



"""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 ViTImageProcessor



class      A__  (    unittest.TestCase				):




	'''simple docstring'''





	def __init__(	self:						Any			,     _SCREAMING_SNAKE_CASE:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Any=13			,     _SCREAMING_SNAKE_CASE:						List[str]=3			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=224			,     _SCREAMING_SNAKE_CASE:						Optional[int]=30			,     _SCREAMING_SNAKE_CASE:						Any=400			,     _SCREAMING_SNAKE_CASE:						Dict=True			,     _SCREAMING_SNAKE_CASE:						List[Any]=None			,     _SCREAMING_SNAKE_CASE:						Optional[int]=True			,     _SCREAMING_SNAKE_CASE:						List[Any]=[0.5, 0.5, 0.5]			,     _SCREAMING_SNAKE_CASE:						List[str]=[0.5, 0.5, 0.5]			,     )							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			size if size is not None else {"height": 18, "width": 18}
				__lowerCAmelCase :    List[str]				=			parent
				__lowerCAmelCase :    Dict				=			batch_size
				__lowerCAmelCase :    int				=			num_channels
				__lowerCAmelCase :    int				=			image_size
				__lowerCAmelCase :    Union[str, Any]				=			min_resolution
				__lowerCAmelCase :    Optional[int]				=			max_resolution
				__lowerCAmelCase :    Optional[int]				=			do_resize
				__lowerCAmelCase :    str				=			size
				__lowerCAmelCase :    Optional[int]				=			do_normalize
				__lowerCAmelCase :    Tuple				=			image_mean
				__lowerCAmelCase :    Union[str, Any]				=			image_std







	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any])							->			List[str]:

				"""simple docstring"""

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






@require_torch
@require_vision
class      A__  (    __SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      ViTImageProcessor if is_vision_available() else None

	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any])							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			EfficientFormerImageProcessorTester(self)

	@property
	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			List[Any]:

				"""simple docstring"""

				return self.image_proc_tester.prepare_image_processor_dict()

	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any])							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			self.image_processing_class(**self.image_processor_dict)
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,     "image_mean"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,     "image_std"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,     "do_normalize"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,     "do_resize"))
				self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE			,     "size"))

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			List[str]:

				"""simple docstring"""

				pass

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			Tuple:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			self.image_processing_class(**self.image_processor_dict)
				# create random PIL images
				__lowerCAmelCase :    Tuple				=			prepare_image_inputs(self.image_proc_tester			,     equal_resolution=_SCREAMING_SNAKE_CASE)
				for image in image_inputs:
							self.assertIsInstance(_SCREAMING_SNAKE_CASE			,     Image.Image)

				# Test not batched input
				__lowerCAmelCase :    Any				=			image_processor(image_inputs[0]			,     return_tensors="pt").pixel_values
				self.assertEqual(
				    encoded_images.shape			,     (
				        1,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )			,     )

				# Test batched
				__lowerCAmelCase :    Tuple				=			image_processor(_SCREAMING_SNAKE_CASE			,     return_tensors="pt").pixel_values
				self.assertEqual(
				    encoded_images.shape			,     (
				        self.image_proc_tester.batch_size,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )			,     )

	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			self.image_processing_class(**self.image_processor_dict)
				# create random numpy tensors
				__lowerCAmelCase :    Any				=			prepare_image_inputs(self.image_proc_tester			,     equal_resolution=_SCREAMING_SNAKE_CASE			,     numpify=_SCREAMING_SNAKE_CASE)
				for image in image_inputs:
							self.assertIsInstance(_SCREAMING_SNAKE_CASE			,     np.ndarray)

				# Test not batched input
				__lowerCAmelCase :    List[Any]				=			image_processor(image_inputs[0]			,     return_tensors="pt").pixel_values
				self.assertEqual(
				    encoded_images.shape			,     (
				        1,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )			,     )

				# Test batched
				__lowerCAmelCase :    int				=			image_processor(_SCREAMING_SNAKE_CASE			,     return_tensors="pt").pixel_values
				self.assertEqual(
				    encoded_images.shape			,     (
				        self.image_proc_tester.batch_size,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )			,     )







	def 				_SCREAMING_SNAKE_CASE  (	self:						Tuple)							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			self.image_processing_class(**self.image_processor_dict)
				# create random PyTorch tensors
				__lowerCAmelCase :    List[str]				=			prepare_image_inputs(self.image_proc_tester			,     equal_resolution=_SCREAMING_SNAKE_CASE			,     torchify=_SCREAMING_SNAKE_CASE)
				for image in image_inputs:
							self.assertIsInstance(_SCREAMING_SNAKE_CASE			,     torch.Tensor)

				# Test not batched input
				__lowerCAmelCase :    Union[str, Any]				=			image_processor(image_inputs[0]			,     return_tensors="pt").pixel_values
				self.assertEqual(
				    encoded_images.shape			,     (
				        1,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )			,     )

				# Test batched
				__lowerCAmelCase :    int				=			image_processor(_SCREAMING_SNAKE_CASE			,     return_tensors="pt").pixel_values
				self.assertEqual(
				    encoded_images.shape			,     (
				        self.image_proc_tester.batch_size,
				        self.image_proc_tester.num_channels,
				        self.image_proc_tester.size["height"],
				        self.image_proc_tester.size["width"],
				    )			,     )




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case				:  int =       {'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  Optional[Any] =       ['PLBartTokenizer']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case				:  List[str] =       [
	    'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'PLBartForCausalLM',
	    'PLBartForConditionalGeneration',
	    'PLBartForSequenceClassification',
	    'PLBartModel',
	    'PLBartPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_plbart import PLBartTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_plbart import (
		    PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PLBartForCausalLM,
		    PLBartForConditionalGeneration,
		    PLBartForSequenceClassification,
		    PLBartModel,
		    PLBartPreTrainedModel,
		)


else:
	import sys

	__snake_case				:  Any =       _LazyModule(__name__, globals()['__file__'], _import_structure)