




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   lowerCAmelCase_   ( self       :       Optional[Any]	,      UpperCamelCase       :       int		):





            """simple docstring"""



            _lowercase					:				Optional[int]           =			list(tokenizer_class.max_model_input_sizes.keys()		)
            for model_name in sa_models[:1]:
                   _lowercase					:				Optional[Any]           =			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		)
                          _lowercase					:				int           =			tokenizer.convert_tokens_to_ids(UpperCamelCase		)
                          self.assertIsInstance(UpperCamelCase	,      UpperCamelCase		)




     def   lowerCAmelCase_   ( self       :       Optional[int]	,      UpperCamelCase       :       BatchEncoding	,      UpperCamelCase       :       Optional[Callable] = None		):





            """simple docstring"""



            _lowercase					:				Tuple           =			pickle.dumps(UpperCamelCase		)
            self.assertIsNotNone(UpperCamelCase		)

            _lowercase					:				int           =			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       :       int		):





            """simple docstring"""



            self.check_tokenizer_from_pretrained(UpperCamelCase		)




     def   lowerCAmelCase_   ( 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   lowerCAmelCase_   ( self       :       Any		):





            """simple docstring"""



            import numpy as np

            _lowercase					:				Dict           =			BertTokenizer.from_pretrained('''bert-base-cased'''		)
            _lowercase					:				List[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   lowerCAmelCase_   ( self       :       Dict		):





            """simple docstring"""



            import tensorflow as tf

            def tf_array_equals(UpperCamelCase       :       Optional[Any]	,      UpperCamelCase       :       List[Any]		):
                   return tf.reduce_all(tf.equal(UpperCamelCase	,      UpperCamelCase		)		)

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





            """simple docstring"""



            import torch

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





            """simple docstring"""



            _lowercase					:				Optional[int]           =			BertTokenizer.from_pretrained('''bert-base-cased'''		)
            _lowercase					:				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"""



            _lowercase					:				List[str]           =			BertTokenizerFast.from_pretrained('''bert-base-cased'''		)
            _lowercase					:				Union[str, Any]           =			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       :       List[str]		):





            """simple docstring"""



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

            _lowercase					:				Tuple           =			BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}		)
            _lowercase					:				str           =			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       :       Optional[int]		):





            """simple docstring"""



            _lowercase					:				Optional[Any]           =			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					:				str           =			batch.convert_to_tensors(tensor_type='''pt'''		)
            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					:				Tuple           =			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"""



            _lowercase					:				Tuple           =			BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}		)
            _lowercase					:				Any           =			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					:				Optional[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"""



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

            _lowercase					:				int           =			BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}		)
            _lowercase					:				str           =			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       :       Tuple		):





            """simple docstring"""



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

            _lowercase					:				List[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]]		)
            _lowercase					:				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

            _lowercase					:				Union[str, Any]           =			[{'''input_ids''': torch.tensor([0, 1, 2]		)}, {'''input_ids''': torch.tensor([0, 1, 2, 3]		)}]
            _lowercase					:				Dict           =			BertTokenizer.from_pretrained('''bert-base-cased'''		)

            _lowercase					:				Tuple           =			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]]		)
            _lowercase					:				Optional[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       :       Tuple		):





            """simple docstring"""



            import tensorflow as tf

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

            _lowercase					:				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]]		)
            _lowercase					:				int           =			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       :       Union[str, Any]		):





            """simple docstring"""



            _lowercase					:				int           =			Tokenizer(WordPiece(unk_token='''[UNK]'''		)		)
            PreTrainedTokenizerFast(tokenizer_object=UpperCamelCase		)




     @require_tokenizers
     def   lowerCAmelCase_   ( self       :       int		):





            """simple docstring"""



            _lowercase					:				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'''		)		)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCamelCase__				       =			{
    'configuration_wav2vec2_conformer': [
        'WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Wav2Vec2ConformerConfig',
    ],
}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase__				       =			[
       'WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
       'Wav2Vec2ConformerForAudioFrameClassification',
       'Wav2Vec2ConformerForCTC',
       'Wav2Vec2ConformerForPreTraining',
       'Wav2Vec2ConformerForSequenceClassification',
       'Wav2Vec2ConformerForXVector',
       'Wav2Vec2ConformerModel',
       'Wav2Vec2ConformerPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_wavaveca_conformer import (
       WAV2VEC2_CONFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       WavaVecaConformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_wavaveca_conformer import (
          WAV2VEC2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          WavaVecaConformerForAudioFrameClassification,
          WavaVecaConformerForCTC,
          WavaVecaConformerForPreTraining,
          WavaVecaConformerForSequenceClassification,
          WavaVecaConformerForXVector,
          WavaVecaConformerModel,
          WavaVecaConformerPreTrainedModel,
      )

else:
   import sys

   UpperCamelCase__				       =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)