





"""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:
						UpperCamelCase_     =False


if is_vision_available():
						from PIL import Image
else:




						class 				_a		:






										@staticmethod
										def        snake_case			(				*lowerCAmelCase__	:					Any,				**lowerCAmelCase__	:					str							)			->   str:




																'''simple docstring'''



																pass




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






				def        snake_case			(				self	:					List[str],				lowerCAmelCase__	:					Any,				lowerCAmelCase__	:					Any,				lowerCAmelCase__	:					List[str]							)			->   str:




										'''simple docstring'''



										_UpperCamelCase    :      Dict						     =					pipeline('''image-to-text''',				model=lowerCAmelCase__,				tokenizer=lowerCAmelCase__,				image_processor=lowerCAmelCase__							)
										_UpperCamelCase    :      Optional[int]						     =					[
										    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							),
										    '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
										]
										return pipe, examples






				def        snake_case			(				self	:					Tuple,				lowerCAmelCase__	:					Union[str, Any],				lowerCAmelCase__	:					str							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Any						     =					pipe(lowerCAmelCase__							)
										self.assertEqual(
										    lowerCAmelCase__,				[
										        [{'''generated_text''': ANY(lowerCAmelCase__							)}],
										        [{'''generated_text''': ANY(lowerCAmelCase__							)}],
										    ],				)






				@require_tf
				def        snake_case			(				self	:					Tuple							)			->   Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[int]						     =					pipeline('''image-to-text''',				model='''hf-internal-testing/tiny-random-vit-gpt2''',				framework='''tf'''							)
										_UpperCamelCase    :      Dict						     =					'''./tests/fixtures/tests_samples/COCO/000000039769.png'''

										_UpperCamelCase    :      Union[str, Any]						     =					pipe(lowerCAmelCase__							)
										self.assertEqual(
										    lowerCAmelCase__,				[
										        {
										            '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
										        },
										    ],				)

										_UpperCamelCase    :      Dict						     =					pipe([image, image]							)
										self.assertEqual(
										    lowerCAmelCase__,				[
										        [
										            {
										                '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
										            }
										        ],
										        [
										            {
										                '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
										            }
										        ],
										    ],				)

										_UpperCamelCase    :      Optional[Any]						     =					pipe(lowerCAmelCase__,				max_new_tokens=1							)
										self.assertEqual(
										    lowerCAmelCase__,				[{'''generated_text''': '''growth'''}],				)






				@require_torch
				def        snake_case			(				self	:					Optional[Any]							)			->   Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase    :      str						     =					pipeline('''image-to-text''',				model='''hf-internal-testing/tiny-random-vit-gpt2'''							)
										_UpperCamelCase    :      Dict						     =					'''./tests/fixtures/tests_samples/COCO/000000039769.png'''

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

										_UpperCamelCase    :      Dict						     =					pipe([image, image]							)
										self.assertEqual(
										    lowerCAmelCase__,				[
										        [
										            {
										                '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
										            }
										        ],
										        [
										            {
										                '''generated_text''': '''growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO'''
										            }
										        ],
										    ],				)






				@require_torch
				def        snake_case			(				self	:					Any							)			->   List[str]:




										'''simple docstring'''



										_UpperCamelCase    :      Any						     =					pipeline('''image-to-text''',				model='''hf-internal-testing/tiny-random-BlipForConditionalGeneration'''							)
										_UpperCamelCase    :      Optional[Any]						     =					'''./tests/fixtures/tests_samples/COCO/000000039769.png'''
										_UpperCamelCase    :      List[Any]						     =					'''a photo of'''

										_UpperCamelCase    :      Any						     =					pipe(lowerCAmelCase__,				prompt=lowerCAmelCase__							)
										self.assertTrue(outputs[0]['''generated_text'''].startswith(lowerCAmelCase__							)							)






				@slow
				@require_torch
				def        snake_case			(				self	:					Tuple							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      Dict						     =					pipeline('''image-to-text''',				model='''ydshieh/vit-gpt2-coco-en'''							)
										_UpperCamelCase    :      Dict						     =					'''./tests/fixtures/tests_samples/COCO/000000039769.png'''

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

										_UpperCamelCase    :      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 '''}],
										    ],				)






				@slow
				@require_torch
				def        snake_case			(				self	:					int							)			->   Optional[int]:




										'''simple docstring'''



										_UpperCamelCase    :      Optional[Any]						     =					pipeline('''image-to-text''',				model='''Salesforce/blip-image-captioning-base'''							)
										_UpperCamelCase    :      str						     =					'''https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'''
										_UpperCamelCase    :      str						     =					Image.open(requests.get(lowerCAmelCase__,				stream=lowerCAmelCase__							).raw							)

										_UpperCamelCase    :      Dict						     =					pipe(lowerCAmelCase__							)
										self.assertEqual(lowerCAmelCase__,				[{'''generated_text''': '''a pink pokemon pokemon with a blue shirt and a blue shirt'''}]							)






				@slow
				@require_torch
				def        snake_case			(				self	:					int							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      str						     =					pipeline('''image-to-text''',				model='''microsoft/git-base-coco'''							)
										_UpperCamelCase    :      str						     =					'''https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png'''
										_UpperCamelCase    :      List[str]						     =					Image.open(requests.get(lowerCAmelCase__,				stream=lowerCAmelCase__							).raw							)

										_UpperCamelCase    :      Dict						     =					pipe(lowerCAmelCase__							)
										self.assertEqual(lowerCAmelCase__,				[{'''generated_text''': '''a cartoon of a purple character.'''}]							)






