

import torch

from transformers import AutoTokenizer
class 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__   ):







						"""simple docstring"""




						def __init__(				self , __UpperCamelCase    )							->	Tuple:




									'''simple docstring'''


									__UpperCamelCase    :    Dict								=      AutoTokenizer.from_pretrained(__UpperCamelCase    )

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




									'''simple docstring'''


									if isinstance(__UpperCamelCase , __UpperCamelCase    ) and all(isinstance(_x , __UpperCamelCase    ) for _x in x    ):
												__UpperCamelCase    :    List[str]								=      None
												for l in x:
															__UpperCamelCase    :    int								=      self.tokenizer(
															    __UpperCamelCase , padding="max_length" , max_length=3_84 , truncation=__UpperCamelCase , return_tensors="pt" , )
															__UpperCamelCase    :    List[Any]								=      torch.tensor([len(__UpperCamelCase    )]    )
															if d is not None:
																		for k in d.keys():
																					__UpperCamelCase    :    str								=      torch.cat((d[k], t[k]) , 0    )
															else:
																		__UpperCamelCase    :    List[Any]								=      t

												__UpperCamelCase    :    Union[str, Any]								=      torch.tensor(self.tokenizer.convert_tokens_to_ids("[E]"    )    )
												__UpperCamelCase    :    Dict								=      torch.tensor(self.tokenizer.convert_tokens_to_ids("[/E]"    )    )

									elif isinstance(__UpperCamelCase , __UpperCamelCase    ) and all(isinstance(_x , __UpperCamelCase    ) for _x in x    ):
												__UpperCamelCase    :    Optional[Any]								=      self.tokenizer(
												    __UpperCamelCase , padding="max_length" , max_length=3_84 , truncation=__UpperCamelCase , return_tensors="pt" , )

									else:
												raise Exception(
												    "Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"
												    " strings` for supports are supported."    )

									return d




						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase=0.70    )							->	Dict:




									'''simple docstring'''


									__UpperCamelCase    :    int								=      []
									for idx in range(len(W_query["input_ids"]    )    ):
												__UpperCamelCase    :    Tuple								=      range(p_start.shape[1]    )

												__UpperCamelCase    :    Union[str, Any]								=      []
												for start_id in start_indexes:
															for end_id in end_indexes:
																		if start_id < end_id:
																					output.append(
																					    (
																					        start_id,
																					        end_id,
																					        p_start[idx][start_id].item(),
																					        p_end[idx][end_id].item(),
																					    )    )

												output.sort(key=lambda __UpperCamelCase    : (tup[2] * tup[3]) , reverse=__UpperCamelCase    )
												__UpperCamelCase    :    int								=      []
												for k in range(len(__UpperCamelCase    )    ):
															if output[k][2] * output[k][3] >= thresh:
																		__UpperCamelCase							,				__UpperCamelCase    :    Dict								=      output[k][0], output[k][1]
																		__UpperCamelCase    :    str								=      self.tokenizer.decode(W_query["input_ids"][idx][c_start_pos:c_end_pos]    )
																		temp.append((decoded, output[k][2] * output[k][3])    )

												final_outputs.append(__UpperCamelCase    )

									return final_outputs


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 		SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):







						"""simple docstring"""




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




									'''simple docstring'''


									__UpperCamelCase    :    List[str]								=      list(tokenizer_class.max_model_input_sizes.keys()    )
									for model_name in sa_models[:1]:
												__UpperCamelCase    :    int								=      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    )
															__UpperCamelCase    :    str								=      tokenizer.convert_tokens_to_ids(__UpperCamelCase    )
															self.assertIsInstance(__UpperCamelCase , __UpperCamelCase    )

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = None    )							->	Tuple:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[Any]								=      pickle.dumps(__UpperCamelCase    )
									self.assertIsNotNone(__UpperCamelCase    )

									__UpperCamelCase    :    List[str]								=      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 __lowerCamelCase					(				self    )							->	List[Any]:




									'''simple docstring'''


									self.check_tokenizer_from_pretrained(__UpperCamelCase    )

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




									'''simple docstring'''


									self.assertEqual(TensorType("tf"    ) , TensorType.TENSORFLOW    )
									self.assertEqual(TensorType("pt"    ) , TensorType.PYTORCH    )
									self.assertEqual(TensorType("np"    ) , TensorType.NUMPY    )

