



import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor






@require_tokenizers
@require_vision
class 						__a (     unittest.TestCase				):



	def 		UpperCamelCase							(						self							:    Any)->							Optional[int]:
						__lowerCAmelCase	   =tempfile.mkdtemp()

						# fmt: off
						__lowerCAmelCase	   =["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
						# fmt: on
						__lowerCAmelCase	   =os.path.join(self.tmpdirname				,	VOCAB_FILES_NAMES["""vocab_file"""])
						with open(self.vocab_file				,	"""w"""				,	encoding="""utf-8""") as vocab_writer:
											vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]))

						__lowerCAmelCase	   ={
						    """do_resize""": True,
						    """size""": {"""height""": 18, """width""": 18},
						    """do_normalize""": True,
						    """image_mean""": [0.5, 0.5, 0.5],
						    """image_std""": [0.5, 0.5, 0.5],
						}
						__lowerCAmelCase	   =os.path.join(self.tmpdirname				,	snake_case_)
						with open(self.image_processor_file				,	"""w"""				,	encoding="""utf-8""") as fp:
											json.dump(snake_case_				,	snake_case_)



	def 		UpperCamelCase							(						self							:    Any				,	**snake_case_							:    Optional[int])->							Tuple:
						return BertTokenizer.from_pretrained(self.tmpdirname				,	**snake_case_)



	def 		UpperCamelCase							(						self							:    List[str]				,	**snake_case_							:    List[Any])->							Union[str, Any]:
						return ViTImageProcessor.from_pretrained(self.tmpdirname				,	**snake_case_)



	def 		UpperCamelCase							(						self							:    Union[str, Any])->							Optional[Any]:
						shutil.rmtree(self.tmpdirname)



	def 		UpperCamelCase							(						self							:    List[str])->							Dict:
						__lowerCAmelCase	   =[np.random.randint(2_55				,	size=(3, 30, 4_00)				,	dtype=np.uinta)]

						__lowerCAmelCase	   =[Image.fromarray(np.moveaxis(snake_case_				,	0				,	-1)) for x in image_inputs]

						return image_inputs



	def 		UpperCamelCase							(						self							:    Tuple)->							Union[str, Any]:
						__lowerCAmelCase	   =self.get_tokenizer()
						__lowerCAmelCase	   =self.get_image_processor()

						__lowerCAmelCase	   =VisionTextDualEncoderProcessor(tokenizer=snake_case_				,	image_processor=snake_case_)

						processor.save_pretrained(self.tmpdirname)
						__lowerCAmelCase	   =VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname)

						self.assertEqual(processor.tokenizer.get_vocab()				,	tokenizer.get_vocab())
						self.assertIsInstance(processor.tokenizer				,	(BertTokenizer, BertTokenizerFast))

						self.assertEqual(processor.image_processor.to_json_string()				,	image_processor.to_json_string())
						self.assertIsInstance(processor.image_processor				,	snake_case_)



	def 		UpperCamelCase							(						self							:    Dict)->							List[Any]:
						__lowerCAmelCase	   =VisionTextDualEncoderProcessor(
						    tokenizer=self.get_tokenizer()				,	image_processor=self.get_image_processor())
						processor.save_pretrained(self.tmpdirname)

						__lowerCAmelCase	   =self.get_tokenizer(bos_token="""(BOS)"""				,	eos_token="""(EOS)""")
						__lowerCAmelCase	   =self.get_image_processor(do_normalize=snake_case_				,	padding_value=1.0)

						__lowerCAmelCase	   =VisionTextDualEncoderProcessor.from_pretrained(
						    self.tmpdirname				,	bos_token="""(BOS)"""				,	eos_token="""(EOS)"""				,	do_normalize=snake_case_				,	padding_value=1.0)

						self.assertEqual(processor.tokenizer.get_vocab()				,	tokenizer_add_kwargs.get_vocab())
						self.assertIsInstance(processor.tokenizer				,	(BertTokenizer, BertTokenizerFast))

						self.assertEqual(processor.image_processor.to_json_string()				,	image_processor_add_kwargs.to_json_string())
						self.assertIsInstance(processor.image_processor				,	snake_case_)



	def 		UpperCamelCase							(						self							:    Tuple)->							List[Any]:
						__lowerCAmelCase	   =self.get_image_processor()
						__lowerCAmelCase	   =self.get_tokenizer()

