




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 		lowercase							(     unittest.TestCase	):

     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  List[Any]    =		tempfile.mkdtemp()

           # fmt: off
           _A							:  Optional[int]    =		["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
           # fmt: on
           _A							:  Optional[int]    =		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]       )       )

           _A							:  Optional[Any]    =		{
               """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],
           }
           _A							:  List[Any]    =		os.path.join(self.tmpdirname	,  _a       )
           with open(self.image_processor_file	,  """w"""	,  encoding="""utf-8"""       ) as fp:
                 json.dump(_a	,  _a       )

     def       a__			(						self	,  **_a       )		->							int:
           return BertTokenizer.from_pretrained(self.tmpdirname	,  **_a       )

     def       a__			(						self	,  **_a       )		->							List[str]:
           return ViTImageProcessor.from_pretrained(self.tmpdirname	,  **_a       )

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

     def       a__			(						self       )		->							List[str]:
           _A							:  List[Any]    =		[np.random.randint(255	,  size=(3, 30, 400)	,  dtype=np.uinta       )]

           _A							:  int    =		[Image.fromarray(np.moveaxis(_a	,  0	,  -1       )       ) for x in image_inputs]

           return image_inputs

     def       a__			(						self       )		->							Dict:
           _A							:  Any    =		self.get_tokenizer()
           _A							:  int    =		self.get_image_processor()

           _A							:  Union[str, Any]    =		VisionTextDualEncoderProcessor(tokenizer=_a	,  image_processor=_a       )

           processor.save_pretrained(self.tmpdirname       )
           _A							:  int    =		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	,  _a       )

     def       a__			(						self       )		->							int:
           _A							:  List[Any]    =		VisionTextDualEncoderProcessor(
               tokenizer=self.get_tokenizer()	,  image_processor=self.get_image_processor()       )
           processor.save_pretrained(self.tmpdirname       )

           _A							:  List[str]    =		self.get_tokenizer(bos_token="""(BOS)"""	,  eos_token="""(EOS)"""       )
           _A							:  Dict    =		self.get_image_processor(do_normalize=_a	,  padding_value=1.0       )

           _A							:  Tuple    =		VisionTextDualEncoderProcessor.from_pretrained(
               self.tmpdirname	,  bos_token="""(BOS)"""	,  eos_token="""(EOS)"""	,  do_normalize=_a	,  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	,  _a       )

     def       a__			(						self       )		->							Optional[int]:
           _A							:  List[Any]    =		self.get_image_processor()
           _A							:  Optional[Any]    =		self.get_tokenizer()

           _A							:  Optional[int]    =		VisionTextDualEncoderProcessor(tokenizer=_a	,  image_processor=_a       )

           _A							:  List[str]    =		self.prepare_image_inputs()

           _A							:  List[str]    =		image_processor(_a	,  return_tensors="""np"""       )
           _A							:  Optional[Any]    =		processor(images=_a	,  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       a__			(						self       )		->							List[Any]:
           _A							:  Optional[Any]    =		self.get_image_processor()
           _A							:  Optional[int]    =		self.get_tokenizer()

           _A							:  str    =		VisionTextDualEncoderProcessor(tokenizer=_a	,  image_processor=_a       )

           _A							:  Dict    =		"""lower newer"""

           _A							:  Tuple    =		processor(text=_a       )

           _A							:  Tuple    =		tokenizer(_a       )

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

     def       a__			(						self       )		->							int:
           _A							:  List[str]    =		self.get_image_processor()
           _A							:  Any    =		self.get_tokenizer()

           _A							:  str    =		VisionTextDualEncoderProcessor(tokenizer=_a	,  image_processor=_a       )

           _A							:  Tuple    =		"""lower newer"""
           _A							:  Any    =		self.prepare_image_inputs()

           _A							:  List[str]    =		processor(text=_a	,  images=_a       )

           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(_a       ):
                 processor()

     def       a__			(						self       )		->							Union[str, Any]:
           _A							:  Dict    =		self.get_image_processor()
           _A							:  Optional[Any]    =		self.get_tokenizer()

           _A							:  Dict    =		VisionTextDualEncoderProcessor(tokenizer=_a	,  image_processor=_a       )

           _A							:  Optional[Any]    =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _A							:  int    =		processor.batch_decode(_a       )
           _A							:  Union[str, Any]    =		tokenizer.batch_decode(_a       )

           self.assertListEqual(_a	,  _a       )



     def       a__			(						self       )		->							Optional[Any]:
           _A							:  int    =		self.get_image_processor()
           _A							:  Any    =		self.get_tokenizer()

           _A							:  str    =		VisionTextDualEncoderProcessor(tokenizer=_a	,  image_processor=_a       )

