





'''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     __a					(lowerCamelCase				,						unittest.TestCase   ):
							__a    :      List[str]  	=				GPTaTokenizer
							__a    :      str  	=				GPTaTokenizerFast
							__a    :      Optional[Any]  	=				True
							__a    :      List[Any]  	=				{"add_prefix_space": True}
							__a    :      Optional[int]  	=				False

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







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

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

													UpperCAmelCase_	:					Optional[Any]     =     os.path.join(self.tmpdirname				,   VOCAB_FILES_NAMES['''vocab_file''']      )
													UpperCAmelCase_	:					Optional[int]     =     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(_lowerCAmelCase      ) + '''\n'''      )
													with open(self.merges_file				,   '''w'''				,   encoding='''utf-8'''      ) as fp:
																			fp.write('''\n'''.join(_lowerCAmelCase      )      )

							def 			UpperCAmelCase__		(							self							:   Tuple				,   **__magic_name__							:   List[str]      )    ->				List[Any]:







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

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







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

							def 			UpperCAmelCase__		(							self							:   Tuple				,   __magic_name__							:   Optional[int]      )    ->				Union[str, Any]:







													"""simple docstring"""
													UpperCAmelCase_	:					List[str]     =     "lower newer"
													UpperCAmelCase_	:					List[Any]     =     "lower newer"
													return input_text, output_text

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







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

													UpperCAmelCase_	:					str     =     tokens + [tokenizer.unk_token]
													UpperCAmelCase_	:					List[Any]     =     [14, 15, 10, 9, 3, 2, 15, 19]
													self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase      )				,   _lowerCAmelCase      )

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







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

													UpperCAmelCase_	:					Union[str, Any]     =     self.get_tokenizer()
													UpperCAmelCase_	:					Any     =     self.get_rust_tokenizer(add_prefix_space=_lowerCAmelCase      )

													UpperCAmelCase_	:					Dict     =     "lower newer"

													# Testing tokenization
													UpperCAmelCase_	:					Union[str, Any]     =     tokenizer.tokenize(_lowerCAmelCase				,   add_prefix_space=_lowerCAmelCase      )
													UpperCAmelCase_	:					Tuple     =     rust_tokenizer.tokenize(_lowerCAmelCase      )
													self.assertListEqual(_lowerCAmelCase				,   _lowerCAmelCase      )

													# Testing conversion to ids without special tokens
													UpperCAmelCase_	:					Dict     =     tokenizer.encode(_lowerCAmelCase				,   add_special_tokens=_lowerCAmelCase				,   add_prefix_space=_lowerCAmelCase      )
													UpperCAmelCase_	:					List[Any]     =     rust_tokenizer.encode(_lowerCAmelCase				,   add_special_tokens=_lowerCAmelCase      )
													self.assertListEqual(_lowerCAmelCase				,   _lowerCAmelCase      )

													# Testing conversion to ids with special tokens
													UpperCAmelCase_	:					Dict     =     self.get_rust_tokenizer(add_prefix_space=_lowerCAmelCase      )
													UpperCAmelCase_	:					Union[str, Any]     =     tokenizer.encode(_lowerCAmelCase				,   add_prefix_space=_lowerCAmelCase      )
													UpperCAmelCase_	:					List[str]     =     rust_tokenizer.encode(_lowerCAmelCase      )
													self.assertListEqual(_lowerCAmelCase				,   _lowerCAmelCase      )

													# Testing the unknown token
													UpperCAmelCase_	:					List[str]     =     tokens + [rust_tokenizer.unk_token]
													UpperCAmelCase_	:					Dict     =     [14, 15, 10, 9, 3, 2, 15, 19]
													self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_lowerCAmelCase      )				,   _lowerCAmelCase      )

							def 			UpperCAmelCase__		(							self							:   Dict				,   *__magic_name__							:   Union[str, Any]				,   **__magic_name__							:   List[Any]      )    ->				Optional[int]:







													"""simple docstring"""
													# 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 			UpperCAmelCase__		(							self							:   Any				,   __magic_name__							:   Union[str, Any]=15      )    ->				Union[str, Any]:







													"""simple docstring"""
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""      ):
																									UpperCAmelCase_	:					str     =     self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase				,   **_lowerCAmelCase      )

