



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   (      lowerCAmelCase__    ,						unittest.TestCase						):
			UpperCamelCase        =					OpenAIGPTTokenizer
			UpperCamelCase        =					OpenAIGPTTokenizerFast
			UpperCamelCase        =					True
			UpperCamelCase        =					False



			def    _lowerCamelCase	(						self       :  Any)       ->   List[Any]:



						"""simple docstring"""






						super().setUp()

						# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
						_UpperCAmelCase			=       [
						    '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>',
						]
						_UpperCAmelCase			=       dict(zip(a__			,		range(len(a__))))
						_UpperCAmelCase			=       ['#version: 0.2', 'l o', 'lo w', 'e r</w>', '']

						_UpperCAmelCase			=       os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['vocab_file'])
						_UpperCAmelCase			=       os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['merges_file'])
						with open(self.vocab_file			,		'w') as fp:
									fp.write(json.dumps(a__))
						with open(self.merges_file			,		'w') as fp:
									fp.write('\n'.join(a__))



			def    _lowerCamelCase	(						self       :  Any			,		A       :  int)       ->   Any:



						"""simple docstring"""






						return "lower newer", "lower newer"



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       OpenAIGPTTokenizer(self.vocab_file			,		self.merges_file)

						_UpperCAmelCase			=       'lower'
						_UpperCAmelCase			=       ['low', 'er</w>']
						_UpperCAmelCase			=       tokenizer.tokenize(a__)
						self.assertListEqual(a__			,		a__)

						_UpperCAmelCase			=       tokens + ['<unk>']
						_UpperCAmelCase			=       [14, 15, 20]
						self.assertListEqual(tokenizer.convert_tokens_to_ids(a__)			,		a__)



			def    _lowerCamelCase	(						self       :  Optional[int]			,		A       :  Union[str, Any]=15)       ->   int:



						"""simple docstring"""






						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
												_UpperCAmelCase			=       self.rust_tokenizer_class.from_pretrained(a__			,		**a__)

												# Simple input
												_UpperCAmelCase			=       'This is a simple input'
												_UpperCAmelCase			=       ['This is a simple input 1', 'This is a simple input 2']
												_UpperCAmelCase			=       ('This is a simple input', 'This is a pair')
												_UpperCAmelCase			=       [
												    ('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(a__			,		tokenizer_r.encode			,		a__			,		max_length=a__			,		padding='max_length')

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

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

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

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

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



			def    _lowerCamelCase	(						self       :  Dict)       ->   Dict:



						"""simple docstring"""






						pass







@require_ftfy
@require_spacy
@require_tokenizers
class       __lowerCAmelCase   (      lowerCAmelCase__						):
			pass




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase__       =	{
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	["LxmertTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "LxmertEncoder",
							    "LxmertForPreTraining",
							    "LxmertForQuestionAnswering",
							    "LxmertModel",
							    "LxmertPreTrainedModel",
							    "LxmertVisualFeatureEncoder",
							    "LxmertXLayer",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__       =	[
							    "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFLxmertForPreTraining",
							    "TFLxmertMainLayer",
							    "TFLxmertModel",
							    "TFLxmertPreTrainedModel",
							    "TFLxmertVisualFeatureEncoder",
							]


if TYPE_CHECKING:
							from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
							from .tokenization_lxmert import LxmertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_lxmert_fast import LxmertTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_lxmert import (
														    LxmertEncoder,
														    LxmertForPreTraining,
														    LxmertForQuestionAnswering,
														    LxmertModel,
														    LxmertPreTrainedModel,
														    LxmertVisualFeatureEncoder,
														    LxmertXLayer,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_lxmert import (
														    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFLxmertForPreTraining,
														    TFLxmertMainLayer,
														    TFLxmertModel,
														    TFLxmertPreTrainedModel,
														    TFLxmertVisualFeatureEncoder,
														)

else:
							import sys

							UpperCAmelCase__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
