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"]       )










def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  if not root or root not in binary_tree_mirror_dictionary:
    return
  a_    , a_		    =  binary_tree_mirror_dictionary[root][:2]
  a_		    =  [right_child, left_child]
  binary_tree_mirror_dict(_A      ,      _A			)
  binary_tree_mirror_dict(_A      ,      _A			)






def 				UpperCAmelCase__  (							_A      ,      _A = 1			):






  """simple docstring"""


  if not binary_tree:
    raise ValueError('''binary tree cannot be empty'''			)
  if root not in binary_tree:
    a_		    =  f"root {root} is not present in the binary_tree"
    raise ValueError(_A			)
  a_		    =  dict(_A			)
  binary_tree_mirror_dict(_A      ,      _A			)
  return binary_tree_mirror_dictionary


if __name__ == "__main__":
  UpperCamelCase__       				=	{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
  print(F"""Binary tree: {binary_tree}""")
  UpperCamelCase__       				=	binary_tree_mirror(binary_tree, 5)
  print(F"""Binary tree mirror: {binary_tree_mirror_dictionary}""")