																									# Simple input
																									UpperCAmelCase_	:					Optional[Any]     =     "This is a simple input"
																									UpperCAmelCase_	:					Union[str, Any]     =     ["This is a simple input 1", "This is a simple input 2"]
																									UpperCAmelCase_	:					Optional[Any]     =     ("This is a simple input", "This is a pair")
																									UpperCAmelCase_	:					Union[str, 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(_lowerCAmelCase				,   tokenizer_r.encode				,   _lowerCAmelCase				,   max_length=_lowerCAmelCase				,   padding='''max_length'''      )

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

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

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

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

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

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







													"""simple docstring"""
													UpperCAmelCase_	:					int     =     GPTaTokenizer.from_pretrained(self.tmpdirname				,   pad_token='''<pad>'''      )

													# Simple input
													UpperCAmelCase_	:					str     =     "This is a simple input"
													UpperCAmelCase_	:					List[str]     =     ["This is a simple input looooooooong", "This is a simple input"]
													UpperCAmelCase_	:					str     =     ("This is a simple input", "This is a pair")
													UpperCAmelCase_	:					Optional[Any]     =     [
													    ("This is a simple input loooooong", "This is a simple input"),
													    ("This is a simple pair loooooong", "This is a simple pair"),
													]

													UpperCAmelCase_	:					List[Any]     =     tokenizer.pad_token_id

													UpperCAmelCase_	:					Any     =     tokenizer(_lowerCAmelCase				,   padding='''max_length'''				,   max_length=30				,   return_tensors='''np'''      )
													UpperCAmelCase_	:					int     =     tokenizer(_lowerCAmelCase				,   padding=_lowerCAmelCase				,   truncate=_lowerCAmelCase				,   return_tensors='''np'''      )
													UpperCAmelCase_	:					Optional[Any]     =     tokenizer(*_lowerCAmelCase				,   padding='''max_length'''				,   max_length=60				,   return_tensors='''np'''      )
													UpperCAmelCase_	:					Tuple     =     tokenizer(_lowerCAmelCase				,   padding=_lowerCAmelCase				,   truncate=_lowerCAmelCase				,   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 			UpperCAmelCase__		(							self							:   Tuple      )    ->				List[Any]:







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

													UpperCAmelCase_	:					List[Any]     =     "This is a simple input"
													UpperCAmelCase_	:					Dict     =     ["This is a simple input 1", "This is a simple input 2"]

													UpperCAmelCase_	:					Union[str, Any]     =     tokenizer.bos_token_id

													UpperCAmelCase_	:					Tuple     =     tokenizer(_lowerCAmelCase      )
													UpperCAmelCase_	:					Optional[Any]     =     tokenizer(_lowerCAmelCase      )

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

													UpperCAmelCase_	:					int     =     tokenizer.decode(out_s.input_ids      )
													UpperCAmelCase_	:					Any     =     tokenizer.batch_decode(out_sa.input_ids      )

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

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







													"""simple docstring"""
													pass



							def 			UpperCAmelCase__		(							self							:   int      )    ->				int:







													"""simple docstring"""
													# TODO: change to self.get_tokenizers() when the fast version is implemented
													UpperCAmelCase_	:					List[Any]     =     [self.get_tokenizer(do_lower_case=_lowerCAmelCase				,   add_bos_token=_lowerCAmelCase      )]
													for tokenizer in tokenizers:
																			with self.subTest(F"""{tokenizer.__class__.__name__}"""      ):
																									UpperCAmelCase_	:					List[Any]     =     "Encode this."
																									UpperCAmelCase_	:					List[Any]     =     "This one too please."
																									UpperCAmelCase_	:					Tuple     =     tokenizer.encode(_lowerCAmelCase				,   add_special_tokens=_lowerCAmelCase      )
																									encoded_sequence += tokenizer.encode(_lowerCAmelCase				,   add_special_tokens=_lowerCAmelCase      )
																									UpperCAmelCase_	:					Optional[Any]     =     tokenizer.encode_plus(
																									    _lowerCAmelCase				,   _lowerCAmelCase				,   add_special_tokens=_lowerCAmelCase				,   return_special_tokens_mask=_lowerCAmelCase				,   )
																									UpperCAmelCase_	:					Any     =     encoded_sequence_dict["input_ids"]
																									UpperCAmelCase_	:					List[Any]     =     encoded_sequence_dict["special_tokens_mask"]
																									self.assertEqual(len(_lowerCAmelCase      )				,   len(_lowerCAmelCase      )      )

