
import copy
import random

from transformers import CLIPTokenizer




class      __A(    _UpperCamelCase			):

  """simple docstring"""







  def __init__(self	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       super().__init__(*_SCREAMING_SNAKE_CASE	,      **_SCREAMING_SNAKE_CASE						)
       UpperCamelCase__				=    {}







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    super().add_tokens(_SCREAMING_SNAKE_CASE	,      *_SCREAMING_SNAKE_CASE	,      **_SCREAMING_SNAKE_CASE						)
       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 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      *SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=1	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    []
       if num_vec_per_token == 1:
            self.try_adding_tokens(_SCREAMING_SNAKE_CASE	,      *_SCREAMING_SNAKE_CASE	,      **_SCREAMING_SNAKE_CASE						)
            output.append(_SCREAMING_SNAKE_CASE						)
       else:
            UpperCamelCase__				=    []
            for i in range(_SCREAMING_SNAKE_CASE						):
                 UpperCamelCase__				=    placeholder_token + F"_{i}"
                 self.try_adding_tokens(_SCREAMING_SNAKE_CASE	,      *_SCREAMING_SNAKE_CASE	,      **_SCREAMING_SNAKE_CASE						)
                 output.append(_SCREAMING_SNAKE_CASE						)
        # 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"						)
       UpperCamelCase__				=    output







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=False	,      SCREAMING_SNAKE_CASE_=1.0						):
       if isinstance(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE						):
            UpperCamelCase__				=    []
            for i in range(len(_SCREAMING_SNAKE_CASE						)						):
                 output.append(self.replace_placeholder_tokens_in_text(text[i]	,      vector_shuffle=_SCREAMING_SNAKE_CASE						)						)
            return output
       for placeholder_token in self.token_map:
            if placeholder_token in text:
                 UpperCamelCase__				=    self.token_map[placeholder_token]
                 UpperCamelCase__				=    tokens[: 1 + int(len(_SCREAMING_SNAKE_CASE						) * prop_tokens_to_load						)]
                 if vector_shuffle:
                      UpperCamelCase__				=    copy.copy(_SCREAMING_SNAKE_CASE						)
                      random.shuffle(_SCREAMING_SNAKE_CASE						)
                 UpperCamelCase__				=    text.replace(_SCREAMING_SNAKE_CASE	,      """ """.join(_SCREAMING_SNAKE_CASE						)						)
       return text







  def __call__(self	,      SCREAMING_SNAKE_CASE_	,      *SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=False	,      SCREAMING_SNAKE_CASE_=1.0	,      **SCREAMING_SNAKE_CASE_						):
       return super().__call__(
           self.replace_placeholder_tokens_in_text(
               _SCREAMING_SNAKE_CASE	,      vector_shuffle=_SCREAMING_SNAKE_CASE	,      prop_tokens_to_load=_SCREAMING_SNAKE_CASE						)	,      *_SCREAMING_SNAKE_CASE	,      **_SCREAMING_SNAKE_CASE	,      )







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      *SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=False	,      SCREAMING_SNAKE_CASE_=1.0	,      **SCREAMING_SNAKE_CASE_						):
       return super().encode(
           self.replace_placeholder_tokens_in_text(
               _SCREAMING_SNAKE_CASE	,      vector_shuffle=_SCREAMING_SNAKE_CASE	,      prop_tokens_to_load=_SCREAMING_SNAKE_CASE						)	,      *_SCREAMING_SNAKE_CASE	,      **_SCREAMING_SNAKE_CASE	,      )







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 GLPNImageProcessor




class      __A(    unittest.TestCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=7	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=18	,      SCREAMING_SNAKE_CASE_=30	,      SCREAMING_SNAKE_CASE_=4_00	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=32	,      SCREAMING_SNAKE_CASE_=True	,      ):
       UpperCamelCase__				=    parent
       UpperCamelCase__				=    batch_size
       UpperCamelCase__				=    num_channels
       UpperCamelCase__				=    image_size
       UpperCamelCase__				=    min_resolution
       UpperCamelCase__				=    max_resolution
       UpperCamelCase__				=    do_resize
       UpperCamelCase__				=    size_divisor
       UpperCamelCase__				=    do_rescale







  def 	UpperCAmelCase_ (self						):
       return {
           "do_resize": self.do_resize,
           "size_divisor": self.size_divisor,
           "do_rescale": self.do_rescale,
       }






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

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      GLPNImageProcessor if is_vision_available() else None







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    GLPNImageProcessingTester(self						)







  @property
  def 	UpperCAmelCase_ (self						):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_resize"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """size_divisor"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """resample"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_rescale"""						)						)







  def 	UpperCAmelCase_ (self						):
       pass







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processing
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      Image.Image						)

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       UpperCamelCase__				=    image_processing(image_inputs[0]	,      return_tensors="""pt"""						).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processing
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_	,      numpify=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      np.ndarray						)

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       UpperCamelCase__				=    image_processing(image_inputs[0]	,      return_tensors="""pt"""						).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processing
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_	,      torchify=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      torch.Tensor						)

       # Test not batched input (GLPNImageProcessor doesn't support batching)
       UpperCamelCase__				=    image_processing(image_inputs[0]	,      return_tensors="""pt"""						).pixel_values
       self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0						)
       self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0						)





