



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_			      =  {
    '''configuration_big_bird''': ['''BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BigBirdConfig''', '''BigBirdOnnxConfig'''],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase_			      =  ['''BigBirdTokenizer''']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase_			      =  ['''BigBirdTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase_			      =  [
						    '''BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''BigBirdForCausalLM''',
						    '''BigBirdForMaskedLM''',
						    '''BigBirdForMultipleChoice''',
						    '''BigBirdForPreTraining''',
						    '''BigBirdForQuestionAnswering''',
						    '''BigBirdForSequenceClassification''',
						    '''BigBirdForTokenClassification''',
						    '''BigBirdLayer''',
						    '''BigBirdModel''',
						    '''BigBirdPreTrainedModel''',
						    '''load_tf_weights_in_big_bird''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCAmelCase_			      =  [
						    '''FlaxBigBirdForCausalLM''',
						    '''FlaxBigBirdForMaskedLM''',
						    '''FlaxBigBirdForMultipleChoice''',
						    '''FlaxBigBirdForPreTraining''',
						    '''FlaxBigBirdForQuestionAnswering''',
						    '''FlaxBigBirdForSequenceClassification''',
						    '''FlaxBigBirdForTokenClassification''',
						    '''FlaxBigBirdModel''',
						    '''FlaxBigBirdPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_big_bird import BigBirdTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_big_bird_fast import BigBirdTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_big_bird import (
												    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BigBirdForCausalLM,
												    BigBirdForMaskedLM,
												    BigBirdForMultipleChoice,
												    BigBirdForPreTraining,
												    BigBirdForQuestionAnswering,
												    BigBirdForSequenceClassification,
												    BigBirdForTokenClassification,
												    BigBirdLayer,
												    BigBirdModel,
												    BigBirdPreTrainedModel,
												    load_tf_weights_in_big_bird,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_big_bird import (
												    FlaxBigBirdForCausalLM,
												    FlaxBigBirdForMaskedLM,
												    FlaxBigBirdForMultipleChoice,
												    FlaxBigBirdForPreTraining,
												    FlaxBigBirdForQuestionAnswering,
												    FlaxBigBirdForSequenceClassification,
												    FlaxBigBirdForTokenClassification,
												    FlaxBigBirdModel,
												    FlaxBigBirdPreTrainedModel,
												)

else:
						import sys

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










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						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   list(tokenizer_class.max_model_input_sizes.keys()      )
					for model_name in sa_models[:1]:
						_snake_case					:							Optional[Any]					     =   tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__      )
						self.assertIsNotNone(SCREAMING_SNAKE_CASE__      )
						self.assertIsInstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
						self.assertIsInstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )

						for special_tok in tokenizer.all_special_tokens:
							self.assertIsInstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )
							_snake_case					:							int					     =   tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__      )
							self.assertIsInstance(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None      ):






					"""simple docstring"""

					_snake_case					:							int					     =   pickle.dumps(SCREAMING_SNAKE_CASE__      )
					self.assertIsNotNone(SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Dict					     =   pickle.loads(SCREAMING_SNAKE_CASE__      )

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







				@slow
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					self.check_tokenizer_from_pretrained(SCREAMING_SNAKE_CASE__      )







				def 		__lowerCamelCase(				self      ):






					"""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      ):






					"""simple docstring"""

					import numpy as np

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






					"""simple docstring"""

					import tensorflow as tf

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

					_snake_case					:							int					     =   BertTokenizer.from_pretrained("""bert-base-cased"""      )
					_snake_case					:							str					     =   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      )						,	SCREAMING_SNAKE_CASE__      )

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







				@require_torch
				@require_tokenizers
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					import torch

					_snake_case					:							Union[str, Any]					     =   BertTokenizer.from_pretrained("""bert-base-cased"""      )
					_snake_case					:							Dict					     =   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      ):






					"""simple docstring"""

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






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   BertTokenizerFast.from_pretrained("""bert-base-cased"""      )
					_snake_case					:							Dict					     =   tokenizer_r(["""Test""", """\xad""", """test"""]						,	is_split_into_words=SCREAMING_SNAKE_CASE__      )

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







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

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

					_snake_case					:							str					     =   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}      )
					_snake_case					:							Optional[int]					     =   batch.convert_to_tensors(tensor_type="""np"""						,	prepend_batch_axis=SCREAMING_SNAKE_CASE__      )
					self.assertEqual(tensor_batch["""inputs"""].shape						,	(1, 3)      )
					self.assertEqual(tensor_batch["""labels"""].shape						,	(1,)      )







				@require_torch
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

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

					_snake_case					:							Union[str, Any]					     =   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}      )
					_snake_case					:							Union[str, Any]					     =   batch.convert_to_tensors(tensor_type="""pt"""						,	prepend_batch_axis=SCREAMING_SNAKE_CASE__      )
					self.assertEqual(tensor_batch["""inputs"""].shape						,	(1, 3)      )
					self.assertEqual(tensor_batch["""labels"""].shape						,	(1,)      )







				@require_tf
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

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

					_snake_case					:							Optional[Any]					     =   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}      )
					_snake_case					:							List[str]					     =   batch.convert_to_tensors(tensor_type="""tf"""						,	prepend_batch_axis=SCREAMING_SNAKE_CASE__      )
					self.assertEqual(tensor_batch["""inputs"""].shape						,	(1, 3)      )
					self.assertEqual(tensor_batch["""labels"""].shape						,	(1,)      )







				@require_flax
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

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

					_snake_case					:							List[str]					     =   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}      )
					_snake_case					:							List[Any]					     =   batch.convert_to_tensors(tensor_type="""jax"""						,	prepend_batch_axis=SCREAMING_SNAKE_CASE__      )
					self.assertEqual(tensor_batch["""inputs"""].shape						,	(1, 3)      )
					self.assertEqual(tensor_batch["""labels"""].shape						,	(1,)      )







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

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

					_snake_case					:							List[Any]					     =   tokenizer.pad(SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__      )
					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]]      )
					_snake_case					:							Optional[int]					     =   tokenizer.pad(SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__						,	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      ):






					"""simple docstring"""

					import torch

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

					_snake_case					:							Dict					     =   tokenizer.pad(SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__      )
					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]]      )
					_snake_case					:							Tuple					     =   tokenizer.pad(SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__						,	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      ):






					"""simple docstring"""

					import tensorflow as tf

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

					_snake_case					:							Optional[Any]					     =   tokenizer.pad(SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__      )
					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]]      )
					_snake_case					:							List[Any]					     =   tokenizer.pad(SCREAMING_SNAKE_CASE__						,	padding=SCREAMING_SNAKE_CASE__						,	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      ):






					"""simple docstring"""

					_snake_case					:							Optional[int]					     =   Tokenizer(WordPiece(unk_token="""[UNK]"""      )      )
					PreTrainedTokenizerFast(tokenizer_object=SCREAMING_SNAKE_CASE__      )







				@require_tokenizers
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							Optional[Any]					     =   Tokenizer(WordPiece(unk_token="""[UNK]"""      )      )
					with tempfile.TemporaryDirectory() as tmpdirname:
						bert_tokenizer.save(os.path.join(SCREAMING_SNAKE_CASE__						,	"""tokenizer.json"""      )      )
						PreTrainedTokenizerFast(tokenizer_file=os.path.join(SCREAMING_SNAKE_CASE__						,	"""tokenizer.json"""      )      )





