





class 	UpperCamelCase_     :



					def __init__(		self    :    List[Any]					,			lowerCamelCase    :    int	):
							lowerCamelCase_  :    Union[str, Any]   	=			val
							# Number of nodes in left subtree
							lowerCamelCase_  :    Dict   	=			0
							lowerCamelCase_  :    str   	=			None
							lowerCamelCase_  :    int   	=			None
							lowerCamelCase_  :    Dict   	=			None





					def      __a    (		self    :    Optional[int]					,			lowerCamelCase    :    List[Any]	):
							assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"

							if self.val < other.val:
									lowerCamelCase_  :    int   	=			self.right
									lowerCamelCase_  :    Any   	=			None
									if self.right:
											lowerCamelCase_  :    Optional[Any]   	=			other
									lowerCamelCase_  :    Union[str, Any]   	=			other
									lowerCamelCase_  :    List[Any]   	=			self.left_tree_size * 2 + 1
									return self
							else:
									lowerCamelCase_  :    str   	=			other.right
									lowerCamelCase_  :    List[Any]   	=			None
									if other.right:
											lowerCamelCase_  :    Union[str, Any]   	=			self
									lowerCamelCase_  :    Optional[int]   	=			self
									lowerCamelCase_  :    Optional[int]   	=			other.left_tree_size * 2 + 1
									return other






class 	UpperCamelCase_     :



					def __init__(		self    :    List[str]					,			lowerCamelCase    :    Optional[int]=None					,			lowerCamelCase    :    str=None					,			lowerCamelCase    :    List[Any]=0	):
							lowerCamelCase_  :    Any   	=			heap_size
							lowerCamelCase_  :    int   	=			bottom_root
							lowerCamelCase_  :    Any   	=			min_node



					def      __a    (		self    :    int					,			lowerCamelCase    :    int	):

							# Empty heaps corner cases
							if other.size == 0:
									return None
							if self.size == 0:
									lowerCamelCase_  :    List[str]   	=			other.size
									lowerCamelCase_  :    Tuple   	=			other.bottom_root
									lowerCamelCase_  :    Tuple   	=			other.min_node
									return None
							# Update size
							lowerCamelCase_  :    Optional[Any]   	=			self.size + other.size

							# Update min.node
							if self.min_node.val > other.min_node.val:
									lowerCamelCase_  :    Optional[Any]   	=			other.min_node
							# Merge

							# Order roots by left_subtree_size
							lowerCamelCase_  :    Dict   	=			[]
							lowerCamelCase_	,			lowerCamelCase_  :    Optional[Any]   	=			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)	)
											lowerCamelCase_  :    Tuple   	=			i.parent
									else:
											combined_roots_list.append((j, False)	)
											lowerCamelCase_  :    Optional[Any]   	=			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]:
											lowerCamelCase_  :    Dict   	=			combined_roots_list[i + 1][0]
											lowerCamelCase_  :    Optional[int]   	=			combined_roots_list[i][0]
        # Consecutively merge roots with same left_tree_size
							lowerCamelCase_  :    Any   	=			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
											lowerCamelCase_  :    Dict   	=			i.left
											lowerCamelCase_  :    int   	=			i.parent.parent

											# Merging trees
											lowerCamelCase_  :    str   	=			i.merge_trees(i.parent	)

											# Updating links
											lowerCamelCase_  :    Union[str, Any]   	=			previous_node
											lowerCamelCase_  :    List[Any]   	=			next_node
											if previous_node:
													lowerCamelCase_  :    List[str]   	=			i
											if next_node:
													lowerCamelCase_  :    Dict   	=			i
									else:
											lowerCamelCase_  :    List[str]   	=			i.parent
        # Updating self.bottom_root
							while i.left:
									lowerCamelCase_  :    Union[str, Any]   	=			i.left
							lowerCamelCase_  :    Any   	=			i

							# Update other
							lowerCamelCase_  :    Optional[int]   	=			self.size
							lowerCamelCase_  :    Optional[Any]   	=			self.bottom_root
							lowerCamelCase_  :    str   	=			self.min_node

							# Return the merged heap
							return self



					def      __a    (		self    :    Any					,			lowerCamelCase    :    Dict	):
							if self.size == 0:
									lowerCamelCase_  :    Tuple   	=			Node(lowerCamelCase	)
									lowerCamelCase_  :    Optional[int]   	=			1
									lowerCamelCase_  :    Optional[int]   	=			self.bottom_root
							else:
									# Create new node
									lowerCamelCase_  :    List[Any]   	=			Node(lowerCamelCase	)

