






import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import BridgeTowerImageProcessor


class      __SCREAMING_SNAKE_CASE(		unittest.TestCase		):
							def __init__(   self:					Any				,		UpperCamelCase:					List[Any]				,		UpperCamelCase:					Dict = True				,		UpperCamelCase:					Optional[Any] = None				,		UpperCamelCase:					List[Any] = 32				,		UpperCamelCase:					Optional[Any] = True				,		UpperCamelCase:					Any = 1 / 2_55				,		UpperCamelCase:					Dict = True				,		UpperCamelCase:					Union[str, Any] = True				,		UpperCamelCase:					int = [0.48_145_466, 0.4_578_275, 0.40_821_073]				,		UpperCamelCase:					Optional[Any] = [0.26_862_954, 0.26_130_258, 0.27_577_711]				,		UpperCamelCase:					int = True				,		UpperCamelCase:					Optional[int]=7				,		UpperCamelCase:					str=30				,		UpperCamelCase:					Tuple=4_00				,		UpperCamelCase:					Tuple=3				,		)						->						Tuple:
											snake_case__													=   parent
											snake_case__													=   do_resize
											snake_case__													=   size if size is not None else {"shortest_edge": 2_88}
											snake_case__													=   size_divisor
											snake_case__													=   do_rescale
											snake_case__													=   rescale_factor
											snake_case__													=   do_normalize
											snake_case__													=   do_center_crop
											snake_case__													=   image_mean
											snake_case__													=   image_std
											snake_case__													=   do_pad
											snake_case__													=   batch_size
											snake_case__													=   num_channels
											snake_case__													=   min_resolution
											snake_case__													=   max_resolution
							def lowerCAmelCase_       (   self:					int  )						->						Optional[int]:
											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "size_divisor": self.size_divisor,
											}


							def lowerCAmelCase_       (   self:					Dict				,		UpperCamelCase:					Any				,		UpperCamelCase:					Optional[Any]=False  )						->						int:
											if not batched:
															snake_case__													=   self.size["shortest_edge"]
															snake_case__													=   image_inputs[0]
															if isinstance(_a				,		Image.Image  ):
																			snake_case__													=   image.size
															else:
																			snake_case__													=   image.shape[1], image.shape[2]
															snake_case__													=   size / min(_a				,		_a  )
															if h < w:
																			snake_case__													=   size, scale * w
															else:
																			snake_case__													=   scale * h, size

															snake_case__													=   int((13_33 / 8_00) * size  )
															if max(_a				,		_a  ) > max_size:
																			snake_case__													=   max_size / max(_a				,		_a  )
																			snake_case__													=   newh * scale
																			snake_case__													=   neww * scale

															snake_case__													=   int(newh + 0.5  ), int(neww + 0.5  )
															snake_case__													=   (
															    newh // self.size_divisor * self.size_divisor,
															    neww // self.size_divisor * self.size_divisor,
															)

											else:
															snake_case__													=   []
															for image in image_inputs:
																			snake_case__													=   self.get_expected_values([image]  )
																			expected_values.append((expected_height, expected_width)  )
															snake_case__													=   max(_a				,		key=lambda UpperCamelCase  : item[0]  )[0]
															snake_case__													=   max(_a				,		key=lambda UpperCamelCase  : item[1]  )[1]

											return expected_height, expected_width


@require_torch
@require_vision
class      __SCREAMING_SNAKE_CASE(		a_			,     unittest.TestCase		):
							_UpperCAmelCase       			=				BridgeTowerImageProcessor if is_vision_available() else None
							def lowerCAmelCase_       (   self:					int  )						->						List[Any]:
											snake_case__													=   BridgeTowerImageProcessingTester(self  )
							@property
							def lowerCAmelCase_       (   self:					List[Any]  )						->						Union[str, Any]:
											return self.image_processor_tester.prepare_image_processor_dict()
							def lowerCAmelCase_       (   self:					List[Any]  )						->						List[str]:
											snake_case__													=   self.image_processing_class(**self.image_processor_dict  )
											self.assertTrue(hasattr(_a				,		'image_mean'  )  )
											self.assertTrue(hasattr(_a				,		'image_std'  )  )
											self.assertTrue(hasattr(_a				,		'do_normalize'  )  )
											self.assertTrue(hasattr(_a				,		'do_resize'  )  )
											self.assertTrue(hasattr(_a				,		'size'  )  )
											self.assertTrue(hasattr(_a				,		'size_divisor'  )  )
							def lowerCAmelCase_       (   self:					List[Any]  )						->						Any:
											pass
							def lowerCAmelCase_       (   self:					Optional[Any]  )						->						Optional[int]:
											# Initialize image processor
											snake_case__													=   self.image_processing_class(**self.image_processor_dict  )
											# create random PIL images
											snake_case__													=   prepare_image_inputs(self.image_processor_tester				,		equal_resolution=_a  )
											for image in image_inputs:
															self.assertIsInstance(_a				,		Image.Image  )

