text: # coding=utf-8
  # Copyright 2018 HuggingFace Inc..
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  import pickle
  import unittest
  from typing import Callable, Optional
  
  import numpy as np
  
  from transformers import BatchEncoding, BertTokenizer, BertTokenizerFast, PreTrainedTokenizer, TensorType
  from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow
  from transformers.tokenization_gpt2 import GPT2Tokenizer
  
  
  class TokenizerUtilsTest(unittest.TestCase):
      def check_tokenizer_from_pretrained(self, tokenizer_class):
          s3_models = list(tokenizer_class.max_model_input_sizes.keys())
          for model_name in s3_models[:1]:
              tokenizer = tokenizer_class.from_pretrained(model_name)
              self.assertIsNotNone(tokenizer)
              self.assertIsInstance(tokenizer, tokenizer_class)
              self.assertIsInstance(tokenizer, PreTrainedTokenizer)
  
              for special_tok in tokenizer.all_special_tokens:
                  self.assertIsInstance(special_tok, str)
                  special_tok_id = tokenizer.convert_tokens_to_ids(special_tok)
                  self.assertIsInstance(special_tok_id, int)
  
      def assert_dump_and_restore(self, be_original: BatchEncoding, equal_op: Optional[Callable] = None):
          batch_encoding_str = pickle.dumps(be_original)
          self.assertIsNotNone(batch_encoding_str)
  
          be_restored = pickle.loads(batch_encoding_str)
  
          # 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(restored_v, original_v))
              else:
                  self.assertEqual(restored_v, original_v)
  
      @slow
      def test_pretrained_tokenizers(self):
          self.check_tokenizer_from_pretrained(GPT2Tokenizer)
  
      def test_tensor_type_from_str(self):
          self.assertEqual(TensorType("tf"), TensorType.TENSORFLOW)
          self.assertEqual(TensorType("pt"), TensorType.PYTORCH)
          self.assertEqual(TensorType("np"), TensorType.NUMPY)
  
      @require_tokenizers
      def test_batch_encoding_pickle(self):
          import numpy as np
  
          tokenizer_p = BertTokenizer.from_pretrained("bert-base-cased")
          tokenizer_r = 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 test_batch_encoding_pickle_tf(self):
          import tensorflow as tf
  
          def tf_array_equals(t1, t2):
              return tf.reduce_all(tf.equal(t1, t2))
  
          tokenizer_p = BertTokenizer.from_pretrained("bert-base-cased")
          tokenizer_r = 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), tf_array_equals
              )
  
          with self.subTest("BatchEncoding (Rust, return_tensors=TENSORFLOW)"):
              self.assert_dump_and_restore(
                  tokenizer_r("Small example to encode", return_tensors=TensorType.TENSORFLOW), tf_array_equals
              )
  
      @require_torch
      @require_tokenizers
      def test_batch_encoding_pickle_pt(self):
          import torch
  
          tokenizer_p = BertTokenizer.from_pretrained("bert-base-cased")
          tokenizer_r = 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 test_batch_encoding_is_fast(self):
          tokenizer_p = BertTokenizer.from_pretrained("bert-base-cased")
          tokenizer_r = 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)
  
      def test_batch_encoding_with_labels(self):
          batch = BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]})
          tensor_batch = batch.convert_to_tensors(tensor_type="np")
          self.assertEqual(tensor_batch["inputs"].shape, (2, 3))
          self.assertEqual(tensor_batch["labels"].shape, (2,))
  
          batch = BatchEncoding({"inputs": [1, 2, 3], "labels": 0})
          tensor_batch = batch.convert_to_tensors(tensor_type="np", prepend_batch_axis=True)
          self.assertEqual(tensor_batch["inputs"].shape, (1, 3))
          self.assertEqual(tensor_batch["labels"].shape, (1,))
  
      @require_torch
      def test_batch_encoding_with_labels_pt(self):
          batch = BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]})
          tensor_batch = batch.convert_to_tensors(tensor_type="pt")
          self.assertEqual(tensor_batch["inputs"].shape, (2, 3))
          self.assertEqual(tensor_batch["labels"].shape, (2,))
  
          batch = BatchEncoding({"inputs": [1, 2, 3], "labels": 0})
          tensor_batch = batch.convert_to_tensors(tensor_type="pt", prepend_batch_axis=True)
          self.assertEqual(tensor_batch["inputs"].shape, (1, 3))
          self.assertEqual(tensor_batch["labels"].shape, (1,))
  
      @require_tf
      def test_batch_encoding_with_labels_tf(self):
          batch = BatchEncoding({"inputs": [[1, 2, 3], [4, 5, 6]], "labels": [0, 1]})
          tensor_batch = batch.convert_to_tensors(tensor_type="tf")
          self.assertEqual(tensor_batch["inputs"].shape, (2, 3))
          self.assertEqual(tensor_batch["labels"].shape, (2,))
  
          batch = BatchEncoding({"inputs": [1, 2, 3], "labels": 0})
          tensor_batch = batch.convert_to_tensors(tensor_type="tf", prepend_batch_axis=True)
          self.assertEqual(tensor_batch["inputs"].shape, (1, 3))
          self.assertEqual(tensor_batch["labels"].shape, (1,))
  
      def test_padding_accepts_tensors(self):
          features = [{"input_ids": np.array([0, 1, 2])}, {"input_ids": np.array([0, 1, 2, 3])}]
          tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
  
          batch = tokenizer.pad(features, padding=True)
          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]])
          batch = tokenizer.pad(features, padding=True, 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 test_padding_accepts_tensors_pt(self):
          import torch
  
          features = [{"input_ids": torch.tensor([0, 1, 2])}, {"input_ids": torch.tensor([0, 1, 2, 3])}]
          tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
  
          batch = tokenizer.pad(features, padding=True)
          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]])
          batch = tokenizer.pad(features, padding=True, 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 test_padding_accepts_tensors_tf(self):
          import tensorflow as tf
  
          features = [{"input_ids": tf.constant([0, 1, 2])}, {"input_ids": tf.constant([0, 1, 2, 3])}]
          tokenizer = BertTokenizer.from_pretrained("bert-base-cased")
  
          batch = tokenizer.pad(features, padding=True)
          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]])
          batch = tokenizer.pad(features, padding=True, 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]])
  