



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   ( A_    ,					unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  Optional[Any]      =			OpenAIGPTTokenizer
			lowercase__      :  Optional[Any]      =			OpenAIGPTTokenizerFast
			lowercase__      :  str      =			True
			lowercase__      :  Any      =			False





			def 		snake_case__			(				self	:       Optional[int]   )						->					List[Any]:
									"""simple docstring"""







									super().setUp()

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

									__lowercase					=				os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES["""vocab_file"""]   )
									__lowercase					=				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 		snake_case__			(				self	:       Dict			,     lowercase	:       Tuple   )						->					Any:
									"""simple docstring"""







									return "lower newer", "lower newer"





			def 		snake_case__			(				self	:       Tuple   )						->					Any:
									"""simple docstring"""







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

									__lowercase					=				"""lower"""
									__lowercase					=				["""low""", """er</w>"""]
									__lowercase					=				tokenizer.tokenize(lowercase   )
									self.assertListEqual(lowercase			,     lowercase   )

									__lowercase					=				tokens + ["""<unk>"""]
									__lowercase					=				[14, 15, 20]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(lowercase   )			,     lowercase   )





			def 		snake_case__			(				self	:       Optional[int]			,     lowercase	:       Any=15   )						->					int:
									"""simple docstring"""







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

																					# Simple input
																					__lowercase					=				"""This is a simple input"""
																					__lowercase					=				["""This is a simple input 1""", """This is a simple input 2"""]
																					__lowercase					=				("""This is a simple input""", """This is a pair""")
																					__lowercase					=				[
																					    ("""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 		snake_case__			(				self	:       Optional[Any]   )						->					List[Any]:
									"""simple docstring"""







									pass





@require_ftfy
@require_spacy
@require_tokenizers
class 			_lowerCAmelCase   ( A_			):






			"""simple docstring"""





			pass








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase__ =						{
    "configuration_ctrl": ["CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP", "CTRLConfig"],
    "tokenization_ctrl": ["CTRLTokenizer"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase__ =						[
			    "CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "CTRLForSequenceClassification",
			    "CTRLLMHeadModel",
			    "CTRLModel",
			    "CTRLPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			UpperCamelCase__ =						[
			    "TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFCTRLForSequenceClassification",
			    "TFCTRLLMHeadModel",
			    "TFCTRLModel",
			    "TFCTRLPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
			from .tokenization_ctrl import CTRLTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ctrl import (
						    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CTRLForSequenceClassification,
						    CTRLLMHeadModel,
						    CTRLModel,
						    CTRLPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_ctrl import (
						    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFCTRLForSequenceClassification,
						    TFCTRLLMHeadModel,
						    TFCTRLModel,
						    TFCTRLPreTrainedModel,
						)

else:
			import sys

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





