






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 :    List[Any]								=				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  __lowercase (		unittest.TestCase					):







     '''simple docstring'''




     def 						lowerCAmelCase_       (			self    :     Optional[int]			):


         """simple docstring"""



         __A             =		tempfile.mkdtemp()

         __A             =		PixaStructImageProcessor()
         __A             =		TaTokenizer.from_pretrained("""t5-small"""			)

         __A             =		PixaStructProcessor(UpperCamelCase_	,		UpperCamelCase_			)

         processor.save_pretrained(self.tmpdirname			)




     def 						lowerCAmelCase_       (			self    :     Optional[Any]	,		**UpperCamelCase_    :     Union[str, Any]			):


         """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     str	,		**UpperCamelCase_    :     Optional[int]			):


         """simple docstring"""



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




     def 						lowerCAmelCase_       (			self    :     Optional[Any]			):


         """simple docstring"""



         shutil.rmtree(self.tmpdirname			)




     def 						lowerCAmelCase_       (			self    :     int			):


         """simple docstring"""



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

         __A             =		[Image.fromarray(np.moveaxis(UpperCamelCase_	,		0	,		-1			)			) for x in image_inputs]

         return image_inputs




     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



         __A             =		PixaStructProcessor(tokenizer=self.get_tokenizer()	,		image_processor=self.get_image_processor()			)
         processor.save_pretrained(self.tmpdirname			)

         __A             =		self.get_tokenizer(bos_token="""(BOS)"""	,		eos_token="""(EOS)"""			)
         __A             =		self.get_image_processor(do_normalize=UpperCamelCase_	,		padding_value=1.0			)

         __A             =		PixaStructProcessor.from_pretrained(
             self.tmpdirname	,		bos_token="""(BOS)"""	,		eos_token="""(EOS)"""	,		do_normalize=UpperCamelCase_	,		padding_value=1.0			)

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

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




     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



         __A             =		self.get_image_processor()
         __A             =		self.get_tokenizer()

         __A             =		PixaStructProcessor(tokenizer=UpperCamelCase_	,		image_processor=UpperCamelCase_			)

         __A             =		self.prepare_image_inputs()

         __A             =		image_processor(UpperCamelCase_	,		return_tensors="""np"""			)
         __A             =		processor(images=UpperCamelCase_	,		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    :     Any			):


         """simple docstring"""



         __A             =		self.get_image_processor()
         __A             =		self.get_tokenizer()

         __A             =		PixaStructProcessor(tokenizer=UpperCamelCase_	,		image_processor=UpperCamelCase_			)

         __A             =		"""lower newer"""

         __A             =		processor(text=UpperCamelCase_			)

         __A             =		tokenizer(UpperCamelCase_	,		return_token_type_ids=UpperCamelCase_	,		add_special_tokens=UpperCamelCase_			)

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




     def 						lowerCAmelCase_       (			self    :     Tuple			):


         """simple docstring"""



         __A             =		self.get_image_processor()
         __A             =		self.get_tokenizer()

         __A             =		PixaStructProcessor(tokenizer=UpperCamelCase_	,		image_processor=UpperCamelCase_			)

         __A             =		"""lower newer"""
         __A             =		self.prepare_image_inputs()

         __A             =		processor(text=UpperCamelCase_	,		images=UpperCamelCase_			)

         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(UpperCamelCase_			):
             processor()




     def 						lowerCAmelCase_       (			self    :     int			):


         """simple docstring"""



         __A             =		self.get_image_processor()
         __A             =		self.get_tokenizer()

         __A             =		PixaStructProcessor(tokenizer=UpperCamelCase_	,		image_processor=UpperCamelCase_			)

         __A             =		"""lower newer"""
         __A             =		self.prepare_image_inputs()

         __A             =		processor(text=UpperCamelCase_	,		images=UpperCamelCase_			)

         __A             =		[512, 1_024, 2_048, 4_096]
         __A             =		[770, 770, 770, 770]
         # with text
         for i, max_patch in enumerate(UpperCamelCase_			):
             __A             =		processor(text=UpperCamelCase_	,		images=UpperCamelCase_	,		max_patches=UpperCamelCase_			)
             self.assertEqual(inputs["""flattened_patches"""][0].shape[0]	,		UpperCamelCase_			)
             self.assertEqual(inputs["""flattened_patches"""][0].shape[1]	,		expected_hidden_size[i]			)

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




     def 						lowerCAmelCase_       (			self    :     List[str]			):


         """simple docstring"""



         __A             =		self.get_image_processor()
         __A             =		self.get_tokenizer()

         __A             =		PixaStructProcessor(tokenizer=UpperCamelCase_	,		image_processor=UpperCamelCase_			)

         __A             =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __A             =		processor.batch_decode(UpperCamelCase_			)
         __A             =		tokenizer.batch_decode(UpperCamelCase_			)

         self.assertListEqual(UpperCamelCase_	,		UpperCamelCase_			)





     def 						lowerCAmelCase_       (			self    :     Optional[Any]			):


         """simple docstring"""



         __A             =		self.get_image_processor()
         __A             =		self.get_tokenizer()

         __A             =		PixaStructProcessor(tokenizer=UpperCamelCase_	,		image_processor=UpperCamelCase_			)

         __A             =		"""lower newer"""
         __A             =		self.prepare_image_inputs()

         __A             =		processor(text=UpperCamelCase_	,		images=UpperCamelCase_			)

         # 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"""]			)

         __A             =		processor(text=UpperCamelCase_			)

         # 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 ..utils import DummyObject, requires_backends


__a :    int								=				None



def      _SCREAMING_SNAKE_CASE  (	*__lowercase				:     Any     ,			**__lowercase				:     Optional[int]      )  -> Tuple:





    """simple docstring"""

    requires_backends(__lowercase     ,			["""sentencepiece""", """tokenizers"""]      )