           _A							:  int    =		"""lower newer"""
           _A							:  Optional[int]    =		self.prepare_image_inputs()

           _A							:  int    =		processor(text=_a	,  images=_a       )

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







import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DPTImageProcessor







class 		lowercase							(     unittest.TestCase	):

     def __init__(						self	,  _a	,  _a=7	,  _a=3	,  _a=18	,  _a=30	,  _a=400	,  _a=True	,  _a=None	,  _a=True	,  _a=[0.5, 0.5, 0.5]	,  _a=[0.5, 0.5, 0.5]	,  )		->							Tuple:
           _A							:  Any    =		size if size is not None else {"""height""": 18, """width""": 18}
           _A							:  Optional[Any]    =		parent
           _A							:  Union[str, Any]    =		batch_size
           _A							:  List[Any]    =		num_channels
           _A							:  List[str]    =		image_size
           _A							:  Optional[Any]    =		min_resolution
           _A							:  List[Any]    =		max_resolution
           _A							:  Optional[Any]    =		do_resize
           _A							:  str    =		size
           _A							:  List[str]    =		do_normalize
           _A							:  Dict    =		image_mean
           _A							:  int    =		image_std



     def       a__			(						self       )		->							Any:
           return {
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_normalize": self.do_normalize,
               "do_resize": self.do_resize,
               "size": self.size,
           }







@require_torch
@require_vision
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		DPTImageProcessor if is_vision_available() else None

     def       a__			(						self       )		->							Optional[int]:
           _A							:  Optional[Any]    =		DPTImageProcessingTester(self       )

     @property
     def       a__			(						self       )		->							Tuple:
           return self.image_processor_tester.prepare_image_processor_dict()

     def       a__			(						self       )		->							Any:
           _A							:  List[str]    =		self.image_processing_class(**self.image_processor_dict       )
           self.assertTrue(hasattr(_a	,  """image_mean"""       )       )
           self.assertTrue(hasattr(_a	,  """image_std"""       )       )
           self.assertTrue(hasattr(_a	,  """do_normalize"""       )       )
           self.assertTrue(hasattr(_a	,  """do_resize"""       )       )
           self.assertTrue(hasattr(_a	,  """size"""       )       )

     def       a__			(						self       )		->							Any:
           _A							:  List[str]    =		self.image_processing_class.from_dict(self.image_processor_dict       )
           self.assertEqual(image_processor.size	,  {"""height""": 18, """width""": 18}       )

           _A							:  str    =		self.image_processing_class.from_dict(self.image_processor_dict	,  size=42       )
           self.assertEqual(image_processor.size	,  {"""height""": 42, """width""": 42}       )

     def       a__			(						self       )		->							List[Any]:
           # Initialize image_processing
           _A							:  Optional[int]    =		self.image_processing_class(**self.image_processor_dict       )
           # create random PIL images
           _A							:  Any    =		prepare_image_inputs(self.image_processor_tester	,  equal_resolution=_a       )
           for image in image_inputs:
                 self.assertIsInstance(_a	,  Image.Image       )

           # Test not batched input
           _A							:  Union[str, Any]    =		image_processing(image_inputs[0]	,  return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape	,  (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )	,  )

           # Test batched
           _A							:  int    =		image_processing(_a	,  return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape	,  (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )	,  )

     def       a__			(						self       )		->							Union[str, Any]:
           # Initialize image_processing
           _A							:  List[Any]    =		self.image_processing_class(**self.image_processor_dict       )
           # create random numpy tensors
           _A							:  Optional[Any]    =		prepare_image_inputs(self.image_processor_tester	,  equal_resolution=_a	,  numpify=_a       )
           for image in image_inputs:
                 self.assertIsInstance(_a	,  np.ndarray       )

           # Test not batched input
           _A							:  Dict    =		image_processing(image_inputs[0]	,  return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape	,  (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )	,  )

           # Test batched
           _A							:  Any    =		image_processing(_a	,  return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape	,  (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )	,  )



     def       a__			(						self       )		->							List[str]:
           # Initialize image_processing
           _A							:  Dict    =		self.image_processing_class(**self.image_processor_dict       )
           # create random PyTorch tensors
           _A							:  str    =		prepare_image_inputs(self.image_processor_tester	,  equal_resolution=_a	,  torchify=_a       )
           for image in image_inputs:
                 self.assertIsInstance(_a	,  torch.Tensor       )

           # Test not batched input
           _A							:  Optional[Any]    =		image_processing(image_inputs[0]	,  return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape	,  (
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )	,  )

           # Test batched
           _A							:  int    =		image_processing(_a	,  return_tensors="""pt"""       ).pixel_values
           self.assertEqual(
               encoded_images.shape	,  (
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )	,  )

