





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_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import MobileViTImageProcessor







class       __A							(   unittest.TestCase	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__=7   ,				a__=3   ,				a__=18   ,				a__=30   ,				a__=400   ,				a__=True   ,				a__=None   ,				a__=True   ,				a__=None   ,				a__=True   ,				):



             """simple docstring"""





             _lowerCamelCase				:  int						=		size if size is not None else {'''shortest_edge''': 20}
             _lowerCamelCase				:  Any						=		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
             _lowerCamelCase				:  Dict						=		parent
             _lowerCamelCase				:  int						=		batch_size
             _lowerCamelCase				:  str						=		num_channels
             _lowerCamelCase				:  str						=		image_size
             _lowerCamelCase				:  Dict						=		min_resolution
             _lowerCamelCase				:  List[Any]						=		max_resolution
             _lowerCamelCase				:  Tuple						=		do_resize
             _lowerCamelCase				:  Optional[int]						=		size
             _lowerCamelCase				:  Optional[Any]						=		do_center_crop
             _lowerCamelCase				:  Dict						=		crop_size
             _lowerCamelCase				:  Tuple						=		do_flip_channel_order



      def 					__snake_case      (		self):



             """simple docstring"""





             return {
                 "do_resize": self.do_resize,
                 "size": self.size,
                 "do_center_crop": self.do_center_crop,
                 "crop_size": self.crop_size,
                 "do_flip_channel_order": self.do_flip_channel_order,
             }







@require_torch
@require_vision
class       __A							(   lowerCamelCase__			,unittest.TestCase	):




      """simple docstring"""







      UpperCAmelCase__							=   MobileViTImageProcessor if is_vision_available() else None


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		MobileViTImageProcessingTester(self)


      @property
      def 					__snake_case      (		self):



             """simple docstring"""





             return self.image_processor_tester.prepare_image_processor_dict()


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  int						=		self.image_processing_class(**self.image_processor_dict)
             self.assertTrue(hasattr(a__   ,				'''do_resize'''))
             self.assertTrue(hasattr(a__   ,				'''size'''))
             self.assertTrue(hasattr(a__   ,				'''do_center_crop'''))
             self.assertTrue(hasattr(a__   ,				'''center_crop'''))
             self.assertTrue(hasattr(a__   ,				'''do_flip_channel_order'''))


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Dict						=		self.image_processing_class.from_dict(self.image_processor_dict)
             self.assertEqual(image_processor.size   ,				{'''shortest_edge''': 20})
             self.assertEqual(image_processor.crop_size   ,				{'''height''': 18, '''width''': 18})

             _lowerCamelCase				:  Any						=		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 					__snake_case      (		self):



             """simple docstring"""





             pass


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  int						=		self.image_processing_class(**self.image_processor_dict)
             # create random PIL images
             _lowerCamelCase				:  Optional[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
             _lowerCamelCase				:  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.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )   ,				)

             # Test batched
             _lowerCamelCase				:  Optional[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.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )   ,				)


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[Any]						=		self.image_processing_class(**self.image_processor_dict)
             # create random numpy tensors
             _lowerCamelCase				:  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
             _lowerCamelCase				:  Tuple						=		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.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )   ,				)

             # Test batched
             _lowerCamelCase				:  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.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )   ,				)



      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  str						=		self.image_processing_class(**self.image_processor_dict)
             # create random PyTorch tensors
             _lowerCamelCase				:  Union[str, Any]						=		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
             _lowerCamelCase				:  Tuple						=		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.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )   ,				)

             # Test batched
             _lowerCamelCase				:  List[str]						=		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.crop_size['''height'''],
                     self.image_processor_tester.crop_size['''width'''],
                 )   ,				)








import copy
import random

from transformers import CLIPTokenizer







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				*a__   ,				**a__):



             """simple docstring"""





             super().__init__(*a__   ,				**a__)
             _lowerCamelCase				:  Optional[Any]						=		{}


      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				**a__):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		super().add_tokens(a__   ,				*a__   ,				**a__)
             if num_added_tokens == 0:
                    raise ValueError(
                        F"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
                        ''' `placeholder_token` that is not already in the tokenizer.''')


      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				a__=1   ,				**a__):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		[]
             if num_vec_per_token == 1:
                    self.try_adding_tokens(a__   ,				*a__   ,				**a__)
                    output.append(a__)
             else:
                    _lowerCamelCase				:  Dict						=		[]
                    for i in range(a__):
                           _lowerCamelCase				:  Optional[Any]						=		placeholder_token + F"""_{i}"""
                           self.try_adding_tokens(a__   ,				*a__   ,				**a__)
                           output.append(a__)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
             for token in self.token_map:
                    if token in placeholder_token:
                           raise ValueError(
                               F"""The tokenizer already has placeholder token {token} that can get confused with"""
                               F""" {placeholder_token}keep placeholder tokens independent""")
             _lowerCamelCase				:  int						=		output


      def 					__snake_case      (		self   ,				a__   ,				a__=False   ,				a__=1.0):



             """simple docstring"""





             if isinstance(a__   ,				a__):
                    _lowerCamelCase				:  Tuple						=		[]
                    for i in range(len(a__)):
                           output.append(self.replace_placeholder_tokens_in_text(text[i]   ,				vector_shuffle=a__))
                    return output
             for placeholder_token in self.token_map:
                    if placeholder_token in text:
                           _lowerCamelCase				:  Union[str, Any]						=		self.token_map[placeholder_token]
                           _lowerCamelCase				:  Optional[Any]						=		tokens[: 1 + int(len(a__) * prop_tokens_to_load)]
                           if vector_shuffle:
                                  _lowerCamelCase				:  int						=		copy.copy(a__)
                                  random.shuffle(a__)
                           _lowerCamelCase				:  List[str]						=		text.replace(a__   ,				''' '''.join(a__))
             return text


      def __call__(		self   ,				a__   ,				*a__   ,				a__=False   ,				a__=1.0   ,				**a__):



             """simple docstring"""





             return super().__call__(
                 self.replace_placeholder_tokens_in_text(
                     a__   ,				vector_shuffle=a__   ,				prop_tokens_to_load=a__)   ,				*a__   ,				**a__   ,				)



      def 					__snake_case      (		self   ,				a__   ,				*a__   ,				a__=False   ,				a__=1.0   ,				**a__):



             """simple docstring"""





             return super().encode(
                 self.replace_placeholder_tokens_in_text(
                     a__   ,				vector_shuffle=a__   ,				prop_tokens_to_load=a__)   ,				*a__   ,				**a__   ,				)