									# Update size
									self.size += 1

									# update min_node
									if val < self.min_node.val:
											lowerCamelCase_  :    List[str]   	=			new_node
									# Put new_node as a bottom_root in heap
									lowerCamelCase_  :    int   	=			new_node
									lowerCamelCase_  :    Tuple   	=			self.bottom_root
									lowerCamelCase_  :    Any   	=			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
											lowerCamelCase_  :    Any   	=			self.bottom_root.parent.parent

											# Merge
											lowerCamelCase_  :    int   	=			self.bottom_root.merge_trees(self.bottom_root.parent	)

											# Update Links
											lowerCamelCase_  :    Dict   	=			next_node
											lowerCamelCase_  :    Optional[Any]   	=			None
											if next_node:
													lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root



					def      __a    (		self    :    Dict	):
							return self.min_node.val



					def      __a    (		self    :    int	):
							return self.size == 0



					def      __a    (		self    :    List[Any]	):
							lowerCamelCase_  :    Tuple   	=			self.min_node.val

							# Last element in heap corner case
							if self.size == 1:
									# Update size
									lowerCamelCase_  :    Optional[Any]   	=			0

									# Update bottom root
									lowerCamelCase_  :    Any   	=			None

									# Update min_node
									lowerCamelCase_  :    List[str]   	=			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
									lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root.parent
									lowerCamelCase_  :    Union[str, Any]   	=			None

									# Update min_node
									lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root
									lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root.parent
									while i:
											if i.val < self.min_node.val:
													lowerCamelCase_  :    Union[str, Any]   	=			i
											lowerCamelCase_  :    Optional[int]   	=			i.parent
									return min_value
							# General case
							# Find the BinomialHeap of the right subtree of min_node
							lowerCamelCase_  :    Dict   	=			self.min_node.right
							lowerCamelCase_  :    Dict   	=			None
							lowerCamelCase_  :    Tuple   	=			bottom_of_new
							lowerCamelCase_  :    Dict   	=			1

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

							# Update size
							lowerCamelCase_  :    Union[str, Any]   	=			self.size - 1 - size_of_new

							# Neighbour nodes
							lowerCamelCase_  :    Union[str, Any]   	=			self.min_node.left
							lowerCamelCase_  :    Optional[Any]   	=			self.min_node.parent

							# Initialize new bottom_root and min_node
							lowerCamelCase_  :    Optional[int]   	=			previous_node or next_node
							lowerCamelCase_  :    Tuple   	=			next_node

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

									# Update bottom_root and search for min_node below
									lowerCamelCase_  :    int   	=			previous_node
									lowerCamelCase_  :    Tuple   	=			previous_node
									while self.bottom_root.left:
											lowerCamelCase_  :    Union[str, Any]   	=			self.bottom_root.left
											if self.bottom_root.val < self.min_node.val:
													lowerCamelCase_  :    Any   	=			self.bottom_root
							if next_node:
									lowerCamelCase_  :    Any   	=			previous_node

									# Search for new min_node above min_node
									lowerCamelCase_  :    List[str]   	=			next_node
									while i:
											if i.val < self.min_node.val:
													lowerCamelCase_  :    List[Any]   	=			i
											lowerCamelCase_  :    Dict   	=			i.parent
        # Merge heaps
							self.merge_heaps(lowerCamelCase	)

							return min_value



					def      __a    (		self    :    Optional[int]	):
							lowerCamelCase_  :    List[Any]   	=			self.bottom_root
							while top_root.parent:
									lowerCamelCase_  :    Tuple   	=			top_root.parent
							# preorder
							lowerCamelCase_  :    str   	=			[]
							self.__traversal(lowerCamelCase					,			lowerCamelCase	)
							return heap_pre_order



					def      __a    (		self    :    Dict					,			lowerCamelCase    :    Tuple					,			lowerCamelCase    :    Dict					,			lowerCamelCase    :    List[str]=0	):
							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[Any]	):
							if self.is_empty():
									return ""
							lowerCamelCase_  :    str   	=			self.pre_order()

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


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

		doctest.testmod()







import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
		_lowercase		:     Dict 					=False


if is_vision_available():
		from PIL import Image
else:






		class 	UpperCamelCase_     :



							@staticmethod
							def      __a    (		*lowerCamelCase    :    Any					,			**lowerCamelCase    :    List[Any]	):
									pass






