
import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin



class __lowerCAmelCase				(					_lowercase     ,				unittest.TestCase      ):




			"""simple docstring"""







			snake_case	      =				CanineTokenizer
			snake_case	      =				False




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







								"""simple docstring"""
								super().setUp()
								A_				 =						CanineTokenizer()
								tokenizer.save_pretrained(self.tmpdirname	)




			@cached_property
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> str:







								"""simple docstring"""
								return CanineTokenizer.from_pretrained("google/canine-s"	)




			def      lowerCamelCase__     (				self						:					Any       ,   **_snake_case						:					Dict	)      -> Any:







								"""simple docstring"""
								A_				 =						self.tokenizer_class.from_pretrained(self.tmpdirname       ,   **__lowerCAmelCase	)
								A_				 =						1_024
								return tokenizer




			@require_torch
			def      lowerCamelCase__     (				self						:					List[str]	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.canine_tokenizer
								A_				 =						["Life is like a box of chocolates.", "You never know what you\'re gonna get."]
								# fmt: off
								A_				 =						[57_344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57_345, 0, 0, 0, 0]
								# fmt: on
								A_				 =						tokenizer(__lowerCAmelCase       ,   padding=__lowerCAmelCase       ,   return_tensors="pt"	)
								self.assertIsInstance(__lowerCAmelCase       ,   __lowerCAmelCase	)

								A_				 =						list(batch.input_ids.numpy()[0]	)

								self.assertListEqual(__lowerCAmelCase       ,   __lowerCAmelCase	)

								self.assertEqual((2, 39)       ,   batch.input_ids.shape	)
								self.assertEqual((2, 39)       ,   batch.attention_mask.shape	)




			@require_torch
			def      lowerCamelCase__     (				self						:					Optional[int]	)      -> Any:







								"""simple docstring"""
								A_				 =						self.canine_tokenizer
								A_				 =						["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
								A_				 =						tokenizer(__lowerCAmelCase       ,   padding=__lowerCAmelCase       ,   return_tensors="pt"	)
								# check if input_ids, attention_mask and token_type_ids are returned
								self.assertIn("input_ids"       ,   __lowerCAmelCase	)
								self.assertIn("attention_mask"       ,   __lowerCAmelCase	)
								self.assertIn("token_type_ids"       ,   __lowerCAmelCase	)




			@require_torch
			def      lowerCamelCase__     (				self						:					Optional[Any]	)      -> Union[str, Any]:







								"""simple docstring"""
								A_				 =						self.canine_tokenizer
								A_				 =						[
								    "What\'s the weater?",
								    "It\'s about 25 degrees.",
								]
								A_				 =						tokenizer(
								    text_target=__lowerCAmelCase       ,   max_length=32       ,   padding="max_length"       ,   truncation=__lowerCAmelCase       ,   return_tensors="pt"	)
								self.assertEqual(32       ,   targets["input_ids"].shape[1]	)




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







								"""simple docstring"""
								A_				 =						self.get_tokenizers()
								for tokenizer in tokenizers:
													with self.subTest(F'{tokenizer.__class__.__name__}'	):
																		self.assertNotEqual(tokenizer.model_max_length       ,   42	)

        # Now let's start the test
								A_				 =						self.get_tokenizers()
								for tokenizer in tokenizers:
													with self.subTest(F'{tokenizer.__class__.__name__}'	):
																		# Isolate this from the other tests because we save additional tokens/etc
																		A_				 =						tempfile.mkdtemp()

																		A_				 =						" He is very happy, UNwant\u00E9d,running"
																		A_				 =						tokenizer.encode(__lowerCAmelCase       ,   add_special_tokens=__lowerCAmelCase	)
																		tokenizer.save_pretrained(__lowerCAmelCase	)

																		A_				 =						tokenizer.__class__.from_pretrained(__lowerCAmelCase	)
																		A_				 =						after_tokenizer.encode(__lowerCAmelCase       ,   add_special_tokens=__lowerCAmelCase	)
																		self.assertListEqual(__lowerCAmelCase       ,   __lowerCAmelCase	)

																		shutil.rmtree(__lowerCAmelCase	)

