



import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_torch_available():
						from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
						A__						:						Optional[int]          =      False

if is_vision_available():
						from PIL import Image

						from transformers import (
						    AutoProcessor,
						    PixaStructImageProcessor,
						    PixaStructProcessor,
						    PreTrainedTokenizerFast,
						    TaTokenizer,
						)



@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11		,reason='''`Pix2StructImageProcessor` requires `torch>=1.11.0`.'''		,)
@require_vision
@require_torch
class        __snake_case		(  unittest.TestCase		):




							def      UpperCAmelCase__    ( self  : List[Any]):
									lowerCAmelCase_    :  Optional[Any]             =				tempfile.mkdtemp()

									lowerCAmelCase_    :  Union[str, Any]             =				PixaStructImageProcessor()
									lowerCAmelCase_    :  List[str]             =				TaTokenizer.from_pretrained('''t5-small''')

									lowerCAmelCase_    :  Any             =				PixaStructProcessor(A_       ,       A_)

									processor.save_pretrained(self.tmpdirname)




							def      UpperCAmelCase__    ( self  : Any       ,       **A_  : Tuple):
									return AutoProcessor.from_pretrained(self.tmpdirname       ,       **A_).tokenizer




							def      UpperCAmelCase__    ( self  : Optional[int]       ,       **A_  : Union[str, Any]):
									return AutoProcessor.from_pretrained(self.tmpdirname       ,       **A_).image_processor




							def      UpperCAmelCase__    ( self  : Any):
									shutil.rmtree(self.tmpdirname)




							def      UpperCAmelCase__    ( self  : int):
									lowerCAmelCase_    :  List[Any]             =				[np.random.randint(2_5_5       ,       size=(3, 3_0, 4_0_0)       ,       dtype=np.uinta)]

									lowerCAmelCase_    :  Optional[int]             =				[Image.fromarray(np.moveaxis(A_       ,       0       ,       -1)) for x in image_inputs]

									return image_inputs




							def      UpperCAmelCase__    ( self  : Optional[Any]):
									lowerCAmelCase_    :  Tuple             =				PixaStructProcessor(tokenizer=self.get_tokenizer()       ,       image_processor=self.get_image_processor())
									processor.save_pretrained(self.tmpdirname)

									lowerCAmelCase_    :  str             =				self.get_tokenizer(bos_token='''(BOS)'''       ,       eos_token='''(EOS)''')
									lowerCAmelCase_    :  List[str]             =				self.get_image_processor(do_normalize=A_       ,       padding_value=1.0)

									lowerCAmelCase_    :  int             =				PixaStructProcessor.from_pretrained(
									    self.tmpdirname       ,       bos_token='''(BOS)'''       ,       eos_token='''(EOS)'''       ,       do_normalize=A_       ,       padding_value=1.0)

									self.assertEqual(processor.tokenizer.get_vocab()       ,       tokenizer_add_kwargs.get_vocab())
									self.assertIsInstance(processor.tokenizer       ,       A_)

									self.assertEqual(processor.image_processor.to_json_string()       ,       image_processor_add_kwargs.to_json_string())
									self.assertIsInstance(processor.image_processor       ,       A_)




							def      UpperCAmelCase__    ( self  : Optional[Any]):
									lowerCAmelCase_    :  Dict             =				self.get_image_processor()
									lowerCAmelCase_    :  str             =				self.get_tokenizer()

									lowerCAmelCase_    :  str             =				PixaStructProcessor(tokenizer=A_       ,       image_processor=A_)

									lowerCAmelCase_    :  Optional[int]             =				self.prepare_image_inputs()

									lowerCAmelCase_    :  Dict             =				image_processor(A_       ,       return_tensors='''np''')
									lowerCAmelCase_    :  Optional[Any]             =				processor(images=A_       ,       return_tensors='''np''')

									for key in input_feat_extract.keys():
											self.assertAlmostEqual(input_feat_extract[key].sum()       ,       input_processor[key].sum()       ,       delta=1e-2)




							def      UpperCAmelCase__    ( self  : Optional[int]):
									lowerCAmelCase_    :  str             =				self.get_image_processor()
									lowerCAmelCase_    :  Optional[int]             =				self.get_tokenizer()

									lowerCAmelCase_    :  Any             =				PixaStructProcessor(tokenizer=A_       ,       image_processor=A_)

									lowerCAmelCase_    :  Union[str, Any]             =				'''lower newer'''

									lowerCAmelCase_    :  Dict             =				processor(text=A_)

