





"""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 			snake_case		(   unittest.TestCase   ):

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						):
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       tempfile.mkdtemp()

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       PixaStructImageProcessor()
       __SCREAMING_SNAKE_CASE :			int												=       TaTokenizer.from_pretrained('''t5-small'''						)

       __SCREAMING_SNAKE_CASE :			List[Any]												=       PixaStructProcessor(_lowerCamelCase						,							_lowerCamelCase						)

       processor.save_pretrained(self.tmpdirname						)

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						,							**_lowerCamelCase   :Optional[Any]						):
       return AutoProcessor.from_pretrained(self.tmpdirname						,							**_lowerCamelCase						).tokenizer

      def 						SCREAMING_SNAKE_CASE_			(			self   :Any						,							**_lowerCamelCase   :Optional[int]						):
       return AutoProcessor.from_pretrained(self.tmpdirname						,							**_lowerCamelCase						).image_processor

      def 						SCREAMING_SNAKE_CASE_			(			self   :Tuple						):
       shutil.rmtree(self.tmpdirname						)

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

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

       return image_inputs

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       __SCREAMING_SNAKE_CASE :			int												=       PixaStructProcessor(tokenizer=self.get_tokenizer()						,							image_processor=self.get_image_processor()						)
       processor.save_pretrained(self.tmpdirname						)

       __SCREAMING_SNAKE_CASE :			Tuple												=       self.get_tokenizer(bos_token='''(BOS)'''						,							eos_token='''(EOS)'''						)
       __SCREAMING_SNAKE_CASE :			int												=       self.get_image_processor(do_normalize=_lowerCamelCase						,							padding_value=1.0						)

       __SCREAMING_SNAKE_CASE :			List[Any]												=       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 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			str												=       self.get_image_processor()
       __SCREAMING_SNAKE_CASE :			Optional[int]												=       self.get_tokenizer()

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

       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.prepare_image_inputs()

       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       image_processor(_lowerCamelCase						,							return_tensors='''np'''						)
       __SCREAMING_SNAKE_CASE :			List[str]												=       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 						SCREAMING_SNAKE_CASE_			(			self   :Optional[int]						):
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.get_image_processor()
       __SCREAMING_SNAKE_CASE :			int												=       self.get_tokenizer()

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       PixaStructProcessor(tokenizer=_lowerCamelCase						,							image_processor=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Dict												=       '''lower newer'''

       __SCREAMING_SNAKE_CASE :			Dict												=       processor(text=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			List[Any]												=       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 						SCREAMING_SNAKE_CASE_			(			self   :Optional[Any]						):
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.get_image_processor()
       __SCREAMING_SNAKE_CASE :			Any												=       self.get_tokenizer()

       __SCREAMING_SNAKE_CASE :			List[Any]												=       PixaStructProcessor(tokenizer=_lowerCamelCase						,							image_processor=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       '''lower newer'''
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.prepare_image_inputs()

       __SCREAMING_SNAKE_CASE :			List[Any]												=       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 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       __SCREAMING_SNAKE_CASE :			str												=       self.get_image_processor()
       __SCREAMING_SNAKE_CASE :			Any												=       self.get_tokenizer()

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

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       '''lower newer'''
       __SCREAMING_SNAKE_CASE :			int												=       self.prepare_image_inputs()

       __SCREAMING_SNAKE_CASE :			str												=       processor(text=_lowerCamelCase						,							images=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			str												=       [5_1_2, 1_0_2_4, 2_0_4_8, 4_0_9_6]
       __SCREAMING_SNAKE_CASE :			List[Any]												=       [7_7_0, 7_7_0, 7_7_0, 7_7_0]
       # with text
       for i, max_patch in enumerate(_lowerCamelCase						):
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       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						):
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       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 						SCREAMING_SNAKE_CASE_			(			self   :List[str]						):
       __SCREAMING_SNAKE_CASE :			Dict												=       self.get_image_processor()
       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       self.get_tokenizer()

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

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

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       processor.batch_decode(_lowerCamelCase						)
       __SCREAMING_SNAKE_CASE :			Tuple												=       tokenizer.batch_decode(_lowerCamelCase						)

       self.assertListEqual(_lowerCamelCase						,							_lowerCamelCase						)



      def 						SCREAMING_SNAKE_CASE_			(			self   :Dict						):
       __SCREAMING_SNAKE_CASE :			List[Any]												=       self.get_image_processor()
       __SCREAMING_SNAKE_CASE :			Optional[Any]												=       self.get_tokenizer()

