
import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class       _lowerCAmelCase   (      __a   , unittest.TestCase			):
				_lowercase     						=GPTaTokenizer
				_lowercase     						=GPTaTokenizerFast
				_lowercase     						=True
				_lowercase     						={'''add_prefix_space''': True}
				_lowercase     						=False




				def 	__a		(   self	)							-> List[str]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							lowerCAmelCase_         =      [
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							    "<|endoftext|>",
							]
							lowerCAmelCase_         =      dict(zip(_UpperCamelCase							,							range(len(_UpperCamelCase	)	)	)	)
							lowerCAmelCase_         =      ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
							lowerCAmelCase_         =      {"unk_token": "<unk>"}

							lowerCAmelCase_         =      os.path.join(self.tmpdirname							,							VOCAB_FILES_NAMES["vocab_file"]	)
							lowerCAmelCase_         =      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"							,							encoding="utf-8"	) as fp:
										fp.write("\n".join(_UpperCamelCase	)	)




				def 	__a		(   self							,							**_UpperCamelCase	)							-> Optional[int]:
							kwargs.update(self.special_tokens_map	)
							return GPTaTokenizer.from_pretrained(self.tmpdirname							,							**_UpperCamelCase	)




				def 	__a		(   self							,							**_UpperCamelCase	)							-> Optional[Any]:
							kwargs.update(self.special_tokens_map	)
							return GPTaTokenizerFast.from_pretrained(self.tmpdirname							,							**_UpperCamelCase	)




				def 	__a		(   self							,							_UpperCamelCase	)							-> Union[str, Any]:
							lowerCAmelCase_         =      "lower newer"
							lowerCAmelCase_         =      "lower newer"
							return input_text, output_text




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      GPTaTokenizer(self.vocab_file							,							self.merges_file							,							**self.special_tokens_map	)
							lowerCAmelCase_         =      "lower newer"
							lowerCAmelCase_         =      ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
							lowerCAmelCase_         =      tokenizer.tokenize(_UpperCamelCase							,							add_prefix_space=_UpperCamelCase	)
							self.assertListEqual(_UpperCamelCase							,							_UpperCamelCase	)

							lowerCAmelCase_         =      tokens + [tokenizer.unk_token]
							lowerCAmelCase_         =      [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCamelCase	)							,							_UpperCamelCase	)




				def 	__a		(   self	)							-> str:
							if not self.test_rust_tokenizer:
										return

							lowerCAmelCase_         =      self.get_tokenizer()
							lowerCAmelCase_         =      self.get_rust_tokenizer(add_prefix_space=_UpperCamelCase	)

							lowerCAmelCase_         =      "lower newer"

							# Testing tokenization
							lowerCAmelCase_         =      tokenizer.tokenize(_UpperCamelCase							,							add_prefix_space=_UpperCamelCase	)
							lowerCAmelCase_         =      rust_tokenizer.tokenize(_UpperCamelCase	)
							self.assertListEqual(_UpperCamelCase							,							_UpperCamelCase	)

							# Testing conversion to ids without special tokens
							lowerCAmelCase_         =      tokenizer.encode(_UpperCamelCase							,							add_special_tokens=_UpperCamelCase							,							add_prefix_space=_UpperCamelCase	)
							lowerCAmelCase_         =      rust_tokenizer.encode(_UpperCamelCase							,							add_special_tokens=_UpperCamelCase	)
							self.assertListEqual(_UpperCamelCase							,							_UpperCamelCase	)

							# Testing conversion to ids with special tokens
							lowerCAmelCase_         =      self.get_rust_tokenizer(add_prefix_space=_UpperCamelCase	)
							lowerCAmelCase_         =      tokenizer.encode(_UpperCamelCase							,							add_prefix_space=_UpperCamelCase	)
							lowerCAmelCase_         =      rust_tokenizer.encode(_UpperCamelCase	)
							self.assertListEqual(_UpperCamelCase							,							_UpperCamelCase	)

