






"""simple docstring"""
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 PoolFormerImageProcessor







class      UpperCamelCase_			(unittest.TestCase	):





	def __init__( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					List[str]=7      ,      lowerCAmelCase_   :					str=3      ,      lowerCAmelCase_   :					Tuple=30      ,      lowerCAmelCase_   :					str=400      ,      lowerCAmelCase_   :					Dict=True      ,      lowerCAmelCase_   :					int=None      ,      lowerCAmelCase_   :					List[str]=0.9      ,      lowerCAmelCase_   :					Union[str, Any]=None      ,      lowerCAmelCase_   :					str=True      ,      lowerCAmelCase_   :					Dict=[0.5, 0.5, 0.5]      ,      lowerCAmelCase_   :					str=[0.5, 0.5, 0.5]      ,      )		->     int:
				UpperCAmelCase_	:					Dict     	=   size if size is not None else {"shortest_edge": 30}
				UpperCAmelCase_	:					Dict     	=   crop_size if crop_size is not None else {"height": 30, "width": 30}
				UpperCAmelCase_	:					int     	=   parent
				UpperCAmelCase_	:					Dict     	=   batch_size
				UpperCAmelCase_	:					List[Any]     	=   num_channels
				UpperCAmelCase_	:					Union[str, Any]     	=   min_resolution
				UpperCAmelCase_	:					List[str]     	=   max_resolution
				UpperCAmelCase_	:					Any     	=   do_resize_and_center_crop
				UpperCAmelCase_	:					str     	=   size
				UpperCAmelCase_	:					int     	=   crop_pct
				UpperCAmelCase_	:					List[str]     	=   crop_size
				UpperCAmelCase_	:					List[str]     	=   do_normalize
				UpperCAmelCase_	:					Dict     	=   image_mean
				UpperCAmelCase_	:					List[Any]     	=   image_std





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     str:
				return {
				    "size": self.size,
				    "do_resize_and_center_crop": self.do_resize_and_center_crop,
				    "crop_pct": self.crop_pct,
				    "crop_size": self.crop_size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				}







@require_torch
@require_vision
class      UpperCamelCase_			(__A							,		unittest.TestCase	):
	__magic_name__       =							PoolFormerImageProcessor if is_vision_available() else None





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Tuple:
				UpperCAmelCase_	:					Tuple     	=   PoolFormerImageProcessingTester(self		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     Union[str, Any]:
				return self.image_processor_tester.prepare_image_processor_dict()





	def 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Optional[Any]:
				UpperCAmelCase_	:					Dict     	=   self.image_processing_class(**self.image_processor_dict		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "do_resize_and_center_crop"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "size"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "crop_pct"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "do_normalize"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "image_mean"		)		)
				self.assertTrue(hasattr(lowerCAmelCase_      ,      "image_std"		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict		)		->     Optional[int]:
				UpperCAmelCase_	:					Union[str, Any]     	=   self.image_processing_class.from_dict(self.image_processor_dict		)
				self.assertEqual(image_processor.size      ,      {"shortest_edge": 30}		)
				self.assertEqual(image_processor.crop_size      ,      {"height": 30, "width": 30}		)

				UpperCAmelCase_	:					Optional[Any]     	=   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   :					Tuple		)		->     Any:
				pass





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]		)		->     List[str]:
				# Initialize image_processing
				UpperCAmelCase_	:					List[str]     	=   self.image_processing_class(**self.image_processor_dict		)
				# create random PIL images
				UpperCAmelCase_	:					int     	=   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_	:					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.crop_size["height"],
				        self.image_processor_tester.crop_size["width"],
				    )      ,      )

				# Test batched
				UpperCAmelCase_	:					Union[str, Any]     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     int:
				# Initialize image_processing
				UpperCAmelCase_	:					List[Any]     	=   self.image_processing_class(**self.image_processor_dict		)
				# create random numpy tensors
				UpperCAmelCase_	:					str     	=   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_	:					Union[str, Any]     	=   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_	:					Union[str, Any]     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Union[str, Any]:
				# Initialize image_processing
				UpperCAmelCase_	:					List[str]     	=   self.image_processing_class(**self.image_processor_dict		)
				# create random PyTorch tensors
				UpperCAmelCase_	:					Tuple     	=   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_	:					Optional[Any]     	=   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_	:					List[Any]     	=   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 typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 						__magic_name__					(							snake_case				):
     UpperCamelCase_	:Dict										=      ["""image_processor""", """tokenizer"""]
     UpperCamelCase_	:str										=      """BridgeTowerImageProcessor"""
     UpperCamelCase_	:Tuple										=      ("""RobertaTokenizer""", """RobertaTokenizerFast""")






     def __init__(				self		,						_lowercase		,						_lowercase	)->     Any:
         super().__init__(_lowercase		,						_lowercase	)






     def __call__(				self		,						_lowercase		,						_lowercase = None		,						_lowercase = True		,						_lowercase = False		,						_lowercase = None		,						_lowercase = None		,						_lowercase = 0		,						_lowercase = None		,						_lowercase = None		,						_lowercase = None		,						_lowercase = False		,						_lowercase = False		,						_lowercase = False		,						_lowercase = False		,						_lowercase = True		,						_lowercase = None		,						**_lowercase		,						)->     BatchEncoding:
         UpperCamelCase_			  =						self.tokenizer(
             text=_lowercase		,						add_special_tokens=_lowercase		,						padding=_lowercase		,						truncation=_lowercase		,						max_length=_lowercase		,						stride=_lowercase		,						pad_to_multiple_of=_lowercase		,						return_token_type_ids=_lowercase		,						return_attention_mask=_lowercase		,						return_overflowing_tokens=_lowercase		,						return_special_tokens_mask=_lowercase		,						return_offsets_mapping=_lowercase		,						return_length=_lowercase		,						verbose=_lowercase		,						return_tensors=_lowercase		,						**_lowercase		,						)
         # add pixel_values + pixel_mask
         UpperCamelCase_			  =						self.image_processor(
             _lowercase		,						return_tensors=_lowercase		,						do_normalize=_lowercase		,						do_center_crop=_lowercase		,						**_lowercase	)
         encoding.update(_lowercase	)

         return encoding






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     Union[str, Any]:
         return self.tokenizer.batch_decode(*_lowercase		,						**_lowercase	)






     def 	UpperCAmelCase_				(				self		,						*_lowercase		,						**_lowercase	)->     List[Any]:
         return self.tokenizer.decode(*_lowercase		,						**_lowercase	)






     @property
     def 	UpperCAmelCase_				(				self	)->     Any:
         UpperCamelCase_			  =						self.tokenizer.model_input_names
         UpperCamelCase_			  =						self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)