								A_				 =						self.get_tokenizers(model_max_length=42	)
								for tokenizer in tokenizers:
													with self.subTest(F'{tokenizer.__class__.__name__}'	):
																		# Isolate this from the other tests because we save additional tokens/etc
																		A_				 =						tempfile.mkdtemp()

																		A_				 =						" He is very happy, UNwant\u00E9d,running"

																		A_				 =						tokenizer.additional_special_tokens

																		# We can add a new special token for Canine as follows:
																		A_				 =						chr(0Xe007	)
																		additional_special_tokens.append(__lowerCAmelCase	)
																		tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}	)
																		A_				 =						tokenizer.encode(__lowerCAmelCase       ,   add_special_tokens=__lowerCAmelCase	)
																		tokenizer.save_pretrained(__lowerCAmelCase	)

																		A_				 =						tokenizer.__class__.from_pretrained(__lowerCAmelCase	)
																		A_				 =						after_tokenizer.encode(__lowerCAmelCase       ,   add_special_tokens=__lowerCAmelCase	)
																		self.assertListEqual(__lowerCAmelCase       ,   __lowerCAmelCase	)
																		self.assertIn(__lowerCAmelCase       ,   after_tokenizer.additional_special_tokens	)
																		self.assertEqual(after_tokenizer.model_max_length       ,   42	)

																		A_				 =						tokenizer.__class__.from_pretrained(__lowerCAmelCase       ,   model_max_length=43	)
																		self.assertEqual(tokenizer.model_max_length       ,   43	)

																		shutil.rmtree(__lowerCAmelCase	)




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







								"""simple docstring"""
								A_				 =						self.get_tokenizers(do_lower_case=__lowerCAmelCase	)
								for tokenizer in tokenizers:
													with self.subTest(F'{tokenizer.__class__.__name__}'	):
																		A_ ,    A_				 =						self.get_clean_sequence(__lowerCAmelCase	)

																		# a special token for Canine can be defined as follows:
																		A_				 =						0Xe005
																		A_				 =						chr(__lowerCAmelCase	)

																		tokenizer.add_special_tokens({"cls_token": special_token}	)
																		A_				 =						tokenizer.encode(__lowerCAmelCase       ,   add_special_tokens=__lowerCAmelCase	)
																		self.assertEqual(len(__lowerCAmelCase	)       ,   1	)

																		A_				 =						tokenizer.decode(ids + encoded_special_token       ,   clean_up_tokenization_spaces=__lowerCAmelCase	)
																		A_				 =						tokenizer.encode(__lowerCAmelCase       ,   add_special_tokens=__lowerCAmelCase	)

																		A_				 =						tokenizer.encode(__lowerCAmelCase       ,   add_special_tokens=__lowerCAmelCase	)
																		A_				 =						tokenizer.encode(__lowerCAmelCase       ,   add_special_tokens=__lowerCAmelCase	)
																		self.assertEqual(__lowerCAmelCase       ,   input_encoded + special_token_id	)

																		A_				 =						tokenizer.decode(__lowerCAmelCase       ,   skip_special_tokens=__lowerCAmelCase	)
																		self.assertTrue(special_token not in decoded	)




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







								"""simple docstring"""
								A_				 =						self.get_tokenizers(do_lower_case=__lowerCAmelCase	)
								for tokenizer in tokenizers:
													with self.subTest(F'{tokenizer.__class__.__name__}'	):
																		A_				 =						chr(0Xe005	)
																		A_				 =						chr(0Xe006	)

																		# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
																		tokenizer.add_tokens([SPECIAL_TOKEN_1]       ,   special_tokens=__lowerCAmelCase	)
																		# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
																		# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
																		tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]}	)

																		A_				 =						tokenizer.tokenize(__lowerCAmelCase	)
																		A_				 =						tokenizer.tokenize(__lowerCAmelCase	)

																		self.assertEqual(len(__lowerCAmelCase	)       ,   1	)
																		self.assertEqual(len(__lowerCAmelCase	)       ,   1	)
																		self.assertEqual(token_a[0]       ,   __lowerCAmelCase	)
																		self.assertEqual(token_a[0]       ,   __lowerCAmelCase	)




			@require_tokenizers
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> Optional[int]:







								"""simple docstring"""
								A_				 =						self.get_tokenizers(do_lower_case=__lowerCAmelCase	)
								for tokenizer in tokenizers:
													with self.subTest(F'{tokenizer.__class__.__name__}'	):
																		# a special token for Canine can be defined as follows:
																		A_				 =						0Xe006
																		A_				 =						chr(__lowerCAmelCase	)

																		A_				 =						AddedToken(__lowerCAmelCase       ,   lstrip=__lowerCAmelCase	)
																		tokenizer.add_special_tokens({"additional_special_tokens": [new_token]}	)

																		with tempfile.TemporaryDirectory() as tmp_dir_name:
																							tokenizer.save_pretrained(__lowerCAmelCase	)
																							tokenizer.from_pretrained(__lowerCAmelCase	)




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







								"""simple docstring"""
								A_				 =						[]
								if self.test_slow_tokenizer:
													tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())	)

								if self.test_rust_tokenizer:
													tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())	)

								for tokenizer_class, tokenizer_utils in tokenizer_list:
													with tempfile.TemporaryDirectory() as tmp_dir:
																		tokenizer_utils.save_pretrained(__lowerCAmelCase	)

																		with open(os.path.join(__lowerCAmelCase       ,   "special_tokens_map.json"	)       ,   encoding="utf-8"	) as json_file:
																							A_				 =						json.load(__lowerCAmelCase	)

																		with open(os.path.join(__lowerCAmelCase       ,   "tokenizer_config.json"	)       ,   encoding="utf-8"	) as json_file:
																							A_				 =						json.load(__lowerCAmelCase	)

																		# a special token for Canine can be defined as follows:
																		A_				 =						0Xe006
																		A_				 =						chr(__lowerCAmelCase	)

																		A_				 =						[new_token_a]
																		A_				 =						[new_token_a]

																		with open(os.path.join(__lowerCAmelCase       ,   "special_tokens_map.json"	)       ,   "w"       ,   encoding="utf-8"	) as outfile:
																							json.dump(__lowerCAmelCase       ,   __lowerCAmelCase	)
																		with open(os.path.join(__lowerCAmelCase       ,   "tokenizer_config.json"	)       ,   "w"       ,   encoding="utf-8"	) as outfile:
																							json.dump(__lowerCAmelCase       ,   __lowerCAmelCase	)

																		# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																		# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																		# "special_tokens_map.json" files
																		A_				 =						tokenizer_class.from_pretrained(__lowerCAmelCase       ,   extra_ids=0	)
																		self.assertIn(__lowerCAmelCase       ,   tokenizer_without_change_in_init.additional_special_tokens	)
																		# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
																		self.assertEqual(
																		    [new_token_a]       ,   tokenizer_without_change_in_init.convert_ids_to_tokens(
																		        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]	)	)       ,   )

																		A_				 =						0Xe007
																		A_				 =						chr(__lowerCAmelCase	)
																		# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																		A_				 =						[AddedToken(__lowerCAmelCase       ,   lstrip=__lowerCAmelCase	)]
																		A_				 =						tokenizer_class.from_pretrained(
																		    __lowerCAmelCase       ,   additional_special_tokens=__lowerCAmelCase       ,   extra_ids=0	)

																		self.assertIn(__lowerCAmelCase       ,   tokenizer.additional_special_tokens	)
																		# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
																		self.assertEqual(
																		    [new_token_a]       ,   tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a]	)	)	)




			@require_tokenizers
			def      lowerCamelCase__     (				self						:					str	)      -> int:







								"""simple docstring"""
								A_				 =						self.get_tokenizers(do_lower_case=__lowerCAmelCase	)
								for tokenizer in tokenizers:
													with self.subTest(F'{tokenizer.__class__.__name__}'	):
																		A_				 =						"hello world"
																		if self.space_between_special_tokens:
																							A_				 =						"[CLS] hello world [SEP]"
																		else:
																							A_				 =						input
																		A_				 =						tokenizer.encode(__lowerCAmelCase       ,   add_special_tokens=__lowerCAmelCase	)
																		A_				 =						tokenizer.decode(__lowerCAmelCase       ,   spaces_between_special_tokens=self.space_between_special_tokens	)
																		self.assertIn(__lowerCAmelCase       ,   [output, output.lower()]	)




