






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:
   _UpperCAmelCase					:						Optional[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 		lowerCAmelCase_				(  unittest.TestCase ):





     def 						lowerCamelCase							(	self  )		->				Dict:
        lowerCamelCase									=tempfile.mkdtemp()

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

        lowerCamelCase									=PixaStructProcessor(_UpperCAmelCase							, _UpperCAmelCase  )

        processor.save_pretrained(self.tmpdirname  )





     def 						lowerCamelCase							(	self							, **_UpperCAmelCase  )		->				Union[str, Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname							, **_UpperCAmelCase  ).tokenizer





     def 						lowerCamelCase							(	self							, **_UpperCAmelCase  )		->				Optional[Any]:
        return AutoProcessor.from_pretrained(self.tmpdirname							, **_UpperCAmelCase  ).image_processor





     def 						lowerCamelCase							(	self  )		->				Any:
        shutil.rmtree(self.tmpdirname  )





     def 						lowerCamelCase							(	self  )		->				Union[str, Any]:
        lowerCamelCase									=[np.random.randint(255							, size=(3, 30, 400)							, dtype=np.uinta  )]

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

        return image_inputs





     def 						lowerCamelCase							(	self  )		->				Optional[Any]:
        lowerCamelCase									=PixaStructProcessor(tokenizer=self.get_tokenizer()							, image_processor=self.get_image_processor()  )
        processor.save_pretrained(self.tmpdirname  )

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

        lowerCamelCase									=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  )		->				Dict:
        lowerCamelCase									=self.get_image_processor()
        lowerCamelCase									=self.get_tokenizer()

        lowerCamelCase									=PixaStructProcessor(tokenizer=_UpperCAmelCase							, image_processor=_UpperCAmelCase  )

        lowerCamelCase									=self.prepare_image_inputs()

        lowerCamelCase									=image_processor(_UpperCAmelCase							, return_tensors="""np"""  )
        lowerCamelCase									=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  )		->				Dict:
        lowerCamelCase									=self.get_image_processor()
        lowerCamelCase									=self.get_tokenizer()

        lowerCamelCase									=PixaStructProcessor(tokenizer=_UpperCAmelCase							, image_processor=_UpperCAmelCase  )

        lowerCamelCase									="""lower newer"""

        lowerCamelCase									=processor(text=_UpperCAmelCase  )

        lowerCamelCase									=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  )		->				List[Any]:
        lowerCamelCase									=self.get_image_processor()
        lowerCamelCase									=self.get_tokenizer()

        lowerCamelCase									=PixaStructProcessor(tokenizer=_UpperCAmelCase							, image_processor=_UpperCAmelCase  )

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

        lowerCamelCase									=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  )		->				List[str]:
        lowerCamelCase									=self.get_image_processor()
        lowerCamelCase									=self.get_tokenizer()

        lowerCamelCase									=PixaStructProcessor(tokenizer=_UpperCAmelCase							, image_processor=_UpperCAmelCase  )

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

        lowerCamelCase									=processor(text=_UpperCAmelCase							, images=_UpperCAmelCase  )

        lowerCamelCase									=[512, 1024, 2048, 4096]
        lowerCamelCase									=[770, 770, 770, 770]
        # with text
        for i, max_patch in enumerate(_UpperCAmelCase  ):
           lowerCamelCase									=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  ):
           lowerCamelCase									=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[Any]:
        lowerCamelCase									=self.get_image_processor()
        lowerCamelCase									=self.get_tokenizer()

        lowerCamelCase									=PixaStructProcessor(tokenizer=_UpperCAmelCase							, image_processor=_UpperCAmelCase  )

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

        lowerCamelCase									=processor.batch_decode(_UpperCAmelCase  )
        lowerCamelCase									=tokenizer.batch_decode(_UpperCAmelCase  )

        self.assertListEqual(_UpperCAmelCase							, _UpperCAmelCase  )





     def 						lowerCamelCase							(	self  )		->				str:
        lowerCamelCase									=self.get_image_processor()
        lowerCamelCase									=self.get_tokenizer()

        lowerCamelCase									=PixaStructProcessor(tokenizer=_UpperCAmelCase							, image_processor=_UpperCAmelCase  )

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

        lowerCamelCase									=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"""]  )

        lowerCamelCase									=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"""]  )

"""simple docstring"""




import safs
class SCREAMING_SNAKE_CASE       (      safs.SaFileSystem					):






	'''simple docstring'''




