




'''simple docstring'''





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 			_snake_case		(     lowercase_						,						unittest.TestCase							):
			lowerCAmelCase_					:				str							=   GPTaTokenizer
			lowerCAmelCase_					:				Union[str, Any]							=   GPTaTokenizerFast
			lowerCAmelCase_					:				Optional[Any]							=   True
			lowerCAmelCase_					:				Union[str, Any]							=   {"add_prefix_space": True}
			lowerCAmelCase_					:				int							=   False




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										super().setUp()

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

										snake_case_      =				os.path.join(self.tmpdirname    ,							VOCAB_FILES_NAMES["vocab_file"]							)
										snake_case_      =				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(a__							) + "\n"							)
										with open(self.merges_file    ,							"w"    ,							encoding="utf-8"							) as fp:
																	fp.write("\n".join(a__							)							)




			def        lowerCAmelCase__    ( self    ,							**a__							)    ->    Union[str, Any]:

										'''simple docstring'''




										kwargs.update(self.special_tokens_map							)
										return GPTaTokenizer.from_pretrained(self.tmpdirname    ,							**a__							)




			def        lowerCAmelCase__    ( self    ,							**a__							)    ->    List[str]:

										'''simple docstring'''




										kwargs.update(self.special_tokens_map							)
										return GPTaTokenizerFast.from_pretrained(self.tmpdirname    ,							**a__							)




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Union[str, Any]:

										'''simple docstring'''




										snake_case_      =				"lower newer"
										snake_case_      =				"lower newer"
										return input_text, output_text




			def        lowerCAmelCase__    ( self							)    ->    Union[str, Any]:

										'''simple docstring'''




										snake_case_      =				GPTaTokenizer(self.vocab_file    ,							self.merges_file    ,							**self.special_tokens_map							)
										snake_case_      =				"lower newer"
										snake_case_      =				["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
										snake_case_      =				tokenizer.tokenize(a__    ,							add_prefix_space=a__							)
										self.assertListEqual(a__    ,							a__							)

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




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




										if not self.test_rust_tokenizer:
																	return

										snake_case_      =				self.get_tokenizer()
										snake_case_      =				self.get_rust_tokenizer(add_prefix_space=a__							)

										snake_case_      =				"lower newer"

										# Testing tokenization
										snake_case_      =				tokenizer.tokenize(a__    ,							add_prefix_space=a__							)
										snake_case_      =				rust_tokenizer.tokenize(a__							)
										self.assertListEqual(a__    ,							a__							)

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

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

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




			def        lowerCAmelCase__    ( self    ,							*a__    ,							**a__							)    ->    Any:

										'''simple docstring'''




										pass




			def        lowerCAmelCase__    ( self    ,							a__=15							)    ->    Any:

										'''simple docstring'''




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

																								# Simple input
																								snake_case_      =				"This is a simple input"
																								snake_case_      =				["This is a simple input 1", "This is a simple input 2"]
																								snake_case_      =				("This is a simple input", "This is a pair")
																								snake_case_      =				[
																								    ("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							)    ->    Any:

										'''simple docstring'''




										snake_case_      =				GPTaTokenizer.from_pretrained(self.tmpdirname    ,							pad_token="<pad>"							)

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

										snake_case_      =				tokenizer.pad_token_id

										snake_case_      =				tokenizer(a__    ,							padding="max_length"    ,							max_length=30    ,							return_tensors="np"							)
										snake_case_      =				tokenizer(a__    ,							padding=a__    ,							truncate=a__    ,							return_tensors="np"							)
										snake_case_      =				tokenizer(*a__    ,							padding="max_length"    ,							max_length=60    ,							return_tensors="np"							)
										snake_case_      =				tokenizer(a__    ,							padding=a__    ,							truncate=a__    ,							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        lowerCAmelCase__    ( self							)    ->    Any:

										'''simple docstring'''




										snake_case_      =				"$$$"
										snake_case_      =				GPTaTokenizer.from_pretrained(self.tmpdirname    ,							bos_token=a__    ,							add_bos_token=a__							)

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

										snake_case_      =				tokenizer.bos_token_id

										snake_case_      =				tokenizer(a__							)
										snake_case_      =				tokenizer(a__							)

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

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

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




			def        lowerCAmelCase__    ( self							)    ->    Tuple:

										'''simple docstring'''




										pass




			def        lowerCAmelCase__    ( self							)    ->    Union[str, Any]:

										'''simple docstring'''




										snake_case_      =				[self.get_tokenizer(do_lower_case=a__    ,							add_bos_token=a__							)]
										for tokenizer in tokenizers:
																	with self.subTest(F'{tokenizer.__class__.__name__}'							):
																								snake_case_      =				"Encode this."
																								snake_case_      =				"This one too please."
																								snake_case_      =				tokenizer.encode(a__    ,							add_special_tokens=a__							)
																								encoded_sequence += tokenizer.encode(a__    ,							add_special_tokens=a__							)
																								snake_case_      =				tokenizer.encode_plus(
																								    a__    ,							a__    ,							add_special_tokens=a__    ,							return_special_tokens_mask=a__    ,							)
																								snake_case_      =				encoded_sequence_dict["input_ids"]
																								snake_case_      =				encoded_sequence_dict["special_tokens_mask"]
																								self.assertEqual(len(a__							)    ,							len(a__							)							)

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




@require_tokenizers
class 			_snake_case		(     unittest.TestCase							):




			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




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

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

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




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

										'''simple docstring'''




										snake_case_      =				AutoTokenizer.from_pretrained("facebook/opt-350m"    ,							use_slow=a__							)
										snake_case_      =				"A photo of a cat"

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




			@unittest.skip("This test is failing because of a bug in the fast tokenizer"							)
			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




										snake_case_      =				AutoTokenizer.from_pretrained("facebook/opt-350m"    ,							from_slow=a__							)

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

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










'''simple docstring'''





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE							:       Optional[int]						=			{
    "configuration_canine": ["CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP", "CanineConfig"],
    "tokenization_canine": ["CanineTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE							:       Dict						=			[
		    "CANINE_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "CanineForMultipleChoice",
		    "CanineForQuestionAnswering",
		    "CanineForSequenceClassification",
		    "CanineForTokenClassification",
		    "CanineLayer",
		    "CanineModel",
		    "CaninePreTrainedModel",
		    "load_tf_weights_in_canine",
		]


if TYPE_CHECKING:
		from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
		from .tokenization_canine import CanineTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_canine import (
				    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CanineForMultipleChoice,
				    CanineForQuestionAnswering,
				    CanineForSequenceClassification,
				    CanineForTokenClassification,
				    CanineLayer,
				    CanineModel,
				    CaninePreTrainedModel,
				    load_tf_weights_in_canine,
				)


else:
		import sys

		_SCREAMING_SNAKE_CASE							:       int						=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




