


import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


_snake_case					:		Optional[int]      =    logging.get_logger(__name__)

_snake_case					:		List[str]      =    OrderedDict(
    [
        ("align", "EfficientNetImageProcessor"),
        ("beit", "BeitImageProcessor"),
        ("bit", "BitImageProcessor"),
        ("blip", "BlipImageProcessor"),
        ("blip-2", "BlipImageProcessor"),
        ("bridgetower", "BridgeTowerImageProcessor"),
        ("chinese_clip", "ChineseCLIPImageProcessor"),
        ("clip", "CLIPImageProcessor"),
        ("clipseg", "ViTImageProcessor"),
        ("conditional_detr", "ConditionalDetrImageProcessor"),
        ("convnext", "ConvNextImageProcessor"),
        ("convnextv2", "ConvNextImageProcessor"),
        ("cvt", "ConvNextImageProcessor"),
        ("data2vec-vision", "BeitImageProcessor"),
        ("deformable_detr", "DeformableDetrImageProcessor"),
        ("deit", "DeiTImageProcessor"),
        ("deta", "DetaImageProcessor"),
        ("detr", "DetrImageProcessor"),
        ("dinat", "ViTImageProcessor"),
        ("donut-swin", "DonutImageProcessor"),
        ("dpt", "DPTImageProcessor"),
        ("efficientformer", "EfficientFormerImageProcessor"),
        ("efficientnet", "EfficientNetImageProcessor"),
        ("flava", "FlavaImageProcessor"),
        ("focalnet", "BitImageProcessor"),
        ("git", "CLIPImageProcessor"),
        ("glpn", "GLPNImageProcessor"),
        ("groupvit", "CLIPImageProcessor"),
        ("imagegpt", "ImageGPTImageProcessor"),
        ("instructblip", "BlipImageProcessor"),
        ("layoutlmv2", "LayoutLMv2ImageProcessor"),
        ("layoutlmv3", "LayoutLMv3ImageProcessor"),
        ("levit", "LevitImageProcessor"),
        ("mask2former", "Mask2FormerImageProcessor"),
        ("maskformer", "MaskFormerImageProcessor"),
        ("mgp-str", "ViTImageProcessor"),
        ("mobilenet_v1", "MobileNetV1ImageProcessor"),
        ("mobilenet_v2", "MobileNetV2ImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevit", "MobileViTImageProcessor"),
        ("mobilevitv2", "MobileViTImageProcessor"),
        ("nat", "ViTImageProcessor"),
        ("oneformer", "OneFormerImageProcessor"),
        ("owlvit", "OwlViTImageProcessor"),
        ("perceiver", "PerceiverImageProcessor"),
        ("pix2struct", "Pix2StructImageProcessor"),
        ("poolformer", "PoolFormerImageProcessor"),
        ("regnet", "ConvNextImageProcessor"),
        ("resnet", "ConvNextImageProcessor"),
        ("sam", "SamImageProcessor"),
        ("segformer", "SegformerImageProcessor"),
        ("swiftformer", "ViTImageProcessor"),
        ("swin", "ViTImageProcessor"),
        ("swin2sr", "Swin2SRImageProcessor"),
        ("swinv2", "ViTImageProcessor"),
        ("table-transformer", "DetrImageProcessor"),
        ("timesformer", "VideoMAEImageProcessor"),
        ("tvlt", "TvltImageProcessor"),
        ("upernet", "SegformerImageProcessor"),
        ("van", "ConvNextImageProcessor"),
        ("videomae", "VideoMAEImageProcessor"),
        ("vilt", "ViltImageProcessor"),
        ("vit", "ViTImageProcessor"),
        ("vit_hybrid", "ViTHybridImageProcessor"),
        ("vit_mae", "ViTImageProcessor"),
        ("vit_msn", "ViTImageProcessor"),
        ("xclip", "CLIPImageProcessor"),
        ("yolos", "YolosImageProcessor"),
    ]
)

_snake_case					:		Union[str, Any]      =    _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)







def     lowerCAmelCase_					( __lowerCamelCase     ):





							for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
														if class_name in extractors:
																					__snake_case		:      Optional[Any]										=       model_type_to_module_name(A__     )

																					__snake_case		:      str										=       importlib.import_module(F'.{module_name}'							,     "transformers.models"     )
																					try:
																												return getattr(A__							,     A__     )
																					except AttributeError:
																												continue

							for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
														if getattr(A__							,     "__name__"							,     A__     ) == class_name:
																					return extractor

														# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
														# init and we return the proper dummy to get an appropriate error message.
							__snake_case		:      List[str]										=       importlib.import_module("transformers"     )
							if hasattr(A__							,     A__     ):
														return getattr(A__							,     A__     )

							return None







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase = None							,     __lowerCamelCase = False							,     __lowerCamelCase = False							,     __lowerCamelCase = None							,     __lowerCamelCase = None							,     __lowerCamelCase = None							,     __lowerCamelCase = False							,     **__lowerCamelCase							,     ):
							__snake_case		:      Optional[Any]										=       get_file_from_repo(
							    A__							,     A__							,     cache_dir=A__							,     force_download=A__							,     resume_download=A__							,     proxies=A__							,     use_auth_token=A__							,     revision=A__							,     local_files_only=A__							,     )
							if resolved_config_file is None:
														logger.info(
														    "Could not locate the image processor configuration file, will try to use the model config instead."     )
														return {}

							with open(A__							,     encoding="utf-8"     ) as reader:
														return json.load(A__     )

class     a		:




							"""simple docstring"""


							def __init__(							self	:  Union[str, Any]    )     ->   str:
														raise EnvironmentError(
														    "AutoImageProcessor is designed to be instantiated "
														    "using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method."    )

							@classmethod
							@replace_list_option_in_docstrings(lowerCamelCase    )
							def    __snake_case (							cls	:  Tuple   ,     lowerCamelCase	:  Dict   ,     **lowerCamelCase	:  Optional[Any]    )     ->   Dict:
														__snake_case		:      List[str]										=       kwargs.pop("config"   ,     lowerCamelCase    )
														__snake_case		:      Tuple										=       kwargs.pop("trust_remote_code"   ,     lowerCamelCase    )
														__snake_case		:      List[str]										=       True

														__snake_case		:      int										=       ImageProcessingMixin.get_image_processor_dict(lowerCamelCase   ,     **lowerCamelCase    )
														__snake_case		:      List[Any]										=       config_dict.get("image_processor_type"   ,     lowerCamelCase    )
														__snake_case		:      int										=       None
														if "AutoImageProcessor" in config_dict.get("auto_map"   ,     {}    ):
																					__snake_case		:      str										=       config_dict["""auto_map"""]["""AutoImageProcessor"""]

														# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
														# and if so, infer the image processor class from there.
														if image_processor_class is None and image_processor_auto_map is None:
																					__snake_case		:      List[Any]										=       config_dict.pop("feature_extractor_type"   ,     lowerCamelCase    )
																					if feature_extractor_class is not None:
																												logger.warning(
																												    "Could not find image processor class in the image processor config or the model config. Loading"
																												    " based on pattern matching with the model's feature extractor configuration."    )
																												__snake_case		:      Optional[int]										=       feature_extractor_class.replace("FeatureExtractor"   ,     "ImageProcessor"    )
																					if "AutoFeatureExtractor" in config_dict.get("auto_map"   ,     {}    ):
																												__snake_case		:      Optional[Any]										=       config_dict["""auto_map"""]["""AutoFeatureExtractor"""]
																												__snake_case		:      Dict										=       feature_extractor_auto_map.replace("FeatureExtractor"   ,     "ImageProcessor"    )
																												logger.warning(
																												    "Could not find image processor auto map in the image processor config or the model config."
																												    " Loading based on pattern matching with the model's feature extractor configuration."    )

																					# If we don't find the image processor class in the image processor config, let's try the model config.
														if image_processor_class is None and image_processor_auto_map is None:
																					if not isinstance(lowerCamelCase   ,     lowerCamelCase    ):
																												__snake_case		:      str										=       AutoConfig.from_pretrained(lowerCamelCase   ,     **lowerCamelCase    )
																					# It could be in `config.image_processor_type``
																					__snake_case		:      Optional[Any]										=       getattr(lowerCamelCase   ,     "image_processor_type"   ,     lowerCamelCase    )
																					if hasattr(lowerCamelCase   ,     "auto_map"    ) and "AutoImageProcessor" in config.auto_map:
																												__snake_case		:      Dict										=       config.auto_map["""AutoImageProcessor"""]

														if image_processor_class is not None:
																					__snake_case		:      Optional[Any]										=       image_processor_class_from_name(lowerCamelCase    )

														__snake_case		:      Optional[Any]										=       image_processor_auto_map is not None
														__snake_case		:      Any										=       image_processor_class is not None or type(lowerCamelCase    ) in IMAGE_PROCESSOR_MAPPING
														__snake_case		:      Union[str, Any]										=       resolve_trust_remote_code(
														    lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase    )

														if has_remote_code and trust_remote_code:
																					__snake_case		:      Dict										=       get_class_from_dynamic_module(
																					    lowerCamelCase   ,     lowerCamelCase   ,     **lowerCamelCase    )
																					__snake_case		:      int										=       kwargs.pop("code_revision"   ,     lowerCamelCase    )
																					if os.path.isdir(lowerCamelCase    ):
																												image_processor_class.register_for_auto_class()
																					return image_processor_class.from_dict(lowerCamelCase   ,     **lowerCamelCase    )
														elif image_processor_class is not None:
																					return image_processor_class.from_dict(lowerCamelCase   ,     **lowerCamelCase    )
														# Last try: we use the IMAGE_PROCESSOR_MAPPING.
														elif type(lowerCamelCase    ) in IMAGE_PROCESSOR_MAPPING:
																					__snake_case		:      List[str]										=       IMAGE_PROCESSOR_MAPPING[type(lowerCamelCase    )]
																					return image_processor_class.from_dict(lowerCamelCase   ,     **lowerCamelCase    )

														raise ValueError(
														    F'Unrecognized image processor in {pretrained_model_name_or_path}. Should have a '
														    F'`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following '
														    F'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys()    )}'    )






							@staticmethod
							def    __snake_case (							lowerCamelCase	:  Optional[Any]   ,     lowerCamelCase	:  Optional[Any]    )     ->   List[Any]:
														IMAGE_PROCESSOR_MAPPING.register(lowerCamelCase   ,     lowerCamelCase    )





# 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}'     )