																									UpperCAmelCase_	:					Any     =     [
																									    (x if not special_tokens_mask[i] else None) for i, x in enumerate(_lowerCAmelCase      )
																									]
																									UpperCAmelCase_	:					int     =     [x for x in filtered_sequence if x is not None]
																									self.assertEqual(_lowerCAmelCase				,   _lowerCAmelCase      )





@require_tokenizers
class     __a					(unittest.TestCase   ):

							def 			UpperCAmelCase__		(							self							:   Optional[int]      )    ->				int:







													"""simple docstring"""
													# 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

													UpperCAmelCase_	:					List[Any]     =     AutoTokenizer.from_pretrained('''facebook/opt-350m'''				,   from_slow=_lowerCAmelCase      )
													UpperCAmelCase_	:					Any     =     "A photo of a cat"

													UpperCAmelCase_	:					Optional[int]     =     tokenizer.encode(
													    _lowerCAmelCase				,   )
													self.assertEqual(_lowerCAmelCase				,   [2, 2_50, 13_45, 9, 10, 47_58]      )
													tokenizer.save_pretrained('''test_opt'''      )

													UpperCAmelCase_	:					List[Any]     =     AutoTokenizer.from_pretrained('''./test_opt'''      )
													UpperCAmelCase_	:					List[Any]     =     tokenizer.encode(
													    _lowerCAmelCase				,   )
													self.assertEqual(_lowerCAmelCase				,   [2, 2_50, 13_45, 9, 10, 47_58]      )

							def 			UpperCAmelCase__		(							self							:   str      )    ->				List[str]:







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

													UpperCAmelCase_	:					int     =     tokenizer.encode(
													    _lowerCAmelCase				,   )
													# Same as above
													self.assertEqual(_lowerCAmelCase				,   [2, 2_50, 13_45, 9, 10, 47_58]      )



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







													"""simple docstring"""
													UpperCAmelCase_	:					Any     =     AutoTokenizer.from_pretrained('''facebook/opt-350m'''				,   from_slow=_lowerCAmelCase      )

													UpperCAmelCase_	:					List[Any]     =     "bos"
													UpperCAmelCase_	:					List[str]     =     tokenizer.get_vocab()["bos"]

													UpperCAmelCase_	:					Tuple     =     "A photo of a cat"
													UpperCAmelCase_	:					Dict     =     tokenizer.encode(
													    _lowerCAmelCase				,   )
													# We changed the bos token
													self.assertEqual(_lowerCAmelCase				,   [3_19_57, 2_50, 13_45, 9, 10, 47_58]      )
													tokenizer.save_pretrained('''./tok'''      )
													UpperCAmelCase_	:					Optional[Any]     =     AutoTokenizer.from_pretrained('''./tok'''      )
													self.assertTrue(tokenizer.is_fast      )
													UpperCAmelCase_	:					Dict     =     tokenizer.encode(
													    _lowerCAmelCase				,   )
													self.assertEqual(_lowerCAmelCase				,   [3_19_57, 2_50, 13_45, 9, 10, 47_58]      )











import os

import pytest
from attr import dataclass


_UpperCAmelCase		:  List[str]	=   "us-east-1"  # defaults region




@dataclass
class       __lowerCAmelCase				:
       _a           =  42
       _a           =  '''arn:aws:iam::558105141721:role/sagemaker_execution_role'''
       _a           =  {
           '''task_name''': '''mnli''',
           '''per_device_train_batch_size''': 16,
           '''per_device_eval_batch_size''': 16,
           '''do_train''': True,
           '''do_eval''': True,
           '''do_predict''': True,
           '''output_dir''': '''/opt/ml/model''',
           '''overwrite_output_dir''': True,
           '''max_steps''': 500,
           '''save_steps''': 5500,
       }
       _a           =  {**hyperparameters, '''max_steps''': 1000}






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    str ):
         if self.framework == "pytorch":
           return [
               {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
               {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
               {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
           ]
         else:
           return [
               {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
               {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
               {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
           ]






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Dict ):
         return F"{self.framework}-transfromers-test"






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Any ):
         return F"./tests/sagemaker/scripts/{self.framework}"






       @property
       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int] ):
         if self.framework == "pytorch":
           return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
         else:
           return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"







@pytest.fixture(scope="class"      )
def 					UpperCAmelCase__     (   lowerCamelCase      ):
  lowercase			:Union[str, Any]							=  SageMakerTestEnvironment(framework=request.cls.framework      )



