
"""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 LevitImageProcessor




class  __lowerCamelCase  ( unittest.TestCase			):




				def __init__(				self							,						UpperCAmelCase							,						UpperCAmelCase=7							,						UpperCAmelCase=3							,						UpperCAmelCase=18							,						UpperCAmelCase=30							,						UpperCAmelCase=400							,						UpperCAmelCase=True							,						UpperCAmelCase=None							,						UpperCAmelCase=True							,						UpperCAmelCase=None							,						UpperCAmelCase=True							,						UpperCAmelCase=[0.5, 0.5, 0.5]							,						UpperCAmelCase=[0.5, 0.5, 0.5]							,						):
							lowerCamelCase_					=  size if size is not None else {'''shortest_edge''': 18}
							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





				def        UpperCAmelCase__						(				self   ):
							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  __lowerCamelCase  ( lowerCAmelCase						,	unittest.TestCase			):
				a__:      Tuple          =    LevitImageProcessor if is_vision_available() else None




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  LevitImageProcessingTester(self   )




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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.image_processing_class(**self.image_processor_dict   )
							self.assertTrue(hasattr(UpperCAmelCase							,						'''image_mean'''   )   )
							self.assertTrue(hasattr(UpperCAmelCase							,						'''image_std'''   )   )
							self.assertTrue(hasattr(UpperCAmelCase							,						'''do_normalize'''   )   )
							self.assertTrue(hasattr(UpperCAmelCase							,						'''do_resize'''   )   )
							self.assertTrue(hasattr(UpperCAmelCase							,						'''do_center_crop'''   )   )
							self.assertTrue(hasattr(UpperCAmelCase							,						'''size'''   )   )




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.image_processing_class.from_dict(self.image_processor_dict   )
							self.assertEqual(image_processor.size							,						{'''shortest_edge''': 18}   )
							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        UpperCAmelCase__						(				self   ):
							pass




				def        UpperCAmelCase__						(				self   ):
							# Initialize image_processing
							lowerCamelCase_					=  self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							lowerCamelCase_					=  prepare_image_inputs(self.image_processor_tester							,						equal_resolution=UpperCAmelCase   )
							for image in image_inputs:
										self.assertIsInstance(UpperCAmelCase							,						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(UpperCAmelCase							,						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        UpperCAmelCase__						(				self   ):
							# Initialize image_processing
							lowerCamelCase_					=  self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							lowerCamelCase_					=  prepare_image_inputs(self.image_processor_tester							,						equal_resolution=UpperCAmelCase							,						numpify=UpperCAmelCase   )
							for image in image_inputs:
										self.assertIsInstance(UpperCAmelCase							,						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(UpperCAmelCase							,						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        UpperCAmelCase__						(				self   ):
							# Initialize image_processing
							lowerCamelCase_					=  self.image_processing_class(**self.image_processor_dict   )
							# create random PyTorch tensors
							lowerCamelCase_					=  prepare_image_inputs(self.image_processor_tester							,						equal_resolution=UpperCAmelCase							,						torchify=UpperCAmelCase   )
							for image in image_inputs:
										self.assertIsInstance(UpperCAmelCase							,						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(UpperCAmelCase							,						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'''],
							    )							,						)





"""simple docstring"""


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__)



