("""attention_mask""", dynamic_axis),
								        ("""token_type_ids""", dynamic_axis),
								    ]					)

