



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


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import CLIPImageProcessor

class 	__snake_case (				unittest.TestCase):







							'''simple docstring'''






							def __init__(							self					,					a_					,					a_=7					,					a_=3					,					a_=18					,					a_=30					,					a_=400					,					a_=True					,					a_=None					,					a_=True					,					a_=None					,					a_=True					,					a_=[0.48_145_466, 0.4_578_275, 0.40_821_073]					,					a_=[0.26_862_954, 0.26_130_258, 0.27_577_711]					,					a_=True					,					):
											a__			 =						size if size is not None else {"""shortest_edge""": 20}
											a__			 =						crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
											a__			 =						parent
											a__			 =						batch_size
											a__			 =						num_channels
											a__			 =						image_size
											a__			 =						min_resolution
											a__			 =						max_resolution
											a__			 =						do_resize
											a__			 =						size
											a__			 =						do_center_crop
											a__			 =						crop_size
											a__			 =						do_normalize
											a__			 =						image_mean
											a__			 =						image_std
											a__			 =						do_convert_rgb


							def _a (							self						):
											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,
											    "do_convert_rgb": self.do_convert_rgb,
											}







							def _a (							self					,					a_=False					,					a_=False					,					a_=False						):
											assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

											if equal_resolution:
															a__			 =						[]
															for i in range(self.batch_size						):
																			image_inputs.append(
																			    np.random.randint(
																			        255					,					size=(self.num_channels, self.max_resolution, self.max_resolution)					,					dtype=np.uinta						)						)
											else:
															a__			 =						[]
															for i in range(self.batch_size						):
																			a__ ,     a__			 =						np.random.choice(np.arange(self.min_resolution					,					self.max_resolution						)					,					2						)
																			image_inputs.append(np.random.randint(255					,					size=(self.num_channels, width, height)					,					dtype=np.uinta						)						)

											if not numpify and not torchify:
															# PIL expects the channel dimension as last dimension
															a__			 =						[Image.fromarray(np.moveaxis(a_					,					0					,					-1						)						) for x in image_inputs]

											if torchify:
															a__			 =						[torch.from_numpy(a_						) for x in image_inputs]

											return image_inputs




@require_torch
@require_vision
class 	__snake_case (				SCREAMING_SNAKE_CASE     ,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__			: List[Any]             =   CLIPImageProcessor if is_vision_available() else None


							def _a (							self						):
											a__			 =						CLIPImageProcessingTester(self						)


							@property
							def _a (							self						):
											return self.image_processor_tester.prepare_image_processor_dict()


							def _a (							self						):
											a__			 =						self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(a_					,					"""do_resize"""						)						)
											self.assertTrue(hasattr(a_					,					"""size"""						)						)
											self.assertTrue(hasattr(a_					,					"""do_center_crop"""						)						)
											self.assertTrue(hasattr(a_					,					"""center_crop"""						)						)
											self.assertTrue(hasattr(a_					,					"""do_normalize"""						)						)
											self.assertTrue(hasattr(a_					,					"""image_mean"""						)						)
											self.assertTrue(hasattr(a_					,					"""image_std"""						)						)
											self.assertTrue(hasattr(a_					,					"""do_convert_rgb"""						)						)


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

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


							def _a (							self						):
											pass


							def _a (							self						):
											# Initialize image_processing
											a__			 =						self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											a__			 =						self.image_processor_tester.prepare_inputs(equal_resolution=a_						)
											for image in image_inputs:
															self.assertIsInstance(a_					,					Image.Image						)

											# Test not batched input
											a__			 =						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
											a__			 =						image_processing(a_					,					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 _a (							self						):
											# Initialize image_processing
											a__			 =						self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											a__			 =						self.image_processor_tester.prepare_inputs(equal_resolution=a_					,					numpify=a_						)
											for image in image_inputs:
															self.assertIsInstance(a_					,					np.ndarray						)

											# Test not batched input
											a__			 =						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
											a__			 =						image_processing(a_					,					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 _a (							self						):
											# Initialize image_processing
											a__			 =						self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											a__			 =						self.image_processor_tester.prepare_inputs(equal_resolution=a_					,					torchify=a_						)
											for image in image_inputs:
															self.assertIsInstance(a_					,					torch.Tensor						)

											# Test not batched input
											a__			 =						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
											a__			 =						image_processing(a_					,					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"""],
											    )					,					)




@require_torch
@require_vision
class 	__snake_case (				SCREAMING_SNAKE_CASE     ,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__			: Dict             =   CLIPImageProcessor if is_vision_available() else None


							def _a (							self						):
											a__			 =						CLIPImageProcessingTester(self					,					num_channels=4						)
											a__			 =						3


							@property
							def _a (							self						):
											return self.image_processor_tester.prepare_image_processor_dict()


							def _a (							self						):
											a__			 =						self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(a_					,					"""do_resize"""						)						)
											self.assertTrue(hasattr(a_					,					"""size"""						)						)
											self.assertTrue(hasattr(a_					,					"""do_center_crop"""						)						)
											self.assertTrue(hasattr(a_					,					"""center_crop"""						)						)
											self.assertTrue(hasattr(a_					,					"""do_normalize"""						)						)
											self.assertTrue(hasattr(a_					,					"""image_mean"""						)						)
											self.assertTrue(hasattr(a_					,					"""image_std"""						)						)
											self.assertTrue(hasattr(a_					,					"""do_convert_rgb"""						)						)


							def _a (							self						):
											pass







							def _a (							self						):
											# Initialize image_processing
											a__			 =						self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											a__			 =						self.image_processor_tester.prepare_inputs(equal_resolution=a_						)
											for image in image_inputs:
															self.assertIsInstance(a_					,					Image.Image						)

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

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






import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 	__snake_case (				unittest.TestCase):







							'''simple docstring'''






							@cached_property
							def _a (							self						):
											return BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""						)


							@cached_property
							def _a (							self						):
											return BlenderbotTokenizerFast.from_pretrained("""facebook/blenderbot-3B"""						)


							def _a (							self						):
											a__			 =						self.tokenizer_ab
											a__			 =						""" I am a small frog."""
											a__			 =						tok([src_text]					,					padding=a_					,					truncation=a_						)["""input_ids"""]
											a__			 =						tok.batch_decode(a_					,					skip_special_tokens=a_					,					clean_up_tokenization_spaces=a_						)[0]
											assert src_text == decoded


							def _a (							self						):
											a__			 =						self.rust_tokenizer_ab
											a__			 =						""" I am a small frog."""
											a__			 =						tok([src_text]					,					padding=a_					,					truncation=a_						)["""input_ids"""]
											a__			 =						tok.batch_decode(a_					,					skip_special_tokens=a_					,					clean_up_tokenization_spaces=a_						)[0]
											assert src_text == decoded


							def _a (							self						):
											assert self.tokenizer_ab.add_prefix_space
											assert self.tokenizer_ab([""" Sam""", """Sam"""]						).input_ids == [[5_502, 2], [5_502, 2]]







							def _a (							self						):
											assert self.rust_tokenizer_ab.add_prefix_space
											assert self.rust_tokenizer_ab([""" Sam""", """Sam"""]						).input_ids == [[5_502, 2], [5_502, 2]]

