




'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase	:      Optional[int]				=       {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase	:      List[str]				=       [
          '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ProphetNetDecoder''',
          '''ProphetNetEncoder''',
          '''ProphetNetForCausalLM''',
          '''ProphetNetForConditionalGeneration''',
          '''ProphetNetModel''',
          '''ProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
      from .tokenization_prophetnet import ProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_prophetnet import (
                PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                ProphetNetDecoder,
                ProphetNetEncoder,
                ProphetNetForCausalLM,
                ProphetNetForConditionalGeneration,
                ProphetNetModel,
                ProphetNetPreTrainedModel,
            )

else:
      import sys

      lowercase	:      int				=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










'''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   SCREAMING_SNAKE_CASE_		(       unittest.TestCase				):



 def    SCREAMING_SNAKE_CASE(  self   ,		A ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      list(tokenizer_class.max_model_input_sizes.keys() )
        for model_name in sa_models[:1]:
               lowercase  :		Dict						=      tokenizer_class.from_pretrained(A )
               self.assertIsNotNone(A )
               self.assertIsInstance(A   ,		A )
               self.assertIsInstance(A   ,		A )

               for special_tok in tokenizer.all_special_tokens:
                      self.assertIsInstance(A   ,		A )
                      lowercase  :		Tuple						=      tokenizer.convert_tokens_to_ids(A )
                      self.assertIsInstance(A   ,		A )



 def    SCREAMING_SNAKE_CASE(  self   ,		A   ,		A = None ):






        '''simple docstring'''




        lowercase  :		List[Any]						=      pickle.dumps(A )
        self.assertIsNotNone(A )

        lowercase  :		str						=      pickle.loads(A )

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



 @slow
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        self.check_tokenizer_from_pretrained(A )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''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 ):






        '''simple docstring'''




        import numpy as np

        lowercase  :		Union[str, Any]						=      BertTokenizer.from_pretrained('''bert-base-cased''' )
        lowercase  :		str						=      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 ):






        '''simple docstring'''




        import tensorflow as tf

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

        lowercase  :		Optional[Any]						=      BertTokenizer.from_pretrained('''bert-base-cased''' )
        lowercase  :		Optional[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 )   ,		A )

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



 @require_torch
 @require_tokenizers
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        import torch

        lowercase  :		Tuple						=      BertTokenizer.from_pretrained('''bert-base-cased''' )
        lowercase  :		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    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		str						=      BertTokenizer.from_pretrained('''bert-base-cased''' )
        lowercase  :		List[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 ):






        '''simple docstring'''




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

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



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

        lowercase  :		Dict						=      BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
        lowercase  :		List[str]						=      batch.convert_to_tensors(tensor_type='''np'''   ,		prepend_batch_axis=A )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(1, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(1,) )



 @require_torch
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

        lowercase  :		Optional[Any]						=      BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
        lowercase  :		List[str]						=      batch.convert_to_tensors(tensor_type='''pt'''   ,		prepend_batch_axis=A )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(1, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(1,) )



 @require_tf
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

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



 @require_flax
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

        lowercase  :		str						=      BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
        lowercase  :		str						=      batch.convert_to_tensors(tensor_type='''jax'''   ,		prepend_batch_axis=A )
        self.assertEqual(tensor_batch['''inputs'''].shape   ,		(1, 3) )
        self.assertEqual(tensor_batch['''labels'''].shape   ,		(1,) )



 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




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

        lowercase  :		Dict						=      tokenizer.pad(A   ,		padding=A )
        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]] )
        lowercase  :		Dict						=      tokenizer.pad(A   ,		padding=A   ,		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 ):






        '''simple docstring'''




        import torch

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

        lowercase  :		Any						=      tokenizer.pad(A   ,		padding=A )
        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]] )
        lowercase  :		Union[str, Any]						=      tokenizer.pad(A   ,		padding=A   ,		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 ):






        '''simple docstring'''




        import tensorflow as tf

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

        lowercase  :		List[Any]						=      tokenizer.pad(A   ,		padding=A )
        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]] )
        lowercase  :		Any						=      tokenizer.pad(A   ,		padding=A   ,		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 ):






        '''simple docstring'''




        lowercase  :		Optional[Any]						=      Tokenizer(WordPiece(unk_token='''[UNK]''' ) )
        PreTrainedTokenizerFast(tokenizer_object=A )





 @require_tokenizers
 def    SCREAMING_SNAKE_CASE(  self ):






        '''simple docstring'''




        lowercase  :		List[str]						=      Tokenizer(WordPiece(unk_token='''[UNK]''' ) )
        with tempfile.TemporaryDirectory() as tmpdirname:
               bert_tokenizer.save(os.path.join(A   ,		'''tokenizer.json''' ) )
               PreTrainedTokenizerFast(tokenizer_file=os.path.join(A   ,		'''tokenizer.json''' ) )