							# Testing the unknown token
							lowerCAmelCase_         =      tokens + [rust_tokenizer.unk_token]
							lowerCAmelCase_         =      [14, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_UpperCamelCase	)							,							_UpperCamelCase	)




				def 	__a		(   self							,							*_UpperCamelCase							,							**_UpperCamelCase	)							-> Optional[int]:
							# It's very difficult to mix/test pretokenization with byte-level
							# And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
							pass




				def 	__a		(   self							,							_UpperCamelCase=15	)							-> Optional[Any]:
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
													lowerCAmelCase_         =      self.rust_tokenizer_class.from_pretrained(_UpperCamelCase							,							**_UpperCamelCase	)

													# Simple input
													lowerCAmelCase_         =      "This is a simple input"
													lowerCAmelCase_         =      ["This is a simple input 1", "This is a simple input 2"]
													lowerCAmelCase_         =      ("This is a simple input", "This is a pair")
													lowerCAmelCase_         =      [
													    ("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(_UpperCamelCase							,							tokenizer_r.encode							,							_UpperCamelCase							,							max_length=_UpperCamelCase							,							padding="max_length"	)

													# Simple input
													self.assertRaises(_UpperCamelCase							,							tokenizer_r.encode_plus							,							_UpperCamelCase							,							max_length=_UpperCamelCase							,							padding="max_length"	)

													# Simple input
													self.assertRaises(
													    _UpperCamelCase							,							tokenizer_r.batch_encode_plus							,							_UpperCamelCase							,							max_length=_UpperCamelCase							,							padding="max_length"							,							)

													# Pair input
													self.assertRaises(_UpperCamelCase							,							tokenizer_r.encode							,							_UpperCamelCase							,							max_length=_UpperCamelCase							,							padding="max_length"	)

													# Pair input
													self.assertRaises(_UpperCamelCase							,							tokenizer_r.encode_plus							,							_UpperCamelCase							,							max_length=_UpperCamelCase							,							padding="max_length"	)

													# Pair input
													self.assertRaises(
													    _UpperCamelCase							,							tokenizer_r.batch_encode_plus							,							_UpperCamelCase							,							max_length=_UpperCamelCase							,							padding="max_length"							,							)




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      GPTaTokenizer.from_pretrained(self.tmpdirname							,							pad_token="<pad>"	)

							# Simple input
							lowerCAmelCase_         =      "This is a simple input"
							lowerCAmelCase_         =      ["This is a simple input looooooooong", "This is a simple input"]
							lowerCAmelCase_         =      ("This is a simple input", "This is a pair")
							lowerCAmelCase_         =      [
							    ("This is a simple input loooooong", "This is a simple input"),
							    ("This is a simple pair loooooong", "This is a simple pair"),
							]

							lowerCAmelCase_         =      tokenizer.pad_token_id

							lowerCAmelCase_         =      tokenizer(_UpperCamelCase							,							padding="max_length"							,							max_length=30							,							return_tensors="np"	)
							lowerCAmelCase_         =      tokenizer(_UpperCamelCase							,							padding=_UpperCamelCase							,							truncate=_UpperCamelCase							,							return_tensors="np"	)
							lowerCAmelCase_         =      tokenizer(*_UpperCamelCase							,							padding="max_length"							,							max_length=60							,							return_tensors="np"	)
							lowerCAmelCase_         =      tokenizer(_UpperCamelCase							,							padding=_UpperCamelCase							,							truncate=_UpperCamelCase							,							return_tensors="np"	)

							# s
							# test single string max_length padding
							self.assertEqual(out_s["input_ids"].shape[-1]							,							30	)
							self.assertTrue(pad_token_id in out_s["input_ids"]	)
							self.assertTrue(0 in out_s["attention_mask"]	)

							# s2
							# test automatic padding
							self.assertEqual(out_sa["input_ids"].shape[-1]							,							33	)
							# long slice doesn't have padding
							self.assertFalse(pad_token_id in out_sa["input_ids"][0]	)
							self.assertFalse(0 in out_sa["attention_mask"][0]	)
							# short slice does have padding
							self.assertTrue(pad_token_id in out_sa["input_ids"][1]	)
							self.assertTrue(0 in out_sa["attention_mask"][1]	)

