

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     a__  (    snake_case							,		unittest.TestCase	):
			"""simple docstring"""







			__lowerCamelCase       				=				CanineTokenizer
			__lowerCamelCase       				=				False


			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






							super().setUp()
							A__						=	CanineTokenizer()
							tokenizer.save_pretrained(self.tmpdirname				)


			@cached_property
			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self					,					**lowercase				)     ->  CanineTokenizer:
							'''simple docstring'''






							A__						=	self.tokenizer_class.from_pretrained(self.tmpdirname					,					**lowercase				)
							A__						=	1024
							return tokenizer


			@require_torch
			def    UpperCamelCase						(			self				)     ->  Optional[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__						=	[57344, 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, 57345, 0, 0, 0, 0]
							# fmt: on
							A__						=	tokenizer(lowercase					,					padding=lowercase					,					return_tensors="pt"				)
							self.assertIsInstance(lowercase					,					lowercase				)

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

							self.assertListEqual(lowercase					,					lowercase				)

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


			@require_torch
			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






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


			@require_torch
			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self				)     ->  int:
							'''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(lowercase					,					add_special_tokens=lowercase				)
															tokenizer.save_pretrained(lowercase				)

															A__						=	tokenizer.__class__.from_pretrained(lowercase				)
															A__						=	after_tokenizer.encode(lowercase					,					add_special_tokens=lowercase				)
															self.assertListEqual(lowercase					,					lowercase				)

															shutil.rmtree(lowercase				)

							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(0XE_0_0_7				)
															additional_special_tokens.append(lowercase				)
															tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}				)
															A__						=	tokenizer.encode(lowercase					,					add_special_tokens=lowercase				)
															tokenizer.save_pretrained(lowercase				)

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

															A__						=	tokenizer.__class__.from_pretrained(lowercase					,					model_max_length=43				)
															self.assertEqual(tokenizer.model_max_length					,					43				)

															shutil.rmtree(lowercase				)


			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	self.get_tokenizers(do_lower_case=lowercase				)
							for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'				):
															A__			, A__						=	self.get_clean_sequence(lowercase				)

															# a special token for Canine can be defined as follows:
															A__						=	0XE_0_0_5
															A__						=	chr(lowercase				)

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

															A__						=	tokenizer.decode(ids + encoded_special_token					,					clean_up_tokenization_spaces=lowercase				)
															A__						=	tokenizer.encode(lowercase					,					add_special_tokens=lowercase				)

															A__						=	tokenizer.encode(lowercase					,					add_special_tokens=lowercase				)
															A__						=	tokenizer.encode(lowercase					,					add_special_tokens=lowercase				)
															self.assertEqual(lowercase					,					input_encoded + special_token_id				)

															A__						=	tokenizer.decode(lowercase					,					skip_special_tokens=lowercase				)
															self.assertTrue(special_token not in decoded				)


			def    UpperCamelCase						(			self				)     ->  List[Any]:
							'''simple docstring'''






							A__						=	self.get_tokenizers(do_lower_case=lowercase				)
							for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'				):
															A__						=	chr(0XE_0_0_5				)
															A__						=	chr(0XE_0_0_6				)

															# `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=lowercase				)
															# `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(lowercase				)
															A__						=	tokenizer.tokenize(lowercase				)

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


			@require_tokenizers
			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							A__						=	self.get_tokenizers(do_lower_case=lowercase				)
							for tokenizer in tokenizers:
											with self.subTest(F'{tokenizer.__class__.__name__}'				):
															# a special token for Canine can be defined as follows:
															A__						=	0XE_0_0_6
															A__						=	chr(lowercase				)

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

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


			def    UpperCamelCase						(			self				)     ->  int:
							'''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(lowercase				)

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

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

															# a special token for Canine can be defined as follows:
															A__						=	0XE_0_0_6
															A__						=	chr(lowercase				)

															A__						=	[new_token_a]
															A__						=	[new_token_a]

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

															# 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(lowercase					,					extra_ids=0				)
															self.assertIn(lowercase					,					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__						=	0XE_0_0_7
															A__						=	chr(lowercase				)
															# Now we test that we can change the value of additional_special_tokens in the from_pretrained
															A__						=	[AddedToken(lowercase					,					lstrip=lowercase				)]
															A__						=	tokenizer_class.from_pretrained(
															    lowercase					,					additional_special_tokens=lowercase					,					extra_ids=0				)

															self.assertIn(lowercase					,					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    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							A__						=	self.get_tokenizers(do_lower_case=lowercase				)
							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(lowercase					,					add_special_tokens=lowercase				)
															A__						=	tokenizer.decode(lowercase					,					spaces_between_special_tokens=self.space_between_special_tokens				)
															self.assertIn(lowercase					,					[output, output.lower()]				)


			def    UpperCamelCase						(			self				)     ->  Dict:
							'''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(lowercase				)

