





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       (__lowerCamelCase							,     unittest.TestCase      ):
							UpperCAmelCase_      						=   GPTaTokenizer
							UpperCAmelCase_      						=   GPTaTokenizerFast
							UpperCAmelCase_      						=   True
							UpperCAmelCase_      						=   {"add_prefix_space": True}
							UpperCAmelCase_      						=   False






							def  A_     ( self     :					str						)				->			Optional[int]:





														"""simple docstring"""
														super().setUp()

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

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES["vocab_file"]						)
														SCREAMING_SNAKE_CASE__       :      str     =						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     :					int,			**_UpperCAmelCase     :					str						)				->			str:





														"""simple docstring"""
														kwargs.update(self.special_tokens_map						)
														return GPTaTokenizer.from_pretrained(self.tmpdirname,			**_UpperCAmelCase						)






							def  A_     ( self     :					str,			**_UpperCAmelCase     :					List[Any]						)				->			Optional[int]:





														"""simple docstring"""
														kwargs.update(self.special_tokens_map						)
														return GPTaTokenizerFast.from_pretrained(self.tmpdirname,			**_UpperCAmelCase						)






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						"lower newer"
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						"lower newer"
														return input_text, output_text






							def  A_     ( self     :					List[Any]						)				->			List[str]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						GPTaTokenizer(self.vocab_file,			self.merges_file,			**self.special_tokens_map						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"lower newer"
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
														SCREAMING_SNAKE_CASE__       :      int     =						tokenizer.tokenize(_UpperCAmelCase,			add_prefix_space=_UpperCAmelCase						)
														self.assertListEqual(_UpperCAmelCase,			_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Tuple     =						tokens + [tokenizer.unk_token]
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase						),			_UpperCAmelCase						)






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





														"""simple docstring"""
														if not self.test_rust_tokenizer:
																					return

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.get_tokenizer()
														SCREAMING_SNAKE_CASE__       :      str     =						self.get_rust_tokenizer(add_prefix_space=_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"lower newer"

														# Testing tokenization
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						tokenizer.tokenize(_UpperCAmelCase,			add_prefix_space=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						rust_tokenizer.tokenize(_UpperCAmelCase						)
														self.assertListEqual(_UpperCAmelCase,			_UpperCAmelCase						)

														# Testing conversion to ids without special tokens
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						tokenizer.encode(_UpperCAmelCase,			add_special_tokens=_UpperCAmelCase,			add_prefix_space=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Any     =						rust_tokenizer.encode(_UpperCAmelCase,			add_special_tokens=_UpperCAmelCase						)
														self.assertListEqual(_UpperCAmelCase,			_UpperCAmelCase						)

														# Testing conversion to ids with special tokens
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self.get_rust_tokenizer(add_prefix_space=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						tokenizer.encode(_UpperCAmelCase,			add_prefix_space=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      int     =						rust_tokenizer.encode(_UpperCAmelCase						)
														self.assertListEqual(_UpperCAmelCase,			_UpperCAmelCase						)

														# Testing the unknown token
														SCREAMING_SNAKE_CASE__       :      Tuple     =						tokens + [rust_tokenizer.unk_token]
														SCREAMING_SNAKE_CASE__       :      List[str]     =						[1_4, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
														self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_UpperCAmelCase						),			_UpperCAmelCase						)






							def  A_     ( self     :					List[str],			*_UpperCAmelCase     :					Optional[Any],			**_UpperCAmelCase     :					Optional[int]						)				->			int:





														"""simple docstring"""
														pass






							def  A_     ( self     :					int,			_UpperCAmelCase     :					str=1_5						)				->			List[str]:





														"""simple docstring"""
														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''						):
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase,			**_UpperCAmelCase						)

																												# Simple input
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						"This is a simple input"
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						["This is a simple input 1", "This is a simple input 2"]
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						("This is a simple input", "This is a pair")
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[
																												    ("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[Any]						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						GPTaTokenizer.from_pretrained(self.tmpdirname,			pad_token="<pad>"						)

														# Simple input
														SCREAMING_SNAKE_CASE__       :      Dict     =						"This is a simple input"
														SCREAMING_SNAKE_CASE__       :      str     =						["This is a simple input looooooooong", "This is a simple input"]
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						("This is a simple input", "This is a pair")
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[
														    ("This is a simple input loooooong", "This is a simple input"),
														    ("This is a simple pair loooooong", "This is a simple pair"),
														]

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						tokenizer.pad_token_id

														SCREAMING_SNAKE_CASE__       :      Tuple     =						tokenizer(_UpperCAmelCase,			padding="max_length",			max_length=3_0,			return_tensors="np"						)
														SCREAMING_SNAKE_CASE__       :      Any     =						tokenizer(_UpperCAmelCase,			padding=_UpperCAmelCase,			truncate=_UpperCAmelCase,			return_tensors="np"						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						tokenizer(*_UpperCAmelCase,			padding="max_length",			max_length=6_0,			return_tensors="np"						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						tokenizer(_UpperCAmelCase,			padding=_UpperCAmelCase,			truncate=_UpperCAmelCase,			return_tensors="np"						)

