code: 
  
  
  
  
  
  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()
  
style_context: 
  
  
  
  
  
  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 '}],
  							    ]					,			)
  