@is_pipeline_test
@require_vision
class 	UpperCamelCase_     (     unittest.TestCase       ):
					_a			:	int							=				MODEL_FOR_VISION_2_SEQ_MAPPING
					_a			:	int							=				TF_MODEL_FOR_VISION_2_SEQ_MAPPING



					def      __a    (		self    :    int					,			lowerCamelCase    :    Union[str, Any]					,			lowerCamelCase    :    Optional[int]					,			lowerCamelCase    :    Union[str, Any]	):
							lowerCamelCase_  :    List[str]   	=			pipeline('image-to-text'					,			model=lowerCamelCase					,			tokenizer=lowerCamelCase					,			image_processor=lowerCamelCase	)
							lowerCamelCase_  :    str   	=			[
							    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	),
							    './tests/fixtures/tests_samples/COCO/000000039769.png',
							]
							return pipe, examples



					def      __a    (		self    :    List[Any]					,			lowerCamelCase    :    Any					,			lowerCamelCase    :    List[str]	):
							lowerCamelCase_  :    str   	=			pipe(lowerCamelCase	)
							self.assertEqual(
							    lowerCamelCase					,			[
							        [{'generated_text': ANY(lowerCamelCase	)}],
							        [{'generated_text': ANY(lowerCamelCase	)}],
							    ]					,			)



					@require_tf
					def      __a    (		self    :    Union[str, Any]	):
							lowerCamelCase_  :    Tuple   	=			pipeline('image-to-text'					,			model='hf-internal-testing/tiny-random-vit-gpt2'					,			framework='tf'	)
							lowerCamelCase_  :    Optional[int]   	=			'./tests/fixtures/tests_samples/COCO/000000039769.png'

							lowerCamelCase_  :    Optional[int]   	=			pipe(lowerCamelCase	)
							self.assertEqual(
							    lowerCamelCase					,			[
							        {
							            'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
							        },
							    ]					,			)

							lowerCamelCase_  :    List[Any]   	=			pipe([image, image]	)
							self.assertEqual(
							    lowerCamelCase					,			[
							        [
							            {
							                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
							            }
							        ],
							        [
							            {
							                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
							            }
							        ],
							    ]					,			)

							lowerCamelCase_  :    str   	=			pipe(lowerCamelCase					,			max_new_tokens=1	)
							self.assertEqual(
							    lowerCamelCase					,			[{'generated_text': 'growth'}]					,			)



					@require_torch
					def      __a    (		self    :    Tuple	):
							lowerCamelCase_  :    Union[str, Any]   	=			pipeline('image-to-text'					,			model='hf-internal-testing/tiny-random-vit-gpt2'	)
							lowerCamelCase_  :    Any   	=			'./tests/fixtures/tests_samples/COCO/000000039769.png'

							lowerCamelCase_  :    int   	=			pipe(lowerCamelCase	)
							self.assertEqual(
							    lowerCamelCase					,			[
							        {
							            'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
							        },
							    ]					,			)

							lowerCamelCase_  :    Tuple   	=			pipe([image, image]	)
							self.assertEqual(
							    lowerCamelCase					,			[
							        [
							            {
							                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
							            }
							        ],
							        [
							            {
							                'generated_text': 'growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'
							            }
							        ],
							    ]					,			)



					@require_torch
					def      __a    (		self    :    Dict	):
							lowerCamelCase_  :    Any   	=			pipeline('image-to-text'					,			model='hf-internal-testing/tiny-random-BlipForConditionalGeneration'	)
							lowerCamelCase_  :    List[Any]   	=			'./tests/fixtures/tests_samples/COCO/000000039769.png'
							lowerCamelCase_  :    List[Any]   	=			'a photo of'

							lowerCamelCase_  :    Dict   	=			pipe(lowerCamelCase					,			prompt=lowerCamelCase	)
							self.assertTrue(outputs[0]['generated_text'].startswith(lowerCamelCase	)	)



					@slow
					@require_torch
					def      __a    (		self    :    Union[str, Any]	):
							lowerCamelCase_  :    List[str]   	=			pipeline('image-to-text'					,			model='ydshieh/vit-gpt2-coco-en'	)
							lowerCamelCase_  :    Any   	=			'./tests/fixtures/tests_samples/COCO/000000039769.png'

							lowerCamelCase_  :    str   	=			pipe(lowerCamelCase	)
							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]	)

							lowerCamelCase_  :    Union[str, Any]   	=			pipe([image, image]	)
							self.assertEqual(
							    lowerCamelCase					,			[
							        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
							        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
							    ]					,			)