														# s
														# test single string max_length padding
														self.assertEqual(out_s["input_ids"].shape[-1],			3_0						)
														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],			3_3						)
														# 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],			6_0						)
														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],			5_2						)
														# 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]						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						"$$$"
														SCREAMING_SNAKE_CASE__       :      List[str]     =						GPTaTokenizer.from_pretrained(self.tmpdirname,			bos_token=_UpperCAmelCase,			add_bos_token=_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						"This is a simple input"
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						["This is a simple input 1", "This is a simple input 2"]

														SCREAMING_SNAKE_CASE__       :      Any     =						tokenizer.bos_token_id

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						tokenizer(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						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						)						)

														SCREAMING_SNAKE_CASE__       :      Dict     =						tokenizer.decode(out_s.input_ids						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						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						)				->			Optional[int]:





														"""simple docstring"""
														pass






							def  A_     ( self     :					Tuple						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[self.get_tokenizer(do_lower_case=_UpperCAmelCase,			add_bos_token=_UpperCAmelCase						)]
														for tokenizer in tokenizers:
																					with self.subTest(F'''{tokenizer.__class__.__name__}'''						):
																												SCREAMING_SNAKE_CASE__       :      str     =						"Encode this."
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						"This one too please."
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						tokenizer.encode(_UpperCAmelCase,			add_special_tokens=_UpperCAmelCase						)
																												encoded_sequence += tokenizer.encode(_UpperCAmelCase,			add_special_tokens=_UpperCAmelCase						)
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						tokenizer.encode_plus(
																												    _UpperCAmelCase,			_UpperCAmelCase,			add_special_tokens=_UpperCAmelCase,			return_special_tokens_mask=_UpperCAmelCase,			)
																												SCREAMING_SNAKE_CASE__       :      Any     =						encoded_sequence_dict["input_ids"]
																												SCREAMING_SNAKE_CASE__       :      int     =						encoded_sequence_dict["special_tokens_mask"]
																												self.assertEqual(len(_UpperCAmelCase						),			len(_UpperCAmelCase						)						)

																												SCREAMING_SNAKE_CASE__       :      Optional[int]     =						[
																												    (x if not special_tokens_mask[i] else None) for i, x in enumerate(_UpperCAmelCase						)
																												]
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[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						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						AutoTokenizer.from_pretrained("facebook/opt-350m",			from_slow=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						"A photo of a cat"

														SCREAMING_SNAKE_CASE__       :      List[str]     =						tokenizer.encode(
														    _UpperCAmelCase,			)
														self.assertEqual(_UpperCAmelCase,			[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]						)
														tokenizer.save_pretrained("test_opt"						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						AutoTokenizer.from_pretrained("./test_opt"						)
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						tokenizer.encode(
														    _UpperCAmelCase,			)
														self.assertEqual(_UpperCAmelCase,			[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]						)






							def  A_     ( self     :					List[str]						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						AutoTokenizer.from_pretrained("facebook/opt-350m",			use_slow=_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						"A photo of a cat"

														SCREAMING_SNAKE_CASE__       :      Any     =						tokenizer.encode(
														    _UpperCAmelCase,			)
														# Same as above
														self.assertEqual(_UpperCAmelCase,			[2, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]						)






							@unittest.skip("This test is failing because of a bug in the fast tokenizer"						)
							def  A_     ( self     :					Optional[Any]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      int     =						AutoTokenizer.from_pretrained("facebook/opt-350m",			from_slow=_UpperCAmelCase						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						"bos"
														SCREAMING_SNAKE_CASE__       :      Any     =						tokenizer.get_vocab()["bos"]

														SCREAMING_SNAKE_CASE__       :      str     =						"A photo of a cat"
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						tokenizer.encode(
														    _UpperCAmelCase,			)
														# We changed the bos token
														self.assertEqual(_UpperCAmelCase,			[3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]						)
														tokenizer.save_pretrained("./tok"						)
														SCREAMING_SNAKE_CASE__       :      str     =						AutoTokenizer.from_pretrained("./tok"						)
														self.assertTrue(tokenizer.is_fast						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						tokenizer.encode(
														    _UpperCAmelCase,			)
														self.assertEqual(_UpperCAmelCase,			[3_1_9_5_7, 2_5_0, 1_3_4_5, 9, 1_0, 4_7_5_8]						)













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase :    Union[str, Any]  							= {
    '''configuration_bigbird_pegasus''': [
        '''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BigBirdPegasusConfig''',
        '''BigBirdPegasusOnnxConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase :    int  							= [
		    '''BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BigBirdPegasusForCausalLM''',
		    '''BigBirdPegasusForConditionalGeneration''',
		    '''BigBirdPegasusForQuestionAnswering''',
		    '''BigBirdPegasusForSequenceClassification''',
		    '''BigBirdPegasusModel''',
		    '''BigBirdPegasusPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_bigbird_pegasus import (
		    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BigBirdPegasusConfig,
		    BigBirdPegasusOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bigbird_pegasus import (
				    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BigBirdPegasusForCausalLM,
				    BigBirdPegasusForConditionalGeneration,
				    BigBirdPegasusForQuestionAnswering,
				    BigBirdPegasusForSequenceClassification,
				    BigBirdPegasusModel,
				    BigBirdPegasusPreTrainedModel,
				)


else:
		import sys

		_lowerCamelCase :    Optional[Any]  							= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






