




import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
   import numpy as np
   import torch

if is_vision_available():
   from PIL import Image

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase=False		,					__UpperCamelCase=False		,					__UpperCamelCase=False)			->     Optional[Any]:
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     a      =      []
     for i in range(image_processor_tester.batch_size):
          if equal_resolution:
               a      =      a      =      image_processor_tester.max_resolution
          else:
               # To avoid getting image width/height 0
               a      =      image_processor_tester.min_resolution
               if getattr(__UpperCamelCase		,					"size_divisor"		,					__UpperCamelCase):
                    # If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
                    a      =      max(image_processor_tester.size_divisor		,					__UpperCamelCase)
               a  ,      a      =      np.random.choice(np.arange(__UpperCamelCase		,					image_processor_tester.max_resolution)		,					2)
          image_inputs.append(
              np.random.randint(2_55		,					size=(image_processor_tester.num_channels, width, height)		,					dtype=np.uinta))

     if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          a      =      [Image.fromarray(np.moveaxis(__UpperCamelCase		,					0		,					-1)) for image in image_inputs]

     if torchify:
          a      =      [torch.from_numpy(__UpperCamelCase) for image in image_inputs]

     return image_inputs

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase=10		,					__UpperCamelCase=10		,					__UpperCamelCase=False		,					__UpperCamelCase=False)			->     int:
     a      =      []
     for i in range(image_processor_tester.num_frames):
          video.append(np.random.randint(2_55		,					size=(image_processor_tester.num_channels, width, height)		,					dtype=np.uinta))

     if not numpify and not torchify:
          # PIL expects the channel dimension as last dimension
          a      =      [Image.fromarray(np.moveaxis(__UpperCamelCase		,					0		,					-1)) for frame in video]

     if torchify:
          a      =      [torch.from_numpy(__UpperCamelCase) for frame in video]

     return video

def 					SCREAMING_SNAKE_CASE							(   __UpperCamelCase		,					__UpperCamelCase=False		,					__UpperCamelCase=False		,					__UpperCamelCase=False)			->     int:
     assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

     a      =      []
     for i in range(image_processor_tester.batch_size):
          if equal_resolution:
               a      =      a      =      image_processor_tester.max_resolution
          else:
               a  ,      a      =      np.random.choice(
                   np.arange(image_processor_tester.min_resolution		,					image_processor_tester.max_resolution)		,					2)
               a      =      prepare_video(
                   image_processor_tester=__UpperCamelCase		,					width=__UpperCamelCase		,					height=__UpperCamelCase		,					numpify=__UpperCamelCase		,					torchify=__UpperCamelCase		,					)
          video_inputs.append(__UpperCamelCase)

     return video_inputs





class 			a__       :
     a		:			Any							=	None







     def 			lowerCAmelCase_				(	self )  ->    Optional[int]:



          '''simple docstring'''



          a      =      self.image_processing_class(**self.image_processor_dict )
          a      =      json.loads(image_processor.to_json_string() )
          for key, value in self.image_processor_dict.items():
               self.assertEqual(obj[key]  ,		A )







     def 			lowerCAmelCase_				(	self )  ->    Optional[Any]:



          '''simple docstring'''



          a      =      self.image_processing_class(**self.image_processor_dict )

          with tempfile.TemporaryDirectory() as tmpdirname:
               a      =      os.path.join(A  ,		"image_processor.json" )
               image_processor_first.to_json_file(A )
               a      =      self.image_processing_class.from_json_file(A )

          self.assertEqual(image_processor_second.to_dict()  ,		image_processor_first.to_dict() )







     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          a      =      self.image_processing_class(**self.image_processor_dict )

          with tempfile.TemporaryDirectory() as tmpdirname:
               a      =      image_processor_first.save_pretrained(A )[0]
               check_json_file_has_correct_format(A )
               a      =      self.image_processing_class.from_pretrained(A )

          self.assertEqual(image_processor_second.to_dict()  ,		image_processor_first.to_dict() )







     def 			lowerCAmelCase_				(	self )  ->    Optional[Any]:



          '''simple docstring'''



          a      =      self.image_processing_class()
          self.assertIsNotNone(A )







     @require_torch
     @require_vision
     def 			lowerCAmelCase_				(	self )  ->    Tuple:



