






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 LevitImageProcessor
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def __init__(      self		:			List[Any]       ,				lowerCamelCase		:			List[Any]       ,				lowerCamelCase		:			Optional[Any]=7       ,				lowerCamelCase		:			int=3       ,				lowerCamelCase		:			int=18       ,				lowerCamelCase		:			Tuple=30       ,				lowerCamelCase		:			Dict=400       ,				lowerCamelCase		:			Optional[int]=True       ,				lowerCamelCase		:			int=None       ,				lowerCamelCase		:			List[str]=True       ,				lowerCamelCase		:			List[Any]=None       ,				lowerCamelCase		:			Any=True       ,				lowerCamelCase		:			int=[0.5, 0.5, 0.5]       ,				lowerCamelCase		:			List[Any]=[0.5, 0.5, 0.5]       ,				)						->	Optional[int]:


         """simple docstring"""
         _UpperCAmelCase							=    size if size is not None else {"""shortest_edge""": 18}
         _UpperCAmelCase							=    crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    image_size
         _UpperCAmelCase							=    min_resolution
         _UpperCAmelCase							=    max_resolution
         _UpperCAmelCase							=    do_resize
         _UpperCAmelCase							=    size
         _UpperCAmelCase							=    do_center_crop
         _UpperCAmelCase							=    crop_size
         _UpperCAmelCase							=    do_normalize
         _UpperCAmelCase							=    image_mean
         _UpperCAmelCase							=    image_std





  def  lowerCamelCase       (      self		:			Dict							)						->	List[str]:


         """simple docstring"""
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "do_center_crop": self.do_center_crop,
             "size": self.size,
             "crop_size": self.crop_size,
         }



@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  LevitImageProcessor if is_vision_available() else None


  def  lowerCamelCase       (      self		:			Dict							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    LevitImageProcessingTester(self							)


  @property
  def  lowerCamelCase       (      self		:			int							)						->	Dict:


         """simple docstring"""
         return self.image_processor_tester.prepare_image_processor_dict()


  def  lowerCamelCase       (      self		:			int							)						->	Tuple:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""image_mean"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""image_std"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_normalize"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_resize"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""do_center_crop"""							)							)
         self.assertTrue(hasattr(lowerCamelCase       ,				"""size"""							)							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	str:


         """simple docstring"""
         _UpperCAmelCase							=    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							=    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  lowerCamelCase       (      self		:			str							)						->	int:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[int]:


         """simple docstring"""
         # 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=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				Image.Image							)

         # Test not batched input
         _UpperCAmelCase							=    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
         _UpperCAmelCase							=    image_processing(lowerCamelCase       ,				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  lowerCamelCase       (      self		:			Optional[int]							)						->	Union[str, Any]:


         """simple docstring"""
         # 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=lowerCamelCase       ,				numpify=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				np.ndarray							)

         # Test not batched input
         _UpperCAmelCase							=    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
         _UpperCAmelCase							=    image_processing(lowerCamelCase       ,				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  lowerCamelCase       (      self		:			Optional[int]							)						->	Any:


         """simple docstring"""
         # 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=lowerCamelCase       ,				torchify=lowerCamelCase							)
         for image in image_inputs:
                self.assertIsInstance(lowerCamelCase       ,				torch.Tensor							)

         # Test not batched input
         _UpperCAmelCase							=    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
         _UpperCAmelCase							=    image_processing(lowerCamelCase       ,				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"""],
             )       ,				)







from queue import Queue
from typing import TYPE_CHECKING, Optional


if TYPE_CHECKING:
  from ..models.auto import AutoTokenizer
class 		SCREAMING_SNAKE_CASE__	:

  '''simple docstring'''







  def  lowerCamelCase       (      self		:			str       ,				lowerCamelCase		:			str							)						->	Optional[int]:


         """simple docstring"""
         raise NotImplementedError()





  def  lowerCamelCase       (      self		:			Any							)						->	Any:


         """simple docstring"""
         raise NotImplementedError()



class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  def __init__(      self		:			str       ,				lowerCamelCase		:			"AutoTokenizer"       ,				lowerCamelCase		:			bool = False       ,				**lowerCamelCase		:			Any							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    tokenizer
         _UpperCAmelCase							=    skip_prompt
         _UpperCAmelCase							=    decode_kwargs

         # variables used in the streaming process
         _UpperCAmelCase							=    []
         _UpperCAmelCase							=    0
         _UpperCAmelCase							=    True


  def  lowerCamelCase       (      self		:			Any       ,				lowerCamelCase		:			List[str]							)						->	List[Any]:


