



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    _SCREAMING_SNAKE_CASE						(unittest.TestCase			):





     def 						__snake_case     (							self			:				List[Any]					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						tempfile.mkdtemp()

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						PixaStructImageProcessor()
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						TaTokenizer.from_pretrained("t5-small"					)

      __SCREAMING_SNAKE_CASE							:     str							=						PixaStructProcessor(UpperCamelCase  ,	UpperCamelCase					)

      processor.save_pretrained(self.tmpdirname					)





     def 						__snake_case     (							self			:				List[str]  ,	**UpperCamelCase			:				List[str]					)->List[str]:
      return AutoProcessor.from_pretrained(self.tmpdirname  ,	**UpperCamelCase					).tokenizer





     def 						__snake_case     (							self			:				Any  ,	**UpperCamelCase			:				List[Any]					)->Any:
      return AutoProcessor.from_pretrained(self.tmpdirname  ,	**UpperCamelCase					).image_processor





     def 						__snake_case     (							self			:				List[str]					)->List[Any]:
      shutil.rmtree(self.tmpdirname					)





     def 						__snake_case     (							self			:				Optional[int]					)->Any:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						[np.random.randint(2_5_5  ,	size=(3, 3_0, 4_0_0)  ,	dtype=np.uinta					)]

      __SCREAMING_SNAKE_CASE							:     Dict							=						[Image.fromarray(np.moveaxis(UpperCamelCase  ,	0  ,	-1					)					) for x in image_inputs]

      return image_inputs





     def 						__snake_case     (							self			:				List[Any]					)->List[Any]:
      __SCREAMING_SNAKE_CASE							:     List[str]							=						PixaStructProcessor(tokenizer=self.get_tokenizer()  ,	image_processor=self.get_image_processor()					)
      processor.save_pretrained(self.tmpdirname					)

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.get_tokenizer(bos_token="(BOS)"  ,	eos_token="(EOS)"					)
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						self.get_image_processor(do_normalize=UpperCamelCase  ,	padding_value=1.0					)

      __SCREAMING_SNAKE_CASE							:     Tuple							=						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 						__snake_case     (							self			:				Optional[int]					)->List[str]:
      __SCREAMING_SNAKE_CASE							:     int							=						self.get_image_processor()
      __SCREAMING_SNAKE_CASE							:     int							=						self.get_tokenizer()

      __SCREAMING_SNAKE_CASE							:     Dict							=						PixaStructProcessor(tokenizer=UpperCamelCase  ,	image_processor=UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE							:     int							=						image_processor(UpperCamelCase  ,	return_tensors="np"					)
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						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 						__snake_case     (							self			:				Union[str, Any]					)->Any:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.get_image_processor()
      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.get_tokenizer()

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						PixaStructProcessor(tokenizer=UpperCamelCase  ,	image_processor=UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Any							=						"lower newer"

      __SCREAMING_SNAKE_CASE							:     Tuple							=						processor(text=UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     str							=						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 						__snake_case     (							self			:				Dict					)->str:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.get_image_processor()
      __SCREAMING_SNAKE_CASE							:     int							=						self.get_tokenizer()

      __SCREAMING_SNAKE_CASE							:     List[str]							=						PixaStructProcessor(tokenizer=UpperCamelCase  ,	image_processor=UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Any							=						"lower newer"
      __SCREAMING_SNAKE_CASE							:     str							=						self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE							:     Any							=						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 						__snake_case     (							self			:				Union[str, Any]					)->int:
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.get_image_processor()
      __SCREAMING_SNAKE_CASE							:     int							=						self.get_tokenizer()

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						PixaStructProcessor(tokenizer=UpperCamelCase  ,	image_processor=UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						"lower newer"
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						processor(text=UpperCamelCase  ,	images=UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Tuple							=						[5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
      __SCREAMING_SNAKE_CASE							:     Tuple							=						[7_7_0, 7_7_0, 7_7_0, 7_7_0]
      # with text
      for i, max_patch in enumerate(UpperCamelCase					):
       __SCREAMING_SNAKE_CASE							:     List[str]							=						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					):
       __SCREAMING_SNAKE_CASE							:     int							=						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 						__snake_case     (							self			:				List[str]					)->Tuple:
      __SCREAMING_SNAKE_CASE							:     str							=						self.get_image_processor()
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.get_tokenizer()