				@slow
				@require_torch
				def        snake_case			(				self	:					Optional[int]							)			->   Tuple:




										'''simple docstring'''



										_UpperCamelCase    :      List[str]						     =					pipeline('''image-to-text''',				model='''Salesforce/blip-image-captioning-base'''							)
										_UpperCamelCase    :      int						     =					'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
										_UpperCamelCase    :      int						     =					Image.open(requests.get(lowerCAmelCase__,				stream=lowerCAmelCase__							).raw							)

										_UpperCamelCase    :      List[str]						     =					'''a photography of'''

										_UpperCamelCase    :      List[str]						     =					pipe(lowerCAmelCase__,				prompt=lowerCAmelCase__							)
										self.assertEqual(lowerCAmelCase__,				[{'''generated_text''': '''a photography of a volcano'''}]							)

										with self.assertRaises(lowerCAmelCase__							):
																_UpperCamelCase    :      str						     =					pipe([image, image],				prompt=[prompt, prompt]							)






				@slow
				@require_torch
				def        snake_case			(				self	:					Optional[Any]							)			->   Any:




										'''simple docstring'''



										_UpperCamelCase    :      Any						     =					pipeline('''image-to-text''',				model='''microsoft/git-base-coco'''							)
										_UpperCamelCase    :      int						     =					'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
										_UpperCamelCase    :      Any						     =					Image.open(requests.get(lowerCAmelCase__,				stream=lowerCAmelCase__							).raw							)

										_UpperCamelCase    :      Dict						     =					'''a photo of a'''

										_UpperCamelCase    :      Optional[Any]						     =					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__							):
																_UpperCamelCase    :      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        snake_case			(				self	:					int							)			->   Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase    :      Dict						     =					pipeline('''image-to-text''',				model='''google/pix2struct-ai2d-base'''							)
										_UpperCamelCase    :      Any						     =					'''https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg'''
										_UpperCamelCase    :      Dict						     =					Image.open(requests.get(lowerCAmelCase__,				stream=lowerCAmelCase__							).raw							)

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

										_UpperCamelCase    :      int						     =					pipe(lowerCAmelCase__,				prompt=lowerCAmelCase__							)
										self.assertEqual(lowerCAmelCase__,				[{'''generated_text''': '''ash cloud'''}]							)

										with self.assertRaises(lowerCAmelCase__							):
																_UpperCamelCase    :      Any						     =					pipe([image, image],				prompt=[prompt, prompt]							)






				@slow
				@require_tf
				def        snake_case			(				self	:					Union[str, Any]							)			->   int:




										'''simple docstring'''



										_UpperCamelCase    :      Union[str, Any]						     =					pipeline('''image-to-text''',				model='''ydshieh/vit-gpt2-coco-en''',				framework='''tf'''							)
										_UpperCamelCase    :      int						     =					'''./tests/fixtures/tests_samples/COCO/000000039769.png'''

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

										_UpperCamelCase    :      Tuple						     =					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 '''}],
										    ],				)










"""simple docstring"""




def  a_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     ):
						return len_board * len_board_column * row + column





def  a_       (							_lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		):

						if board[row][column] != word[word_index]:
												return False

						if word_index == len(_lowercase     ) - 1:
												return True

						_UpperCamelCase    :      Union[str, Any]						     =					[(0, 1), (0, -1), (-1, 0), (1, 0)]
						_UpperCamelCase    :      List[str]						     =					len(_lowercase     )
						_UpperCamelCase    :      Optional[Any]						     =					len(board[0]     )
						for direction in traverts_directions:
												_UpperCamelCase    :      Union[str, Any]						     =					row + direction[0]
												_UpperCamelCase    :      List[Any]						     =					column + direction[1]
												if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
																		continue

												_UpperCamelCase    :      Optional[Any]						     =					get_point_key(_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     )
												if key in visited_points_set:
																		continue

												visited_points_set.add(_lowercase     )
												if exits_word(_lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		word_index + 1 ,		_lowercase     ):
																		return True

												visited_points_set.remove(_lowercase     )

						return False





def  a_       (							_lowercase ,		_lowercase     ):
						_UpperCamelCase    :      Optional[Any]						     =					(
						    '''The board should be a non empty matrix of single chars strings.'''
						)

						_UpperCamelCase    :      int						     =					len(_lowercase     )
						if not isinstance(_lowercase ,		_lowercase     ) or len(_lowercase     ) == 0:
												raise ValueError(_lowercase     )

						for row in board:
												if not isinstance(_lowercase ,		_lowercase     ) or len(_lowercase     ) == 0:
																		raise ValueError(_lowercase     )

												for item in row:
																		if not isinstance(_lowercase ,		_lowercase     ) or len(_lowercase     ) != 1:
																								raise ValueError(_lowercase     )

    # Validate word
						if not isinstance(_lowercase ,		_lowercase     ) or len(_lowercase     ) == 0:
												raise ValueError(
												    '''The word parameter should be a string of length greater than 0.'''     )

						_UpperCamelCase    :      List[str]						     =					len(board[0]     )
						for i in range(_lowercase     ):
												for j in range(_lowercase     ):
																		if exits_word(
																		    _lowercase ,		_lowercase ,		_lowercase ,		_lowercase ,		0 ,		{get_point_key(_lowercase ,		_lowercase ,		_lowercase ,		_lowercase     )}     ):
																								return True

						return False


if __name__ == "__main__":
						import doctest

						doctest.testmod()



