


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     __snake_case							(      unittest.TestCase):
					def   a (	self					:	Union[str, Any]  ,			_lowerCAmelCase					:	Union[str, Any]     )       ->				int:



											'''simple docstring'''





											__magic_name__ 		=			list(tokenizer_class.max_model_input_sizes.keys()     )
											for model_name in sa_models[:1]:
																	__magic_name__ 		=			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     )
																							__magic_name__ 		=			tokenizer.convert_tokens_to_ids(_lowerCAmelCase     )
																							self.assertIsInstance(_lowerCAmelCase  ,			_lowerCAmelCase     )
					def   a (	self					:	Tuple  ,			_lowerCAmelCase					:	BatchEncoding  ,			_lowerCAmelCase					:	Optional[Callable] = None     )       ->				int:



											'''simple docstring'''





											__magic_name__ 		=			pickle.dumps(_lowerCAmelCase     )
											self.assertIsNotNone(_lowerCAmelCase     )

											__magic_name__ 		=			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   a (	self					:	Tuple     )       ->				str:



											'''simple docstring'''





											self.check_tokenizer_from_pretrained(_lowerCAmelCase     )
					def   a (	self					:	int     )       ->				Union[str, Any]:



											'''simple docstring'''





											self.assertEqual(TensorType('tf'     )  ,			TensorType.TENSORFLOW     )
											self.assertEqual(TensorType('pt'     )  ,			TensorType.PYTORCH     )
											self.assertEqual(TensorType('np'     )  ,			TensorType.NUMPY     )
					@require_tokenizers
					def   a (	self					:	Optional[Any]     )       ->				Union[str, Any]:



											'''simple docstring'''





											import numpy as np

											__magic_name__ 		=			BertTokenizer.from_pretrained('bert-base-cased'     )
											__magic_name__ 		=			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   a (	self					:	Optional[Any]     )       ->				Tuple:



											'''simple docstring'''





											import tensorflow as tf

											def tf_array_equals(_lowerCAmelCase					:	Union[str, Any]  ,			_lowerCAmelCase					:	Union[str, Any]     ):
																	return tf.reduce_all(tf.equal(_lowerCAmelCase  ,			_lowerCAmelCase     )     )

											__magic_name__ 		=			BertTokenizer.from_pretrained('bert-base-cased'     )
											__magic_name__ 		=			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   a (	self					:	Any     )       ->				Tuple:



											'''simple docstring'''





											import torch

											__magic_name__ 		=			BertTokenizer.from_pretrained('bert-base-cased'     )
											__magic_name__ 		=			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   a (	self					:	Optional[Any]     )       ->				Dict:



											'''simple docstring'''





											__magic_name__ 		=			BertTokenizer.from_pretrained('bert-base-cased'     )
											__magic_name__ 		=			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   a (	self					:	int     )       ->				List[str]:



											'''simple docstring'''





											__magic_name__ 		=			BertTokenizerFast.from_pretrained('bert-base-cased'     )
											__magic_name__ 		=			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   a (	self					:	Union[str, Any]     )       ->				Union[str, Any]:



											'''simple docstring'''





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

											__magic_name__ 		=			BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}     )
											__magic_name__ 		=			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   a (	self					:	Optional[int]     )       ->				int:



											'''simple docstring'''





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

											__magic_name__ 		=			BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}     )
											__magic_name__ 		=			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   a (	self					:	Optional[Any]     )       ->				Optional[Any]:



											'''simple docstring'''





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

											__magic_name__ 		=			BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}     )
											__magic_name__ 		=			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   a (	self					:	Tuple     )       ->				Optional[Any]:



											'''simple docstring'''





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

											__magic_name__ 		=			BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}     )
											__magic_name__ 		=			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   a (	self					:	str     )       ->				Any:



											'''simple docstring'''





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

											__magic_name__ 		=			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]]     )
											__magic_name__ 		=			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   a (	self					:	Any     )       ->				int:



											'''simple docstring'''





											import torch

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

											__magic_name__ 		=			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]]     )
											__magic_name__ 		=			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   a (	self					:	Union[str, Any]     )       ->				str:



