

from typing import TYPE_CHECKING

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


lowercase     :       int        =       {
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase     :       Optional[Any]        =       [
					    "DPRContextEncoderTokenizerFast",
					    "DPRQuestionEncoderTokenizerFast",
					    "DPRReaderTokenizerFast",
					]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase     :       int        =       [
					    "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "DPRContextEncoder",
					    "DPRPretrainedContextEncoder",
					    "DPRPreTrainedModel",
					    "DPRPretrainedQuestionEncoder",
					    "DPRPretrainedReader",
					    "DPRQuestionEncoder",
					    "DPRReader",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase     :       List[str]        =       [
					    "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFDPRContextEncoder",
					    "TFDPRPretrainedContextEncoder",
					    "TFDPRPretrainedQuestionEncoder",
					    "TFDPRPretrainedReader",
					    "TFDPRQuestionEncoder",
					    "TFDPRReader",
					]


if TYPE_CHECKING:
					from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
					from .tokenization_dpr import (
					    DPRContextEncoderTokenizer,
					    DPRQuestionEncoderTokenizer,
					    DPRReaderOutput,
					    DPRReaderTokenizer,
					)

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_dpr_fast import (
										    DPRContextEncoderTokenizerFast,
										    DPRQuestionEncoderTokenizerFast,
										    DPRReaderTokenizerFast,
										)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_dpr import (
										    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DPRContextEncoder,
										    DPRPretrainedContextEncoder,
										    DPRPreTrainedModel,
										    DPRPretrainedQuestionEncoder,
										    DPRPretrainedReader,
										    DPRQuestionEncoder,
										    DPRReader,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_dpr import (
										    TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDPRContextEncoder,
										    TFDPRPretrainedContextEncoder,
										    TFDPRPretrainedQuestionEncoder,
										    TFDPRPretrainedReader,
										    TFDPRQuestionEncoder,
										    TFDPRReader,
										)

else:
					import sys

					lowercase     :       List[Any]        =       _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 , __UpperCamelCase    )							->	Union[str, Any]:




									'''simple docstring'''


									__UpperCamelCase    :    List[str]								=      list(tokenizer_class.max_model_input_sizes.keys()    )
									for model_name in sa_models[:1]:
												__UpperCamelCase    :    int								=      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    )
															__UpperCamelCase    :    str								=      tokenizer.convert_tokens_to_ids(__UpperCamelCase    )
															self.assertIsInstance(__UpperCamelCase , __UpperCamelCase    )

						def __lowerCamelCase					(				self , __UpperCamelCase , __UpperCamelCase = None    )							->	Tuple:




									'''simple docstring'''


									__UpperCamelCase    :    Optional[Any]								=      pickle.dumps(__UpperCamelCase    )
									self.assertIsNotNone(__UpperCamelCase    )

									__UpperCamelCase    :    List[str]								=      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    )							->	List[Any]:




									'''simple docstring'''


									self.check_tokenizer_from_pretrained(__UpperCamelCase    )

						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''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    )							->	List[str]:




									'''simple docstring'''


									import numpy as np

									__UpperCamelCase    :    str								=      BertTokenizer.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Optional[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    )							->	Union[str, Any]:




									'''simple docstring'''


									import tensorflow as tf

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

									__UpperCamelCase    :    int								=      BertTokenizer.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    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    )							->	Any:




									'''simple docstring'''


									import torch

									__UpperCamelCase    :    List[Any]								=      BertTokenizer.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Optional[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 __lowerCamelCase					(				self    )							->	Any:




									'''simple docstring'''


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


									__UpperCamelCase    :    Any								=      BertTokenizerFast.from_pretrained("bert-base-cased"    )
									__UpperCamelCase    :    Dict								=      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    )							->	Optional[int]:




									'''simple docstring'''


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

									__UpperCamelCase    :    Union[str, Any]								=      BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
									__UpperCamelCase    :    int								=      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    )							->	List[str]:




									'''simple docstring'''


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

									__UpperCamelCase    :    Union[str, Any]								=      BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
									__UpperCamelCase    :    List[Any]								=      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'''


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

									__UpperCamelCase    :    str								=      BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
									__UpperCamelCase    :    Union[str, 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'''


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

									__UpperCamelCase    :    Tuple								=      BatchEncoding({"inputs": [1, 2, 3], "labels": 0}    )
									__UpperCamelCase    :    Union[str, Any]								=      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    )							->	int:




									'''simple docstring'''


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

									__UpperCamelCase    :    Optional[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]]    )
									__UpperCamelCase    :    Optional[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

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

									__UpperCamelCase    :    str								=      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]]    )
									__UpperCamelCase    :    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    )							->	List[Any]:




									'''simple docstring'''


									import tensorflow as tf

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

									__UpperCamelCase    :    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]]    )
									__UpperCamelCase    :    List[str]								=      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    )							->	int:




									'''simple docstring'''


									__UpperCamelCase    :    List[Any]								=      Tokenizer(WordPiece(unk_token="[UNK]"    )    )
									PreTrainedTokenizerFast(tokenizer_object=__UpperCamelCase    )




						@require_tokenizers
						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, 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"    )    )