											# Test not batched input
											snake_case__													=   image_processing(image_inputs[0]				,		return_tensors='pt'  ).pixel_values

											snake_case__													=   self.image_processor_tester.get_expected_values(_a  )
											self.assertEqual(
											    encoded_images.shape				,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,		)

											# Test batched
											snake_case__													=   image_processing(_a				,		return_tensors='pt'  ).pixel_values

											snake_case__													=   self.image_processor_tester.get_expected_values(_a				,		batched=_a  )
											self.assertEqual(
											    encoded_images.shape				,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )				,		)
							def lowerCAmelCase_       (   self:					Optional[int]  )						->						List[Any]:
											# Initialize image processor
											snake_case__													=   self.image_processing_class(**self.image_processor_dict  )
											# create random numpy tensors
											snake_case__													=   prepare_image_inputs(self.image_processor_tester				,		equal_resolution=_a				,		numpify=_a  )
											for image in image_inputs:
															self.assertIsInstance(_a				,		np.ndarray  )

											# Test not batched input
											snake_case__													=   image_processing(image_inputs[0]				,		return_tensors='pt'  ).pixel_values

											snake_case__													=   self.image_processor_tester.get_expected_values(_a  )
											self.assertEqual(
											    encoded_images.shape				,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,		)

											# Test batched
											snake_case__													=   image_processing(_a				,		return_tensors='pt'  ).pixel_values

											snake_case__													=   self.image_processor_tester.get_expected_values(_a				,		batched=_a  )
											self.assertEqual(
											    encoded_images.shape				,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )				,		)


							def lowerCAmelCase_       (   self:					List[str]  )						->						Tuple:
											# Initialize image processor
											snake_case__													=   self.image_processing_class(**self.image_processor_dict  )
											# create random PyTorch tensors
											snake_case__													=   prepare_image_inputs(self.image_processor_tester				,		equal_resolution=_a				,		torchify=_a  )
											for image in image_inputs:
															self.assertIsInstance(_a				,		torch.Tensor  )

											# Test not batched input
											snake_case__													=   image_processing(image_inputs[0]				,		return_tensors='pt'  ).pixel_values

											snake_case__													=   self.image_processor_tester.get_expected_values(_a  )
											self.assertEqual(
											    encoded_images.shape				,		(1, self.image_processor_tester.num_channels, expected_height, expected_width)				,		)

											# Test batched
											snake_case__													=   image_processing(_a				,		return_tensors='pt'  ).pixel_values

											snake_case__													=   self.image_processor_tester.get_expected_values(_a				,		batched=_a  )
											self.assertEqual(
											    encoded_images.shape				,		(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )				,		)






"""simple docstring"""




import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


_A			:       int   		=       """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.

In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]

On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""

class 		a__						(      unittest.TestCase,			a_						):
   def 			__magic_name__			(			self				):
          lowercase    :				Tuple       		=							load_tool("text-question-answering"				)
          self.tool.setup()
          lowercase    :				Dict       		=							load_tool("text-question-answering"	,    remote=_a				)
   def 			__magic_name__			(			self				):
          lowercase    :				str       		=							self.tool(_a	,    "What did Hugging Face do in April 2021?"				)
          self.assertEqual(_a	,    "launched the BigScience Research Workshop"				)
   def 			__magic_name__			(			self				):
          lowercase    :				Union[str, Any]       		=							self.remote_tool(_a	,    "What did Hugging Face do in April 2021?"				)
          self.assertEqual(_a	,    "launched the BigScience Research Workshop"				)
   def 			__magic_name__			(			self				):
          lowercase    :				int       		=							self.tool(text=_a	,    question="What did Hugging Face do in April 2021?"				)
          self.assertEqual(_a	,    "launched the BigScience Research Workshop"				)

   def 			__magic_name__			(			self				):
          lowercase    :				Optional[Any]       		=							self.remote_tool(text=_a	,    question="What did Hugging Face do in April 2021?"				)
          self.assertEqual(_a	,    "launched the BigScience Research Workshop"				)


