'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import CLIPImageProcessor, CLIPProcessor

@require_vision
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    tempfile.mkdtemp()

             # fmt: off
             _UpperCAmelCase     :  Dict							      =    ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
             # fmt: on
             _UpperCAmelCase     :  Optional[int]							      =    dict(zip(A						,					range(len(A       )       )       )       )
             _UpperCAmelCase     :  Tuple							      =    ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
             _UpperCAmelCase     :  int							      =    {"unk_token": "<unk>"}

             _UpperCAmelCase     :  List[Any]							      =    os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES["vocab_file"]       )
             _UpperCAmelCase     :  List[Any]							      =    os.path.join(self.tmpdirname						,					VOCAB_FILES_NAMES["merges_file"]       )
             with open(self.vocab_file						,					"w"						,					encoding="utf-8"       ) as fp:
                    fp.write(json.dumps(A       ) + "\n"       )
             with open(self.merges_file						,					"w"						,					encoding="utf-8"       ) as fp:
                    fp.write("\n".join(A       )       )

             _UpperCAmelCase     :  str							      =    {
                 "do_resize": True,
                 "size": 20,
                 "do_center_crop": True,
                 "crop_size": 18,
                 "do_normalize": True,
                 "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
                 "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
             }
             _UpperCAmelCase     :  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						:				Union[str, Any]						,					**A						:				Dict       ):
             return CLIPTokenizer.from_pretrained(self.tmpdirname						,					**A       )




      def      _A	(       self						:				Optional[int]						,					**A						:				List[Any]       ):
             return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,					**A       )




      def      _A	(       self						:				Optional[Any]						,					**A						:				Union[str, Any]       ):
             return CLIPImageProcessor.from_pretrained(self.tmpdirname						,					**A       )




      def      _A	(       self						:				Dict       ):
             shutil.rmtree(self.tmpdirname       )




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

             _UpperCAmelCase     :  List[Any]							      =    [Image.fromarray(np.moveaxis(A						,					0						,					-1       )       ) for x in image_inputs]

             return image_inputs




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    self.get_tokenizer()
             _UpperCAmelCase     :  Tuple							      =    self.get_rust_tokenizer()
             _UpperCAmelCase     :  Any							      =    self.get_image_processor()

             _UpperCAmelCase     :  int							      =    CLIPProcessor(tokenizer=A						,					image_processor=A       )
             processor_slow.save_pretrained(self.tmpdirname       )
             _UpperCAmelCase     :  int							      =    CLIPProcessor.from_pretrained(self.tmpdirname						,					use_fast=A       )

             _UpperCAmelCase     :  Optional[int]							      =    CLIPProcessor(tokenizer=A						,					image_processor=A       )
             processor_fast.save_pretrained(self.tmpdirname       )
             _UpperCAmelCase     :  Optional[int]							      =    CLIPProcessor.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      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  Dict							      =    CLIPProcessor(tokenizer=self.get_tokenizer()						,					image_processor=self.get_image_processor()       )
             processor.save_pretrained(self.tmpdirname       )

             _UpperCAmelCase     :  Union[str, Any]							      =    self.get_tokenizer(bos_token="(BOS)"						,					eos_token="(EOS)"       )
             _UpperCAmelCase     :  List[str]							      =    self.get_image_processor(do_normalize=A						,					padding_value=1.0       )

             _UpperCAmelCase     :  List[Any]							      =    CLIPProcessor.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						,					A       )

             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[Any]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    self.get_image_processor()
             _UpperCAmelCase     :  List[str]							      =    self.get_tokenizer()

             _UpperCAmelCase     :  Union[str, Any]							      =    CLIPProcessor(tokenizer=A						,					image_processor=A       )

             _UpperCAmelCase     :  Optional[int]							      =    self.prepare_image_inputs()

             _UpperCAmelCase     :  List[Any]							      =    image_processor(A						,					return_tensors="np"       )
             _UpperCAmelCase     :  Tuple							      =    processor(images=A						,					return_tensors="np"       )

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




      def      _A	(       self						:				str       ):
             _UpperCAmelCase     :  Any							      =    self.get_image_processor()
             _UpperCAmelCase     :  Union[str, Any]							      =    self.get_tokenizer()

             _UpperCAmelCase     :  int							      =    CLIPProcessor(tokenizer=A						,					image_processor=A       )

