




from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   SqueezeBertTokenizer
				lowerCAmelCase__			=   SqueezeBertTokenizerFast
				lowerCAmelCase__			=   True



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]						,		**_UpperCAmelCase					:						Tuple					)     ->			Any:





								'''simple docstring'''




								return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname						,		**UpperCAmelCase_					)



				@slow
				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]					)     ->			Tuple:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[Any]        =					SqueezeBertTokenizer.from_pretrained("""squeezebert/squeezebert-mnli-headless"""					)

								_lowerCAmelCase			:			Dict        =					tokenizer.encode("""sequence builders"""						,		add_special_tokens=UpperCAmelCase_					)
								_lowerCAmelCase			:			int        =					tokenizer.encode("""multi-sequence build"""						,		add_special_tokens=UpperCAmelCase_					)

								_lowerCAmelCase			:			Any        =					tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_					)
								_lowerCAmelCase			:			Tuple        =					tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_						,		UpperCAmelCase_					)

								assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
								assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
								    tokenizer.sep_token_id
								]









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 ConvNextImageProcessor


class        SCREAMING_SNAKE_CASE	(	unittest.TestCase    ):
     '''simple docstring'''


     def __init__( self				:		Optional[Any]    ,       UpperCAmelCase_				:		Optional[int]    ,       UpperCAmelCase_				:		Any=7    ,       UpperCAmelCase_				:		Union[str, Any]=3    ,       UpperCAmelCase_				:		List[str]=18    ,       UpperCAmelCase_				:		Optional[int]=30    ,       UpperCAmelCase_				:		int=400    ,       UpperCAmelCase_				:		int=True    ,       UpperCAmelCase_				:		List[Any]=None    ,       UpperCAmelCase_				:		Tuple=0.875    ,       UpperCAmelCase_				:		str=True    ,       UpperCAmelCase_				:		Optional[Any]=[0.5, 0.5, 0.5]    ,       UpperCAmelCase_				:		List[Any]=[0.5, 0.5, 0.5]    ,       ):
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			size if size is not None else {"shortest_edge": 20}
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			parent
       SCREAMING_SNAKE_CASE				:					int		  =			batch_size
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			num_channels
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			image_size
       SCREAMING_SNAKE_CASE				:					Any		  =			min_resolution
       SCREAMING_SNAKE_CASE				:					List[str]		  =			max_resolution
       SCREAMING_SNAKE_CASE				:					int		  =			do_resize
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			size
       SCREAMING_SNAKE_CASE				:					Any		  =			crop_pct
       SCREAMING_SNAKE_CASE				:					Dict		  =			do_normalize
       SCREAMING_SNAKE_CASE				:					List[str]		  =			image_mean
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			image_std






     def  _A				( self				:		str					):
       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
           "crop_pct": self.crop_pct,
       }





@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE	(	lowerCAmelCase					,						unittest.TestCase    ):
     '''simple docstring'''


     UpperCamelCase_							:						str								=		ConvNextImageProcessor if is_vision_available() else None


     def  _A				( self				:		Optional[int]					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			ConvNextImageProcessingTester(self					)


     @property
     def  _A				( self				:		str					):
       return self.image_processor_tester.prepare_image_processor_dict()


     def  _A				( self				:		str					):
       SCREAMING_SNAKE_CASE				:					str		  =			self.image_processing_class(**self.image_processor_dict					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "do_resize"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "size"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "crop_pct"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "do_normalize"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "image_mean"					)					)
       self.assertTrue(hasattr(UpperCAmelCase_    ,       "image_std"					)					)


     def  _A				( self				:		Union[str, Any]					):
       SCREAMING_SNAKE_CASE				:					Optional[int]		  =			self.image_processing_class.from_dict(self.image_processor_dict					)
       self.assertEqual(image_processor.size    ,       {"shortest_edge": 20}					)

       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			self.image_processing_class.from_dict(self.image_processor_dict    ,       size=42					)
       self.assertEqual(image_processor.size    ,       {"shortest_edge": 42}					)


     def  _A				( self				:		List[str]					):
       pass


     def  _A				( self				:		Optional[Any]					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random PIL images
       SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCAmelCase_					)
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_    ,       Image.Image					)

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					Dict		  =			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.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					str		  =			image_processing(UpperCAmelCase_    ,       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.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )


     def  _A				( self				:		Optional[Any]					):
       # Initialize image_processing
       SCREAMING_SNAKE_CASE				:					List[str]		  =			self.image_processing_class(**self.image_processor_dict					)
       # create random numpy tensors
       SCREAMING_SNAKE_CASE				:					List[Any]		  =			prepare_image_inputs(self.image_processor_tester    ,       equal_resolution=UpperCAmelCase_    ,       numpify=UpperCAmelCase_					)
       for image in image_inputs:
         self.assertIsInstance(UpperCAmelCase_    ,       np.ndarray					)

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					str		  =			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.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					Dict		  =			image_processing(UpperCAmelCase_    ,       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.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )






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

       # Test not batched input
       SCREAMING_SNAKE_CASE				:					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.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )

       # Test batched
       SCREAMING_SNAKE_CASE				:					Any		  =			image_processing(UpperCAmelCase_    ,       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.size["shortest_edge"],
               self.image_processor_tester.size["shortest_edge"],
           )    ,       )






