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 GLPNImageProcessor





class       lowercase__		(     unittest.TestCase):


						def __init__( self			:       Union[str, Any]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       str=7					,	UpperCamelCase__			:       Any=3					,	UpperCamelCase__			:       str=18					,	UpperCamelCase__			:       Dict=30					,	UpperCamelCase__			:       Any=400					,	UpperCamelCase__			:       str=True					,	UpperCamelCase__			:       str=32					,	UpperCamelCase__			:       str=True					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							parent
												SCREAMING_SNAKE_CASE							:					Any           =							batch_size
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							num_channels
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							image_size
												SCREAMING_SNAKE_CASE							:					Any           =							min_resolution
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							max_resolution
												SCREAMING_SNAKE_CASE							:					int           =							do_resize
												SCREAMING_SNAKE_CASE							:					Dict           =							size_divisor
												SCREAMING_SNAKE_CASE							:					Dict           =							do_rescale



						def  __A ( self			:       Tuple		):





												'''simple docstring'''





												return {
												    "do_resize": self.do_resize,
												    "size_divisor": self.size_divisor,
												    "do_rescale": self.do_rescale,
												}






@require_torch
@require_vision
class       lowercase__		(     UpperCamelCase_			,							unittest.TestCase):
						UpperCamelCase_  =	GLPNImageProcessor if is_vision_available() else None


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							GLPNImageProcessingTester(self		)


						@property
						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												return self.image_processor_tester.prepare_image_processor_dict()


						def  __A ( self			:       Optional[int]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							self.image_processing_class(**self.image_processor_dict		)
												self.assertTrue(hasattr(UpperCamelCase__					,	'''do_resize'''		)		)
												self.assertTrue(hasattr(UpperCamelCase__					,	'''size_divisor'''		)		)
												self.assertTrue(hasattr(UpperCamelCase__					,	'''resample'''		)		)
												self.assertTrue(hasattr(UpperCamelCase__					,	'''do_rescale'''		)		)


						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												pass


						def  __A ( self			:       Any		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					List[str]           =							self.image_processing_class(**self.image_processor_dict		)
												# create random PIL images
												SCREAMING_SNAKE_CASE							:					Dict           =							prepare_image_inputs(self.image_processor_tester					,	equal_resolution=UpperCamelCase__		)
												for image in image_inputs:
																		self.assertIsInstance(UpperCamelCase__					,	Image.Image		)

												# Test not batched input (GLPNImageProcessor doesn't support batching)
												SCREAMING_SNAKE_CASE							:					List[str]           =							image_processing(image_inputs[0]					,	return_tensors='''pt'''		).pixel_values
												self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0		)
												self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0		)


						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Tuple           =							self.image_processing_class(**self.image_processor_dict		)
												# create random numpy tensors
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							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 (GLPNImageProcessor doesn't support batching)
												SCREAMING_SNAKE_CASE							:					Tuple           =							image_processing(image_inputs[0]					,	return_tensors='''pt'''		).pixel_values
												self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0		)
												self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0		)



						def  __A ( self			:       str		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Any           =							self.image_processing_class(**self.image_processor_dict		)
												# create random PyTorch tensors
												SCREAMING_SNAKE_CASE							:					str           =							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 (GLPNImageProcessor doesn't support batching)
												SCREAMING_SNAKE_CASE							:					Tuple           =							image_processing(image_inputs[0]					,	return_tensors='''pt'''		).pixel_values
												self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0		)
												self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0		)






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse

from ...utils.dataclasses import (
    ComputeEnvironment,
    DistributedType,
    DynamoBackend,
    PrecisionType,
    SageMakerDistributedType,
)
from ..menu import BulletMenu


__UpperCamelCase	: Dict    		=  [
    'EAGER',
    'AOT_EAGER',
    'INDUCTOR',
    'NVFUSER',
    'AOT_NVFUSER',
    'AOT_CUDAGRAPHS',
    'OFI',
    'FX2TRT',
    'ONNXRT',
    'IPEX',
]
def    A			(					_lowercase	,						_lowercase=None	,						_lowercase=None	,						_lowercase=None		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							True
						while ask_again:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input(_lowercase		)
												try:
																		if default is not None and len(_lowercase		) == 0:
																								return default
																		return convert_value(_lowercase		) if convert_value is not None else result
												except Exception:
																		if error_message is not None:
																								print(_lowercase		)
def    A			(					_lowercase	,						_lowercase=[]	,						_lowercase=None	,						_lowercase=0		):
						SCREAMING_SNAKE_CASE							:					Dict           =							BulletMenu(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					str           =							menu.run(default_choice=_lowercase		)
						return convert_value(_lowercase		) if convert_value is not None else result
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		)
						return ComputeEnvironment(['''LOCAL_MACHINE''', '''AMAZON_SAGEMAKER'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Any           =							int(_lowercase		)
						return DistributedType(['''NO''', '''MULTI_CPU''', '''MULTI_XPU''', '''MULTI_GPU''', '''MULTI_NPU''', '''TPU'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							int(_lowercase		)
						return DynamoBackend(DYNAMO_BACKENDS[value]		).value
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							int(_lowercase		)
						return PrecisionType(['''no''', '''fp16''', '''bf16''', '''fp8'''][value]		)
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Dict           =							int(_lowercase		)
						return SageMakerDistributedType(['''NO''', '''DATA_PARALLEL''', '''MODEL_PARALLEL'''][value]		)
def    A			(					_lowercase		):
						return {"yes": True, "no": False}[value.lower()]





class       lowercase__		(     argparse.RawDescriptionHelpFormatter):


						def  __A ( self			:       List[str]					,	UpperCamelCase__			:       List[str]					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Dict					,	UpperCamelCase__			:       Dict		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					str           =							super()._format_usage(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							usage.replace('''<command> [<args>] '''					,	''''''		)
												return usage