             _UpperCAmelCase     :  Tuple							      =    "lower newer"

             _UpperCAmelCase     :  str							      =    processor(text=A       )

             _UpperCAmelCase     :  List[str]							      =    tokenizer(A       )

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




      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  str							      =    self.get_image_processor()
             _UpperCAmelCase     :  List[str]							      =    self.get_tokenizer()

             _UpperCAmelCase     :  int							      =    CLIPProcessor(tokenizer=A						,					image_processor=A       )

             _UpperCAmelCase     :  Any							      =    "lower newer"
             _UpperCAmelCase     :  List[str]							      =    self.prepare_image_inputs()

             _UpperCAmelCase     :  int							      =    processor(text=A						,					images=A       )

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

             # test if it raises when no input is passed
             with pytest.raises(A       ):
                    processor()




      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  int							      =    self.get_image_processor()
             _UpperCAmelCase     :  Dict							      =    self.get_tokenizer()

             _UpperCAmelCase     :  Dict							      =    CLIPProcessor(tokenizer=A						,					image_processor=A       )

             _UpperCAmelCase     :  int							      =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             _UpperCAmelCase     :  int							      =    processor.batch_decode(A       )
             _UpperCAmelCase     :  Tuple							      =    tokenizer.batch_decode(A       )

             self.assertListEqual(A						,					A       )





      def      _A	(       self						:				Optional[Any]       ):
             _UpperCAmelCase     :  Tuple							      =    self.get_image_processor()
             _UpperCAmelCase     :  str							      =    self.get_tokenizer()

             _UpperCAmelCase     :  List[Any]							      =    CLIPProcessor(tokenizer=A						,					image_processor=A       )

             _UpperCAmelCase     :  Union[str, Any]							      =    "lower newer"
             _UpperCAmelCase     :  Optional[Any]							      =    self.prepare_image_inputs()

             _UpperCAmelCase     :  Union[str, Any]							      =    processor(text=A						,					images=A       )

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


'''simple docstring'''




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 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

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

             _UpperCAmelCase     :  Tuple							      =    parent
             _UpperCAmelCase     :  Any							      =    batch_size
             _UpperCAmelCase     :  Optional[int]							      =    num_channels
             _UpperCAmelCase     :  Optional[Any]							      =    num_frames
             _UpperCAmelCase     :  Any							      =    image_size
             _UpperCAmelCase     :  Dict							      =    min_resolution
             _UpperCAmelCase     :  Any							      =    max_resolution
             _UpperCAmelCase     :  Optional[int]							      =    do_resize
             _UpperCAmelCase     :  str							      =    size
             _UpperCAmelCase     :  List[Any]							      =    do_normalize
             _UpperCAmelCase     :  Any							      =    image_mean
             _UpperCAmelCase     :  Tuple							      =    image_std
             _UpperCAmelCase     :  Any							      =    crop_size





      def      _A	(       self						:				List[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 	lowerCamelCase_	(snake_case__							, unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  Dict						=						VivitImageProcessor if is_vision_available() else None




      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Tuple							      =    VivitImageProcessingTester(self       )




      @property
      def      _A	(       self						:				Optional[Any]       ):
             return self.image_processor_tester.prepare_image_processor_dict()




      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  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						,					"do_center_crop"       )       )
             self.assertTrue(hasattr(A						,					"size"       )       )




      def      _A	(       self						:				List[Any]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    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}       )

             _UpperCAmelCase     :  Optional[int]							      =    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      _A	(       self						:				Tuple       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Optional[Any]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PIL videos
             _UpperCAmelCase     :  Any							      =    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
             _UpperCAmelCase     :  str							      =    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
             _UpperCAmelCase     :  List[Any]							      =    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      _A	(       self						:				List[Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  List[str]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random numpy tensors
             _UpperCAmelCase     :  List[Any]							      =    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
             _UpperCAmelCase     :  Tuple							      =    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
             _UpperCAmelCase     :  Optional[int]							      =    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      _A	(       self						:				List[Any]       ):
             # Initialize image_processing
             _UpperCAmelCase     :  Optional[Any]							      =    self.image_processing_class(**self.image_processor_dict       )
             # create random PyTorch tensors
             _UpperCAmelCase     :  Optional[int]							      =    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
             _UpperCAmelCase     :  Optional[Any]							      =    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
             _UpperCAmelCase     :  List[Any]							      =    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"],
                 )						,					)