							# p
							# test single pair max_length padding
							self.assertEqual(out_p["input_ids"].shape[-1]							,							60	)
							self.assertTrue(pad_token_id in out_p["input_ids"]	)
							self.assertTrue(0 in out_p["attention_mask"]	)

							# p2
							# test automatic padding pair
							self.assertEqual(out_pa["input_ids"].shape[-1]							,							52	)
							# long slice pair doesn't have padding
							self.assertFalse(pad_token_id in out_pa["input_ids"][0]	)
							self.assertFalse(0 in out_pa["attention_mask"][0]	)
							# short slice pair does have padding
							self.assertTrue(pad_token_id in out_pa["input_ids"][1]	)
							self.assertTrue(0 in out_pa["attention_mask"][1]	)




				def 	__a		(   self	)							-> Optional[Any]:
							lowerCAmelCase_         =      "$$$"
							lowerCAmelCase_         =      GPTaTokenizer.from_pretrained(self.tmpdirname							,							bos_token=_UpperCamelCase							,							add_bos_token=_UpperCamelCase	)

							lowerCAmelCase_         =      "This is a simple input"
							lowerCAmelCase_         =      ["This is a simple input 1", "This is a simple input 2"]

							lowerCAmelCase_         =      tokenizer.bos_token_id

							lowerCAmelCase_         =      tokenizer(_UpperCamelCase	)
							lowerCAmelCase_         =      tokenizer(_UpperCamelCase	)

							self.assertEqual(out_s.input_ids[0]							,							_UpperCamelCase	)
							self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids	)	)

							lowerCAmelCase_         =      tokenizer.decode(out_s.input_ids	)
							lowerCAmelCase_         =      tokenizer.batch_decode(out_sa.input_ids	)

							self.assertEqual(decode_s.split()[0]							,							_UpperCamelCase	)
							self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa	)	)




				def 	__a		(   self	)							-> Any:
							pass




				def 	__a		(   self	)							-> Union[str, Any]:
							# TODO: change to self.get_tokenizers() when the fast version is implemented
							lowerCAmelCase_         =      [self.get_tokenizer(do_lower_case=_UpperCamelCase							,							add_bos_token=_UpperCamelCase	)]
							for tokenizer in tokenizers:
										with self.subTest(f"""{tokenizer.__class__.__name__}"""	):
													lowerCAmelCase_         =      "Encode this."
													lowerCAmelCase_         =      "This one too please."
													lowerCAmelCase_         =      tokenizer.encode(_UpperCamelCase							,							add_special_tokens=_UpperCamelCase	)
													encoded_sequence += tokenizer.encode(_UpperCamelCase							,							add_special_tokens=_UpperCamelCase	)
													lowerCAmelCase_         =      tokenizer.encode_plus(
													    _UpperCamelCase							,							_UpperCamelCase							,							add_special_tokens=_UpperCamelCase							,							return_special_tokens_mask=_UpperCamelCase							,							)
													lowerCAmelCase_         =      encoded_sequence_dict["input_ids"]
													lowerCAmelCase_         =      encoded_sequence_dict["special_tokens_mask"]
													self.assertEqual(len(_UpperCamelCase	)							,							len(_UpperCamelCase	)	)

													lowerCAmelCase_         =      [
													    (x if not special_tokens_mask[i] else None) for i, x in enumerate(_UpperCamelCase	)
													]
													lowerCAmelCase_         =      [x for x in filtered_sequence if x is not None]
													self.assertEqual(_UpperCamelCase							,							_UpperCamelCase	)





@require_tokenizers
class       _lowerCAmelCase   (      unittest.TestCase			):




				def 	__a		(   self	)							-> Any:
							# More context:
							# https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
							# https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
							# https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

							lowerCAmelCase_         =      AutoTokenizer.from_pretrained("facebook/opt-350m"							,							from_slow=_UpperCamelCase	)
							lowerCAmelCase_         =      "A photo of a cat"

							lowerCAmelCase_         =      tokenizer.encode(
							    _UpperCamelCase							,							)
							self.assertEqual(_UpperCamelCase							,							[2, 250, 1_345, 9, 10, 4_758]	)
							tokenizer.save_pretrained("test_opt"	)

