





"""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 ConvNextImageProcessor





class 							_lowerCamelCase   (							unittest.TestCase			):


						def __init__(self ,				__a ,				__a=7 ,				__a=3 ,				__a=18 ,				__a=30 ,				__a=4_00 ,				__a=True ,				__a=None ,				__a=0.875 ,				__a=True ,				__a=[0.5, 0.5, 0.5] ,				__a=[0.5, 0.5, 0.5] ,				)			-> Dict:
								UpperCamelCase =						size if size is not None else {"shortest_edge": 20}
								UpperCamelCase =						parent
								UpperCamelCase =						batch_size
								UpperCamelCase =						num_channels
								UpperCamelCase =						image_size
								UpperCamelCase =						min_resolution
								UpperCamelCase =						max_resolution
								UpperCamelCase =						do_resize
								UpperCamelCase =						size
								UpperCamelCase =						crop_pct
								UpperCamelCase =						do_normalize
								UpperCamelCase =						image_mean
								UpperCamelCase =						image_std




						def      snake_case_ (self		)			-> Optional[int]:
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "crop_pct": self.crop_pct,
								}





@require_torch
@require_vision
class 							_lowerCamelCase   (							_lowercase	,      unittest.TestCase			):
						UpperCAmelCase_			=      ConvNextImageProcessor if is_vision_available() else None


						def      snake_case_ (self		)			-> str:
								UpperCamelCase =						ConvNextImageProcessingTester(self		)


						@property
						def      snake_case_ (self		)			-> int:
								return self.image_processor_tester.prepare_image_processor_dict()


						def      snake_case_ (self		)			-> List[str]:
								UpperCamelCase =						self.image_processing_class(**self.image_processor_dict		)
								self.assertTrue(hasattr(__a ,				"do_resize"		)		)
								self.assertTrue(hasattr(__a ,				"size"		)		)
								self.assertTrue(hasattr(__a ,				"crop_pct"		)		)
								self.assertTrue(hasattr(__a ,				"do_normalize"		)		)
								self.assertTrue(hasattr(__a ,				"image_mean"		)		)
								self.assertTrue(hasattr(__a ,				"image_std"		)		)


						def      snake_case_ (self		)			-> List[str]:
								UpperCamelCase =						self.image_processing_class.from_dict(self.image_processor_dict		)
								self.assertEqual(image_processor.size ,				{"shortest_edge": 20}		)

								UpperCamelCase =						self.image_processing_class.from_dict(self.image_processor_dict ,				size=42		)
								self.assertEqual(image_processor.size ,				{"shortest_edge": 42}		)


						def      snake_case_ (self		)			-> Optional[int]:
								pass


						def      snake_case_ (self		)			-> List[Any]:
								# Initialize image_processing
								UpperCamelCase =						self.image_processing_class(**self.image_processor_dict		)
								# create random PIL images
								UpperCamelCase =						prepare_image_inputs(self.image_processor_tester ,				equal_resolution=__a		)
								for image in image_inputs:
										self.assertIsInstance(__a ,				Image.Image		)

								# Test not batched input
								UpperCamelCase =						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.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    ) ,				)

								# Test batched
								UpperCamelCase =						image_processing(__a ,				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.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    ) ,				)


						def      snake_case_ (self		)			-> Tuple:
								# Initialize image_processing
								UpperCamelCase =						self.image_processing_class(**self.image_processor_dict		)
								# create random numpy tensors
								UpperCamelCase =						prepare_image_inputs(self.image_processor_tester ,				equal_resolution=__a ,				numpify=__a		)
								for image in image_inputs:
										self.assertIsInstance(__a ,				np.ndarray		)

								# Test not batched input
								UpperCamelCase =						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.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    ) ,				)

								# Test batched
								UpperCamelCase =						image_processing(__a ,				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.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    ) ,				)




						def      snake_case_ (self		)			-> List[str]:
								# Initialize image_processing
								UpperCamelCase =						self.image_processing_class(**self.image_processor_dict		)
								# create random PyTorch tensors
								UpperCamelCase =						prepare_image_inputs(self.image_processor_tester ,				equal_resolution=__a ,				torchify=__a		)
								for image in image_inputs:
										self.assertIsInstance(__a ,				torch.Tensor		)

								# Test not batched input
								UpperCamelCase =						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.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    ) ,				)

								# Test batched
								UpperCamelCase =						image_processing(__a ,				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.size["shortest_edge"],
								        self.image_processor_tester.size["shortest_edge"],
								    ) ,				)













"""simple docstring"""





from typing import TYPE_CHECKING

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


lowerCAmelCase__        =      {
    '''configuration_openai''': ['''OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OpenAIGPTConfig'''],
    '''tokenization_openai''': ['''OpenAIGPTTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      ['''OpenAIGPTTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''OpenAIGPTDoubleHeadsModel''',
						    '''OpenAIGPTForSequenceClassification''',
						    '''OpenAIGPTLMHeadModel''',
						    '''OpenAIGPTModel''',
						    '''OpenAIGPTPreTrainedModel''',
						    '''load_tf_weights_in_openai_gpt''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase__        =      [
						    '''TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFOpenAIGPTDoubleHeadsModel''',
						    '''TFOpenAIGPTForSequenceClassification''',
						    '''TFOpenAIGPTLMHeadModel''',
						    '''TFOpenAIGPTMainLayer''',
						    '''TFOpenAIGPTModel''',
						    '''TFOpenAIGPTPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
						from .tokenization_openai import OpenAIGPTTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_openai_fast import OpenAIGPTTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_openai import (
												    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    OpenAIGPTDoubleHeadsModel,
												    OpenAIGPTForSequenceClassification,
												    OpenAIGPTLMHeadModel,
												    OpenAIGPTModel,
												    OpenAIGPTPreTrainedModel,
												    load_tf_weights_in_openai_gpt,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_openai import (
												    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFOpenAIGPTDoubleHeadsModel,
												    TFOpenAIGPTForSequenceClassification,
												    TFOpenAIGPTLMHeadModel,
												    TFOpenAIGPTMainLayer,
												    TFOpenAIGPTModel,
												    TFOpenAIGPTPreTrainedModel,
												)

else:
						import sys

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






