




"""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 MobileNetVaImageProcessor
class      lowerCamelCase__      (					unittest.TestCase     ):


			def __init__(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE=7			,	SCREAMING_SNAKE_CASE=3			,	SCREAMING_SNAKE_CASE=18			,	SCREAMING_SNAKE_CASE=30			,	SCREAMING_SNAKE_CASE=400			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=None			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=None			,	):



					"""simple docstring"""







					snake_case						:  Tuple        =			size if size is not None else {"shortest_edge": 20}
					snake_case						:  str        =			crop_size if crop_size is not None else {"height": 18, "width": 18}
					snake_case						:  List[str]        =			parent
					snake_case						:  List[Any]        =			batch_size
					snake_case						:  List[Any]        =			num_channels
					snake_case						:  Optional[Any]        =			image_size
					snake_case						:  Union[str, Any]        =			min_resolution
					snake_case						:  List[Any]        =			max_resolution
					snake_case						:  Dict        =			do_resize
					snake_case						:  Union[str, Any]        =			size
					snake_case						:  List[Any]        =			do_center_crop
					snake_case						:  str        =			crop_size



			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







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







@require_torch
@require_vision
class      lowerCamelCase__      (					lowerCamelCase_			,					unittest.TestCase     ):
			a__	: Optional[Any]        =       MobileNetVaImageProcessor if is_vision_available() else None


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			MobileNetVaImageProcessingTester(self							)


			@property
			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					return self.image_processor_tester.prepare_image_processor_dict()


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Union[str, Any]        =			self.image_processing_class(**self.image_processor_dict							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,	"do_resize"							)							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,	"size"							)							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,	"do_center_crop"							)							)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE			,	"center_crop"							)							)


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  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}							)

					snake_case						:  List[Any]        =			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 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					pass


			def 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  int        =			self.image_processing_class(**self.image_processor_dict							)
					# create random PIL images
					snake_case						:  Optional[int]        =			prepare_image_inputs(self.image_processor_tester			,	equal_resolution=SCREAMING_SNAKE_CASE							)
					for image in image_inputs:
							self.assertIsInstance(SCREAMING_SNAKE_CASE			,	Image.Image							)

					# Test not batched input
					snake_case						:  List[str]        =			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
					snake_case						:  str        =			image_processing(SCREAMING_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 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			self.image_processing_class(**self.image_processor_dict							)
					# create random numpy tensors
					snake_case						:  List[Any]        =			prepare_image_inputs(self.image_processor_tester			,	equal_resolution=SCREAMING_SNAKE_CASE			,	numpify=SCREAMING_SNAKE_CASE							)
					for image in image_inputs:
							self.assertIsInstance(SCREAMING_SNAKE_CASE			,	np.ndarray							)

					# Test not batched input
					snake_case						:  List[str]        =			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
					snake_case						:  Tuple        =			image_processing(SCREAMING_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 							lowerCamelCase_			(       self							):



					"""simple docstring"""







					snake_case						:  Optional[int]        =			self.image_processing_class(**self.image_processor_dict							)
					# create random PyTorch tensors
					snake_case						:  int        =			prepare_image_inputs(self.image_processor_tester			,	equal_resolution=SCREAMING_SNAKE_CASE			,	torchify=SCREAMING_SNAKE_CASE							)
					for image in image_inputs:
							self.assertIsInstance(SCREAMING_SNAKE_CASE			,	torch.Tensor							)

					# Test not batched input
					snake_case						:  Tuple        =			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
					snake_case						:  Any        =			image_processing(SCREAMING_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"],
					    )			,	)







"""simple docstring"""

from typing import TYPE_CHECKING

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


__A							=     {"configuration_gptj": ["GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTJConfig", "GPTJOnnxConfig"]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "GPTJForCausalLM",
							    "GPTJForQuestionAnswering",
							    "GPTJForSequenceClassification",
							    "GPTJModel",
							    "GPTJPreTrainedModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "TFGPTJForCausalLM",
							    "TFGPTJForQuestionAnswering",
							    "TFGPTJForSequenceClassification",
							    "TFGPTJModel",
							    "TFGPTJPreTrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__A							=     [
							    "FlaxGPTJForCausalLM",
							    "FlaxGPTJModel",
							    "FlaxGPTJPreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gptj import (
														    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTJForCausalLM,
														    GPTJForQuestionAnswering,
														    GPTJForSequenceClassification,
														    GPTJModel,
														    GPTJPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_gptj import (
														    TFGPTJForCausalLM,
														    TFGPTJForQuestionAnswering,
														    TFGPTJForSequenceClassification,
														    TFGPTJModel,
														    TFGPTJPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
							import sys

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