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

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

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

							A__						=	0XE_0_0_6
							A__						=	chr(lowercase				)
							setattr(lowercase					,					"additional_special_tokens_ids"					,					[additional_special_token_id]				)
							self.assertListEqual(getattr(lowercase					,					"additional_special_tokens"				)					,					[additional_special_token]				)
							self.assertListEqual(getattr(lowercase					,					"additional_special_tokens_ids"				)					,					[additional_special_token_id]				)


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Optional[Any]:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Tuple:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  List[str]:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Any:
							'''simple docstring'''






							pass


			def    UpperCamelCase						(			self				)     ->  Optional[Any]:
							'''simple docstring'''






							pass


'''simple docstring'''

import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType







class 				_lowercase     :


 '''simple docstring'''






 def __init__(      self   :	Optional[int]      ,       SCREAMING_SNAKE_CASE__   :	List[str]=2      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=3      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=64      ,       SCREAMING_SNAKE_CASE__   :	int=None							)  ->    Optional[int]:
  __lowerCAmelCase											=						np.random.default_rng(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						length
  __lowerCAmelCase											=						rng.normal(size=(length,)							).astype(np.floataa							)
  __lowerCAmelCase											=						a * self.x + b + rng.normal(scale=0.1      ,       size=(length,)							).astype(np.floataa							)
 def __len__(      self   :	Union[str, Any]							)  ->    Optional[Any]:
  return self.length





 def __getitem__(      self   :	str      ,       SCREAMING_SNAKE_CASE__   :	Dict							)  ->    Optional[int]:
  return {"x": self.x[i], "y": self.y[i]}







class 				_lowercase     (				torch.nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Tuple=0      ,       SCREAMING_SNAKE_CASE__   :	Tuple=False							)  ->    Any:
  super().__init__()
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor([2, 3]							).float()							)
  __lowerCAmelCase											=						True





 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=None							)  ->    str:
  if self.first_batch:
   print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""							)
   __lowerCAmelCase											=						False
  return x * self.a[0] + self.b[0]







class 				_lowercase     (				torch.nn.Module			):


 '''simple docstring'''






 def __init__(      self   :	Any      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=0      ,       SCREAMING_SNAKE_CASE__   :	Optional[Any]=False							)  ->    Optional[Any]:
  super().__init__()
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__							).float()							)
  __lowerCAmelCase											=						torch.nn.Parameter(torch.tensor(SCREAMING_SNAKE_CASE__							).float()							)
  __lowerCAmelCase											=						True





 def a					(      self   :	List[str]      ,       SCREAMING_SNAKE_CASE__   :	Any=None							)  ->    int:
  if self.first_batch:
   print(f"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"""							)
   __lowerCAmelCase											=						False
  return x * self.a + self.b







def      UpperCamelCase_						(					snake_case_       :       List[str]						,		snake_case_       :       int = 16      )					->     int:






 '''simple docstring'''

 from datasets import load_dataset
 from transformers import AutoTokenizer

 __lowerCAmelCase											=						AutoTokenizer.from_pretrained("""bert-base-cased"""      )
 __lowerCAmelCase											=						{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
 __lowerCAmelCase											=						load_dataset("""csv"""						,		data_files=snake_case_      )
 __lowerCAmelCase											=						datasets["""train"""].unique("""label"""      )

 __lowerCAmelCase											=						{v: i for i, v in enumerate(snake_case_      )}

 def tokenize_function(snake_case_       :       Optional[int]      ):
  # max_length=None => use the model max length (it's actually the default)
  __lowerCAmelCase											=						tokenizer(
      examples["""sentence1"""]						,		examples["""sentence2"""]						,		truncation=snake_case_						,		max_length=snake_case_						,		padding="""max_length"""      )
  if "label" in examples:
   __lowerCAmelCase											=						[label_to_id[l] for l in examples["""label"""]]
  return outputs

 # Apply the method we just defined to all the examples in all the splits of the dataset
 __lowerCAmelCase											=						datasets.map(
     snake_case_						,		batched=snake_case_						,		remove_columns=["""sentence1""", """sentence2""", """label"""]						,		)

 def collate_fn(snake_case_       :       List[Any]      ):
  # On TPU it's best to pad everything to the same length or training will be very slow.
  if accelerator.distributed_type == DistributedType.TPU:
   return tokenizer.pad(snake_case_						,		padding="""max_length"""						,		max_length=1_28						,		return_tensors="""pt"""      )
  return tokenizer.pad(snake_case_						,		padding="""longest"""						,		return_tensors="""pt"""      )

 # Instantiate dataloaders.
 __lowerCAmelCase											=						DataLoader(tokenized_datasets["""train"""]						,		shuffle=snake_case_						,		collate_fn=snake_case_						,		batch_size=2      )
 __lowerCAmelCase											=						DataLoader(tokenized_datasets["""validation"""]						,		shuffle=snake_case_						,		collate_fn=snake_case_						,		batch_size=1      )

 return train_dataloader, eval_dataloader






