
"""simple docstring"""
import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
			from tokenizers import Tokenizer
			from tokenizers.models import WordPiece
class      lowerCAmelCase_						(							unittest.TestCase				):



							'''simple docstring'''





							def  A__		(				self  ,							snake_case_							)   ->       List[Any]:
													__lowerCAmelCase        =       list(tokenizer_class.max_model_input_sizes.keys()							)
													for model_name in sa_models[:1]:
																			__lowerCAmelCase        =       tokenizer_class.from_pretrained(snake_case_							)
																			self.assertIsNotNone(snake_case_							)
																			self.assertIsInstance(snake_case_  ,							snake_case_							)
																			self.assertIsInstance(snake_case_  ,							snake_case_							)

																			for special_tok in tokenizer.all_special_tokens:
																									self.assertIsInstance(snake_case_  ,							snake_case_							)
																									__lowerCAmelCase        =       tokenizer.convert_tokens_to_ids(snake_case_							)
																									self.assertIsInstance(snake_case_  ,							snake_case_							)




							def  A__		(				self  ,							snake_case_  ,							snake_case_ = None							)   ->       Optional[Any]:
													__lowerCAmelCase        =       pickle.dumps(snake_case_							)
													self.assertIsNotNone(snake_case_							)

													__lowerCAmelCase        =       pickle.loads(snake_case_							)

													# Ensure is_fast is correctly restored
													self.assertEqual(be_restored.is_fast  ,							be_original.is_fast							)

													# Ensure encodings are potentially correctly restored
													if be_original.is_fast:
																			self.assertIsNotNone(be_restored.encodings							)
													else:
																			self.assertIsNone(be_restored.encodings							)

													# Ensure the keys are the same
													for original_v, restored_v in zip(be_original.values()  ,							be_restored.values()							):
																			if equal_op:
																									self.assertTrue(equal_op(snake_case_  ,							snake_case_							)							)
																			else:
																									self.assertEqual(snake_case_  ,							snake_case_							)




							@slow
							def  A__		(				self							)   ->       Tuple:
													self.check_tokenizer_from_pretrained(snake_case_							)




							def  A__		(				self							)   ->       Tuple:
													self.assertEqual(TensorType("""tf"""							)  ,							TensorType.TENSORFLOW							)
													self.assertEqual(TensorType("""pt"""							)  ,							TensorType.PYTORCH							)
													self.assertEqual(TensorType("""np"""							)  ,							TensorType.NUMPY							)




							@require_tokenizers
							def  A__		(				self							)   ->       str:
													import numpy as np

													__lowerCAmelCase        =       BertTokenizer.from_pretrained("""bert-base-cased"""							)
													__lowerCAmelCase        =       BertTokenizerFast.from_pretrained("""bert-base-cased"""							)

													# Python no tensor
													with self.subTest("""BatchEncoding (Python, return_tensors=None)"""							):
																			self.assert_dump_and_restore(tokenizer_p("""Small example to encode"""							)							)

													with self.subTest("""BatchEncoding (Python, return_tensors=NUMPY)"""							):
																			self.assert_dump_and_restore(
																			    tokenizer_p("""Small example to encode"""  ,							return_tensors=TensorType.NUMPY							)  ,							np.array_equal							)

													with self.subTest("""BatchEncoding (Rust, return_tensors=None)"""							):
																			self.assert_dump_and_restore(tokenizer_r("""Small example to encode"""							)							)

													with self.subTest("""BatchEncoding (Rust, return_tensors=NUMPY)"""							):
																			self.assert_dump_and_restore(
																			    tokenizer_r("""Small example to encode"""  ,							return_tensors=TensorType.NUMPY							)  ,							np.array_equal							)




							@require_tf
							@require_tokenizers
							def  A__		(				self							)   ->       Optional[Any]:
													import tensorflow as tf

													def tf_array_equals(snake_case_  ,							snake_case_							):
																			return tf.reduce_all(tf.equal(snake_case_  ,							snake_case_							)							)

													__lowerCAmelCase        =       BertTokenizer.from_pretrained("""bert-base-cased"""							)
													__lowerCAmelCase        =       BertTokenizerFast.from_pretrained("""bert-base-cased"""							)

													with self.subTest("""BatchEncoding (Python, return_tensors=TENSORFLOW)"""							):
																			self.assert_dump_and_restore(
																			    tokenizer_p("""Small example to encode"""  ,							return_tensors=TensorType.TENSORFLOW							)  ,							snake_case_							)

													with self.subTest("""BatchEncoding (Rust, return_tensors=TENSORFLOW)"""							):
																			self.assert_dump_and_restore(
																			    tokenizer_r("""Small example to encode"""  ,							return_tensors=TensorType.TENSORFLOW							)  ,							snake_case_							)




