

import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_vision_available():
 from PIL import Image

 from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor



@require_vision
class   lowercase_   (  unittest.TestCase					):



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

        _snake_case						=							[
            '''[UNK]''',
            '''[CLS]''',
            '''[SEP]''',
            '''[PAD]''',
            '''[MASK]''',
            '''的''',
            '''价''',
            '''格''',
            '''是''',
            '''15''',
            '''便''',
            '''alex''',
            '''##andra''',
            '''，''',
            '''。''',
            '''-''',
            '''t''',
            '''shirt''',
        ]
        _snake_case						=							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]		)		)

        _snake_case						=							{
            '''do_resize''': True,
            '''size''': {'''height''': 224, '''width''': 224},
            '''do_center_crop''': True,
            '''crop_size''': {'''height''': 18, '''width''': 18},
            '''do_normalize''': True,
            '''image_mean''': [0.4814_5466, 0.457_8275, 0.4082_1073],
            '''image_std''': [0.2686_2954, 0.2613_0258, 0.2757_7711],
            '''do_convert_rgb''': True,
        }
        _snake_case						=							os.path.join(self.tmpdirname      , A__		)
        with open(self.image_processor_file      , '''w'''      , encoding='''utf-8'''		) as fp:
          json.dump(A__      , A__		)



      def 						UpperCamelCase_		(      self				:    Any      , **A__				:    str		)   ->					str:
        return BertTokenizer.from_pretrained(self.tmpdirname      , **A__		)



      def 						UpperCamelCase_		(      self				:    Any      , **A__				:    List[str]		)   ->					Tuple:
        return BertTokenizerFast.from_pretrained(self.tmpdirname      , **A__		)



      def 						UpperCamelCase_		(      self				:    int      , **A__				:    Optional[Any]		)   ->					str:
        return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname      , **A__		)



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



      def 						UpperCamelCase_		(      self				:    str		)   ->					Dict:
        _snake_case						=							[np.random.randint(255      , size=(3, 30, 400)      , dtype=np.uinta		)]

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

        return image_inputs



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					List[Any]:
        _snake_case						=							self.get_tokenizer()
        _snake_case						=							self.get_rust_tokenizer()
        _snake_case						=							self.get_image_processor()

        _snake_case						=							ChineseCLIPProcessor(tokenizer=A__      , image_processor=A__		)
        processor_slow.save_pretrained(self.tmpdirname		)
        _snake_case						=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname      , use_fast=A__		)

        _snake_case						=							ChineseCLIPProcessor(tokenizer=A__      , image_processor=A__		)
        processor_fast.save_pretrained(self.tmpdirname		)
        _snake_case						=							ChineseCLIPProcessor.from_pretrained(self.tmpdirname		)

        self.assertEqual(processor_slow.tokenizer.get_vocab()      , tokenizer_slow.get_vocab()		)
        self.assertEqual(processor_fast.tokenizer.get_vocab()      , tokenizer_fast.get_vocab()		)
        self.assertEqual(tokenizer_slow.get_vocab()      , tokenizer_fast.get_vocab()		)
        self.assertIsInstance(processor_slow.tokenizer      , A__		)
        self.assertIsInstance(processor_fast.tokenizer      , A__		)

        self.assertEqual(processor_slow.image_processor.to_json_string()      , image_processor.to_json_string()		)
        self.assertEqual(processor_fast.image_processor.to_json_string()      , image_processor.to_json_string()		)
        self.assertIsInstance(processor_slow.image_processor      , A__		)
        self.assertIsInstance(processor_fast.image_processor      , A__		)



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					List[Any]:
        _snake_case						=							ChineseCLIPProcessor(tokenizer=self.get_tokenizer()      , image_processor=self.get_image_processor()		)
        processor.save_pretrained(self.tmpdirname		)

        _snake_case						=							self.get_tokenizer(cls_token='''(CLS)'''      , sep_token='''(SEP)'''		)
        _snake_case						=							self.get_image_processor(do_normalize=A__		)

        _snake_case						=							ChineseCLIPProcessor.from_pretrained(
            self.tmpdirname      , cls_token='''(CLS)'''      , sep_token='''(SEP)'''      , do_normalize=A__		)

        self.assertEqual(processor.tokenizer.get_vocab()      , tokenizer_add_kwargs.get_vocab()		)
        self.assertIsInstance(processor.tokenizer      , A__		)

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



      def 						UpperCamelCase_		(      self				:    str		)   ->					Dict:
        _snake_case						=							self.get_image_processor()
        _snake_case						=							self.get_tokenizer()

        _snake_case						=							ChineseCLIPProcessor(tokenizer=A__      , image_processor=A__		)

        _snake_case						=							self.prepare_image_inputs()

        _snake_case						=							image_processor(A__      , return_tensors='''np'''		)
        _snake_case						=							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 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Any:
        _snake_case						=							self.get_image_processor()
        _snake_case						=							self.get_tokenizer()

        _snake_case						=							ChineseCLIPProcessor(tokenizer=A__      , image_processor=A__		)

        _snake_case						=							'''Alexandra，T-shirt的价格是15便士。'''

