



'''simple docstring'''







from __future__ import annotations

from typing import Generic, TypeVar

_UpperCamelCase  						=			TypeVar("""T""")

class      lowerCamelCase__     (     _UpperCAmelCase				):






	'''simple docstring'''

	pass





class      lowerCamelCase__     (     _UpperCAmelCase				):






	'''simple docstring'''

	pass





class      lowerCamelCase__     (     Generic[T]				):






	'''simple docstring'''

	def __init__(  self		:  Any   ,		__A		:  str = 10					)     ->			Optional[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   []
								lowerCAmelCase__  =   limit




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




								'''simple docstring'''





								return bool(self.stack					)




	def __str__(  self		:  int					)     ->			str:




								'''simple docstring'''





								return str(self.stack					)




	def      lowercase__							(  self		:  List[Any]   ,		__A		:  List[str]					)     ->			Dict:




								'''simple docstring'''





								if len(self.stack					) >= self.limit:
															raise StackOverflowError
								self.stack.append(snake_case_					)




	def      lowercase__							(  self		:  int					)     ->			Dict:




								'''simple docstring'''





								if not self.stack:
															raise StackUnderflowError
								return self.stack.pop()




	def      lowercase__							(  self		:  List[Any]					)     ->			List[str]:




								'''simple docstring'''





								if not self.stack:
															raise StackUnderflowError
								return self.stack[-1]




	def      lowercase__							(  self		:  Dict					)     ->			List[Any]:




								'''simple docstring'''





								return not bool(self.stack					)




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




								'''simple docstring'''





								return self.size() == self.limit




	def      lowercase__							(  self		:  str					)     ->			Optional[int]:




								'''simple docstring'''





								return len(self.stack					)




	def __contains__(  self		:  Optional[int]   ,		__A		:  Union[str, Any]					)     ->			Tuple:




								'''simple docstring'''





								return item in self.stack





def 			_lowerCAmelCase(      ) ->							None:
							lowerCAmelCase__  =   Stack(10						)
							assert bool(_lowerCAmelCase						) is False
							assert stack.is_empty() is True
							assert stack.is_full() is False
							assert str(_lowerCAmelCase						) == "[]"

							try:
														lowerCAmelCase__  =   stack.pop()
														raise AssertionError  # This should not happen
							except StackUnderflowError:
														assert True  # This should happen

							try:
														lowerCAmelCase__  =   stack.peek()
														raise AssertionError  # This should not happen
							except StackUnderflowError:
														assert True  # This should happen

							for i in range(10						):
														assert stack.size() == i
														stack.push(_lowerCAmelCase						)

							assert bool(_lowerCAmelCase						)
							assert not stack.is_empty()
							assert stack.is_full()
							assert str(_lowerCAmelCase						) == str(list(range(10						)						)						)
							assert stack.pop() == 9
							assert stack.peek() == 8

							stack.push(100						)
							assert str(_lowerCAmelCase						) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]						)

							try:
														stack.push(200						)
														raise AssertionError  # This should not happen
							except StackOverflowError:
														assert True  # This should happen

							assert not stack.is_empty()
							assert stack.size() == 10

							assert 5 in stack
							assert 55 not in stack


if __name__ == "__main__":
		test_stack()






'''simple docstring'''

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:
						A_	   = False


if is_vision_available():
						from PIL import Image
else:




						class 		SCREAMING_SNAKE_CASE_					:



									"""simple docstring"""






									@staticmethod
									def   lowerCamelCase (							*snake_case_				,			**snake_case_					):
															pass




@is_pipeline_test
@require_vision
class 		SCREAMING_SNAKE_CASE_					(unittest.TestCase      ):



			"""simple docstring"""






			__magic_name__ :				Optional[int]   							=       MODEL_FOR_VISION_2_SEQ_MAPPING
			__magic_name__ :				Optional[int]   							=       TF_MODEL_FOR_VISION_2_SEQ_MAPPING



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       str				=    pipeline("image-to-text"				,			model=snake_case_				,			tokenizer=snake_case_				,			image_processor=snake_case_					)
									snake_case :       List[Any]				=    [
									    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					),
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]
									return pipe, examples



