


"""simple docstring"""






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 ):





				"""simple docstring"""







				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					tempfile.mkdtemp()

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

								SCREAMING_SNAKE_CASE__  				=					PixaStructProcessor(_lowercase							,		_lowercase  )

								processor.save_pretrained(self.tmpdirname  )



				def 	lowerCAmelCase__		(    self							,		**UpperCAmelCase__  ):
								return AutoProcessor.from_pretrained(self.tmpdirname							,		**_lowercase  ).tokenizer



				def 	lowerCAmelCase__		(    self							,		**UpperCAmelCase__  ):
								return AutoProcessor.from_pretrained(self.tmpdirname							,		**_lowercase  ).image_processor



				def 	lowerCAmelCase__		(    self  ):
								shutil.rmtree(self.tmpdirname  )



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					[np.random.randint(255							,		size=(3, 30, 400)							,		dtype=np.uinta  )]

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

								return image_inputs



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					PixaStructProcessor(tokenizer=self.get_tokenizer()							,		image_processor=self.get_image_processor()  )
								processor.save_pretrained(self.tmpdirname  )

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

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

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

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



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					self.get_image_processor()
								SCREAMING_SNAKE_CASE__  				=					self.get_tokenizer()

								SCREAMING_SNAKE_CASE__  				=					PixaStructProcessor(tokenizer=_lowercase							,		image_processor=_lowercase  )

								SCREAMING_SNAKE_CASE__  				=					self.prepare_image_inputs()

								SCREAMING_SNAKE_CASE__  				=					image_processor(_lowercase							,		return_tensors="np"  )
								SCREAMING_SNAKE_CASE__  				=					processor(images=_lowercase							,		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  ):
								SCREAMING_SNAKE_CASE__  				=					self.get_image_processor()
								SCREAMING_SNAKE_CASE__  				=					self.get_tokenizer()

								SCREAMING_SNAKE_CASE__  				=					PixaStructProcessor(tokenizer=_lowercase							,		image_processor=_lowercase  )

								SCREAMING_SNAKE_CASE__  				=					"""lower newer"""

								SCREAMING_SNAKE_CASE__  				=					processor(text=_lowercase  )

								SCREAMING_SNAKE_CASE__  				=					tokenizer(_lowercase							,		return_token_type_ids=_lowercase							,		add_special_tokens=_lowercase  )

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



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					self.get_image_processor()
								SCREAMING_SNAKE_CASE__  				=					self.get_tokenizer()

								SCREAMING_SNAKE_CASE__  				=					PixaStructProcessor(tokenizer=_lowercase							,		image_processor=_lowercase  )

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

								SCREAMING_SNAKE_CASE__  				=					processor(text=_lowercase							,		images=_lowercase  )

								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(_lowercase  ):
												processor()



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					self.get_image_processor()
								SCREAMING_SNAKE_CASE__  				=					self.get_tokenizer()

								SCREAMING_SNAKE_CASE__  				=					PixaStructProcessor(tokenizer=_lowercase							,		image_processor=_lowercase  )

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

								SCREAMING_SNAKE_CASE__  				=					processor(text=_lowercase							,		images=_lowercase  )

								SCREAMING_SNAKE_CASE__  				=					[512, 1024, 2048, 4096]
								SCREAMING_SNAKE_CASE__  				=					[770, 770, 770, 770]
								# with text
								for i, max_patch in enumerate(_lowercase  ):
												SCREAMING_SNAKE_CASE__  				=					processor(text=_lowercase							,		images=_lowercase							,		max_patches=_lowercase  )
												self.assertEqual(inputs["flattened_patches"][0].shape[0]							,		_lowercase  )
												self.assertEqual(inputs["flattened_patches"][0].shape[1]							,		expected_hidden_size[i]  )

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



				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					self.get_image_processor()
								SCREAMING_SNAKE_CASE__  				=					self.get_tokenizer()

								SCREAMING_SNAKE_CASE__  				=					PixaStructProcessor(tokenizer=_lowercase							,		image_processor=_lowercase  )

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

								SCREAMING_SNAKE_CASE__  				=					processor.batch_decode(_lowercase  )
								SCREAMING_SNAKE_CASE__  				=					tokenizer.batch_decode(_lowercase  )

								self.assertListEqual(_lowercase							,		_lowercase  )






				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					self.get_image_processor()
								SCREAMING_SNAKE_CASE__  				=					self.get_tokenizer()

								SCREAMING_SNAKE_CASE__  				=					PixaStructProcessor(tokenizer=_lowercase							,		image_processor=_lowercase  )

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

								SCREAMING_SNAKE_CASE__  				=					processor(text=_lowercase							,		images=_lowercase  )

								# 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"]  )

								SCREAMING_SNAKE_CASE__  				=					processor(text=_lowercase  )

								# 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_tokenizers_available, is_torch_available


UpperCAmelCase_       : List[Any]							=    {
    """configuration_retribert""": ["""RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RetriBertConfig"""],
    """tokenization_retribert""": ["""RetriBertTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : str							=    ["""RetriBertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : Optional[Any]							=    [
          """RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """RetriBertModel""",
          """RetriBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
      from .tokenization_retribert import RetriBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_retribert_fast import RetriBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_retribert import (
                RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RetriBertModel,
                RetriBertPreTrainedModel,
            )

else:
      import sys

      UpperCAmelCase_       : Tuple							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
