
import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
		from PIL import Image


class 		__magic_name__   (						unittest.TestCase,				snake_case    ):





		def       _A							(							self    :	Optional[Any]				):
							lowerCAmelCase		:					Dict  	=     load_tool('''image-question-answering'''				)
							self.tool.setup()
							lowerCAmelCase		:					List[str]  	=     load_tool('''image-question-answering'''						,  remote=lowerCamelCase__				)





		def       _A							(							self    :	Tuple				):
							lowerCAmelCase		:					Any  	=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''				)
							lowerCAmelCase		:					Optional[Any]  	=     self.tool(lowerCamelCase__						,  '''How many cats are sleeping on the couch?'''				)
							self.assertEqual(lowerCamelCase__						,  '''2'''				)





		def       _A							(							self    :	Dict				):
							lowerCAmelCase		:					Tuple  	=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''				)
							lowerCAmelCase		:					str  	=     self.remote_tool(lowerCamelCase__						,  '''How many cats are sleeping on the couch?'''				)
							self.assertEqual(lowerCamelCase__						,  '''2'''				)





		def       _A							(							self    :	int				):
							lowerCAmelCase		:					Dict  	=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''				)
							lowerCAmelCase		:					List[Any]  	=     self.tool(image=lowerCamelCase__						,  question='''How many cats are sleeping on the couch?'''				)
							self.assertEqual(lowerCamelCase__						,  '''2'''				)





		def       _A							(							self    :	Dict				):
							lowerCAmelCase		:					int  	=     Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''				)				) / '''000000039769.png'''				)
							lowerCAmelCase		:					Union[str, Any]  	=     self.remote_tool(image=lowerCamelCase__						,  question='''How many cats are sleeping on the couch?'''				)
							self.assertEqual(lowerCamelCase__						,  '''2'''				)


from ctypes import c_float, sizeof
from enum import Enum
from typing import TYPE_CHECKING, Optional, Union


if TYPE_CHECKING:
		from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore


class 		__magic_name__   (						snake_case    ):
		_lowerCAmelCase		=	c_float





		@property
		def       _A							(							self    :	int				):
							return sizeof(self.value				)



def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     int							,  __magic_name__						:     int = 0  ):


					'''simple docstring'''

					if dimension <= 0:
										lowerCAmelCase		:					Tuple  	=     fixed_dimension

					dimension -= num_token_to_add
					return dimension
def     UpperCAmelCase__							(       __magic_name__						:     int							,  __magic_name__						:     ParameterFormat  ):


					'''simple docstring'''

					return num_parameters * dtype.size
def     UpperCAmelCase__							(       __magic_name__						:     str  ):


					'''simple docstring'''

					from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer  # tests_ignore

					try:
										return AutoProcessor.from_pretrained(__magic_name__  )
					except (ValueError, OSError, KeyError):
										lowerCAmelCase	,							lowerCAmelCase		:					Any  	=     None, None
										try:
															lowerCAmelCase		:					Union[str, Any]  	=     AutoTokenizer.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass
										try:
															lowerCAmelCase		:					List[Any]  	=     AutoFeatureExtractor.from_pretrained(__magic_name__  )
										except (OSError, KeyError):
															pass

										if tokenizer is not None and feature_extractor is not None:
															raise ValueError(
															    f'''Couldn\'t auto-detect preprocessor for {model_name}. Found both a tokenizer and a feature extractor.'''  )
										elif tokenizer is None and feature_extractor is None:
															return None
										elif tokenizer is not None:
															return tokenizer
										else:
															return feature_extractor
