





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 LevitImageProcessor






class       __A					(					unittest.TestCase				):

			'''simple docstring'''







			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase=7 ,						__lowerCAmelCase=3 ,						__lowerCAmelCase=1_8 ,						__lowerCAmelCase=3_0 ,						__lowerCAmelCase=4_0_0 ,						__lowerCAmelCase=True ,						__lowerCAmelCase=None ,						__lowerCAmelCase=True ,						__lowerCAmelCase=None ,						__lowerCAmelCase=True ,						__lowerCAmelCase=[0.5, 0.5, 0.5] ,						__lowerCAmelCase=[0.5, 0.5, 0.5] ,						):



				'''simple docstring'''







				lowerCamelCase__              =	size if size is not None else {'''shortest_edge''': 1_8}
				lowerCamelCase__              =	crop_size if crop_size is not None else {'''height''': 1_8, '''width''': 1_8}
				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 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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






@require_torch
@require_vision
class       __A					(					lowerCAmelCase    ,       unittest.TestCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		LevitImageProcessor if is_vision_available() else None





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	LevitImageProcessingTester(self							)





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return self.image_processor_tester.prepare_image_processor_dict()





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.image_processing_class(**self.image_processor_dict							)
				self.assertTrue(hasattr(__lowerCAmelCase ,						'''image_mean'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase ,						'''image_std'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase ,						'''do_normalize'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase ,						'''do_resize'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase ,						'''do_center_crop'''							)							)
				self.assertTrue(hasattr(__lowerCAmelCase ,						'''size'''							)							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.image_processing_class.from_dict(self.image_processor_dict							)
				self.assertEqual(image_processor.size ,						{'''shortest_edge''': 1_8}							)
				self.assertEqual(image_processor.crop_size ,						{'''height''': 1_8, '''width''': 1_8}							)

				lowerCamelCase__              =	self.image_processing_class.from_dict(self.image_processor_dict ,						size=4_2 ,						crop_size=8_4							)
				self.assertEqual(image_processor.size ,						{'''shortest_edge''': 4_2}							)
				self.assertEqual(image_processor.crop_size ,						{'''height''': 8_4, '''width''': 8_4}							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				pass





			def 	__lowerCamelCase       (					self							):



				'''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=__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 	__lowerCamelCase       (					self							):



				'''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=__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 	__lowerCamelCase       (					self							):



				'''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=__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'''],
				    ) ,						)










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_a						=   {
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "GPTNeoForCausalLM",
			    "GPTNeoForQuestionAnswering",
			    "GPTNeoForSequenceClassification",
			    "GPTNeoForTokenClassification",
			    "GPTNeoModel",
			    "GPTNeoPreTrainedModel",
			    "load_tf_weights_in_gpt_neo",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a						=   [
			    "FlaxGPTNeoForCausalLM",
			    "FlaxGPTNeoModel",
			    "FlaxGPTNeoPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_neo import (
						    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTNeoForCausalLM,
						    GPTNeoForQuestionAnswering,
						    GPTNeoForSequenceClassification,
						    GPTNeoForTokenClassification,
						    GPTNeoModel,
						    GPTNeoPreTrainedModel,
						    load_tf_weights_in_gpt_neo,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
			import sys

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



