


import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor

class     a		(unittest.TestCase				):




							"""simple docstring"""


							def __init__(							self	:  str   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Tuple=7   ,     lowerCamelCase	:  List[Any]=3   ,     lowerCamelCase	:  Union[str, Any]=18   ,     lowerCamelCase	:  List[Any]=30   ,     lowerCamelCase	:  Union[str, Any]=400   ,     lowerCamelCase	:  Optional[int]=True   ,     lowerCamelCase	:  List[Any]=None   ,     lowerCamelCase	:  Optional[int]=True   ,     lowerCamelCase	:  Any=None   ,     lowerCamelCase	:  str=True   ,     lowerCamelCase	:  Optional[int]=[0.5, 0.5, 0.5]   ,     lowerCamelCase	:  Dict=[0.5, 0.5, 0.5]   ,     lowerCamelCase	:  Optional[int]=False   ,     )     ->   Union[str, Any]:
														__snake_case		:      Tuple										=       size if size is not None else {"height": 20, "width": 20}
														__snake_case		:      Optional[int]										=       crop_size if crop_size is not None else {"height": 18, "width": 18}
														__snake_case		:      str										=       parent
														__snake_case		:      Dict										=       batch_size
														__snake_case		:      Optional[Any]										=       num_channels
														__snake_case		:      Any										=       image_size
														__snake_case		:      int										=       min_resolution
														__snake_case		:      Dict										=       max_resolution
														__snake_case		:      Optional[Any]										=       do_resize
														__snake_case		:      Union[str, Any]										=       size
														__snake_case		:      Tuple										=       do_center_crop
														__snake_case		:      Optional[Any]										=       crop_size
														__snake_case		:      Tuple										=       do_normalize
														__snake_case		:      Tuple										=       image_mean
														__snake_case		:      Dict										=       image_std
														__snake_case		:      List[str]										=       do_reduce_labels






							def    __snake_case (							self	:  Optional[Any]    )     ->   Any:
														return {
														    "do_resize": self.do_resize,
														    "size": self.size,
														    "do_center_crop": self.do_center_crop,
														    "crop_size": self.crop_size,
														    "do_normalize": self.do_normalize,
														    "image_mean": self.image_mean,
														    "image_std": self.image_std,
														    "do_reduce_labels": self.do_reduce_labels,
														}







def     lowerCAmelCase_					( ):
							__snake_case		:      Tuple										=       load_dataset("hf-internal-testing/fixtures_ade20k"							,     split="test"     )

							__snake_case		:      Optional[int]										=       Image.open(dataset[0]["file"]     )
							__snake_case		:      Tuple										=       Image.open(dataset[1]["file"]     )

							return image, map







def     lowerCAmelCase_					( ):
							__snake_case		:      Optional[Any]										=       load_dataset("hf-internal-testing/fixtures_ade20k"							,     split="test"     )

							__snake_case		:      Optional[Any]										=       Image.open(ds[0]["file"]     )
							__snake_case		:      Optional[int]										=       Image.open(ds[1]["file"]     )
							__snake_case		:      List[Any]										=       Image.open(ds[2]["file"]     )
							__snake_case		:      Dict										=       Image.open(ds[3]["file"]     )

							return [imagea, imagea], [mapa, mapa]

@require_torch
@require_vision
class     a		(_lowerCAmelCase					,    unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							List[str]					  =  BeitImageProcessor if is_vision_available() else None

							def    __snake_case (							self	:  Union[str, Any]    )     ->   int:
														__snake_case		:      Tuple										=       BeitImageProcessingTester(self    )

							@property
							def    __snake_case (							self	:  Any    )     ->   Union[str, Any]:
														return self.image_processor_tester.prepare_image_processor_dict()

							def    __snake_case (							self	:  List[Any]    )     ->   Optional[int]:
														__snake_case		:      str										=       self.image_processing_class(**self.image_processor_dict    )
														self.assertTrue(hasattr(lowerCamelCase   ,     "do_resize"    )    )
														self.assertTrue(hasattr(lowerCamelCase   ,     "size"    )    )
														self.assertTrue(hasattr(lowerCamelCase   ,     "do_center_crop"    )    )
														self.assertTrue(hasattr(lowerCamelCase   ,     "center_crop"    )    )
														self.assertTrue(hasattr(lowerCamelCase   ,     "do_normalize"    )    )
														self.assertTrue(hasattr(lowerCamelCase   ,     "image_mean"    )    )
														self.assertTrue(hasattr(lowerCamelCase   ,     "image_std"    )    )

							def    __snake_case (							self	:  str    )     ->   Dict:
														__snake_case		:      Optional[int]										=       self.image_processing_class.from_dict(self.image_processor_dict    )
														self.assertEqual(image_processor.size   ,     {"height": 20, "width": 20}    )
														self.assertEqual(image_processor.crop_size   ,     {"height": 18, "width": 18}    )
														self.assertEqual(image_processor.do_reduce_labels   ,     lowerCamelCase    )

