




"""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


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      :  Optional[Any]					,							snake_case      :  List[str]					,							snake_case      :  Union[str, Any]=7					,							snake_case      :  List[str]=3					,							snake_case      :  Optional[Any]=18					,							snake_case      :  str=30					,							snake_case      :  Union[str, Any]=400					,							snake_case      :  Dict=True					,							snake_case      :  Union[str, Any]=None					,							snake_case      :  Optional[Any]=True					,							snake_case      :  List[str]=None					,							snake_case      :  Any=True					,							snake_case      :  str=[0.48_145_466, 0.4_578_275, 0.40_821_073]					,							snake_case      :  str=[0.26_862_954, 0.26_130_258, 0.27_577_711]					,							snake_case      :  Union[str, Any]=True					,							):
								UpperCAmelCase_					:Dict      			=      size if size is not None else {'''shortest_edge''': 20}
								UpperCAmelCase_					:Dict      			=      crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								UpperCAmelCase_					:Tuple      			=      parent
								UpperCAmelCase_					:List[str]      			=      batch_size
								UpperCAmelCase_					:Dict      			=      num_channels
								UpperCAmelCase_					:Dict      			=      image_size
								UpperCAmelCase_					:Tuple      			=      min_resolution
								UpperCAmelCase_					:str      			=      max_resolution
								UpperCAmelCase_					:Union[str, Any]      			=      do_resize
								UpperCAmelCase_					:int      			=      size
								UpperCAmelCase_					:str      			=      do_center_crop
								UpperCAmelCase_					:str      			=      crop_size
								UpperCAmelCase_					:List[str]      			=      do_normalize
								UpperCAmelCase_					:List[str]      			=      image_mean
								UpperCAmelCase_					:int      			=      image_std
								UpperCAmelCase_					:Tuple      			=      do_convert_rgb





				def 				snake_case_ (							self      :  List[Any]     ):
								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 				snake_case_ (							self      :  Optional[int]					,							snake_case      :  List[Any]=False					,							snake_case      :  Optional[Any]=False					,							snake_case      :  Union[str, Any]=False     ):
								assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

								if equal_resolution:
												UpperCAmelCase_					:Dict      			=      []
												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:
												UpperCAmelCase_					:List[Any]      			=      []
												for i in range(self.batch_size     ):
																UpperCAmelCase_			,UpperCAmelCase_					:int      			=      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
												UpperCAmelCase_					:str      			=      [Image.fromarray(np.moveaxis(snake_case					,							0					,							-1     )     ) for x in image_inputs]

								if torchify:
												UpperCAmelCase_					:Tuple      			=      [torch.from_numpy(snake_case     ) for x in image_inputs]

								return image_inputs



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

				'''simple docstring'''




				UpperCamelCase__		     =CLIPImageProcessor if is_vision_available() else None





				def 				snake_case_ (							self      :  List[Any]     ):
								UpperCAmelCase_					:int      			=      CLIPImageProcessingTester(self     )





				@property
				def 				snake_case_ (							self      :  Any     ):
								return self.image_processor_tester.prepare_image_processor_dict()





				def 				snake_case_ (							self      :  List[Any]     ):
								UpperCAmelCase_					:Dict      			=      self.image_processing_class(**self.image_processor_dict     )
								self.assertTrue(hasattr(snake_case					,							'''do_resize'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''size'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''do_center_crop'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''center_crop'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''do_normalize'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''image_mean'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''image_std'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''do_convert_rgb'''     )     )





				def 				snake_case_ (							self      :  List[str]     ):
								UpperCAmelCase_					:Union[str, Any]      			=      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}     )

								UpperCAmelCase_					:int      			=      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 				snake_case_ (							self      :  int     ):
								pass





				def 				snake_case_ (							self      :  str     ):
								# Initialize image_processing
								UpperCAmelCase_					:int      			=      self.image_processing_class(**self.image_processor_dict     )
								# create random PIL images
								UpperCAmelCase_					:Union[str, Any]      			=      self.image_processor_tester.prepare_inputs(equal_resolution=snake_case     )
								for image in image_inputs:
												self.assertIsInstance(snake_case					,							Image.Image     )

								# Test not batched input
								UpperCAmelCase_					:int      			=      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
								UpperCAmelCase_					:List[Any]      			=      image_processing(snake_case					,							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 				snake_case_ (							self      :  List[Any]     ):
								# Initialize image_processing
								UpperCAmelCase_					:Dict      			=      self.image_processing_class(**self.image_processor_dict     )
								# create random numpy tensors
								UpperCAmelCase_					:Union[str, Any]      			=      self.image_processor_tester.prepare_inputs(equal_resolution=snake_case					,							numpify=snake_case     )
								for image in image_inputs:
												self.assertIsInstance(snake_case					,							np.ndarray     )

