

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	):




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Union[str, Any]:
							lowerCAmelCase_      =     list(tokenizer_class.max_model_input_sizes.keys()					)
							for model_name in sa_models[:1]:
										lowerCAmelCase_      =     tokenizer_class.from_pretrained(_UpperCamelCase					)
										self.assertIsNotNone(_UpperCamelCase					)
										self.assertIsInstance(_UpperCamelCase    ,    _UpperCamelCase					)
										self.assertIsInstance(_UpperCamelCase    ,    _UpperCamelCase					)

										for special_tok in tokenizer.all_special_tokens:
													self.assertIsInstance(_UpperCamelCase    ,    _UpperCamelCase					)
													lowerCAmelCase_      =     tokenizer.convert_tokens_to_ids(_UpperCamelCase					)
													self.assertIsInstance(_UpperCamelCase    ,    _UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase = None					)    ->				Union[str, Any]:
							lowerCAmelCase_      =     pickle.dumps(_UpperCamelCase					)
							self.assertIsNotNone(_UpperCamelCase					)

							lowerCAmelCase_      =     pickle.loads(_UpperCamelCase					)

							# 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(_UpperCamelCase    ,    _UpperCamelCase					)					)
										else:
													self.assertEqual(_UpperCamelCase    ,    _UpperCamelCase					)




				@slow
				def 					__a						( self					)    ->				List[Any]:
							self.check_tokenizer_from_pretrained(_UpperCamelCase					)




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




				@require_tokenizers
				def 					__a						( self					)    ->				List[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					)    ->				int:
							import tensorflow as tf

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

							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					)    ,    _UpperCamelCase					)

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




				@require_torch
				@require_tokenizers
				def 					__a						( self					)    ->				str:
							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					)    ->				Union[str, Any]:
							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					)    ->				Optional[int]:
							lowerCAmelCase_      =     BertTokenizerFast.from_pretrained("bert-base-cased"					)
							lowerCAmelCase_      =     tokenizer_r(["Test", "\xad", "test"]    ,    is_split_into_words=_UpperCamelCase					)

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




				def 					__a						( self					)    ->				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=_UpperCamelCase					)
							self.assertEqual(tensor_batch["inputs"].shape    ,    (1, 3)					)
							self.assertEqual(tensor_batch["labels"].shape    ,    (1,)					)




				@require_torch
				def 					__a						( self					)    ->				Tuple:
							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=_UpperCamelCase					)
							self.assertEqual(tensor_batch["inputs"].shape    ,    (1, 3)					)
							self.assertEqual(tensor_batch["labels"].shape    ,    (1,)					)




				@require_tf
				def 					__a						( self					)    ->				Optional[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=_UpperCamelCase					)
							self.assertEqual(tensor_batch["inputs"].shape    ,    (1, 3)					)
							self.assertEqual(tensor_batch["labels"].shape    ,    (1,)					)




				@require_flax
				def 					__a						( self					)    ->				int:
							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=_UpperCamelCase					)
							self.assertEqual(tensor_batch["inputs"].shape    ,    (1, 3)					)
							self.assertEqual(tensor_batch["labels"].shape    ,    (1,)					)




				def 					__a						( self					)    ->				Optional[Any]:
							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(_UpperCamelCase    ,    padding=_UpperCamelCase					)
							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(_UpperCamelCase    ,    padding=_UpperCamelCase    ,    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[Any]:
							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(_UpperCamelCase    ,    padding=_UpperCamelCase					)
							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(_UpperCamelCase    ,    padding=_UpperCamelCase    ,    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					)    ->				Union[str, Any]:
							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(_UpperCamelCase    ,    padding=_UpperCamelCase					)
							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(_UpperCamelCase    ,    padding=_UpperCamelCase    ,    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					)    ->				Any:
							lowerCAmelCase_      =     Tokenizer(WordPiece(unk_token="[UNK]"					)					)
							PreTrainedTokenizerFast(tokenizer_object=_UpperCamelCase					)




				@require_tokenizers
				def 					__a						( self					)    ->				Optional[Any]:
							lowerCAmelCase_      =     Tokenizer(WordPiece(unk_token="[UNK]"					)					)
							with tempfile.TemporaryDirectory() as tmpdirname:
										bert_tokenizer.save(os.path.join(_UpperCamelCase    ,    "tokenizer.json"					)					)
										PreTrainedTokenizerFast(tokenizer_file=os.path.join(_UpperCamelCase    ,    "tokenizer.json"					)					)





from __future__ import annotations






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			assert n > 1 and a > 0 and greatest_common_divisor(__lowerCAmelCase   ,							__lowerCAmelCase    ) == 1
			((lowerCAmelCase_)     ,  (lowerCAmelCase_)     ,  (lowerCAmelCase_))      =     extended_gcd(__lowerCAmelCase   ,							__lowerCAmelCase    )  # Implemented below
			lowerCAmelCase_      =     (b * s) % n
			return x






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			((lowerCAmelCase_)     ,  (lowerCAmelCase_))      =     extended_euclid(__lowerCAmelCase   ,							__lowerCAmelCase    )  # Implemented below
			if b < 0:
						lowerCAmelCase_      =     (b % n + n) % n
			return b






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			lowerCAmelCase_      =     invert_modulo(__lowerCAmelCase   ,							__lowerCAmelCase    )
			lowerCAmelCase_      =     (b * s) % n
			return x






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			assert a >= 0 and b >= 0

			if b == 0:
						lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_      =     a, 1, 0
			else:
						((lowerCAmelCase_)     ,  (lowerCAmelCase_)     ,  (lowerCAmelCase_))      =     extended_gcd(__lowerCAmelCase   ,							a % b    )
						lowerCAmelCase_      =     q
						lowerCAmelCase_      =     p - q * (a // b)

			assert a % d == 0 and b % d == 0
			assert d == a * x + b * y

			return (d, x, y)






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			if b == 0:
						return (1, 0)
			((lowerCAmelCase_)     ,  (lowerCAmelCase_))      =     extended_euclid(__lowerCAmelCase   ,							a % b    )
			lowerCAmelCase_      =     a // b
			return (y, x - k * y)






def        lowerCamelCase__    (    __lowerCAmelCase  :  int   ,							__lowerCAmelCase  :  int    ):




			"""simple docstring"""


			if a < b:
						lowerCAmelCase_     ,  lowerCAmelCase_      =     b, a

			while a % b != 0:
						lowerCAmelCase_     ,  lowerCAmelCase_      =     b, a % b

			return b


if __name__ == "__main__":
			from doctest import testmod

			testmod(name="modular_division", verbose=True)
			testmod(name="modular_division2", verbose=True)
			testmod(name="invert_modulo", verbose=True)
			testmod(name="extended_gcd", verbose=True)
			testmod(name="extended_euclid", verbose=True)
			testmod(name="greatest_common_divisor", verbose=True)