						__lowerCAmelCase	   =VisionTextDualEncoderProcessor(tokenizer=snake_case_				,	image_processor=snake_case_)

						__lowerCAmelCase	   =self.prepare_image_inputs()

						__lowerCAmelCase	   =image_processor(snake_case_				,	return_tensors="""np""")
						__lowerCAmelCase	   =processor(images=snake_case_				,	return_tensors="""np""")

						for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()				,	input_processor[key].sum()				,	delta=1e-2)



	def 		UpperCamelCase							(						self							:    Dict)->							Optional[int]:
						__lowerCAmelCase	   =self.get_image_processor()
						__lowerCAmelCase	   =self.get_tokenizer()

						__lowerCAmelCase	   =VisionTextDualEncoderProcessor(tokenizer=snake_case_				,	image_processor=snake_case_)

						__lowerCAmelCase	   ="""lower newer"""

						__lowerCAmelCase	   =processor(text=snake_case_)

						__lowerCAmelCase	   =tokenizer(snake_case_)

						for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]				,	encoded_processor[key])



	def 		UpperCamelCase							(						self							:    Tuple)->							Union[str, Any]:
						__lowerCAmelCase	   =self.get_image_processor()
						__lowerCAmelCase	   =self.get_tokenizer()

						__lowerCAmelCase	   =VisionTextDualEncoderProcessor(tokenizer=snake_case_				,	image_processor=snake_case_)

						__lowerCAmelCase	   ="""lower newer"""
						__lowerCAmelCase	   =self.prepare_image_inputs()

						__lowerCAmelCase	   =processor(text=snake_case_				,	images=snake_case_)

						self.assertListEqual(list(inputs.keys())				,	["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""])

						# test if it raises when no input is passed
						with self.assertRaises(snake_case_):
											processor()



	def 		UpperCamelCase							(						self							:    Any)->							List[Any]:
						__lowerCAmelCase	   =self.get_image_processor()
						__lowerCAmelCase	   =self.get_tokenizer()

						__lowerCAmelCase	   =VisionTextDualEncoderProcessor(tokenizer=snake_case_				,	image_processor=snake_case_)

						__lowerCAmelCase	   =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						__lowerCAmelCase	   =processor.batch_decode(snake_case_)
						__lowerCAmelCase	   =tokenizer.batch_decode(snake_case_)

						self.assertListEqual(snake_case_				,	snake_case_)



	def 		UpperCamelCase							(						self							:    List[str])->							Tuple:
						__lowerCAmelCase	   =self.get_image_processor()
						__lowerCAmelCase	   =self.get_tokenizer()

						__lowerCAmelCase	   =VisionTextDualEncoderProcessor(tokenizer=snake_case_				,	image_processor=snake_case_)

						__lowerCAmelCase	   ="""lower newer"""
						__lowerCAmelCase	   =self.prepare_image_inputs()

						__lowerCAmelCase	   =processor(text=snake_case_				,	images=snake_case_)

						self.assertListEqual(list(inputs.keys())				,	processor.model_input_names)









import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


lowercase_	       =			logging.get_logger(__name__)






class 						__a (     SCREAMING_SNAKE_CASE				):



	def __init__(						self							:    Optional[Any]				,	*snake_case_							:    List[str]				,	**snake_case_							:    Union[str, Any])->							None:
						warnings.warn(
						    """The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
						    """ Please use PoolFormerImageProcessor instead."""				,	snake_case_				,	)
						super().__init__(*snake_case_				,	**snake_case_)




