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 	_a  (							unittest.TestCase					):



	"""simple docstring"""



	def __lowerCAmelCase      (   self       ):
		_lowercase   =tempfile.mkdtemp()

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

		_lowercase   =PixaStructProcessor(lowerCAmelCase_       ,  lowerCAmelCase_       )

		processor.save_pretrained(self.tmpdirname       )


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		return AutoProcessor.from_pretrained(self.tmpdirname       ,  **lowerCAmelCase_       ).tokenizer


	def __lowerCAmelCase      (   self       ,  **lowerCAmelCase_       ):
		return AutoProcessor.from_pretrained(self.tmpdirname       ,  **lowerCAmelCase_       ).image_processor


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


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

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

		return image_inputs


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

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

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

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

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_image_processor()
		_lowercase   =self.get_tokenizer()

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

		_lowercase   =self.prepare_image_inputs()

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

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

		_lowercase   ="lower newer"

		_lowercase   =processor(text=lowerCAmelCase_       )

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

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_image_processor()
		_lowercase   =self.get_tokenizer()

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

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

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

		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(lowerCAmelCase_       ):
			processor()


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_image_processor()
		_lowercase   =self.get_tokenizer()

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

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

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

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

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


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_image_processor()
		_lowercase   =self.get_tokenizer()

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

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

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

		self.assertListEqual(lowerCAmelCase_       ,  lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.get_image_processor()
		_lowercase   =self.get_tokenizer()

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

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

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

		# 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"]       )

		_lowercase   =processor(text=lowerCAmelCase_       )

		# 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 os
import unittest

import pytest
from parameterized import parameterized

from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device


# skipping in unittest tests

a__		: Tuple  							=							[(1,)]





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







      """simple docstring"""
      __A													=      bool(os.getenv('RUN_SLOW'   ,	0						)						)
      if run_slow:
            assert True
      else:
            assert False, "should have been skipped"





def 							snake_case				(		)->      Tuple:







      """simple docstring"""
      __A													=      bool(os.getenv('RUN_SLOW'   ,	0						)						)
      if run_slow and torch_device == "cuda":
            assert True
      else:
            assert False, "should have been skipped"


@require_torch
class 			UpperCamelCase__	(     unittest.TestCase):






 @slow
 @require_torch_gpu
 def 						lowercase_    (      self	:Dict      )					->      List[Any]:







       '''simple docstring'''



       check_slow_torch_cuda()






 @require_torch_gpu
 @slow
 def 						lowercase_    (      self	:int      )					->      int:







       '''simple docstring'''



       check_slow_torch_cuda()






 @parameterized.expand(_A      )
 @slow
 def 						lowercase_    (      self	:Dict						,					_A	:Union[str, Any]=None      )					->      Dict:







       '''simple docstring'''



       check_slow()





@slow
@require_torch_gpu
def 							snake_case				(		)->      Optional[int]:







      """simple docstring"""
      check_slow_torch_cuda()





@require_torch_gpu
@slow
def 							snake_case				(		)->      List[str]:







      """simple docstring"""
      check_slow_torch_cuda()





@slow
@pytest.mark.parametrize('param'   ,	[1]						)
def 							snake_case				(		UpperCAmelCase						)->      int:







      """simple docstring"""
      check_slow()





@pytest.mark.parametrize('param'   ,	[1]						)
@slow
def 							snake_case				(		UpperCAmelCase						)->      Any:







      """simple docstring"""
      check_slow()






