
'''simple docstring'''

import unittest

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 DeiTImageProcessor






class 				A       (		unittest.TestCase      ):





	def __init__(   self     :    Union[str, Any] ,							__magic_name__     :    int ,							__magic_name__     :    Union[str, Any]=7 ,							__magic_name__     :    List[str]=3 ,							__magic_name__     :    int=18 ,							__magic_name__     :    Tuple=30 ,							__magic_name__     :    Union[str, Any]=400 ,							__magic_name__     :    Any=True ,							__magic_name__     :    Union[str, Any]=None ,							__magic_name__     :    Optional[Any]=True ,							__magic_name__     :    List[str]=None ,							__magic_name__     :    Dict=True ,							__magic_name__     :    int=[0.5, 0.5, 0.5] ,							__magic_name__     :    int=[0.5, 0.5, 0.5] ,							):


						"""simple docstring"""

						lowerCAmelCase__									=							size if size is not None else {"height": 20, "width": 20}
						lowerCAmelCase__									=							crop_size if crop_size is not None else {"height": 18, "width": 18}

						lowerCAmelCase__									=							parent
						lowerCAmelCase__									=							batch_size
						lowerCAmelCase__									=							num_channels
						lowerCAmelCase__									=							image_size
						lowerCAmelCase__									=							min_resolution
						lowerCAmelCase__									=							max_resolution
						lowerCAmelCase__									=							do_resize
						lowerCAmelCase__									=							size
						lowerCAmelCase__									=							do_center_crop
						lowerCAmelCase__									=							crop_size
						lowerCAmelCase__									=							do_normalize
						lowerCAmelCase__									=							image_mean
						lowerCAmelCase__									=							image_std







	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


						"""simple docstring"""

						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						}






@require_torch
@require_vision
class 				A       (		SCREAMING_SNAKE_CASE__					, unittest.TestCase      ):
	snake_case__ :Tuple				       =       DeiTImageProcessor if is_vision_available() else None
	snake_case__ :int				       =       True





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str							):


						"""simple docstring"""

						lowerCAmelCase__									=							DeiTImageProcessingTester(self							)





	@property
	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


						"""simple docstring"""

						return self.image_processor_tester.prepare_image_processor_dict()





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str							):


						"""simple docstring"""

						lowerCAmelCase__									=							self.image_processing_class(**self.image_processor_dict							)
						self.assertTrue(hasattr(__magic_name__ ,							"do_resize"							)							)
						self.assertTrue(hasattr(__magic_name__ ,							"size"							)							)
						self.assertTrue(hasattr(__magic_name__ ,							"do_center_crop"							)							)
						self.assertTrue(hasattr(__magic_name__ ,							"center_crop"							)							)
						self.assertTrue(hasattr(__magic_name__ ,							"do_normalize"							)							)
						self.assertTrue(hasattr(__magic_name__ ,							"image_mean"							)							)
						self.assertTrue(hasattr(__magic_name__ ,							"image_std"							)							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


						"""simple docstring"""

						lowerCAmelCase__									=							self.image_processing_class.from_dict(self.image_processor_dict							)
						self.assertEqual(image_processor.size ,							{"height": 20, "width": 20}							)
						self.assertEqual(image_processor.crop_size ,							{"height": 18, "width": 18}							)

						lowerCAmelCase__									=							self.image_processing_class.from_dict(self.image_processor_dict ,							size=42 ,							crop_size=84							)
						self.assertEqual(image_processor.size ,							{"height": 42, "width": 42}							)
						self.assertEqual(image_processor.crop_size ,							{"height": 84, "width": 84}							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


						"""simple docstring"""

						pass





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str							):


						"""simple docstring"""

						lowerCAmelCase__									=							self.image_processing_class(**self.image_processor_dict							)
						# create random PIL images
						lowerCAmelCase__									=							prepare_image_inputs(self.image_processor_tester ,							equal_resolution=__magic_name__							)
						for image in image_inputs:
											self.assertIsInstance(__magic_name__ ,							Image.Image							)

						# Test not batched input
						lowerCAmelCase__									=							image_processing(image_inputs[0] ,							return_tensors="pt"							).pixel_values
						self.assertEqual(
						    encoded_images.shape ,							(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    ) ,							)

						# Test batched
						lowerCAmelCase__									=							image_processing(__magic_name__ ,							return_tensors="pt"							).pixel_values
						self.assertEqual(
						    encoded_images.shape ,							(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    ) ,							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[str]							):


