





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:
	lowerCAmelCase_												=       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       __lowerCAmelCase (    unittest.TestCase  ):






			def     lowerCamelCase						(self					)  ->     Tuple:


							'''simple docstring'''



							snake_case_		:					int			    =						tempfile.mkdtemp()

							snake_case_		:					Any			    =						PixaStructImageProcessor()
							snake_case_		:					Dict			    =						TaTokenizer.from_pretrained('''t5-small'''					)

							snake_case_		:					str			    =						PixaStructProcessor(__magic_name__      ,							__magic_name__					)

							processor.save_pretrained(self.tmpdirname					)






			def     lowerCamelCase						(self      ,							**__magic_name__					)  ->     str:


							'''simple docstring'''



							return AutoProcessor.from_pretrained(self.tmpdirname      ,							**__magic_name__					).tokenizer






			def     lowerCamelCase						(self      ,							**__magic_name__					)  ->     str:


							'''simple docstring'''



							return AutoProcessor.from_pretrained(self.tmpdirname      ,							**__magic_name__					).image_processor






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


							'''simple docstring'''



							shutil.rmtree(self.tmpdirname					)






			def     lowerCamelCase						(self					)  ->     Dict:


							'''simple docstring'''



							snake_case_		:					Any			    =						[np.random.randint(255      ,							size=(3, 30, 400)      ,							dtype=np.uinta					)]

							snake_case_		:					List[str]			    =						[Image.fromarray(np.moveaxis(__magic_name__      ,							0      ,							-1					)					) for x in image_inputs]

							return image_inputs






			def     lowerCamelCase						(self					)  ->     int:


							'''simple docstring'''



							snake_case_		:					Optional[int]			    =						PixaStructProcessor(tokenizer=self.get_tokenizer()      ,							image_processor=self.get_image_processor()					)
							processor.save_pretrained(self.tmpdirname					)

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

							snake_case_		:					str			    =						PixaStructProcessor.from_pretrained(
							    self.tmpdirname      ,							bos_token='''(BOS)'''      ,							eos_token='''(EOS)'''      ,							do_normalize=__magic_name__      ,							padding_value=1.0					)

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

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






			def     lowerCamelCase						(self					)  ->     Dict:


							'''simple docstring'''



							snake_case_		:					Optional[int]			    =						self.get_image_processor()
							snake_case_		:					str			    =						self.get_tokenizer()

							snake_case_		:					Tuple			    =						PixaStructProcessor(tokenizer=__magic_name__      ,							image_processor=__magic_name__					)

							snake_case_		:					Dict			    =						self.prepare_image_inputs()

							snake_case_		:					Dict			    =						image_processor(__magic_name__      ,							return_tensors='''np'''					)
							snake_case_		:					Dict			    =						processor(images=__magic_name__      ,							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     lowerCamelCase						(self					)  ->     Tuple:


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						self.get_image_processor()
							snake_case_		:					Dict			    =						self.get_tokenizer()

							snake_case_		:					Dict			    =						PixaStructProcessor(tokenizer=__magic_name__      ,							image_processor=__magic_name__					)

							snake_case_		:					List[Any]			    =						'lower newer'

							snake_case_		:					List[str]			    =						processor(text=__magic_name__					)

							snake_case_		:					List[str]			    =						tokenizer(__magic_name__      ,							return_token_type_ids=__magic_name__      ,							add_special_tokens=__magic_name__					)

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






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


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						self.get_image_processor()
							snake_case_		:					Union[str, Any]			    =						self.get_tokenizer()

							snake_case_		:					Optional[Any]			    =						PixaStructProcessor(tokenizer=__magic_name__      ,							image_processor=__magic_name__					)

							snake_case_		:					int			    =						'lower newer'
							snake_case_		:					Optional[Any]			    =						self.prepare_image_inputs()

							snake_case_		:					List[Any]			    =						processor(text=__magic_name__      ,							images=__magic_name__					)

							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(__magic_name__					):
											processor()






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


							'''simple docstring'''



							snake_case_		:					Dict			    =						self.get_image_processor()
							snake_case_		:					Dict			    =						self.get_tokenizer()

							snake_case_		:					List[Any]			    =						PixaStructProcessor(tokenizer=__magic_name__      ,							image_processor=__magic_name__					)

							snake_case_		:					str			    =						'lower newer'
							snake_case_		:					Dict			    =						self.prepare_image_inputs()

							snake_case_		:					int			    =						processor(text=__magic_name__      ,							images=__magic_name__					)

							snake_case_		:					Dict			    =						[512, 1024, 2048, 4096]
							snake_case_		:					List[Any]			    =						[770, 770, 770, 770]
							# with text
							for i, max_patch in enumerate(__magic_name__					):
											snake_case_		:					str			    =						processor(text=__magic_name__      ,							images=__magic_name__      ,							max_patches=__magic_name__					)
											self.assertEqual(inputs['''flattened_patches'''][0].shape[0]      ,							__magic_name__					)
											self.assertEqual(inputs['''flattened_patches'''][0].shape[1]      ,							expected_hidden_size[i]					)

							# without text input
							for i, max_patch in enumerate(__magic_name__					):
											snake_case_		:					Dict			    =						processor(images=__magic_name__      ,							max_patches=__magic_name__					)
											self.assertEqual(inputs['''flattened_patches'''][0].shape[0]      ,							__magic_name__					)
											self.assertEqual(inputs['''flattened_patches'''][0].shape[1]      ,							expected_hidden_size[i]					)






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


							'''simple docstring'''



							snake_case_		:					Union[str, Any]			    =						self.get_image_processor()
							snake_case_		:					Optional[Any]			    =						self.get_tokenizer()

							snake_case_		:					Union[str, Any]			    =						PixaStructProcessor(tokenizer=__magic_name__      ,							image_processor=__magic_name__					)

							snake_case_		:					Tuple			    =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							snake_case_		:					Any			    =						processor.batch_decode(__magic_name__					)
							snake_case_		:					List[Any]			    =						tokenizer.batch_decode(__magic_name__					)

							self.assertListEqual(__magic_name__      ,							__magic_name__					)






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


							'''simple docstring'''



							snake_case_		:					Optional[Any]			    =						self.get_image_processor()
							snake_case_		:					Optional[Any]			    =						self.get_tokenizer()

							snake_case_		:					Optional[int]			    =						PixaStructProcessor(tokenizer=__magic_name__      ,							image_processor=__magic_name__					)

							snake_case_		:					List[str]			    =						'lower newer'
							snake_case_		:					Dict			    =						self.prepare_image_inputs()

							snake_case_		:					int			    =						processor(text=__magic_name__      ,							images=__magic_name__					)

							# 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''']					)

							snake_case_		:					Optional[int]			    =						processor(text=__magic_name__					)

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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase_												=       {'''configuration_dinat''': ['''DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DinatConfig''']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_												=       [
	    '''DINAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''DinatForImageClassification''',
	    '''DinatModel''',
	    '''DinatPreTrainedModel''',
	    '''DinatBackbone''',
	]

if TYPE_CHECKING:
	from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_dinat import (
		    DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DinatBackbone,
		    DinatForImageClassification,
		    DinatModel,
		    DinatPreTrainedModel,
		)

else:
	import sys

	lowerCAmelCase_												=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