       __SCREAMING_SNAKE_CASE :			List[Any]												=       PixaStructProcessor(tokenizer=_lowerCamelCase						,							image_processor=_lowerCamelCase						)

       __SCREAMING_SNAKE_CASE :			Dict												=       '''lower newer'''
       __SCREAMING_SNAKE_CASE :			List[Any]												=       self.prepare_image_inputs()

       __SCREAMING_SNAKE_CASE :			str												=       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''']						)

       __SCREAMING_SNAKE_CASE :			Optional[int]												=       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'''



class 							__lowercase				:







	'''simple docstring'''

	def __init__(self   ,_lowerCamelCase       )							->							Optional[Any]:







				'''simple docstring'''





				__lowercase   =							val
				# Number of nodes in left subtree
				__lowercase   =							0
				__lowercase   =							None
				__lowercase   =							None
				__lowercase   =							None


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Tuple:







				'''simple docstring'''





				assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

				if self.val < other.val:
							__lowercase   =							self.right
							__lowercase   =							None
							if self.right:
										__lowercase   =							other
							__lowercase   =							other
							__lowercase   =							self.left_tree_size * 2 + 1
							return self
				else:
							__lowercase   =							other.right
							__lowercase   =							None
							if other.right:
										__lowercase   =							self
							__lowercase   =							self
							__lowercase   =							other.left_tree_size * 2 + 1
							return other






class 							__lowercase				:







	'''simple docstring'''

	def __init__(self   ,_lowerCamelCase=None   ,_lowerCamelCase=None   ,_lowerCamelCase=0       )							->							int:







				'''simple docstring'''





				__lowercase   =							heap_size
				__lowercase   =							bottom_root
				__lowercase   =							min_node


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Tuple:







				'''simple docstring'''





				if other.size == 0:
							return None
				if self.size == 0:
							__lowercase   =							other.size
							__lowercase   =							other.bottom_root
							__lowercase   =							other.min_node
							return None
				# Update size
				__lowercase   =							self.size + other.size

				# Update min.node
				if self.min_node.val > other.min_node.val:
							__lowercase   =							other.min_node
				# Merge

				# Order roots by left_subtree_size
				__lowercase   =							[]
				__lowercase  ,						__lowercase   =							self.bottom_root, other.bottom_root
				while i or j:
							if i and ((not j) or i.left_tree_size < j.left_tree_size):
										combined_roots_list.append((i, True)       )
										__lowercase   =							i.parent
							else:
										combined_roots_list.append((j, False)       )
										__lowercase   =							j.parent
        # Insert links between them
				for i in range(len(_lowerCamelCase       ) - 1       ):
							if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
										__lowercase   =							combined_roots_list[i + 1][0]
										__lowercase   =							combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
				__lowercase   =							combined_roots_list[0][0]
				while i.parent:
							if (
							    (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
							) or (
							    i.left_tree_size == i.parent.left_tree_size
							    and i.left_tree_size != i.parent.parent.left_tree_size
							):
										# Neighbouring Nodes
										__lowercase   =							i.left
										__lowercase   =							i.parent.parent

										# Merging trees
										__lowercase   =							i.merge_trees(i.parent       )

										# Updating links
										__lowercase   =							previous_node
										__lowercase   =							next_node
										if previous_node:
													__lowercase   =							i
										if next_node:
													__lowercase   =							i
							else:
										__lowercase   =							i.parent
        # Updating self.bottom_root
				while i.left:
							__lowercase   =							i.left
				__lowercase   =							i

				# Update other
				__lowercase   =							self.size
				__lowercase   =							self.bottom_root
				__lowercase   =							self.min_node

				# Return the merged heap
				return self


	def   _UpperCAmelCase   (self   ,_lowerCamelCase       )							->							Dict:







				'''simple docstring'''





				if self.size == 0:
							__lowercase   =							Node(_lowerCamelCase       )
							__lowercase   =							1
							__lowercase   =							self.bottom_root
				else:
							# Create new node
							__lowercase   =							Node(_lowerCamelCase       )

							# Update size
							self.size += 1

							# update min_node
							if val < self.min_node.val:
										__lowercase   =							new_node
							# Put new_node as a bottom_root in heap
							__lowercase   =							new_node
							__lowercase   =							self.bottom_root
							__lowercase   =							new_node

							# Consecutively merge roots with same left_tree_size
							while (
							    self.bottom_root.parent
							    and self.bottom_root.left_tree_size
							    == self.bottom_root.parent.left_tree_size
							):
										# Next node
										__lowercase   =							self.bottom_root.parent.parent

