
import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer







class 			UpperCamelCase__		(     tf.keras.layers.Layer ):







	'''simple docstring'''





	def __init__(      self					,							UpperCamelCase__					,							UpperCamelCase__					,							UpperCamelCase__ = None					,							UpperCamelCase__ = None					,							UpperCamelCase__ = None					,							UpperCamelCase__ = "longest"					,							UpperCamelCase__ = True					,							UpperCamelCase__ = 512					,							UpperCamelCase__ = None					,							UpperCamelCase__ = True					,							UpperCamelCase__ = True					,							UpperCamelCase__ = True					,							**UpperCamelCase__					,							):
					super().__init__()
					if use_fast_bert_tokenizer:
									A__	:						List[str]								=				FastBertTokenizer(
									    UpperCamelCase__					,							token_out_type=tf.intaa					,							lower_case_nfd_strip_accents=UpperCamelCase__					,							**UpperCamelCase__		)
					else:
									A__	:						Any								=				tf.lookup.StaticVocabularyTable(
									    tf.lookup.KeyValueTensorInitializer(
									        keys=UpperCamelCase__					,							key_dtype=tf.string					,							values=tf.range(tf.size(UpperCamelCase__					,							out_type=tf.intaa		)					,							dtype=tf.intaa		)					,							value_dtype=tf.intaa					,							)					,							num_oov_buckets=1					,							)
									A__	:						Optional[int]								=				BertTokenizerLayer(
									    UpperCamelCase__					,							token_out_type=tf.intaa					,							lower_case=UpperCamelCase__					,							**UpperCamelCase__		)

					A__	:						Union[str, Any]								=				vocab_list
					A__	:						Dict								=				do_lower_case
					A__	:						List[str]								=				cls_token_id or vocab_list.index('''[CLS]'''		)
					A__	:						Dict								=				sep_token_id or vocab_list.index('''[SEP]'''		)
					A__	:						Tuple								=				pad_token_id or vocab_list.index('''[PAD]'''		)
					A__	:						Dict								=				ShrinkLongestTrimmer(max_length - 3					,							axis=1		)  # Allow room for special tokens
					A__	:						List[Any]								=				max_length
					A__	:						str								=				padding
					A__	:						Any								=				truncation
					A__	:						Tuple								=				pad_to_multiple_of
					A__	:						List[str]								=				return_token_type_ids
					A__	:						Any								=				return_attention_mask





	@classmethod
	def 	__snake_case					(      cls					,							UpperCamelCase__					,							**UpperCamelCase__		):  # noqa: F821
					A__	:						Optional[int]								=				kwargs.pop('''do_lower_case'''					,							UpperCamelCase__		)
					A__	:						int								=				tokenizer.do_lower_case if do_lower_case is None else do_lower_case
					A__	:						str								=				kwargs.pop('''cls_token_id'''					,							UpperCamelCase__		)
					A__	:						Optional[int]								=				tokenizer.cls_token_id if cls_token_id is None else cls_token_id
					A__	:						Dict								=				kwargs.pop('''sep_token_id'''					,							UpperCamelCase__		)
					A__	:						List[Any]								=				tokenizer.sep_token_id if sep_token_id is None else sep_token_id
					A__	:						List[str]								=				kwargs.pop('''pad_token_id'''					,							UpperCamelCase__		)
					A__	:						Dict								=				tokenizer.pad_token_id if pad_token_id is None else pad_token_id

					A__	:						List[Any]								=				tokenizer.get_vocab()
					A__	:						Dict								=				sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]					,							key=lambda UpperCamelCase__		: x[1]		)
					A__	:						Union[str, Any]								=				[entry[0] for entry in vocab]
					return cls(
					    vocab_list=UpperCamelCase__					,							do_lower_case=UpperCamelCase__					,							cls_token_id=UpperCamelCase__					,							sep_token_id=UpperCamelCase__					,							pad_token_id=UpperCamelCase__					,							**UpperCamelCase__					,							)





	@classmethod
	def 	__snake_case					(      cls					,							UpperCamelCase__					,							*UpperCamelCase__					,							**UpperCamelCase__		):
					try:
									A__	:						Dict								=				BertTokenizer.from_pretrained(UpperCamelCase__					,							*UpperCamelCase__					,							**UpperCamelCase__		)
					except:  # noqa: E722
									from .tokenization_bert_fast import BertTokenizerFast