        _snake_case						=							processor(text=A__		)

        _snake_case						=							tokenizer(A__		)

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



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

        _snake_case						=							ChineseCLIPProcessor(tokenizer=A__      , image_processor=A__		)

        _snake_case						=							'''Alexandra，T-shirt的价格是15便士。'''
        _snake_case						=							self.prepare_image_inputs()

        _snake_case						=							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 pytest.raises(A__		):
          processor()



      def 						UpperCamelCase_		(      self				:    int		)   ->					Union[str, Any]:
        _snake_case						=							self.get_image_processor()
        _snake_case						=							self.get_tokenizer()

        _snake_case						=							ChineseCLIPProcessor(tokenizer=A__      , image_processor=A__		)

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

        _snake_case						=							processor.batch_decode(A__		)
        _snake_case						=							tokenizer.batch_decode(A__		)

        self.assertListEqual(A__      , A__		)







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

        _snake_case						=							ChineseCLIPProcessor(tokenizer=A__      , image_processor=A__		)

        _snake_case						=							'''Alexandra，T-shirt的价格是15便士。'''
        _snake_case						=							self.prepare_image_inputs()

        _snake_case						=							processor(text=A__      , images=A__		)

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




import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import VivitImageProcessor



class   lowercase_   (  unittest.TestCase					):



      def __init__(      self				:    Union[str, Any]      , A__				:    int      , A__				:    List[str]=7      , A__				:    Tuple=3      , A__				:    List[str]=10      , A__				:    Optional[int]=18      , A__				:    int=30      , A__				:    Tuple=400      , A__				:    Dict=True      , A__				:    str=None      , A__				:    str=True      , A__				:    List[str]=[0.5, 0.5, 0.5]      , A__				:    int=[0.5, 0.5, 0.5]      , A__				:    List[Any]=None      , )   ->					int:
        _snake_case						=							size if size is not None else {'''shortest_edge''': 18}
        _snake_case						=							crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

        _snake_case						=							parent
        _snake_case						=							batch_size
        _snake_case						=							num_channels
        _snake_case						=							num_frames
        _snake_case						=							image_size
        _snake_case						=							min_resolution
        _snake_case						=							max_resolution
        _snake_case						=							do_resize
        _snake_case						=							size
        _snake_case						=							do_normalize
        _snake_case						=							image_mean
        _snake_case						=							image_std
        _snake_case						=							crop_size







      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Union[str, 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,
            "crop_size": self.crop_size,
        }



@require_torch
@require_vision
class   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Tuple										=    VivitImageProcessor if is_vision_available() else None



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					List[str]:
        _snake_case						=							VivitImageProcessingTester(self		)



      @property
      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Optional[Any]:
        return self.image_processor_tester.prepare_image_processor_dict()



      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Optional[Any]:
        _snake_case						=							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__      , '''do_center_crop'''		)		)
        self.assertTrue(hasattr(A__      , '''size'''		)		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					List[Any]:
        _snake_case						=							self.image_processing_class.from_dict(self.image_processor_dict		)
        self.assertEqual(image_processor.size      , {'''shortest_edge''': 18}		)
        self.assertEqual(image_processor.crop_size      , {'''height''': 18, '''width''': 18}		)

        _snake_case						=							self.image_processing_class.from_dict(self.image_processor_dict      , size=42      , crop_size=84		)
        self.assertEqual(image_processor.size      , {'''shortest_edge''': 42}		)
        self.assertEqual(image_processor.crop_size      , {'''height''': 84, '''width''': 84}		)



      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Optional[Any]:
        # Initialize image_processing
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random PIL videos
        _snake_case						=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=A__		)
        for video in video_inputs:
          self.assertIsInstance(A__      , A__		)
          self.assertIsInstance(video[0]      , Image.Image		)

        # Test not batched input
        _snake_case						=							image_processing(video_inputs[0]      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

        # Test batched
        _snake_case						=							image_processing(A__      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )



      def 						UpperCamelCase_		(      self				:    Any		)   ->					List[str]:
        # Initialize image_processing
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        _snake_case						=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=A__      , numpify=A__		)
        for video in video_inputs:
          self.assertIsInstance(A__      , A__		)
          self.assertIsInstance(video[0]      , np.ndarray		)

        # Test not batched input
        _snake_case						=							image_processing(video_inputs[0]      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

        # Test batched
        _snake_case						=							image_processing(A__      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )







      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					int:
        # Initialize image_processing
        _snake_case						=							self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        _snake_case						=							prepare_video_inputs(self.image_processor_tester      , equal_resolution=A__      , torchify=A__		)
        for video in video_inputs:
          self.assertIsInstance(A__      , A__		)
          self.assertIsInstance(video[0]      , torch.Tensor		)

        # Test not batched input
        _snake_case						=							image_processing(video_inputs[0]      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                1,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

        # Test batched
        _snake_case						=							image_processing(A__      , return_tensors='''pt'''		).pixel_values
        self.assertEqual(
            encoded_videos.shape      , (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_frames,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size['''height'''],
                self.image_processor_tester.crop_size['''width'''],
            )      , )