							@require_torch
							@require_tokenizers
							def  A__		(				self							)   ->       Optional[int]:
													import torch

													__lowerCAmelCase        =       BertTokenizer.from_pretrained("""bert-base-cased"""							)
													__lowerCAmelCase        =       BertTokenizerFast.from_pretrained("""bert-base-cased"""							)

													with self.subTest("""BatchEncoding (Python, return_tensors=PYTORCH)"""							):
																			self.assert_dump_and_restore(
																			    tokenizer_p("""Small example to encode"""  ,							return_tensors=TensorType.PYTORCH							)  ,							torch.equal							)

													with self.subTest("""BatchEncoding (Rust, return_tensors=PYTORCH)"""							):
																			self.assert_dump_and_restore(
																			    tokenizer_r("""Small example to encode"""  ,							return_tensors=TensorType.PYTORCH							)  ,							torch.equal							)




							@require_tokenizers
							def  A__		(				self							)   ->       int:
													__lowerCAmelCase        =       BertTokenizer.from_pretrained("""bert-base-cased"""							)
													__lowerCAmelCase        =       BertTokenizerFast.from_pretrained("""bert-base-cased"""							)

													with self.subTest("""Python Tokenizer"""							):
																			self.assertFalse(tokenizer_p("""Small example to_encode"""							).is_fast							)

													with self.subTest("""Rust Tokenizer"""							):
																			self.assertTrue(tokenizer_r("""Small example to_encode"""							).is_fast							)




							@require_tokenizers
							def  A__		(				self							)   ->       int:
													__lowerCAmelCase        =       BertTokenizerFast.from_pretrained("""bert-base-cased"""							)
													__lowerCAmelCase        =       tokenizer_r(["""Test""", """\xad""", """test"""]  ,							is_split_into_words=snake_case_							)

													self.assertEqual(encoded.word_to_tokens(0							)  ,							TokenSpan(start=1  ,							end=2							)							)
													self.assertEqual(encoded.word_to_tokens(1							)  ,							snake_case_							)
													self.assertEqual(encoded.word_to_tokens(2							)  ,							TokenSpan(start=2  ,							end=3							)							)




							def  A__		(				self							)   ->       List[str]:
													__lowerCAmelCase        =       BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}							)
													__lowerCAmelCase        =       batch.convert_to_tensors(tensor_type="""np"""							)
													self.assertEqual(tensor_batch["""inputs"""].shape  ,							(2, 3)							)
													self.assertEqual(tensor_batch["""labels"""].shape  ,							(2,)							)
													# test converting the converted
													with CaptureStderr() as cs:
																			__lowerCAmelCase        =       batch.convert_to_tensors(tensor_type="""np"""							)
													self.assertFalse(len(cs.err							)  ,							msg=f"""should have no warning, but got {cs.err}"""							)

													__lowerCAmelCase        =       BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}							)
													__lowerCAmelCase        =       batch.convert_to_tensors(tensor_type="""np"""  ,							prepend_batch_axis=snake_case_							)
													self.assertEqual(tensor_batch["""inputs"""].shape  ,							(1, 3)							)
													self.assertEqual(tensor_batch["""labels"""].shape  ,							(1,)							)




							@require_torch
							def  A__		(				self							)   ->       int:
													__lowerCAmelCase        =       BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}							)
													__lowerCAmelCase        =       batch.convert_to_tensors(tensor_type="""pt"""							)
													self.assertEqual(tensor_batch["""inputs"""].shape  ,							(2, 3)							)
													self.assertEqual(tensor_batch["""labels"""].shape  ,							(2,)							)
													# test converting the converted
													with CaptureStderr() as cs:
																			__lowerCAmelCase        =       batch.convert_to_tensors(tensor_type="""pt"""							)
													self.assertFalse(len(cs.err							)  ,							msg=f"""should have no warning, but got {cs.err}"""							)

													__lowerCAmelCase        =       BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}							)
													__lowerCAmelCase        =       batch.convert_to_tensors(tensor_type="""pt"""  ,							prepend_batch_axis=snake_case_							)
													self.assertEqual(tensor_batch["""inputs"""].shape  ,							(1, 3)							)
													self.assertEqual(tensor_batch["""labels"""].shape  ,							(1,)							)




