



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       _lowercase				(  lowerCAmelCase, unittest.TestCase      ):





	"""simple docstring"""
	__A		      =				SpeechaTextaTokenizer
	__A		      =				False




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						super().setUp()

						a   	=  "<s> <pad> </s> <unk> here@@ a couple of@@ words for the he@@ re@@ vocab".split(" "						)
						a   	=  ["he re</w> 123", "here a 1456"]
						a   	=  dict(zip(lowerCamelCase_						,			range(len(lowerCamelCase_						)						)						)						)

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

						a   	=  tempfile.mkdtemp()
						a   	=  os.path.join(self.tmpdirname						,			VOCAB_FILES_NAMES["vocab_file"]						)
						a   	=  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(lowerCamelCase_						) + "\n"						)

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




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  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(lowerCamelCase_						)						,			14						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  SpeechaTextaTokenizer.from_pretrained(self.tmpdirname						)

						# make sure @@ is correctly concatenated
						a   	=  [4, 6, 8, 7, 10]  # ["here@@", "couple", "words", "of@@", "the"]
						a   	=  tokenizer.decode(lowerCamelCase_						)

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




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						a   	=  SpeechaTextaTokenizer.from_pretrained(self.tmpdirname						)

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

											# load tokenizer without merges file should not throw an error
											a   	=  SpeechaTextaTokenizer.from_pretrained(lowerCamelCase_						)

						with tempfile.TemporaryDirectory() as tmp_dirname:
											# save tokenizer and load again
											tokenizer.save_pretrained(lowerCamelCase_						)
											a   	=  SpeechaTextaTokenizer.from_pretrained(lowerCamelCase_						)

						self.assertIsNotNone(lowerCamelCase_						)




	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

						if not self.test_slow_tokenizer:
											return

						a   	=  inspect.signature(self.tokenizer_class.__init__						)
						a   	=  self.get_tokenizer()

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





from collections import OrderedDict
from typing import Mapping

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


_lowercase:					int      =						logging.get_logger(__name__)

_lowercase:					Dict      =						{
    "google/mobilebert-uncased": "https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json"
}



class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""
	__A		      =				MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
	__A		      =				"mobilebert"




	def __init__(self						,			lowerCamelCase_=30522						,			lowerCamelCase_=512						,			lowerCamelCase_=24						,			lowerCamelCase_=4						,			lowerCamelCase_=512						,			lowerCamelCase_="relu"						,			lowerCamelCase_=0.0						,			lowerCamelCase_=0.1						,			lowerCamelCase_=512						,			lowerCamelCase_=2						,			lowerCamelCase_=0.02						,			lowerCamelCase_=1E-1_2						,			lowerCamelCase_=0						,			lowerCamelCase_=128						,			lowerCamelCase_=True						,			lowerCamelCase_=True						,			lowerCamelCase_=128						,			lowerCamelCase_=False						,			lowerCamelCase_=True						,			lowerCamelCase_=4						,			lowerCamelCase_="no_norm"						,			lowerCamelCase_=True						,			lowerCamelCase_=None						,			**lowerCamelCase_						,			):
						"""simple docstring"""

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

						a   	=  vocab_size
						a   	=  hidden_size
						a   	=  num_hidden_layers
						a   	=  num_attention_heads
						a   	=  hidden_act
						a   	=  intermediate_size
						a   	=  hidden_dropout_prob
						a   	=  attention_probs_dropout_prob
						a   	=  max_position_embeddings
						a   	=  type_vocab_size
						a   	=  initializer_range
						a   	=  layer_norm_eps
						a   	=  embedding_size
						a   	=  trigram_input
						a   	=  use_bottleneck
						a   	=  intra_bottleneck_size
						a   	=  use_bottleneck_attention
						a   	=  key_query_shared_bottleneck
						a   	=  num_feedforward_networks
						a   	=  normalization_type
						a   	=  classifier_activation

						if self.use_bottleneck:
											a   	=  intra_bottleneck_size
						else:
											a   	=  hidden_size

						a   	=  classifier_dropout




class       _lowercase				(  lowerCAmelCase      ):





	"""simple docstring"""




	@property
	def 	UpperCamelCase_			(self						):
						"""simple docstring"""

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