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_					):
									snake_case :       Union[str, Any]				=    pipe(snake_case_					)
									self.assertEqual(
									    snake_case_				,			[
									        [{"generated_text": ANY(snake_case_					)}],
									        [{"generated_text": ANY(snake_case_					)}],
									    ]				,			)



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

									snake_case :       Dict				=    pipe(snake_case_					)
									self.assertEqual(
									    snake_case_				,			[
									        {
									            "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									        },
									    ]				,			)

									snake_case :       Tuple				=    pipe([image, image]					)
									self.assertEqual(
									    snake_case_				,			[
									        [
									            {
									                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									            }
									        ],
									        [
									            {
									                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									            }
									        ],
									    ]				,			)

									snake_case :       List[Any]				=    pipe(snake_case_				,			max_new_tokens=1					)
									self.assertEqual(
									    snake_case_				,			[{"generated_text": "growth"}]				,			)



			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       List[Any]				=    pipeline("image-to-text"				,			model="hf-internal-testing/tiny-random-vit-gpt2"					)
									snake_case :       Tuple				=    "./tests/fixtures/tests_samples/COCO/000000039769.png"

									snake_case :       Any				=    pipe(snake_case_					)
									self.assertEqual(
									    snake_case_				,			[
									        {
									            "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									        },
									    ]				,			)

									snake_case :       str				=    pipe([image, image]					)
									self.assertEqual(
									    snake_case_				,			[
									        [
									            {
									                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									            }
									        ],
									        [
									            {
									                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									            }
									        ],
									    ]				,			)



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

									snake_case :       List[str]				=    pipe(snake_case_				,			prompt=snake_case_					)
									self.assertTrue(outputs[0]["generated_text"].startswith(snake_case_					)					)



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

									snake_case :       Tuple				=    pipe(snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}]					)

									snake_case :       Tuple				=    pipe([image, image]					)
									self.assertEqual(
									    snake_case_				,			[
									        [{"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   lowerCamelCase (							self					):
									snake_case :       Optional[Any]				=    pipeline("image-to-text"				,			model="Salesforce/blip-image-captioning-base"					)
									snake_case :       Any				=    "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
									snake_case :       Union[str, Any]				=    Image.open(requests.get(snake_case_				,			stream=snake_case_					).raw					)

									snake_case :       Optional[Any]				=    pipe(snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a pink pokemon pokemon with a blue shirt and a blue shirt"}]					)



			@slow
			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       Tuple				=    pipeline("image-to-text"				,			model="microsoft/git-base-coco"					)
									snake_case :       Tuple				=    "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
									snake_case :       Tuple				=    Image.open(requests.get(snake_case_				,			stream=snake_case_					).raw					)

									snake_case :       Any				=    pipe(snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a cartoon of a purple character."}]					)



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

									snake_case :       str				=    "a photography of"

									snake_case :       int				=    pipe(snake_case_				,			prompt=snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a photography of a volcano"}]					)

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



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

									snake_case :       Any				=    "a photo of a"

									snake_case :       Dict				=    pipe(snake_case_				,			prompt=snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a photo of a tent with a tent and a tent in the background."}]					)

									with self.assertRaises(snake_case_					):
															snake_case :       List[str]				=    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   lowerCamelCase (							self					):
									snake_case :       List[str]				=    pipeline("image-to-text"				,			model="google/pix2struct-ai2d-base"					)
									snake_case :       Optional[int]				=    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
									snake_case :       str				=    Image.open(requests.get(snake_case_				,			stream=snake_case_					).raw					)

									snake_case :       Any				=    "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"

									snake_case :       str				=    pipe(snake_case_				,			prompt=snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "ash cloud"}]					)

									with self.assertRaises(snake_case_					):
															snake_case :       List[str]				=    pipe([image, image]				,			prompt=[prompt, prompt]					)






			@slow
			@require_tf
			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    pipeline("image-to-text"				,			model="ydshieh/vit-gpt2-coco-en"				,			framework="tf"					)
									snake_case :       Union[str, Any]				=    "./tests/fixtures/tests_samples/COCO/000000039769.png"

									snake_case :       Any				=    pipe(snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}]					)

									snake_case :       Optional[int]				=    pipe([image, image]					)
									self.assertEqual(
									    snake_case_				,			[
									        [{"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 "}],
									    ]				,			)