							@require_tf
							def  A__		(				self							)   ->       Any:
													__lowerCAmelCase        =       BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}							)
													__lowerCAmelCase        =       batch.convert_to_tensors(tensor_type="""tf"""							)
													self.assertEqual(tensor_batch["""inputs"""].shape  ,							(2, 3)							)
													self.assertEqual(tensor_batch["""labels"""].shape  ,							(2,)							)
													# test converting the converted
													with CaptureStderr() as cs:
																			__lowerCAmelCase        =       batch.convert_to_tensors(tensor_type="""tf"""							)
													self.assertFalse(len(cs.err							)  ,							msg=f"""should have no warning, but got {cs.err}"""							)

													__lowerCAmelCase        =       BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}							)
													__lowerCAmelCase        =       batch.convert_to_tensors(tensor_type="""tf"""  ,							prepend_batch_axis=snake_case_							)
													self.assertEqual(tensor_batch["""inputs"""].shape  ,							(1, 3)							)
													self.assertEqual(tensor_batch["""labels"""].shape  ,							(1,)							)




							@require_flax
							def  A__		(				self							)   ->       Union[str, Any]:
													__lowerCAmelCase        =       BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}							)
													__lowerCAmelCase        =       batch.convert_to_tensors(tensor_type="""jax"""							)
													self.assertEqual(tensor_batch["""inputs"""].shape  ,							(2, 3)							)
													self.assertEqual(tensor_batch["""labels"""].shape  ,							(2,)							)
													# test converting the converted
													with CaptureStderr() as cs:
																			__lowerCAmelCase        =       batch.convert_to_tensors(tensor_type="""jax"""							)
													self.assertFalse(len(cs.err							)  ,							msg=f"""should have no warning, but got {cs.err}"""							)

													__lowerCAmelCase        =       BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}							)
													__lowerCAmelCase        =       batch.convert_to_tensors(tensor_type="""jax"""  ,							prepend_batch_axis=snake_case_							)
													self.assertEqual(tensor_batch["""inputs"""].shape  ,							(1, 3)							)
													self.assertEqual(tensor_batch["""labels"""].shape  ,							(1,)							)




							def  A__		(				self							)   ->       Dict:
													__lowerCAmelCase        =       [{"""input_ids""": np.array([0, 1, 2]							)}, {"""input_ids""": np.array([0, 1, 2, 3]							)}]
													__lowerCAmelCase        =       BertTokenizer.from_pretrained("""bert-base-cased"""							)

													__lowerCAmelCase        =       tokenizer.pad(snake_case_  ,							padding=snake_case_							)
													self.assertTrue(isinstance(batch["""input_ids"""]  ,							np.ndarray							)							)
													self.assertEqual(batch["""input_ids"""].tolist()  ,							[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]							)
													__lowerCAmelCase        =       tokenizer.pad(snake_case_  ,							padding=snake_case_  ,							return_tensors="""np"""							)
													self.assertTrue(isinstance(batch["""input_ids"""]  ,							np.ndarray							)							)
													self.assertEqual(batch["""input_ids"""].tolist()  ,							[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]							)




							@require_torch
							def  A__		(				self							)   ->       Optional[int]:
													import torch

													__lowerCAmelCase        =       [{"""input_ids""": torch.tensor([0, 1, 2]							)}, {"""input_ids""": torch.tensor([0, 1, 2, 3]							)}]
													__lowerCAmelCase        =       BertTokenizer.from_pretrained("""bert-base-cased"""							)

													__lowerCAmelCase        =       tokenizer.pad(snake_case_  ,							padding=snake_case_							)
													self.assertTrue(isinstance(batch["""input_ids"""]  ,							torch.Tensor							)							)
													self.assertEqual(batch["""input_ids"""].tolist()  ,							[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]							)
													__lowerCAmelCase        =       tokenizer.pad(snake_case_  ,							padding=snake_case_  ,							return_tensors="""pt"""							)
													self.assertTrue(isinstance(batch["""input_ids"""]  ,							torch.Tensor							)							)
													self.assertEqual(batch["""input_ids"""].tolist()  ,							[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]							)




							@require_tf
							def  A__		(				self							)   ->       Dict:
													import tensorflow as tf

