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 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






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


          """simple docstring"""


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




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->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 	__snake_case	( __lowerCAmelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       LevitImageProcessor if is_vision_available() else None



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


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						LevitImageProcessingTester(self)



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Dict:


          """simple docstring"""


          return self.image_processor_tester.prepare_image_processor_dict()



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


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						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})

          _lowerCamelCase     :		List[str] 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Any:


          """simple docstring"""


          pass



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


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          _lowerCamelCase     :		List[str] 						=						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[int] 						=						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[Any] 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Tuple:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          _lowerCamelCase     :		Optional[int] 						=						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     :		int 						=						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[Any] 						=						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 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->str:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          _lowerCamelCase     :		str 						=						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     :		Optional[int] 						=						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[Any] 						=						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 json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase			:							Dict     =logging.get_logger(__name__)

lowerCAmelCase			:							Dict     ={"vocab_file": "vocab.json"}

lowerCAmelCase			:							List[str]     ={
    "vocab_file": {
        "mgp-str": "https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json",
    }
}

lowerCAmelCase			:							int     ={"mgp-str": 27}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



      def __init__(					self     :				List[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int="[GO]"    ,  _UpperCamelCase     :				Any="[GO]"    ,  _UpperCamelCase     :				Optional[Any]="[s]"    ,  _UpperCamelCase     :				List[str]="[GO]"    ,  **_UpperCamelCase     :				Dict)				->Union[str, Any]:


          """simple docstring"""


          super().__init__(
              unk_token=_UpperCamelCase    ,  bos_token=_UpperCamelCase    ,  eos_token=_UpperCamelCase    ,  pad_token=_UpperCamelCase    ,  **_UpperCamelCase    ,  )

          with open(_UpperCamelCase    ,  encoding="""utf-8""") as vocab_handle:
              _lowerCamelCase     :		Optional[Any] 						=						json.load(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						{v: k for k, v in self.vocab.items()}



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Any:


          """simple docstring"""


          return len(self.vocab)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[Any]:


          """simple docstring"""


          return dict(self.vocab    ,  **self.added_tokens_encoder)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						[]
          for s in text:
              char_tokens.extend(_UpperCamelCase)
          return char_tokens



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				int)				->Optional[int]:


          """simple docstring"""


          return self.vocab.get(_UpperCamelCase    ,  self.vocab.get(self.unk_token))



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Optional[Any])				->Dict:


          """simple docstring"""


          return self.decoder.get(_UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[str] = None)				->Tuple[str]:


          """simple docstring"""


          if not os.path.isdir(_UpperCamelCase):
              logger.error("""Vocabulary path ({}) should be a directory""".format(_UpperCamelCase))
              return
          _lowerCamelCase     :		Tuple 						=						os.path.join(
              _UpperCamelCase    ,  (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])

          with open(_UpperCamelCase    ,  """w"""    ,  encoding="""utf-8""") as f:
              f.write(json.dumps(self.vocab    ,  indent=2    ,  sort_keys=_UpperCamelCase    ,  ensure_ascii=_UpperCamelCase) + """\n""")

          return (vocab_file,)