						"""simple docstring"""

						lowerCAmelCase__									=							self.image_processing_class(**self.image_processor_dict							)
						# create random numpy tensors
						lowerCAmelCase__									=							prepare_image_inputs(self.image_processor_tester ,							equal_resolution=__magic_name__ ,							numpify=__magic_name__							)
						for image in image_inputs:
											self.assertIsInstance(__magic_name__ ,							np.ndarray							)

						# Test not batched input
						lowerCAmelCase__									=							image_processing(image_inputs[0] ,							return_tensors="pt"							).pixel_values
						self.assertEqual(
						    encoded_images.shape ,							(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    ) ,							)

						# Test batched
						lowerCAmelCase__									=							image_processing(__magic_name__ ,							return_tensors="pt"							).pixel_values
						self.assertEqual(
						    encoded_images.shape ,							(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    ) ,							)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    Dict							):


						"""simple docstring"""

						lowerCAmelCase__									=							self.image_processing_class(**self.image_processor_dict							)
						# create random PyTorch tensors
						lowerCAmelCase__									=							prepare_image_inputs(self.image_processor_tester ,							equal_resolution=__magic_name__ ,							torchify=__magic_name__							)
						for image in image_inputs:
											self.assertIsInstance(__magic_name__ ,							torch.Tensor							)

						# Test not batched input
						lowerCAmelCase__									=							image_processing(image_inputs[0] ,							return_tensors="pt"							).pixel_values
						self.assertEqual(
						    encoded_images.shape ,							(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    ) ,							)

						# Test batched
						lowerCAmelCase__									=							image_processing(__magic_name__ ,							return_tensors="pt"							).pixel_values
						self.assertEqual(
						    encoded_images.shape ,							(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size["height"],
						        self.image_processor_tester.crop_size["width"],
						    ) ,							)







'''simple docstring'''

import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types






class 				A       (		unittest.TestCase					, SCREAMING_SNAKE_CASE__      ):





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							load_tool("translation"							)
				self.tool.setup()
				lowerCAmelCase__									=							load_tool("translation" ,							remote=__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Tuple							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.tool("Hey, what's up?" ,							src_lang="English" ,							tgt_lang="French"							)
				self.assertEqual(__magic_name__ ,							"- Hé, comment ça va?"							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    Optional[int]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.remote_tool("Hey, what's up?" ,							src_lang="English" ,							tgt_lang="French"							)
				self.assertEqual(__magic_name__ ,							"- Hé, comment ça va?"							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    List[Any]							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.tool(text="Hey, what's up?" ,							src_lang="English" ,							tgt_lang="French"							)
				self.assertEqual(__magic_name__ ,							"- Hé, comment ça va?"							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				lowerCAmelCase__									=							self.remote_tool(text="Hey, what's up?" ,							src_lang="English" ,							tgt_lang="French"							)
				self.assertEqual(__magic_name__ ,							"- Hé, comment ça va?"							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				lowerCAmelCase__									=							["Hey, what's up?", "English", "Spanish"]
				lowerCAmelCase__									=							self.tool(*__magic_name__							)

				# There is a single output
				if len(self.tool.outputs							) == 1:
							lowerCAmelCase__									=							[outputs]

				self.assertListEqual(output_types(__magic_name__							) ,							self.tool.outputs							)





	def 							__SCREAMING_SNAKE_CASE					(   self     :    str							):


				"""simple docstring"""

				lowerCAmelCase__									=							["Hey, what's up?", "English", "Spanish"]
				lowerCAmelCase__									=							self.tool(*__magic_name__							)

				if not isinstance(__magic_name__ ,							__magic_name__							):
							lowerCAmelCase__									=							[outputs]

				self.assertEqual(len(__magic_name__							) ,							len(self.tool.outputs							)							)

				for output, output_type in zip(__magic_name__ ,							self.tool.outputs							):
							lowerCAmelCase__									=							AGENT_TYPE_MAPPING[output_type]
							self.assertTrue(isinstance(__magic_name__ ,							__magic_name__							)							)







	def 							__SCREAMING_SNAKE_CASE					(   self     :    int							):


				"""simple docstring"""

				lowerCAmelCase__									=							["Hey, what's up?", "English", "Spanish"]

				lowerCAmelCase__									=							[]

				for _input, input_type in zip(__magic_name__ ,							self.tool.inputs							):
							if isinstance(__magic_name__ ,							__magic_name__							):
										_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input							) for _input_type in input_type]							)
							else:
										_inputs.append(AGENT_TYPE_MAPPING[input_type](_input							)							)

        # Should not raise an error
				lowerCAmelCase__									=							self.tool(*__magic_name__							)

				if not isinstance(__magic_name__ ,							__magic_name__							):
							lowerCAmelCase__									=							[outputs]

				self.assertEqual(len(__magic_name__							) ,							len(self.tool.outputs							)							)