								# Test not batched input
								UpperCAmelCase_					:List[Any]      			=      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
								UpperCAmelCase_					:Optional[Any]      			=      image_processing(snake_case					,							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 				snake_case_ (							self      :  Any     ):
								# Initialize image_processing
								UpperCAmelCase_					:int      			=      self.image_processing_class(**self.image_processor_dict     )
								# create random PyTorch tensors
								UpperCAmelCase_					:Any      			=      self.image_processor_tester.prepare_inputs(equal_resolution=snake_case					,							torchify=snake_case     )
								for image in image_inputs:
												self.assertIsInstance(snake_case					,							torch.Tensor     )

								# Test not batched input
								UpperCAmelCase_					:int      			=      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
								UpperCAmelCase_					:List[Any]      			=      image_processing(snake_case					,							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				(							A__							,						unittest.TestCase       ):

				'''simple docstring'''




				UpperCamelCase__		     =CLIPImageProcessor if is_vision_available() else None





				def 				snake_case_ (							self      :  int     ):
								UpperCAmelCase_					:Tuple      			=      CLIPImageProcessingTester(self					,							num_channels=4     )
								UpperCAmelCase_					:Dict      			=      3





				@property
				def 				snake_case_ (							self      :  Any     ):
								return self.image_processor_tester.prepare_image_processor_dict()





				def 				snake_case_ (							self      :  int     ):
								UpperCAmelCase_					:Any      			=      self.image_processing_class(**self.image_processor_dict     )
								self.assertTrue(hasattr(snake_case					,							'''do_resize'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''size'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''do_center_crop'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''center_crop'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''do_normalize'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''image_mean'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''image_std'''     )     )
								self.assertTrue(hasattr(snake_case					,							'''do_convert_rgb'''     )     )





				def 				snake_case_ (							self      :  List[Any]     ):
								pass





				def 				snake_case_ (							self      :  List[str]     ):
								# Initialize image_processing
								UpperCAmelCase_					:Any      			=      self.image_processing_class(**self.image_processor_dict     )
								# create random PIL images
								UpperCAmelCase_					:Tuple      			=      self.image_processor_tester.prepare_inputs(equal_resolution=snake_case     )
								for image in image_inputs:
												self.assertIsInstance(snake_case					,							Image.Image     )

								# Test not batched input
								UpperCAmelCase_					:List[str]      			=      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
								UpperCAmelCase_					:Dict      			=      image_processing(snake_case					,							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'''],
								    )					,							)








"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase								=						{
    "configuration_bart": ["BART_PRETRAINED_CONFIG_ARCHIVE_MAP", "BartConfig", "BartOnnxConfig"],
    "tokenization_bart": ["BartTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase								=						["BartTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase								=						[
						    "BART_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "BartForCausalLM",
						    "BartForConditionalGeneration",
						    "BartForQuestionAnswering",
						    "BartForSequenceClassification",
						    "BartModel",
						    "BartPretrainedModel",
						    "PretrainedBartModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase								=						[
						    "TFBartForConditionalGeneration",
						    "TFBartForSequenceClassification",
						    "TFBartModel",
						    "TFBartPretrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__lowerCamelCase								=						[
						    "FlaxBartDecoderPreTrainedModel",
						    "FlaxBartForCausalLM",
						    "FlaxBartForConditionalGeneration",
						    "FlaxBartForQuestionAnswering",
						    "FlaxBartForSequenceClassification",
						    "FlaxBartModel",
						    "FlaxBartPreTrainedModel",
						]

if TYPE_CHECKING:
						from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
						from .tokenization_bart import BartTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bart_fast import BartTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bart import (
												    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BartForCausalLM,
												    BartForConditionalGeneration,
												    BartForQuestionAnswering,
												    BartForSequenceClassification,
												    BartModel,
												    BartPretrainedModel,
												    PretrainedBartModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_bart import (
												    TFBartForConditionalGeneration,
												    TFBartForSequenceClassification,
												    TFBartModel,
												    TFBartPretrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_bart import (
												    FlaxBartDecoderPreTrainedModel,
												    FlaxBartForCausalLM,
												    FlaxBartForConditionalGeneration,
												    FlaxBartForQuestionAnswering,
												    FlaxBartForSequenceClassification,
												    FlaxBartModel,
												    FlaxBartPreTrainedModel,
												)

else:
						import sys

						__lowerCamelCase								=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