														__snake_case		:      str										=       self.image_processing_class.from_dict(
														    self.image_processor_dict   ,     size=42   ,     crop_size=84   ,     reduce_labels=lowerCamelCase    )
														self.assertEqual(image_processor.size   ,     {"height": 42, "width": 42}    )
														self.assertEqual(image_processor.crop_size   ,     {"height": 84, "width": 84}    )
														self.assertEqual(image_processor.do_reduce_labels   ,     lowerCamelCase    )

							def    __snake_case (							self	:  str    )     ->   str:
														pass

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

														# Test not batched input
														__snake_case		:      Optional[Any]										=       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		:      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.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )   ,     )

							def    __snake_case (							self	:  int    )     ->   Tuple:
														# Initialize image_processing
														__snake_case		:      Any										=       self.image_processing_class(**self.image_processor_dict    )
														# create random numpy tensors
														__snake_case		:      Optional[Any]										=       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
														__snake_case		:      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.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )   ,     )

														# Test batched
														__snake_case		:      Optional[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.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )   ,     )

							def    __snake_case (							self	:  Optional[int]    )     ->   Optional[Any]:
														# Initialize image_processing
														__snake_case		:      Tuple										=       self.image_processing_class(**self.image_processor_dict    )
														# create random PyTorch tensors
														__snake_case		:      Dict										=       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
														__snake_case		:      Any										=       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		:      int										=       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.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )   ,     )

							def    __snake_case (							self	:  Tuple    )     ->   Dict:
														# Initialize image_processing
														__snake_case		:      Optional[Any]										=       self.image_processing_class(**self.image_processor_dict    )
														# create random PyTorch tensors
														__snake_case		:      int										=       prepare_image_inputs(self.image_processor_tester   ,     equal_resolution=lowerCamelCase   ,     torchify=lowerCamelCase    )
														__snake_case		:      List[Any]										=       []
														for image in image_inputs:
																					self.assertIsInstance(lowerCamelCase   ,     torch.Tensor    )
																					maps.append(torch.zeros(image.shape[-2:]    ).long()    )

														# Test not batched input
														__snake_case		:      Union[str, Any]										=       image_processing(image_inputs[0]   ,     maps[0]   ,     return_tensors="pt"    )
														self.assertEqual(
														    encoding["pixel_values"].shape   ,     (
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )   ,     )
														self.assertEqual(
														    encoding["labels"].shape   ,     (
														        1,
														        self.image_processor_tester.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )   ,     )
														self.assertEqual(encoding["labels"].dtype   ,     torch.long    )
														self.assertTrue(encoding["labels"].min().item() >= 0    )
														self.assertTrue(encoding["labels"].max().item() <= 255    )

														# Test batched
														__snake_case		:      int										=       image_processing(lowerCamelCase   ,     lowerCamelCase   ,     return_tensors="pt"    )
														self.assertEqual(
														    encoding["pixel_values"].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"],
														    )   ,     )
														self.assertEqual(
														    encoding["labels"].shape   ,     (
														        self.image_processor_tester.batch_size,
														        self.image_processor_tester.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )   ,     )
														self.assertEqual(encoding["labels"].dtype   ,     torch.long    )
														self.assertTrue(encoding["labels"].min().item() >= 0    )
														self.assertTrue(encoding["labels"].max().item() <= 255    )

														# Test not batched input (PIL images)
														__snake_case		:      List[str]										=       prepare_semantic_single_inputs()

														__snake_case		:      List[str]										=       image_processing(lowerCamelCase   ,     lowerCamelCase   ,     return_tensors="pt"    )
														self.assertEqual(
														    encoding["pixel_values"].shape   ,     (
														        1,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )   ,     )
														self.assertEqual(
														    encoding["labels"].shape   ,     (
														        1,
														        self.image_processor_tester.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )   ,     )
														self.assertEqual(encoding["labels"].dtype   ,     torch.long    )
														self.assertTrue(encoding["labels"].min().item() >= 0    )
														self.assertTrue(encoding["labels"].max().item() <= 255    )

														# Test batched input (PIL images)
														__snake_case		:      int										=       prepare_semantic_batch_inputs()

