




"""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 ChineseCLIPImageProcessor






class       SCREAMING_SNAKE_CASE__					(					unittest.TestCase					):


						def __init__(							self				:			Tuple						,						lowerCAmelCase				:			Any						,						lowerCAmelCase				:			str=7						,						lowerCAmelCase				:			int=3						,						lowerCAmelCase				:			str=18						,						lowerCAmelCase				:			Any=30						,						lowerCAmelCase				:			Dict=400						,						lowerCAmelCase				:			Tuple=True						,						lowerCAmelCase				:			List[str]=None						,						lowerCAmelCase				:			List[Any]=True						,						lowerCAmelCase				:			Optional[Any]=None						,						lowerCAmelCase				:			Union[str, Any]=True						,						lowerCAmelCase				:			Dict=[0.4814_5466, 0.457_8275, 0.4082_1073]						,						lowerCAmelCase				:			List[Any]=[0.2686_2954, 0.2613_0258, 0.2757_7711]						,						lowerCAmelCase				:			Any=True						,						):
										lowerCAmelCase  =				size if size is not None else {"""height""": 224, """width""": 224}
										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
										lowerCAmelCase  =				do_convert_rgb


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

										if equal_resolution:
														lowerCAmelCase  =				[]
														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:
														lowerCAmelCase  =				[]
														for i in range(self.batch_size					):
																		lowerCAmelCase ,						lowerCAmelCase  =				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
														lowerCAmelCase  =				[Image.fromarray(np.moveaxis(lowerCAmelCase						,						0						,						-1					)					) for x in image_inputs]

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

										return image_inputs






@require_torch
@require_vision
class       SCREAMING_SNAKE_CASE__					(					_a						,				unittest.TestCase					):
						_a    						=						ChineseCLIPImageProcessor if is_vision_available() else None


						def   __lowercase (							self				:			List[Any]					):
										lowerCAmelCase  =				ChineseCLIPImageProcessingTester(self						,						do_center_crop=lowerCAmelCase					)


						@property
						def   __lowercase (							self				:			Optional[int]					):
										return self.image_processor_tester.prepare_image_processor_dict()


						def   __lowercase (							self				:			Union[str, Any]					):
										lowerCAmelCase  =				self.image_processing_class(**self.image_processor_dict					)
										self.assertTrue(hasattr(lowerCAmelCase						,						"""do_resize"""					)					)
										self.assertTrue(hasattr(lowerCAmelCase						,						"""size"""					)					)
										self.assertTrue(hasattr(lowerCAmelCase						,						"""do_center_crop"""					)					)
										self.assertTrue(hasattr(lowerCAmelCase						,						"""center_crop"""					)					)
										self.assertTrue(hasattr(lowerCAmelCase						,						"""do_normalize"""					)					)
										self.assertTrue(hasattr(lowerCAmelCase						,						"""image_mean"""					)					)
										self.assertTrue(hasattr(lowerCAmelCase						,						"""image_std"""					)					)
										self.assertTrue(hasattr(lowerCAmelCase						,						"""do_convert_rgb"""					)					)


						def   __lowercase (							self				:			Optional[Any]					):
										lowerCAmelCase  =				self.image_processing_class.from_dict(self.image_processor_dict					)
										self.assertEqual(image_processor.size						,						{"""height""": 224, """width""": 224}					)
										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						,						{"""shortest_edge""": 42}					)
										self.assertEqual(image_processor.crop_size						,						{"""height""": 84, """width""": 84}					)


						def   __lowercase (							self				:			Dict					):
										pass


						def   __lowercase (							self				:			Union[str, Any]					):
										# Initialize image_processing
										lowerCAmelCase  =				self.image_processing_class(**self.image_processor_dict					)
										# create random PIL images
										lowerCAmelCase  =				self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase					)
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase						,						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(lowerCAmelCase						,						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   __lowercase (							self				:			List[str]					):
										# Initialize image_processing
										lowerCAmelCase  =				self.image_processing_class(**self.image_processor_dict					)
										# create random numpy tensors
										lowerCAmelCase  =				self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase						,						numpify=lowerCAmelCase					)
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase						,						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(lowerCAmelCase						,						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   __lowercase (							self				:			str					):
										# Initialize image_processing
										lowerCAmelCase  =				self.image_processing_class(**self.image_processor_dict					)
										# create random PyTorch tensors
										lowerCAmelCase  =				self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase						,						torchify=lowerCAmelCase					)
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase						,						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(lowerCAmelCase						,						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       SCREAMING_SNAKE_CASE__					(					_a						,				unittest.TestCase					):
						_a    						=						ChineseCLIPImageProcessor if is_vision_available() else None


						def   __lowercase (							self				:			Optional[Any]					):
										lowerCAmelCase  =				ChineseCLIPImageProcessingTester(self						,						num_channels=4						,						do_center_crop=lowerCAmelCase					)
										lowerCAmelCase  =				3


						@property
						def   __lowercase (							self				:			Union[str, Any]					):
										return self.image_processor_tester.prepare_image_processor_dict()


						def   __lowercase (							self				:			int					):
										lowerCAmelCase  =				self.image_processing_class(**self.image_processor_dict					)
										self.assertTrue(hasattr(lowerCAmelCase						,						"""do_resize"""					)					)
										self.assertTrue(hasattr(lowerCAmelCase						,						"""size"""					)					)
										self.assertTrue(hasattr(lowerCAmelCase						,						"""do_center_crop"""					)					)
										self.assertTrue(hasattr(lowerCAmelCase						,						"""center_crop"""					)					)
										self.assertTrue(hasattr(lowerCAmelCase						,						"""do_normalize"""					)					)
										self.assertTrue(hasattr(lowerCAmelCase						,						"""image_mean"""					)					)
										self.assertTrue(hasattr(lowerCAmelCase						,						"""image_std"""					)					)
										self.assertTrue(hasattr(lowerCAmelCase						,						"""do_convert_rgb"""					)					)


						def   __lowercase (							self				:			Optional[Any]					):
										pass




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

										# Test not batched input
										lowerCAmelCase  =				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
										lowerCAmelCase  =				image_processing(lowerCAmelCase						,						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_tokenizers_available, is_torch_available


a      =							{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BioGptForCausalLM',
		    'BioGptForTokenClassification',
		    'BioGptForSequenceClassification',
		    'BioGptModel',
		    'BioGptPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
		from .tokenization_biogpt import BioGptTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_biogpt import (
				    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptPreTrainedModel,
				)


else:
		import sys

		a      =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





