'''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   UpperCamelCase					(       unittest.TestCase       ):







    """simple docstring"""





    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple							,							UpperCAmelCase_     :						Dict):


         """simple docstring"""







         a					:							Dict							= list(tokenizer_class.max_model_input_sizes.keys())
         for model_name in sa_models[:1]:
              a					:							Tuple							= tokenizer_class.from_pretrained(lowerCamelCase_)
              self.assertIsNotNone(lowerCamelCase_)
              self.assertIsInstance(lowerCamelCase_							,							lowerCamelCase_)
              self.assertIsInstance(lowerCamelCase_							,							lowerCamelCase_)

              for special_tok in tokenizer.all_special_tokens:
                   self.assertIsInstance(lowerCamelCase_							,							lowerCamelCase_)
                   a					:							Union[str, Any]							= tokenizer.convert_tokens_to_ids(lowerCamelCase_)
                   self.assertIsInstance(lowerCamelCase_							,							lowerCamelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						Any							,							UpperCAmelCase_     :						BatchEncoding							,							UpperCAmelCase_     :						Optional[Callable] = None):


         """simple docstring"""







         a					:							Optional[int]							= pickle.dumps(lowerCamelCase_)
         self.assertIsNotNone(lowerCamelCase_)

         a					:							Any							= pickle.loads(lowerCamelCase_)

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





    @slow
    def    SCREAMING_SNAKE_CASE_     (					self     :						Optional[Any]):


         """simple docstring"""







         self.check_tokenizer_from_pretrained(lowerCamelCase_)





    def    SCREAMING_SNAKE_CASE_     (					self     :						List[str]):


         """simple docstring"""







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





    @require_tokenizers
    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple):


         """simple docstring"""







         import numpy as np

         a					:							Tuple							= BertTokenizer.from_pretrained('bert-base-cased')
         a					:							Dict							= 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    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]):


         """simple docstring"""







         import tensorflow as tf

         def tf_array_equals(UpperCAmelCase_     :						int							,							UpperCAmelCase_     :						Optional[int]):
              return tf.reduce_all(tf.equal(lowerCamelCase_							,							lowerCamelCase_))

         a					:							Optional[int]							= BertTokenizer.from_pretrained('bert-base-cased')
         a					:							Union[str, Any]							= 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)							,							lowerCamelCase_)

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





    @require_torch
    @require_tokenizers
    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







         import torch

         a					:							Any							= BertTokenizer.from_pretrained('bert-base-cased')
         a					:							List[str]							= 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    SCREAMING_SNAKE_CASE_     (					self     :						Optional[Any]):


         """simple docstring"""







         a					:							Dict							= BertTokenizer.from_pretrained('bert-base-cased')
         a					:							Any							= 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    SCREAMING_SNAKE_CASE_     (					self     :						Dict):


         """simple docstring"""







         a					:							int							= BertTokenizerFast.from_pretrained('bert-base-cased')
         a					:							str							= tokenizer_r(['Test', '\xad', 'test']							,							is_split_into_words=lowerCamelCase_)

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





    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







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

         a					:							Any							= BatchEncoding({'inputs': [1, 2, 3], 'labels': 0})
         a					:							Union[str, Any]							= batch.convert_to_tensors(tensor_type='np'							,							prepend_batch_axis=lowerCamelCase_)
         self.assertEqual(tensor_batch['inputs'].shape							,							(1, 3))
         self.assertEqual(tensor_batch['labels'].shape							,							(1,))





    @require_torch
    def    SCREAMING_SNAKE_CASE_     (					self     :						int):


         """simple docstring"""







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

         a					:							Tuple							= BatchEncoding({'inputs': [1, 2, 3], 'labels': 0})
         a					:							str							= batch.convert_to_tensors(tensor_type='pt'							,							prepend_batch_axis=lowerCamelCase_)
         self.assertEqual(tensor_batch['inputs'].shape							,							(1, 3))
         self.assertEqual(tensor_batch['labels'].shape							,							(1,))





    @require_tf
    def    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







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

         a					:							Any							= BatchEncoding({'inputs': [1, 2, 3], 'labels': 0})
         a					:							str							= batch.convert_to_tensors(tensor_type='tf'							,							prepend_batch_axis=lowerCamelCase_)
         self.assertEqual(tensor_batch['inputs'].shape							,							(1, 3))
         self.assertEqual(tensor_batch['labels'].shape							,							(1,))





    @require_flax
    def    SCREAMING_SNAKE_CASE_     (					self     :						List[Any]):


         """simple docstring"""







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

         a					:							int							= BatchEncoding({'inputs': [1, 2, 3], 'labels': 0})
         a					:							Optional[Any]							= batch.convert_to_tensors(tensor_type='jax'							,							prepend_batch_axis=lowerCamelCase_)
         self.assertEqual(tensor_batch['inputs'].shape							,							(1, 3))
         self.assertEqual(tensor_batch['labels'].shape							,							(1,))





    def    SCREAMING_SNAKE_CASE_     (					self     :						Tuple):


         """simple docstring"""







         a					:							int							= [{'input_ids': np.array([0, 1, 2])}, {'input_ids': np.array([0, 1, 2, 3])}]
         a					:							List[str]							= BertTokenizer.from_pretrained('bert-base-cased')

         a					:							int							= tokenizer.pad(lowerCamelCase_							,							padding=lowerCamelCase_)
         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]])
         a					:							int							= tokenizer.pad(lowerCamelCase_							,							padding=lowerCamelCase_							,							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    SCREAMING_SNAKE_CASE_     (					self     :						Union[str, Any]):


         """simple docstring"""







         import torch

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

         a					:							List[Any]							= tokenizer.pad(lowerCamelCase_							,							padding=lowerCamelCase_)
         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]])
         a					:							int							= tokenizer.pad(lowerCamelCase_							,							padding=lowerCamelCase_							,							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    SCREAMING_SNAKE_CASE_     (					self     :						Optional[Any]):


         """simple docstring"""







         import tensorflow as tf

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

         a					:							int							= tokenizer.pad(lowerCamelCase_							,							padding=lowerCamelCase_)
         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]])
         a					:							Optional[Any]							= tokenizer.pad(lowerCamelCase_							,							padding=lowerCamelCase_							,							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    SCREAMING_SNAKE_CASE_     (					self     :						Tuple):


         """simple docstring"""







         a					:							Optional[int]							= Tokenizer(WordPiece(unk_token='[UNK]'))
         PreTrainedTokenizerFast(tokenizer_object=lowerCamelCase_)







    @require_tokenizers
    def    SCREAMING_SNAKE_CASE_     (					self     :						int):


         """simple docstring"""







         a					:							int							= Tokenizer(WordPiece(unk_token='[UNK]'))
         with tempfile.TemporaryDirectory() as tmpdirname:
              bert_tokenizer.save(os.path.join(lowerCamelCase_							,							'tokenizer.json'))
              PreTrainedTokenizerFast(tokenizer_file=os.path.join(lowerCamelCase_							,							'tokenizer.json'))





def 		snake_case_   (				lowercase__     :    str     ):






				'''simple docstring'''






				_lowerCAmelCase  						=53_81
				for x in s:
								_lowerCAmelCase  						=((hash_value << 5) + hash_value) + ord(lowercase__     )
				return hash_value & 0Xffff_ffff