									lowerCAmelCase_    :  List[str]             =				tokenizer(A_       ,       return_token_type_ids=A_       ,       add_special_tokens=A_)

									for key in encoded_tok.keys():
											self.assertListEqual(encoded_tok[key]       ,       encoded_processor[key])




							def      UpperCAmelCase__    ( self  : List[Any]):
									lowerCAmelCase_    :  Optional[Any]             =				self.get_image_processor()
									lowerCAmelCase_    :  Union[str, Any]             =				self.get_tokenizer()

									lowerCAmelCase_    :  Optional[int]             =				PixaStructProcessor(tokenizer=A_       ,       image_processor=A_)

									lowerCAmelCase_    :  Tuple             =				'''lower newer'''
									lowerCAmelCase_    :  Union[str, Any]             =				self.prepare_image_inputs()

									lowerCAmelCase_    :  Union[str, Any]             =				processor(text=A_       ,       images=A_)

									self.assertListEqual(
									    list(inputs.keys())       ,       ['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids'''])

									# test if it raises when no input is passed
									with pytest.raises(A_):
											processor()




							def      UpperCAmelCase__    ( self  : Any):
									lowerCAmelCase_    :  Union[str, Any]             =				self.get_image_processor()
									lowerCAmelCase_    :  Optional[int]             =				self.get_tokenizer()

									lowerCAmelCase_    :  str             =				PixaStructProcessor(tokenizer=A_       ,       image_processor=A_)

									lowerCAmelCase_    :  Optional[Any]             =				'''lower newer'''
									lowerCAmelCase_    :  Dict             =				self.prepare_image_inputs()

									lowerCAmelCase_    :  str             =				processor(text=A_       ,       images=A_)

									lowerCAmelCase_    :  List[Any]             =				[5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
									lowerCAmelCase_    :  Optional[int]             =				[7_7_0, 7_7_0, 7_7_0, 7_7_0]
									# with text
									for i, max_patch in enumerate(A_):
											lowerCAmelCase_    :  int             =				processor(text=A_       ,       images=A_       ,       max_patches=A_)
											self.assertEqual(inputs['''flattened_patches'''][0].shape[0]       ,       A_)
											self.assertEqual(inputs['''flattened_patches'''][0].shape[1]       ,       expected_hidden_size[i])

									# without text input
									for i, max_patch in enumerate(A_):
											lowerCAmelCase_    :  Union[str, Any]             =				processor(images=A_       ,       max_patches=A_)
											self.assertEqual(inputs['''flattened_patches'''][0].shape[0]       ,       A_)
											self.assertEqual(inputs['''flattened_patches'''][0].shape[1]       ,       expected_hidden_size[i])




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									lowerCAmelCase_    :  List[Any]             =				self.get_image_processor()
									lowerCAmelCase_    :  Union[str, Any]             =				self.get_tokenizer()

									lowerCAmelCase_    :  str             =				PixaStructProcessor(tokenizer=A_       ,       image_processor=A_)

									lowerCAmelCase_    :  List[str]             =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									lowerCAmelCase_    :  Optional[int]             =				processor.batch_decode(A_)
									lowerCAmelCase_    :  Any             =				tokenizer.batch_decode(A_)

									self.assertListEqual(A_       ,       A_)







							def      UpperCAmelCase__    ( self  : Tuple):
									lowerCAmelCase_    :  List[str]             =				self.get_image_processor()
									lowerCAmelCase_    :  str             =				self.get_tokenizer()

									lowerCAmelCase_    :  List[Any]             =				PixaStructProcessor(tokenizer=A_       ,       image_processor=A_)

									lowerCAmelCase_    :  Optional[Any]             =				'''lower newer'''
									lowerCAmelCase_    :  str             =				self.prepare_image_inputs()

									lowerCAmelCase_    :  List[str]             =				processor(text=A_       ,       images=A_)

									# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
									self.assertListEqual(
									    list(inputs.keys())       ,       ['''flattened_patches''', '''attention_mask''', '''decoder_attention_mask''', '''decoder_input_ids'''])

									lowerCAmelCase_    :  List[str]             =				processor(text=A_)

									# For now the processor supports only ["flattened_patches", "input_ids", "attention_mask", "decoder_attention_mask"]
									self.assertListEqual(list(inputs.keys())       ,       ['''input_ids''', '''attention_mask'''])