					@slow
					@require_torch
					def      __a    (		self    :    List[Any]	):
							lowerCamelCase_  :    int   	=			pipeline('image-to-text'					,			model='Salesforce/blip-image-captioning-base'	)
							lowerCamelCase_  :    Tuple   	=			'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
							lowerCamelCase_  :    List[Any]   	=			Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase	).raw	)

							lowerCamelCase_  :    Optional[Any]   	=			pipe(lowerCamelCase	)
							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'a pink pokemon pokemon with a blue shirt and a blue shirt'}]	)



					@slow
					@require_torch
					def      __a    (		self    :    List[str]	):
							lowerCamelCase_  :    Optional[Any]   	=			pipeline('image-to-text'					,			model='microsoft/git-base-coco'	)
							lowerCamelCase_  :    str   	=			'https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'
							lowerCamelCase_  :    Dict   	=			Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase	).raw	)

							lowerCamelCase_  :    Optional[int]   	=			pipe(lowerCamelCase	)
							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'a cartoon of a purple character.'}]	)



					@slow
					@require_torch
					def      __a    (		self    :    List[str]	):
							lowerCamelCase_  :    List[Any]   	=			pipeline('image-to-text'					,			model='Salesforce/blip-image-captioning-base'	)
							lowerCamelCase_  :    int   	=			'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
							lowerCamelCase_  :    List[str]   	=			Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase	).raw	)

							lowerCamelCase_  :    int   	=			'a photography of'

							lowerCamelCase_  :    Tuple   	=			pipe(lowerCamelCase					,			prompt=lowerCamelCase	)
							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'a photography of a volcano'}]	)

							with self.assertRaises(lowerCamelCase	):
									lowerCamelCase_  :    Union[str, Any]   	=			pipe([image, image]					,			prompt=[prompt, prompt]	)



					@slow
					@require_torch
					def      __a    (		self    :    int	):
							lowerCamelCase_  :    Optional[int]   	=			pipeline('image-to-text'					,			model='microsoft/git-base-coco'	)
							lowerCamelCase_  :    int   	=			'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
							lowerCamelCase_  :    Optional[Any]   	=			Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase	).raw	)

							lowerCamelCase_  :    Optional[int]   	=			'a photo of a'

							lowerCamelCase_  :    Optional[int]   	=			pipe(lowerCamelCase					,			prompt=lowerCamelCase	)
							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'a photo of a tent with a tent and a tent in the background.'}]	)

							with self.assertRaises(lowerCamelCase	):
									lowerCamelCase_  :    List[Any]   	=			pipe([image, image]					,			prompt=[prompt, prompt]	)



					@unittest.skipIf(
					    not is_torch_greater_or_equal_than_1_11					,			reason='`Pix2StructImageProcessor` requires `torch>=1.11.0`.'	)
					@slow
					@require_torch
					def      __a    (		self    :    Any	):
							lowerCamelCase_  :    Union[str, Any]   	=			pipeline('image-to-text'					,			model='google/pix2struct-ai2d-base'	)
							lowerCamelCase_  :    List[str]   	=			'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'
							lowerCamelCase_  :    Tuple   	=			Image.open(requests.get(lowerCamelCase					,			stream=lowerCamelCase	).raw	)

							lowerCamelCase_  :    str   	=			'What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud'

							lowerCamelCase_  :    Union[str, Any]   	=			pipe(lowerCamelCase					,			prompt=lowerCamelCase	)
							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'ash cloud'}]	)

							with self.assertRaises(lowerCamelCase	):
									lowerCamelCase_  :    Optional[Any]   	=			pipe([image, image]					,			prompt=[prompt, prompt]	)





					@slow
					@require_tf
					def      __a    (		self    :    Dict	):
							lowerCamelCase_  :    List[str]   	=			pipeline('image-to-text'					,			model='ydshieh/vit-gpt2-coco-en'					,			framework='tf'	)
							lowerCamelCase_  :    Dict   	=			'./tests/fixtures/tests_samples/COCO/000000039769.png'

							lowerCamelCase_  :    Optional[Any]   	=			pipe(lowerCamelCase	)
							self.assertEqual(lowerCamelCase					,			[{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}]	)

							lowerCamelCase_  :    List[Any]   	=			pipe([image, image]	)
							self.assertEqual(
							    lowerCamelCase					,			[
							        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
							        [{'generated_text': 'a cat laying on a blanket next to a cat laying on a bed '}],
							    ]					,			)
