


import warnings
from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 						lowerCamelCase_							(					SCREAMING_SNAKE_CASE_					):



					'''simple docstring'''







					__UpperCAmelCase							  =					["image_processor", "tokenizer"]
					__UpperCAmelCase							  =					"FlavaImageProcessor"
					__UpperCAmelCase							  =					("BertTokenizer", "BertTokenizerFast")






					def __init__(					self			,							snake_case_=None			,							snake_case_=None			,							**snake_case_    ) ->      Any:







								'''simple docstring'''

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

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

								super().__init__(__snake_case			,							__snake_case    )
								__lowercase			       = self.image_processor






					def __call__(					self			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = True			,							snake_case_ = False			,							snake_case_ = False			,							snake_case_ = None			,							snake_case_ = 0			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = False			,							snake_case_ = False			,							snake_case_ = False			,							snake_case_ = False			,							snake_case_ = True			,							snake_case_ = None			,							**snake_case_			,							) ->      Optional[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:
											__lowercase			       = self.tokenizer(
											    text=__snake_case			,							add_special_tokens=__snake_case			,							padding=__snake_case			,							truncation=__snake_case			,							max_length=__snake_case			,							stride=__snake_case			,							pad_to_multiple_of=__snake_case			,							return_token_type_ids=__snake_case			,							return_attention_mask=__snake_case			,							return_overflowing_tokens=__snake_case			,							return_special_tokens_mask=__snake_case			,							return_offsets_mapping=__snake_case			,							return_length=__snake_case			,							verbose=__snake_case			,							return_tensors=__snake_case			,							**__snake_case			,							)
								if images is not None:
											__lowercase			       = self.image_processor(
											    __snake_case			,							return_image_mask=__snake_case			,							return_codebook_pixels=__snake_case			,							return_tensors=__snake_case			,							**__snake_case			,							)

								if text is not None and images is not None:
											encoding.update(__snake_case    )
											return encoding
								elif text is not None:
											return encoding
								else:
											return BatchEncoding(data=dict(**__snake_case    )			,							tensor_type=__snake_case    )






					def A   (					self			,							*snake_case_			,							**snake_case_    ) ->      Dict:







								'''simple docstring'''

								return self.tokenizer.batch_decode(*__snake_case			,							**__snake_case    )






					def A   (					self			,							*snake_case_			,							**snake_case_    ) ->      int:







								'''simple docstring'''

								return self.tokenizer.decode(*__snake_case			,							**__snake_case    )






					@property
					def A   (					self    ) ->      str:







								'''simple docstring'''

								__lowercase			       = self.tokenizer.model_input_names
								__lowercase			       = self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )






					@property
					def A   (					self    ) ->      Union[str, Any]:







								'''simple docstring'''

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






					@property
					def A   (					self    ) ->      List[str]:







								'''simple docstring'''

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










"""simple docstring"""





import json
import os
import unittest

from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class       __A		(   SCREAMING_SNAKE_CASE_     ,unittest.TestCase			):
					UpperCAmelCase__								=				OpenAIGPTTokenizer
					UpperCAmelCase__								=				OpenAIGPTTokenizerFast
					UpperCAmelCase__								=				True
					UpperCAmelCase__								=				False

					def   lowerCamelCase__   (			self  :      Dict					)			->							Optional[Any]:
											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											__magic_name__:	str							=       [
											    """l""",
											    """o""",
											    """w""",
											    """e""",
											    """r""",
											    """s""",
											    """t""",
											    """i""",
											    """d""",
											    """n""",
											    """w</w>""",
											    """r</w>""",
											    """t</w>""",
											    """lo""",
											    """low""",
											    """er</w>""",
											    """low</w>""",
											    """lowest</w>""",
											    """newer</w>""",
											    """wider</w>""",
											    """<unk>""",
											]
											__magic_name__:	Any							=       dict(zip(__snake_case     ,		range(len(__snake_case					)					)					)					)
											__magic_name__:	List[str]							=       ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]

											__magic_name__:	Optional[Any]							=       os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES["""vocab_file"""]					)
											__magic_name__:	Any							=       os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES["""merges_file"""]					)
											with open(self.vocab_file     ,		"""w"""					) as fp:
																	fp.write(json.dumps(__snake_case					)					)
											with open(self.merges_file     ,		"""w"""					) as fp:
																	fp.write("""\n""".join(__snake_case					)					)

					def   lowerCamelCase__   (			self  :      List[Any]     ,		__snake_case  :      Optional[Any]					)			->							Optional[int]:
											return "lower newer", "lower newer"

					def   lowerCamelCase__   (			self  :      List[Any]					)			->							Union[str, Any]:
											__magic_name__:	Optional[Any]							=       OpenAIGPTTokenizer(self.vocab_file     ,		self.merges_file					)

											__magic_name__:	Union[str, Any]							=       """lower"""
											__magic_name__:	Optional[int]							=       ["""low""", """er</w>"""]
											__magic_name__:	List[Any]							=       tokenizer.tokenize(__snake_case					)
											self.assertListEqual(__snake_case     ,		__snake_case					)

											__magic_name__:	Optional[int]							=       tokens + ["""<unk>"""]
											__magic_name__:	List[str]							=       [1_4, 1_5, 2_0]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case					)     ,		__snake_case					)

					def   lowerCamelCase__   (			self  :      Optional[Any]     ,		__snake_case  :      Any=1_5					)			->							Tuple:
											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'					):
																							__magic_name__:	str							=       self.rust_tokenizer_class.from_pretrained(__snake_case     ,		**__snake_case					)

																							# Simple input
																							__magic_name__:	Dict							=       """This is a simple input"""
																							__magic_name__:	Any							=       ["""This is a simple input 1""", """This is a simple input 2"""]
																							__magic_name__:	int							=       ("""This is a simple input""", """This is a pair""")
																							__magic_name__:	int							=       [
																							    ("""This is a simple input 1""", """This is a simple input 2"""),
																							    ("""This is a simple pair 1""", """This is a simple pair 2"""),
																							]

																							# Simple input tests
																							self.assertRaises(__snake_case     ,		tokenizer_r.encode     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""					)

																							# Simple input
																							self.assertRaises(__snake_case     ,		tokenizer_r.encode_plus     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""					)

																							# Simple input
																							self.assertRaises(
																							    __snake_case     ,		tokenizer_r.batch_encode_plus     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""     ,		)

																							# Pair input
																							self.assertRaises(__snake_case     ,		tokenizer_r.encode     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""					)

																							# Pair input
																							self.assertRaises(__snake_case     ,		tokenizer_r.encode_plus     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""					)

																							# Pair input
																							self.assertRaises(
																							    __snake_case     ,		tokenizer_r.batch_encode_plus     ,		__snake_case     ,		max_length=__snake_case     ,		padding="""max_length"""     ,		)






					def   lowerCamelCase__   (			self  :      Dict					)			->							Any:
											pass



@require_ftfy
@require_spacy
@require_tokenizers
class       __A		(   SCREAMING_SNAKE_CASE_			):
					pass