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 DeiTImageProcessor



class        __snake_case		(  unittest.TestCase		):




							def __init__( self  : Union[str, Any]       ,       A_  : Optional[Any]       ,       A_  : Optional[Any]=7       ,       A_  : str=3       ,       A_  : Optional[Any]=1_8       ,       A_  : Union[str, Any]=3_0       ,       A_  : str=4_0_0       ,       A_  : List[str]=True       ,       A_  : str=None       ,       A_  : str=True       ,       A_  : List[Any]=None       ,       A_  : List[Any]=True       ,       A_  : Optional[Any]=[0.5, 0.5, 0.5]       ,       A_  : Optional[int]=[0.5, 0.5, 0.5]       ,       ):
									lowerCAmelCase_    :  List[str]             =				size if size is not None else {'''height''': 2_0, '''width''': 2_0}
									lowerCAmelCase_    :  List[Any]             =				crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}

									lowerCAmelCase_    :  str             =				parent
									lowerCAmelCase_    :  Union[str, Any]             =				batch_size
									lowerCAmelCase_    :  Optional[Any]             =				num_channels
									lowerCAmelCase_    :  List[str]             =				image_size
									lowerCAmelCase_    :  List[str]             =				min_resolution
									lowerCAmelCase_    :  Union[str, Any]             =				max_resolution
									lowerCAmelCase_    :  Optional[int]             =				do_resize
									lowerCAmelCase_    :  Dict             =				size
									lowerCAmelCase_    :  Dict             =				do_center_crop
									lowerCAmelCase_    :  str             =				crop_size
									lowerCAmelCase_    :  List[Any]             =				do_normalize
									lowerCAmelCase_    :  str             =				image_mean
									lowerCAmelCase_    :  Dict             =				image_std







							def      UpperCAmelCase__    ( self  : int):
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "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        __snake_case		(  UpperCamelCase_		,unittest.TestCase		):
							_a       =				DeiTImageProcessor if is_vision_available() else None
							_a       =				True




							def      UpperCAmelCase__    ( self  : Tuple):
									lowerCAmelCase_    :  int             =				DeiTImageProcessingTester(self)




							@property
							def      UpperCAmelCase__    ( self  : Optional[Any]):
									return self.image_processor_tester.prepare_image_processor_dict()




							def      UpperCAmelCase__    ( self  : str):
									lowerCAmelCase_    :  List[Any]             =				self.image_processing_class(**self.image_processor_dict)
									self.assertTrue(hasattr(A_       ,       '''do_resize'''))
									self.assertTrue(hasattr(A_       ,       '''size'''))
									self.assertTrue(hasattr(A_       ,       '''do_center_crop'''))
									self.assertTrue(hasattr(A_       ,       '''center_crop'''))
									self.assertTrue(hasattr(A_       ,       '''do_normalize'''))
									self.assertTrue(hasattr(A_       ,       '''image_mean'''))
									self.assertTrue(hasattr(A_       ,       '''image_std'''))




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									lowerCAmelCase_    :  Tuple             =				self.image_processing_class.from_dict(self.image_processor_dict)
									self.assertEqual(image_processor.size       ,       {'''height''': 2_0, '''width''': 2_0})
									self.assertEqual(image_processor.crop_size       ,       {'''height''': 1_8, '''width''': 1_8})

									lowerCAmelCase_    :  Any             =				self.image_processing_class.from_dict(self.image_processor_dict       ,       size=4_2       ,       crop_size=8_4)
									self.assertEqual(image_processor.size       ,       {'''height''': 4_2, '''width''': 4_2})
									self.assertEqual(image_processor.crop_size       ,       {'''height''': 8_4, '''width''': 8_4})




							def      UpperCAmelCase__    ( self  : Union[str, Any]):
									pass




							def      UpperCAmelCase__    ( self  : Any):
									# Initialize image_processing
									lowerCAmelCase_    :  List[str]             =				self.image_processing_class(**self.image_processor_dict)
									# create random PIL images
									lowerCAmelCase_    :  Any             =				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_    :  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
									lowerCAmelCase_    :  Dict             =				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      UpperCAmelCase__    ( self  : List[str]):
									# Initialize image_processing
									lowerCAmelCase_    :  Union[str, Any]             =				self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									lowerCAmelCase_    :  Union[str, Any]             =				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_    :  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
									lowerCAmelCase_    :  str             =				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      UpperCAmelCase__    ( self  : Any):
									# Initialize image_processing
									lowerCAmelCase_    :  str             =				self.image_processing_class(**self.image_processor_dict)
									# create random PyTorch tensors
									lowerCAmelCase_    :  Union[str, Any]             =				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_    :  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
									lowerCAmelCase_    :  Tuple             =				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'''],
									    )       ,       )





