

'''simple docstring'''



import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor



class lowerCamelCase					(    unittest.TestCase		):



			'''simple docstring'''






			def __init__( self			:		List[Any]						,     lowerCAmelCase_			:		Tuple						,     lowerCAmelCase_			:		List[str]=7						,     lowerCAmelCase_			:		Any=3						,     lowerCAmelCase_			:		List[str]=18						,     lowerCAmelCase_			:		str=30						,     lowerCAmelCase_			:		Optional[int]=4_00						,     lowerCAmelCase_			:		int=True						,     lowerCAmelCase_			:		int=None						,     lowerCAmelCase_			:		Dict=True						,     lowerCAmelCase_			:		int=[0.5, 0.5, 0.5]						,     lowerCAmelCase_			:		Union[str, Any]=[0.5, 0.5, 0.5]						,     )     ->						Tuple:





									'''simple docstring'''
									A__	:  str           =size if size is not None else {'height': 18, 'width': 18}
									A__	:  Dict           =parent
									A__	:  Any           =batch_size
									A__	:  Any           =num_channels
									A__	:  Union[str, Any]           =image_size
									A__	:  Tuple           =min_resolution
									A__	:  List[Any]           =max_resolution
									A__	:  Optional[Any]           =do_resize
									A__	:  Any           =size
									A__	:  Optional[Any]           =do_normalize
									A__	:  Optional[Any]           =image_mean
									A__	:  str           =image_std



			def lowercase__		( self			:		List[str]					)     ->						str:





									'''simple docstring'''
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									}



@require_torch
@require_vision
class lowerCamelCase					(    SCREAMING_SNAKE_CASE_			,		unittest.TestCase		):



			'''simple docstring'''






			__snake_case             =			DPTImageProcessor if is_vision_available() else None


			def lowercase__		( self			:		List[Any]					)     ->						int:





									'''simple docstring'''
									A__	:  Optional[int]           =DPTImageProcessingTester(self					)


			@property
			def lowercase__		( self			:		Dict					)     ->						Tuple:





									'''simple docstring'''
									return self.image_processor_tester.prepare_image_processor_dict()


			def lowercase__		( self			:		Any					)     ->						Tuple:





									'''simple docstring'''
									A__	:  Any           =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_						,     """size"""					)					)


			def lowercase__		( self			:		Optional[int]					)     ->						List[Any]:





									'''simple docstring'''
									A__	:  List[str]           =self.image_processing_class.from_dict(self.image_processor_dict					)
									self.assertEqual(image_processor.size						,     {"""height""": 18, """width""": 18}					)

									A__	:  List[str]           =self.image_processing_class.from_dict(self.image_processor_dict						,     size=42					)
									self.assertEqual(image_processor.size						,     {"""height""": 42, """width""": 42}					)


			def lowercase__		( self			:		Dict					)     ->						List[str]:





									'''simple docstring'''
									# Initialize image_processing
									A__	:  Union[str, Any]           =self.image_processing_class(**self.image_processor_dict					)
									# create random PIL images
									A__	:  int           =prepare_image_inputs(self.image_processor_tester						,     equal_resolution=lowerCAmelCase_					)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase_						,     Image.Image					)

									# Test not batched input
									A__	:  Dict           =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["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )						,     )

									# Test batched
									A__	:  Tuple           =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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )						,     )


			def lowercase__		( self			:		Optional[Any]					)     ->						Dict:





									'''simple docstring'''
									# Initialize image_processing
									A__	:  int           =self.image_processing_class(**self.image_processor_dict					)
									# create random numpy tensors
									A__	:  Optional[int]           =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
									A__	:  Dict           =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["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )						,     )

									# Test batched
									A__	:  List[Any]           =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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )						,     )



			def lowercase__		( self			:		List[Any]					)     ->						int:





									'''simple docstring'''
									# Initialize image_processing
									A__	:  int           =self.image_processing_class(**self.image_processor_dict					)
									# create random PyTorch tensors
									A__	:  List[str]           =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
									A__	:  int           =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["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )						,     )

									# Test batched
									A__	:  List[str]           =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.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )						,     )






"""simple docstring"""







from typing import TYPE_CHECKING

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


a_							=    {"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    """OPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """OPTForCausalLM""",
	    """OPTModel""",
	    """OPTPreTrainedModel""",
	    """OPTForSequenceClassification""",
	    """OPTForQuestionAnswering""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    ["""TFOPTForCausalLM""", """TFOPTModel""", """TFOPTPreTrainedModel"""]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    """FlaxOPTForCausalLM""",
	    """FlaxOPTModel""",
	    """FlaxOPTPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_opt import (
		    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OPTForCausalLM,
		    OPTForQuestionAnswering,
		    OPTForSequenceClassification,
		    OPTModel,
		    OPTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
	import sys

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