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







								"""simple docstring"""
								A_				 =						self.get_tokenizers()
								for tokenizer in tokenizers:
													with self.subTest(F'{tokenizer.__class__.__name__}'	):
																		A_				 =						[
																		    "bos_token",
																		    "eos_token",
																		    "unk_token",
																		    "sep_token",
																		    "pad_token",
																		    "cls_token",
																		    "mask_token",
																		]

								A_				 =						"a"
								A_				 =						ord(__lowerCAmelCase	)

								for attr in attributes_list:
													setattr(__lowerCAmelCase       ,   attr + "_id"       ,   __lowerCAmelCase	)
													self.assertEqual(getattr(__lowerCAmelCase       ,   __lowerCAmelCase	)       ,   __lowerCAmelCase	)
													self.assertEqual(getattr(__lowerCAmelCase       ,   attr + "_id"	)       ,   __lowerCAmelCase	)

													setattr(__lowerCAmelCase       ,   attr + "_id"       ,   __lowerCAmelCase	)
													self.assertEqual(getattr(__lowerCAmelCase       ,   __lowerCAmelCase	)       ,   __lowerCAmelCase	)
													self.assertEqual(getattr(__lowerCAmelCase       ,   attr + "_id"	)       ,   __lowerCAmelCase	)

								setattr(__lowerCAmelCase       ,   "additional_special_tokens_ids"       ,   []	)
								self.assertListEqual(getattr(__lowerCAmelCase       ,   "additional_special_tokens"	)       ,   []	)
								self.assertListEqual(getattr(__lowerCAmelCase       ,   "additional_special_tokens_ids"	)       ,   []	)

								A_				 =						0Xe006
								A_				 =						chr(__lowerCAmelCase	)
								setattr(__lowerCAmelCase       ,   "additional_special_tokens_ids"       ,   [additional_special_token_id]	)
								self.assertListEqual(getattr(__lowerCAmelCase       ,   "additional_special_tokens"	)       ,   [additional_special_token]	)
								self.assertListEqual(getattr(__lowerCAmelCase       ,   "additional_special_tokens_ids"	)       ,   [additional_special_token_id]	)




			def      lowerCamelCase__     (				self						:					Tuple	)      -> str:







								"""simple docstring"""
								pass




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







								"""simple docstring"""
								pass




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







								"""simple docstring"""
								pass




			def      lowerCamelCase__     (				self						:					Union[str, Any]	)      -> Optional[int]:







								"""simple docstring"""
								pass




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







								"""simple docstring"""
								pass




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







								"""simple docstring"""
								pass




			def      lowerCamelCase__     (				self						:					Tuple	)      -> Optional[int]:







								"""simple docstring"""
								pass






			def      lowerCamelCase__     (				self						:					Dict	)      -> str:







								"""simple docstring"""
								pass






"""simple docstring"""





import os

import pytest
from attr import dataclass


UpperCamelCase_					:						str				= '''us-east-1'''  # defaults region



@dataclass
class __lowerCAmelCase				:




			"""simple docstring"""







			snake_case	      =				42
			snake_case	      =				"arn:aws:iam::558105141721:role/sagemaker_execution_role"
			snake_case	      =				{
			    "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": 5_00,
			    "save_steps": 55_00,
			}
			snake_case	      =				{**hyperparameters, "max_steps": 10_00}




			@property
			def      lowerCamelCase__     (				self						:					int	)      -> str:







								"""simple docstring"""
								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      lowerCamelCase__     (				self						:					Any	)      -> str:







								"""simple docstring"""
								return F'{self.framework}-transfromers-test'




			@property
			def      lowerCamelCase__     (				self						:					List[Any]	)      -> str:







								"""simple docstring"""
								return F'./tests/sagemaker/scripts/{self.framework}'






			@property
			def      lowerCamelCase__     (				self						:					Any	)      -> str:







								"""simple docstring"""
								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 							A_     (__a				):

					'''simple docstring'''





					A_				 =						SageMakerTestEnvironment(framework=request.cls.framework				)