						@require_tokenizers
						def __lowerCamelCase					(				self    )							->	List[str]:




									'''simple docstring'''


									import numpy as np

									__UpperCamelCase    :    str								=      BertTokenizer.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Optional[Any]								=      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 __lowerCamelCase					(				self    )							->	Union[str, Any]:




									'''simple docstring'''


									import tensorflow as tf

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

									__UpperCamelCase    :    int								=      BertTokenizer.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Dict								=      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 __lowerCamelCase					(				self    )							->	Any:




									'''simple docstring'''


									import torch

									__UpperCamelCase    :    List[Any]								=      BertTokenizer.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Optional[Any]								=      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 __lowerCamelCase					(				self    )							->	Any:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[int]								=      BertTokenizer.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Tuple								=      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 __lowerCamelCase					(				self    )							->	Union[str, Any]:




									'''simple docstring'''


									__UpperCamelCase    :    Any								=      BertTokenizerFast.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Dict								=      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 __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									__UpperCamelCase    :    str								=      BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}    )
									__UpperCamelCase    :    List[str]								=      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:
												__UpperCamelCase    :    Optional[Any]								=      batch.convert_to_tensors(tensor_type="np"    )
									self.assertFalse(len(cs.err    ) , msg=f'''should have no warning, but got {cs.err}'''    )

									__UpperCamelCase    :    Union[str, Any]								=      BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
									__UpperCamelCase    :    int								=      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 __lowerCamelCase					(				self    )							->	List[str]:




									'''simple docstring'''


									__UpperCamelCase    :    Dict								=      BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}    )
									__UpperCamelCase    :    List[Any]								=      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:
												__UpperCamelCase    :    Tuple								=      batch.convert_to_tensors(tensor_type="pt"    )
									self.assertFalse(len(cs.err    ) , msg=f'''should have no warning, but got {cs.err}'''    )

									__UpperCamelCase    :    Union[str, Any]								=      BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
									__UpperCamelCase    :    List[Any]								=      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 __lowerCamelCase					(				self    )							->	Optional[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}    )
									__UpperCamelCase    :    Tuple								=      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:
												__UpperCamelCase    :    Optional[int]								=      batch.convert_to_tensors(tensor_type="tf"    )
									self.assertFalse(len(cs.err    ) , msg=f'''should have no warning, but got {cs.err}'''    )

									__UpperCamelCase    :    str								=      BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
									__UpperCamelCase    :    Union[str, Any]								=      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 __lowerCamelCase					(				self    )							->	List[Any]:




									'''simple docstring'''


									__UpperCamelCase    :    str								=      BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]}    )
									__UpperCamelCase    :    int								=      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:
												__UpperCamelCase    :    Dict								=      batch.convert_to_tensors(tensor_type="jax"    )
									self.assertFalse(len(cs.err    ) , msg=f'''should have no warning, but got {cs.err}'''    )

									__UpperCamelCase    :    Tuple								=      BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
									__UpperCamelCase    :    Union[str, Any]								=      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 __lowerCamelCase					(				self    )							->	int:




									'''simple docstring'''


									__UpperCamelCase    :    str								=      [{"input_ids": np.array([0, 1, 2]    )}, {"input_ids": np.array([0, 1, 2, 3]    )}]
									__UpperCamelCase    :    Any								=      BertTokenizer.from_pretrained("bert-base-cased"    )

									__UpperCamelCase    :    Optional[Any]								=      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]]    )
									__UpperCamelCase    :    Optional[Any]								=      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 __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									import torch

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

									__UpperCamelCase    :    str								=      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]]    )
									__UpperCamelCase    :    int								=      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 __lowerCamelCase					(				self    )							->	List[Any]:




									'''simple docstring'''


									import tensorflow as tf

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

									__UpperCamelCase    :    Optional[Any]								=      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]]    )
									__UpperCamelCase    :    List[str]								=      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 __lowerCamelCase					(				self    )							->	int:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      Tokenizer(WordPiece(unk_token="[UNK]"    )    )
									PreTrainedTokenizerFast(tokenizer_object=__UpperCamelCase    )




						@require_tokenizers
						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, Any]								=      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"    )    )