         """simple docstring"""
         if len(value.shape							) > 1 and value.shape[0] > 1:
                raise ValueError("""TextStreamer only supports batch size 1"""							)
         elif len(value.shape							) > 1:
                _UpperCAmelCase							=    value[0]

         if self.skip_prompt and self.next_tokens_are_prompt:
                _UpperCAmelCase							=    False
                return

         # Add the new token to the cache and decodes the entire thing.
         self.token_cache.extend(value.tolist()							)
         _UpperCAmelCase							=    self.tokenizer.decode(self.token_cache       ,				**self.decode_kwargs							)

         # After the symbol for a new line, we flush the cache.
         if text.endswith("""\n"""							):
                _UpperCAmelCase							=    text[self.print_len :]
                _UpperCAmelCase							=    []
                _UpperCAmelCase							=    0
         # If the last token is a CJK character, we print the characters.
         elif len(lowerCamelCase							) > 0 and self._is_chinese_char(ord(text[-1]							)							):
                _UpperCAmelCase							=    text[self.print_len :]
                self.print_len += len(lowerCamelCase							)
         # Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
         # which may change with the subsequent token -- there are probably smarter ways to do this!)
         else:
                _UpperCAmelCase							=    text[self.print_len : text.rfind(""" """							) + 1]
                self.print_len += len(lowerCamelCase							)

         self.on_finalized_text(lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Dict							)						->	Optional[Any]:


         """simple docstring"""
         # Flush the cache, if it exists
         if len(self.token_cache							) > 0:
                _UpperCAmelCase							=    self.tokenizer.decode(self.token_cache       ,				**self.decode_kwargs							)
                _UpperCAmelCase							=    text[self.print_len :]
                _UpperCAmelCase							=    []
                _UpperCAmelCase							=    0
         else:
                _UpperCAmelCase							=    """"""

         _UpperCAmelCase							=    True
         self.on_finalized_text(lowerCamelCase       ,				stream_end=lowerCamelCase							)


  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			bool = False							)						->	Any:


         """simple docstring"""
         print(lowerCamelCase       ,				flush=lowerCamelCase       ,				end="""""" if not stream_end else None							)





  def  lowerCamelCase       (      self		:			int       ,				lowerCamelCase		:			Any							)						->	int:


         """simple docstring"""
         # This defines a "chinese character" as anything in the CJK Unicode block:
         #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
         #
         # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
         # despite its name. The modern Korean Hangul alphabet is a different block,
         # as is Japanese Hiragana and Katakana. Those alphabets are used to write
         # space-separated words, so they are not treated specially and handled
         # like the all of the other languages.
         if (
             (cp >= 0X4E00 and cp <= 0X9FFF)
             or (cp >= 0X3400 and cp <= 0X4DBF)  #
             or (cp >= 0X2_0000 and cp <= 0X2_A6DF)  #
             or (cp >= 0X2_A700 and cp <= 0X2_B73F)  #
             or (cp >= 0X2_B740 and cp <= 0X2_B81F)  #
             or (cp >= 0X2_B820 and cp <= 0X2_CEAF)  #
             or (cp >= 0XF900 and cp <= 0XFAFF)
             or (cp >= 0X2_F800 and cp <= 0X2_FA1F)  #
         ):  #
                return True

         return False



class 		SCREAMING_SNAKE_CASE__	(  UpperCAmelCase     ):

  '''simple docstring'''







  def __init__(      self		:			Optional[int]       ,				lowerCamelCase		:			"AutoTokenizer"       ,				lowerCamelCase		:			bool = False       ,				lowerCamelCase		:			Optional[float] = None       ,				**lowerCamelCase		:			Optional[Any]							)						->	Tuple:


         """simple docstring"""
         super().__init__(lowerCamelCase       ,				lowerCamelCase       ,				**lowerCamelCase							)
         _UpperCAmelCase							=    Queue()
         _UpperCAmelCase							=    None
         _UpperCAmelCase							=    timeout


  def  lowerCamelCase       (      self		:			Optional[Any]       ,				lowerCamelCase		:			str       ,				lowerCamelCase		:			bool = False							)						->	Optional[int]:


         """simple docstring"""
         self.text_queue.put(lowerCamelCase       ,				timeout=self.timeout							)
         if stream_end:
                self.text_queue.put(self.stop_signal       ,				timeout=self.timeout							)


  def __iter__(      self		:			Any							)						->	Union[str, Any]:


         """simple docstring"""
         return self





  def  lowerCamelCase       (      self		:			int							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    self.text_queue.get(timeout=self.timeout							)
         if value == self.stop_signal:
                raise StopIteration()
         else:
                return value