														__snake_case		:      Dict										=       image_processing(lowerCamelCase   ,     lowerCamelCase   ,     return_tensors="pt"    )
														self.assertEqual(
														    encoding["pixel_values"].shape   ,     (
														        2,
														        self.image_processor_tester.num_channels,
														        self.image_processor_tester.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )   ,     )
														self.assertEqual(
														    encoding["labels"].shape   ,     (
														        2,
														        self.image_processor_tester.crop_size["height"],
														        self.image_processor_tester.crop_size["width"],
														    )   ,     )
														self.assertEqual(encoding["labels"].dtype   ,     torch.long    )
														self.assertTrue(encoding["labels"].min().item() >= 0    )
														self.assertTrue(encoding["labels"].max().item() <= 255    )






							def    __snake_case (							self	:  Union[str, Any]    )     ->   Tuple:
														# Initialize image_processing
														__snake_case		:      str										=       self.image_processing_class(**self.image_processor_dict    )

														# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
														__snake_case		:      str										=       prepare_semantic_single_inputs()
														__snake_case		:      List[str]										=       image_processing(lowerCamelCase   ,     lowerCamelCase   ,     return_tensors="pt"    )
														self.assertTrue(encoding["labels"].min().item() >= 0    )
														self.assertTrue(encoding["labels"].max().item() <= 150    )

														__snake_case		:      Union[str, Any]										=       True
														__snake_case		:      Optional[Any]										=       image_processing(lowerCamelCase   ,     lowerCamelCase   ,     return_tensors="pt"    )
														self.assertTrue(encoding["labels"].min().item() >= 0    )
														self.assertTrue(encoding["labels"].max().item() <= 255    )





# 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.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


_snake_case					:		Optional[Any]      =    "Create a default config file for Accelerate with only a few flags set."







def     lowerCAmelCase_					( __lowerCamelCase="no"							,     __lowerCamelCase = default_json_config_file							,     __lowerCamelCase = False     ):
							__snake_case		:      int										=       Path(__lowerCamelCase     )
							path.parent.mkdir(parents=__lowerCamelCase							,     exist_ok=__lowerCamelCase     )
							if path.exists():
														print(
														    F'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'     )
														return False
							__snake_case		:      Any										=       mixed_precision.lower()
							if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
														raise ValueError(
														    F'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'     )
							__snake_case		:      Optional[int]										=       {
							    "compute_environment": "LOCAL_MACHINE",
							    "mixed_precision": mixed_precision,
							}
							if torch.cuda.is_available():
														__snake_case		:      Dict										=       torch.cuda.device_count()
														__snake_case		:      Tuple										=       num_gpus
														__snake_case		:      List[str]										=       False
														if num_gpus > 1:
																					__snake_case		:      Optional[int]										=       "MULTI_GPU"
														else:
																					__snake_case		:      Dict										=       "NO"
							elif is_xpu_available() and use_xpu:
														__snake_case		:      List[str]										=       torch.xpu.device_count()
														__snake_case		:      str										=       num_xpus
														__snake_case		:      int										=       False
														if num_xpus > 1:
																					__snake_case		:      Optional[int]										=       "MULTI_XPU"
														else:
																					__snake_case		:      str										=       "NO"
							elif is_npu_available():
														__snake_case		:      Any										=       torch.npu.device_count()
														__snake_case		:      str										=       num_npus
														__snake_case		:      str										=       False
														if num_npus > 1:
																					__snake_case		:      Optional[int]										=       "MULTI_NPU"
														else:
																					__snake_case		:      int										=       "NO"
							else:
														__snake_case		:      List[Any]										=       0
														__snake_case		:      Dict										=       True
														__snake_case		:      Tuple										=       1
														__snake_case		:      Tuple										=       "NO"
							__snake_case		:      str										=       ClusterConfig(**__lowerCamelCase     )
							config.to_json_file(__lowerCamelCase     )
							return path







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							__snake_case		:      Optional[Any]										=       parser.add_parser("default"							,     parents=__lowerCamelCase							,     help=__lowerCamelCase							,     formatter_class=__lowerCamelCase     )
							parser.add_argument(
							    "--config_file"							,     default=__lowerCamelCase							,     help=(
							        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
							        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
							        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
							        "with 'huggingface'."
							    )							,     dest="save_location"							,     )

							parser.add_argument(
							    "--mixed_precision"							,     choices=["no", "fp16", "bf16"]							,     type=__lowerCamelCase							,     help="Whether or not to use mixed precision training. "
							    "Choose between FP16 and BF16 (bfloat16) training. "
							    "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."							,     default="no"							,     )
							parser.set_defaults(func=__lowerCamelCase     )
							return parser







def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      List[Any]										=       write_basic_config(args.mixed_precision							,     args.save_location     )
							if config_file:
														print(F'accelerate configuration saved at {config_file}'     )