										# Merge
										__lowercase   =							self.bottom_root.merge_trees(self.bottom_root.parent       )

										# Update Links
										__lowercase   =							next_node
										__lowercase   =							None
										if next_node:
													__lowercase   =							self.bottom_root


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







				'''simple docstring'''





				return self.min_node.val


	def   _UpperCAmelCase   (self       )							->							List[Any]:







				'''simple docstring'''





				return self.size == 0


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







				'''simple docstring'''





				__lowercase   =							self.min_node.val

				# Last element in heap corner case
				if self.size == 1:
							# Update size
							__lowercase   =							0

							# Update bottom root
							__lowercase   =							None

							# Update min_node
							__lowercase   =							None

							return min_value
				# No right subtree corner case
				# The structure of the tree implies that this should be the bottom root
				# and there is at least one other root
				if self.min_node.right is None:
							# Update size
							self.size -= 1

							# Update bottom root
							__lowercase   =							self.bottom_root.parent
							__lowercase   =							None

							# Update min_node
							__lowercase   =							self.bottom_root
							__lowercase   =							self.bottom_root.parent
							while i:
										if i.val < self.min_node.val:
													__lowercase   =							i
										__lowercase   =							i.parent
							return min_value
				# General case
				# Find the BinomialHeap of the right subtree of min_node
				__lowercase   =							self.min_node.right
				__lowercase   =							None
				__lowercase   =							bottom_of_new
				__lowercase   =							1

				# Size, min_node and bottom_root
				while bottom_of_new.left:
							__lowercase   =							size_of_new * 2 + 1
							__lowercase   =							bottom_of_new.left
							if bottom_of_new.val < min_of_new.val:
										__lowercase   =							bottom_of_new
        # Corner case of single root on top left path
				if (not self.min_node.left) and (not self.min_node.parent):
							__lowercase   =							size_of_new
							__lowercase   =							bottom_of_new
							__lowercase   =							min_of_new
							# print("Single root, multiple nodes case")
							return min_value
				# Remaining cases
				# Construct heap of right subtree
				__lowercase   =							BinomialHeap(
				    bottom_root=_lowerCamelCase   ,min_node=_lowerCamelCase   ,heap_size=_lowerCamelCase       )

				# Update size
				__lowercase   =							self.size - 1 - size_of_new

				# Neighbour nodes
				__lowercase   =							self.min_node.left
				__lowercase   =							self.min_node.parent

				# Initialize new bottom_root and min_node
				__lowercase   =							previous_node or next_node
				__lowercase   =							next_node

				# Update links of previous_node and search below for new min_node and
				# bottom_root
				if previous_node:
							__lowercase   =							next_node

							# Update bottom_root and search for min_node below
							__lowercase   =							previous_node
							__lowercase   =							previous_node
							while self.bottom_root.left:
										__lowercase   =							self.bottom_root.left
										if self.bottom_root.val < self.min_node.val:
													__lowercase   =							self.bottom_root
				if next_node:
							__lowercase   =							previous_node

							# Search for new min_node above min_node
							__lowercase   =							next_node
							while i:
										if i.val < self.min_node.val:
													__lowercase   =							i
										__lowercase   =							i.parent
        # Merge heaps
				self.merge_heaps(_lowerCamelCase       )

				return min_value


	def   _UpperCAmelCase   (self       )							->							str:







				'''simple docstring'''





				__lowercase   =							self.bottom_root
				while top_root.parent:
							__lowercase   =							top_root.parent
				# preorder
				__lowercase   =							[]
				self.__traversal(_lowerCamelCase   ,_lowerCamelCase       )
				return heap_pre_order


	def   _UpperCAmelCase   (self   ,_lowerCamelCase   ,_lowerCamelCase   ,_lowerCamelCase=0       )							->							str:







				'''simple docstring'''





				if curr_node:
							preorder.append((curr_node.val, level)       )
							self.__traversal(curr_node.left   ,_lowerCamelCase   ,level + 1       )
							self.__traversal(curr_node.right   ,_lowerCamelCase   ,level + 1       )
				else:
							preorder.append(('''#''', level)       )


	def __str__(self       )							->							List[str]:







				'''simple docstring'''





				if self.is_empty():
							return ""
				__lowercase   =							self.pre_order()

				return "\n".join(('''-''' * level + str(_lowerCamelCase       )) for value, level in preorder_heap       )


# Unit Tests
if __name__ == "__main__":
	import doctest

	doctest.testmod()