									A__	:						Optional[Any]								=				BertTokenizerFast.from_pretrained(UpperCamelCase__					,							*UpperCamelCase__					,							**UpperCamelCase__		)
					return cls.from_tokenizer(UpperCamelCase__					,							**UpperCamelCase__		)





	def 	__snake_case					(      self					,							UpperCamelCase__		):
					if self.do_lower_case:
									A__	:						str								=				case_fold_utfa(UpperCamelCase__		)
					A__	:						Optional[int]								=				self.tf_tokenizer.tokenize(UpperCamelCase__		)
					return tokens.merge_dims(1					,							-1		)





	def 	__snake_case					(      self					,							UpperCamelCase__					,							UpperCamelCase__=None					,							UpperCamelCase__=None					,							UpperCamelCase__=None					,							UpperCamelCase__=None					,							UpperCamelCase__=None					,							UpperCamelCase__=None					,							UpperCamelCase__=None					,							):
					if padding is None:
									A__	:						str								=				self.padding
					if padding not in ("longest", "max_length"):
									raise ValueError('''Padding must be either \'longest\' or \'max_length\'!'''		)
					if max_length is not None and text_pair is not None:
									# Because we have to instantiate a Trimmer to do it properly
									raise ValueError('''max_length cannot be overridden at call time when truncating paired texts!'''		)
					if max_length is None:
									A__	:						List[Any]								=				self.max_length
					if truncation is None:
									A__	:						Optional[int]								=				self.truncation
					if pad_to_multiple_of is None:
									A__	:						Optional[int]								=				self.pad_to_multiple_of
					if return_token_type_ids is None:
									A__	:						Union[str, Any]								=				self.return_token_type_ids
					if return_attention_mask is None:
									A__	:						Optional[int]								=				self.return_attention_mask
					if not isinstance(UpperCamelCase__					,							tf.Tensor		):
									A__	:						Union[str, Any]								=				tf.convert_to_tensor(UpperCamelCase__		)
					if text_pair is not None and not isinstance(UpperCamelCase__					,							tf.Tensor		):
									A__	:						List[str]								=				tf.convert_to_tensor(UpperCamelCase__		)
					if text_pair is not None:
									if text.shape.rank > 1:
													raise ValueError('''text argument should not be multidimensional when a text pair is supplied!'''		)
									if text_pair.shape.rank > 1:
													raise ValueError('''text_pair should not be multidimensional!'''		)
					if text.shape.rank == 2:
									A__							,				A__	:						Union[str, Any]								=				text[:, 0], text[:, 1]
					A__	:						List[str]								=				self.unpaired_tokenize(UpperCamelCase__		)
					if text_pair is None:  # Unpaired text
									if truncation:
													A__	:						Optional[int]								=				text[:, : max_length - 2]  # Allow room for special tokens
									A__							,				A__	:						Tuple								=				combine_segments(
									    (text,)					,							start_of_sequence_id=self.cls_token_id					,							end_of_segment_id=self.sep_token_id		)
					else:  # Paired text
									A__	:						Union[str, Any]								=				self.unpaired_tokenize(UpperCamelCase__		)
									if truncation:
													A__							,				A__	:						Any								=				self.paired_trimmer.trim([text, text_pair]		)
									A__							,				A__	:						str								=				combine_segments(
									    (text, text_pair)					,							start_of_sequence_id=self.cls_token_id					,							end_of_segment_id=self.sep_token_id		)
					if padding == "longest":
									A__	:						Any								=				input_ids.bounding_shape(axis=1		)
									if pad_to_multiple_of is not None:
													# No ceiling division in tensorflow, so we negate floordiv instead
													A__	:						Any								=				pad_to_multiple_of * (-tf.math.floordiv(-pad_length					,							UpperCamelCase__		))
					else:
									A__	:						int								=				max_length

					A__							,				A__	:						Any								=				pad_model_inputs(UpperCamelCase__					,							max_seq_length=UpperCamelCase__					,							pad_value=self.pad_token_id		)
					A__	:						Any								=				{'''input_ids''': input_ids}
					if return_attention_mask:
									A__	:						Union[str, Any]								=				attention_mask
					if return_token_type_ids:
									A__							,				A__	:						List[str]								=				pad_model_inputs(
									    UpperCamelCase__					,							max_seq_length=UpperCamelCase__					,							pad_value=self.pad_token_id		)
									A__	:						int								=				token_type_ids
					return output





	def 	__snake_case					(      self		):
					return {
					    "vocab_list": self.vocab_list,
					    "do_lower_case": self.do_lower_case,
					    "cls_token_id": self.cls_token_id,
					    "sep_token_id": self.sep_token_id,
					    "pad_token_id": self.pad_token_id,
					}

import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
					from PIL import Image







class 			UpperCamelCase__		(     unittest.TestCase,      SCREAMING_SNAKE_CASE_ ):







	'''simple docstring'''





	def 	__snake_case					(      self		):
					A__	:						Dict								=				load_tool('''image-question-answering'''		)
					self.tool.setup()
					A__	:						List[Any]								=				load_tool('''image-question-answering'''					,							remote=UpperCamelCase__		)





	def 	__snake_case					(      self		):
					A__	:						str								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						List[str]								=				self.tool(UpperCamelCase__					,							'''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)





	def 	__snake_case					(      self		):
					A__	:						Tuple								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						Dict								=				self.remote_tool(UpperCamelCase__					,							'''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)





	def 	__snake_case					(      self		):
					A__	:						Dict								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						int								=				self.tool(image=UpperCamelCase__					,							question='''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)





	def 	__snake_case					(      self		):
					A__	:						Tuple								=				Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		)
					A__	:						str								=				self.remote_tool(image=UpperCamelCase__					,							question='''How many cats are sleeping on the couch?'''		)
					self.assertEqual(UpperCamelCase__					,							'''2'''		)