




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__       				=	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    ):



     def 				__magic_name__		(  self   :							List[str]       ):
       a_		    =  tempfile.mkdtemp()

       a_		    =  PixaStructImageProcessor()
       a_		    =  TaTokenizer.from_pretrained('''t5-small'''       )

       a_		    =  PixaStructProcessor(UpperCamelCase_      ,		UpperCamelCase_       )

       processor.save_pretrained(self.tmpdirname       )



     def 				__magic_name__		(  self   :							Optional[int]      ,		**lowercase__   :							Any       ):
       return AutoProcessor.from_pretrained(self.tmpdirname      ,		**UpperCamelCase_       ).tokenizer



     def 				__magic_name__		(  self   :							Optional[int]      ,		**lowercase__   :							int       ):
       return AutoProcessor.from_pretrained(self.tmpdirname      ,		**UpperCamelCase_       ).image_processor



     def 				__magic_name__		(  self   :							List[Any]       ):
       shutil.rmtree(self.tmpdirname       )



     def 				__magic_name__		(  self   :							Tuple       ):
       a_		    =  [np.random.randint(2_5_5      ,		size=(3, 3_0, 4_0_0)      ,		dtype=np.uinta       )]

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

       return image_inputs



     def 				__magic_name__		(  self   :							List[str]       ):
       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 				__magic_name__		(  self   :							List[Any]       ):
       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 				__magic_name__		(  self   :							Dict       ):
       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 				__magic_name__		(  self   :							str       ):
       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 				__magic_name__		(  self   :							Any       ):
       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_		    =  [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
       a_		    =  [7_7_0, 7_7_0, 7_7_0, 7_7_0]
       # 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 				__magic_name__		(  self   :							Tuple       ):
       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 				__magic_name__		(  self   :							Optional[int]       ):
       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''']       )


'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowercase	=    {'''configuration_detr''': ['''DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DetrConfig''', '''DetrOnnxConfig''']}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    ['''DetrFeatureExtractor''']
							__lowercase	=    ['''DetrImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DetrForObjectDetection''',
							    '''DetrForSegmentation''',
							    '''DetrModel''',
							    '''DetrPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_detr import DetrFeatureExtractor
														from .image_processing_detr import DetrImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_detr import (
														    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DetrForObjectDetection,
														    DetrForSegmentation,
														    DetrModel,
														    DetrPreTrainedModel,
														)

else:
							import sys

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