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						PixaStructProcessor(tokenizer=UpperCamelCase  ,	image_processor=UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     str							=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						processor.batch_decode(UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						tokenizer.batch_decode(UpperCamelCase					)

      self.assertListEqual(UpperCamelCase  ,	UpperCamelCase					)






     def 						__snake_case     (							self			:				List[str]					)->int:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.get_image_processor()
      __SCREAMING_SNAKE_CASE							:     str							=						self.get_tokenizer()

      __SCREAMING_SNAKE_CASE							:     Dict							=						PixaStructProcessor(tokenizer=UpperCamelCase  ,	image_processor=UpperCamelCase					)

      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						"lower newer"
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						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"]					)

      __SCREAMING_SNAKE_CASE							:     Tuple							=						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"]					)











from __future__ import annotations

from collections.abc import Iterable, Iterator
from typing import Any, Generic, TypeVar

_lowerCamelCase         =	TypeVar("""T""", bound=bool)

class    _SCREAMING_SNAKE_CASE						(Generic[T]			):





     def __init__(							self			:				List[Any]  ,	UpperCamelCase			:				T					)->None:
      __SCREAMING_SNAKE_CASE							:     T							=						value
      __SCREAMING_SNAKE_CASE							:     SkewNode[T] | None							=						None
      __SCREAMING_SNAKE_CASE							:     SkewNode[T] | None							=						None





     @property
     def 						__snake_case     (							self			:				Dict					)->T:
      return self._value






     @staticmethod
     def 						__snake_case     (							UpperCamelCase			:				SkewNode[T] | None  ,	UpperCamelCase			:				SkewNode[T] | None					)->SkewNode[T] | None:
      if not roota:
       return roota

      if not roota:
       return roota

      if roota.value > roota.value:
       __SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE							:     List[Any]							=						roota, roota

      __SCREAMING_SNAKE_CASE							:     int							=						roota
      __SCREAMING_SNAKE_CASE							:     str							=						roota.right
      __SCREAMING_SNAKE_CASE							:     int							=						roota.left
      __SCREAMING_SNAKE_CASE							:     Optional[int]							=						SkewNode.merge(UpperCamelCase  ,	UpperCamelCase					)

      return result






class    _SCREAMING_SNAKE_CASE						(Generic[T]			):





     def __init__(							self			:				str  ,	UpperCamelCase			:				Iterable[T] | None = ()					)->None:
      __SCREAMING_SNAKE_CASE							:     SkewNode[T] | None							=						None
      if data:
       for item in data:
        self.insert(UpperCamelCase					)





     def __bool__(							self			:				str					)->bool:
      return self._root is not None





     def __iter__(							self			:				Any					)->Iterator[T]:
      __SCREAMING_SNAKE_CASE							:     list[Any]							=						[]
      while self:
       result.append(self.pop()					)

      # Pushing items back to the heap not to clear it.
      for item in result:
       self.insert(UpperCamelCase					)

      return iter(UpperCamelCase					)





     def 						__snake_case     (							self			:				List[Any]  ,	UpperCamelCase			:				T					)->None:
      __SCREAMING_SNAKE_CASE							:     str							=						SkewNode.merge(self._root  ,	SkewNode(UpperCamelCase					)					)





     def 						__snake_case     (							self			:				List[Any]					)->T | None:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.top()
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						(
          SkewNode.merge(self._root.left  ,	self._root.right					) if self._root else None
      )

      return result





     def 						__snake_case     (							self			:				Optional[int]					)->T:
      if not self._root:
       raise IndexError("Can't get top element for the empty heap."					)
      return self._root.value






     def 						__snake_case     (							self			:				Dict					)->None:
      __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						None


if __name__ == "__main__":
      import doctest

      doctest.testmod()