													__lowerCAmelCase        =       [{"""input_ids""": tf.constant([0, 1, 2]							)}, {"""input_ids""": tf.constant([0, 1, 2, 3]							)}]
													__lowerCAmelCase        =       BertTokenizer.from_pretrained("""bert-base-cased"""							)

													__lowerCAmelCase        =       tokenizer.pad(snake_case_  ,							padding=snake_case_							)
													self.assertTrue(isinstance(batch["""input_ids"""]  ,							tf.Tensor							)							)
													self.assertEqual(batch["""input_ids"""].numpy().tolist()  ,							[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]							)
													__lowerCAmelCase        =       tokenizer.pad(snake_case_  ,							padding=snake_case_  ,							return_tensors="""tf"""							)
													self.assertTrue(isinstance(batch["""input_ids"""]  ,							tf.Tensor							)							)
													self.assertEqual(batch["""input_ids"""].numpy().tolist()  ,							[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]							)




							@require_tokenizers
							def  A__		(				self							)   ->       str:
													__lowerCAmelCase        =       Tokenizer(WordPiece(unk_token="""[UNK]"""							)							)
													PreTrainedTokenizerFast(tokenizer_object=snake_case_							)




							@require_tokenizers
							def  A__		(				self							)   ->       List[str]:
													__lowerCAmelCase        =       Tokenizer(WordPiece(unk_token="""[UNK]"""							)							)
													with tempfile.TemporaryDirectory() as tmpdirname:
																			bert_tokenizer.save(os.path.join(snake_case_  ,							"""tokenizer.json"""							)							)
																			PreTrainedTokenizerFast(tokenizer_file=os.path.join(snake_case_  ,							"""tokenizer.json"""							)							)


"""simple docstring"""
from __future__ import annotations

from typing import Generic, TypeVar

SCREAMING_SNAKE_CASE_			      =					TypeVar('''T''')
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							pass
class      lowerCAmelCase_						(							A__				):



							'''simple docstring'''





							pass
class      lowerCAmelCase_						(							Generic[T]				):



							'''simple docstring'''





							def __init__(				self  ,							snake_case_ = 10							)   ->       Tuple:
													__lowerCAmelCase        =       []
													__lowerCAmelCase        =       limit




							def __bool__(				self							)   ->       bool:
													return bool(self.stack							)




							def __str__(				self							)   ->       str:
													return str(self.stack							)




							def  A__		(				self  ,							snake_case_							)   ->       None:
													if len(self.stack							) >= self.limit:
																			raise StackOverflowError
													self.stack.append(snake_case_							)




							def  A__		(				self							)   ->       T:
													if not self.stack:
																			raise StackUnderflowError
													return self.stack.pop()




							def  A__		(				self							)   ->       T:
													if not self.stack:
																			raise StackUnderflowError
													return self.stack[-1]




							def  A__		(				self							)   ->       bool:
													return not bool(self.stack							)




							def  A__		(				self							)   ->       bool:
													return self.size() == self.limit




							def  A__		(				self							)   ->       int:
													return len(self.stack							)




							def __contains__(				self  ,							snake_case_							)   ->       bool:
													return item in self.stack

def 				lowercase      ():
						__lowerCAmelCase        =       Stack(10		)
						assert bool(_lowerCAmelCase		) is False
						assert stack.is_empty() is True
						assert stack.is_full() is False
						assert str(_lowerCAmelCase		) == "[]"

						try:
												__lowerCAmelCase        =       stack.pop()
												raise AssertionError  # This should not happen
						except StackUnderflowError:
												assert True  # This should happen

						try:
												__lowerCAmelCase        =       stack.peek()
												raise AssertionError  # This should not happen
						except StackUnderflowError:
												assert True  # This should happen

						for i in range(10		):
												assert stack.size() == i
												stack.push(_lowerCAmelCase		)

						assert bool(_lowerCAmelCase		)
						assert not stack.is_empty()
						assert stack.is_full()
						assert str(_lowerCAmelCase		) == str(list(range(10		)		)		)
						assert stack.pop() == 9
						assert stack.peek() == 8

						stack.push(100		)
						assert str(_lowerCAmelCase		) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]		)

						try:
												stack.push(200		)
												raise AssertionError  # This should not happen
						except StackOverflowError:
												assert True  # This should happen

						assert not stack.is_empty()
						assert stack.size() == 10

						assert 5 in stack
						assert 55 not in stack


if __name__ == "__main__":
			test_stack()