          '''simple docstring'''



          if self.test_cast_dtype is not None:
               # Initialize image_processor
               a      =      self.image_processing_class(**self.image_processor_dict )

               # create random PyTorch tensors
               a      =      prepare_image_inputs(self.image_processor_tester  ,		equal_resolution=A  ,		torchify=A )

               a      =      image_processor(A  ,		return_tensors="pt" )
               # for layoutLM compatiblity
               self.assertEqual(encoding.pixel_values.device  ,		torch.device("cpu" ) )
               self.assertEqual(encoding.pixel_values.dtype  ,		torch.floataa )

               a      =      image_processor(A  ,		return_tensors="pt" ).to(torch.floataa )
               self.assertEqual(encoding.pixel_values.device  ,		torch.device("cpu" ) )
               self.assertEqual(encoding.pixel_values.dtype  ,		torch.floataa )

               a      =      image_processor(A  ,		return_tensors="pt" ).to("cpu"  ,		torch.bfloataa )
               self.assertEqual(encoding.pixel_values.device  ,		torch.device("cpu" ) )
               self.assertEqual(encoding.pixel_values.dtype  ,		torch.bfloataa )

               with self.assertRaises(A ):
                    a      =      image_processor(A  ,		return_tensors="pt" ).to(torch.bfloataa  ,		"cpu" )

               # Try with text + image feature
               a      =      image_processor(A  ,		return_tensors="pt" )
               encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]] )} )
               a      =      encoding.to(torch.floataa )

               self.assertEqual(encoding.pixel_values.device  ,		torch.device("cpu" ) )
               self.assertEqual(encoding.pixel_values.dtype  ,		torch.floataa )
               self.assertEqual(encoding.input_ids.dtype  ,		torch.long )







import os
import unittest

from transformers import MPNetTokenizerFast
from transformers.models.mpnet.tokenization_mpnet import VOCAB_FILES_NAMES, MPNetTokenizer
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 			a__       (					UpperCamelCase__   ,					unittest.TestCase      ):
     a		:			Dict							=	MPNetTokenizer
     a		:			Dict							=	MPNetTokenizerFast
     a		:			Optional[int]							=	True
     a		:			Any							=	True







     def 			lowerCAmelCase_				(	self )  ->    Any:



          '''simple docstring'''



          super().setUp()

          a      =      [
              "[UNK]",
              "[CLS]",
              "[SEP]",
              "[PAD]",
              "[MASK]",
              "want",
              "##want",
              "##ed",
              "wa",
              "un",
              "runn",
              "##ing",
              ",",
              "low",
              "lowest",
          ]
          a      =      os.path.join(self.tmpdirname  ,		VOCAB_FILES_NAMES["vocab_file"] )
          with open(self.vocab_file  ,		"w"  ,		encoding="utf-8" ) as vocab_writer:
               vocab_writer.write("".join([x + "\n" for x in vocab_tokens] ) )







     def 			lowerCAmelCase_				(	self  ,		A )  ->    Any:



          '''simple docstring'''



          a      =      "UNwant\u00E9d,running"
          a      =      "unwanted, running"
          return input_text, output_text







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



          '''simple docstring'''



          a      =      self.tokenizer_class(self.vocab_file )

          a      =      tokenizer.tokenize("UNwant\u00E9d,running" )
          self.assertListEqual(A  ,		["un", "##want", "##ed", ",", "runn", "##ing"] )
          self.assertListEqual(tokenizer.convert_tokens_to_ids(A )  ,		[9, 6, 7, 12, 10, 11] )







     @slow
     def 			lowerCAmelCase_				(	self )  ->    Dict:



          '''simple docstring'''



          a      =      self.tokenizer_class.from_pretrained("microsoft/mpnet-base" )

          a      =      tokenizer.encode("sequence builders"  ,		add_special_tokens=A )
          a      =      tokenizer.encode("multi-sequence build"  ,		add_special_tokens=A )

          a      =      tokenizer.build_inputs_with_special_tokens(A )
          a      =      tokenizer.build_inputs_with_special_tokens(A  ,		A )

          assert encoded_sentence == [0] + text + [2]
          assert encoded_pair == [0] + text + [2] + [2] + text_a + [2]