							lowerCAmelCase_         =      AutoTokenizer.from_pretrained("./test_opt"	)
							lowerCAmelCase_         =      tokenizer.encode(
							    _UpperCamelCase							,							)
							self.assertEqual(_UpperCamelCase							,							[2, 250, 1_345, 9, 10, 4_758]	)




				def 	__a		(   self	)							-> Optional[int]:
							lowerCAmelCase_         =      AutoTokenizer.from_pretrained("facebook/opt-350m"							,							use_slow=_UpperCamelCase	)
							lowerCAmelCase_         =      "A photo of a cat"

							lowerCAmelCase_         =      tokenizer.encode(
							    _UpperCamelCase							,							)
							# Same as above
							self.assertEqual(_UpperCamelCase							,							[2, 250, 1_345, 9, 10, 4_758]	)




				@unittest.skip("This test is failing because of a bug in the fast tokenizer"	)
				def 	__a		(   self	)							-> int:
							lowerCAmelCase_         =      AutoTokenizer.from_pretrained("facebook/opt-350m"							,							from_slow=_UpperCamelCase	)

							lowerCAmelCase_         =      "bos"
							lowerCAmelCase_         =      tokenizer.get_vocab()["bos"]

							lowerCAmelCase_         =      "A photo of a cat"
							lowerCAmelCase_         =      tokenizer.encode(
							    _UpperCamelCase							,							)
							# We changed the bos token
							self.assertEqual(_UpperCamelCase							,							[31_957, 250, 1_345, 9, 10, 4_758]	)
							tokenizer.save_pretrained("./tok"	)
							lowerCAmelCase_         =      AutoTokenizer.from_pretrained("./tok"	)
							self.assertTrue(tokenizer.is_fast	)
							lowerCAmelCase_         =      tokenizer.encode(
							    _UpperCamelCase							,							)
							self.assertEqual(_UpperCamelCase							,							[31_957, 250, 1_345, 9, 10, 4_758]	)





from collections import OrderedDict
from typing import Mapping

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


_A  =	logging.get_logger(__name__)

_A  =	{
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}





class       _lowerCAmelCase   (      __a			):
				_lowercase     						='''ibert'''




				def __init__(   self							,							_UpperCamelCase=30_522							,							_UpperCamelCase=768							,							_UpperCamelCase=12							,							_UpperCamelCase=12							,							_UpperCamelCase=3_072							,							_UpperCamelCase="gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=512							,							_UpperCamelCase=2							,							_UpperCamelCase=0.02							,							_UpperCamelCase=1e-1_2							,							_UpperCamelCase=1							,							_UpperCamelCase=0							,							_UpperCamelCase=2							,							_UpperCamelCase="absolute"							,							_UpperCamelCase=False							,							_UpperCamelCase="none"							,							**_UpperCamelCase							,							)							-> List[str]:
							super().__init__(pad_token_id=_UpperCamelCase							,							bos_token_id=_UpperCamelCase							,							eos_token_id=_UpperCamelCase							,							**_UpperCamelCase	)

							lowerCAmelCase_         =      vocab_size
							lowerCAmelCase_         =      hidden_size
							lowerCAmelCase_         =      num_hidden_layers
							lowerCAmelCase_         =      num_attention_heads
							lowerCAmelCase_         =      hidden_act
							lowerCAmelCase_         =      intermediate_size
							lowerCAmelCase_         =      hidden_dropout_prob
							lowerCAmelCase_         =      attention_probs_dropout_prob
							lowerCAmelCase_         =      max_position_embeddings
							lowerCAmelCase_         =      type_vocab_size
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      layer_norm_eps
							lowerCAmelCase_         =      position_embedding_type
							lowerCAmelCase_         =      quant_mode
							lowerCAmelCase_         =      force_dequant





class       _lowerCAmelCase   (      __a			):




				@property
				def 	__a		(   self	)							-> Mapping[str, Mapping[int, str]]:
							if self.task == "multiple-choice":
										lowerCAmelCase_         =      {0: "batch", 1: "choice", 2: "sequence"}
							else:
										lowerCAmelCase_         =      {0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							    ]	)