											'''simple docstring'''





											import tensorflow as tf

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

											__magic_name__ 		=			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]]     )
											__magic_name__ 		=			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   a (	self					:	int     )       ->				List[str]:



											'''simple docstring'''





											__magic_name__ 		=			Tokenizer(WordPiece(unk_token='[UNK]'     )     )
											PreTrainedTokenizerFast(tokenizer_object=_lowerCAmelCase     )







					@require_tokenizers
					def   a (	self					:	Dict     )       ->				Union[str, Any]:



											'''simple docstring'''





											__magic_name__ 		=			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'     )     )





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class     __snake_case							(      _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:					Union[str, Any] 				= ["image_processor", "tokenizer"]
					SCREAMING_SNAKE_CASE_		:					Tuple 				= "AutoImageProcessor"
					SCREAMING_SNAKE_CASE_		:					Dict 				= "AutoTokenizer"
					def __init__(	self					:	List[str]  ,			_lowerCAmelCase					:	str=None  ,			_lowerCAmelCase					:	Dict=None  ,			**_lowerCAmelCase					:	List[str]     )       ->				Optional[int]:



											'''simple docstring'''





											__magic_name__ 		=			None
											if "feature_extractor" in kwargs:
																	warnings.warn(
																	    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																	    ' instead.'  ,			_lowerCAmelCase  ,			)
																	__magic_name__ 		=			kwargs.pop('feature_extractor'     )

											__magic_name__ 		=			image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																	raise ValueError('You have to specify an image_processor.'     )
											if tokenizer is None:
																	raise ValueError('You have to specify a tokenizer.'     )

											super().__init__(_lowerCAmelCase  ,			_lowerCAmelCase     )
											__magic_name__ 		=			self.image_processor
					def __call__(	self					:	str  ,			_lowerCAmelCase					:	Any=None  ,			_lowerCAmelCase					:	int=None  ,			_lowerCAmelCase					:	List[str]=None  ,			**_lowerCAmelCase					:	int     )       ->				int:



											'''simple docstring'''





											if text is None and images is None:
																	raise ValueError('You have to specify either text or images. Both cannot be none.'     )

											if text is not None:
																	__magic_name__ 		=			self.tokenizer(_lowerCAmelCase  ,			return_tensors=_lowerCAmelCase  ,			**_lowerCAmelCase     )

											if images is not None:
																	__magic_name__ 		=			self.image_processor(_lowerCAmelCase  ,			return_tensors=_lowerCAmelCase  ,			**_lowerCAmelCase     )

											if text is not None and images is not None:
																	__magic_name__ 		=			image_features.pixel_values
																	return encoding
											elif text is not None:
																	return encoding
											else:
																	return BatchEncoding(data=dict(**_lowerCAmelCase     )  ,			tensor_type=_lowerCAmelCase     )
					def   a (	self					:	Optional[int]  ,			*_lowerCAmelCase					:	Optional[int]  ,			**_lowerCAmelCase					:	Optional[Any]     )       ->				Optional[int]:



											'''simple docstring'''





											return self.tokenizer.batch_decode(*_lowerCAmelCase  ,			**_lowerCAmelCase     )
					def   a (	self					:	Union[str, Any]  ,			*_lowerCAmelCase					:	str  ,			**_lowerCAmelCase					:	Tuple     )       ->				int:



											'''simple docstring'''





											return self.tokenizer.decode(*_lowerCAmelCase  ,			**_lowerCAmelCase     )
					@property
					def   a (	self					:	List[Any]     )       ->				Tuple:



											'''simple docstring'''





											__magic_name__ 		=			self.tokenizer.model_input_names
											__magic_name__ 		=			self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
					@property
					def   a (	self					:	Optional[Any]     )       ->				Dict:



											'''simple docstring'''





											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,			_lowerCAmelCase  ,			)
											return self.image_processor_class







					@property
					def   a (	self					:	Dict     )       ->				Tuple:



											'''simple docstring'''





											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,			_lowerCAmelCase  ,			)
											return self.image_processor

