

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,
)


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  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"),
    ]
)

lowercase_			=  _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     str				)							->				Union[str, Any]:





    '''simple docstring'''






    for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
        if class_name in extractors:
            A__   				=       model_type_to_module_name(SCREAMING_SNAKE_CASE__				)

            A__   				=       importlib.import_module(f'.{module_name}'   ,      'transformers.models'				)
            try:
                return getattr(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
            except AttributeError:
                continue

    for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
        if getattr(SCREAMING_SNAKE_CASE__   ,      '__name__'   ,      SCREAMING_SNAKE_CASE__				) == 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.
    A__   				=       importlib.import_module('transformers'				)
    if hasattr(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				):
        return getattr(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

    return None
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Union[str, os.PathLike]   ,      SCREAMING_SNAKE_CASE__      :     Optional[Union[str, os.PathLike]] = None   ,      SCREAMING_SNAKE_CASE__      :     bool = False   ,      SCREAMING_SNAKE_CASE__      :     bool = False   ,      SCREAMING_SNAKE_CASE__      :     Optional[Dict[str, str]] = None   ,      SCREAMING_SNAKE_CASE__      :     Optional[Union[bool, str]] = None   ,      SCREAMING_SNAKE_CASE__      :     Optional[str] = None   ,      SCREAMING_SNAKE_CASE__      :     bool = False   ,      **SCREAMING_SNAKE_CASE__      :     List[Any]   ,      )							->				int:





    '''simple docstring'''






    A__   				=       get_file_from_repo(
        SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      cache_dir=SCREAMING_SNAKE_CASE__   ,      force_download=SCREAMING_SNAKE_CASE__   ,      resume_download=SCREAMING_SNAKE_CASE__   ,      proxies=SCREAMING_SNAKE_CASE__   ,      use_auth_token=SCREAMING_SNAKE_CASE__   ,      revision=SCREAMING_SNAKE_CASE__   ,      local_files_only=SCREAMING_SNAKE_CASE__   ,      )
    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(SCREAMING_SNAKE_CASE__   ,      encoding='utf-8'				) as reader:
        return json.load(SCREAMING_SNAKE_CASE__				)


class 			A						:







    """simple docstring"""





    def __init__(				self			:	Any		)->   Dict:





        '''simple docstring'''







        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(lowercase_		)
    def 						snake_case__					(				cls			:	Tuple,lowercase_			:	str,**lowercase_			:	Union[str, Any]		)->   List[Any]:





        '''simple docstring'''







        A__   				=       kwargs.pop('config',lowercase_		)
        A__   				=       kwargs.pop('trust_remote_code',lowercase_		)
        A__   				=       True

        A__				,						A__   				=       ImageProcessingMixin.get_image_processor_dict(lowercase_,**lowercase_		)
        A__   				=       config_dict.get('image_processor_type',lowercase_		)
        A__   				=       None
        if "AutoImageProcessor" in config_dict.get('auto_map',{}		):
            A__   				=       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:
            A__   				=       config_dict.pop('feature_extractor_type',lowercase_		)
            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.'		)
                A__   				=       feature_extractor_class.replace('FeatureExtractor','ImageProcessor'		)
            if "AutoFeatureExtractor" in config_dict.get('auto_map',{}		):
                A__   				=       config_dict['auto_map']['AutoFeatureExtractor']
                A__   				=       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(lowercase_,lowercase_		):
                A__   				=       AutoConfig.from_pretrained(lowercase_,**lowercase_		)
            # It could be in `config.image_processor_type``
            A__   				=       getattr(lowercase_,'image_processor_type',lowercase_		)
            if hasattr(lowercase_,'auto_map'		) and "AutoImageProcessor" in config.auto_map:
                A__   				=       config.auto_map['AutoImageProcessor']

        if image_processor_class is not None:
            A__   				=       image_processor_class_from_name(lowercase_		)

        A__   				=       image_processor_auto_map is not None
        A__   				=       image_processor_class is not None or type(lowercase_		) in IMAGE_PROCESSOR_MAPPING
        A__   				=       resolve_trust_remote_code(
            lowercase_,lowercase_,lowercase_,lowercase_		)

        if has_remote_code and trust_remote_code:
            A__   				=       get_class_from_dynamic_module(
                lowercase_,lowercase_,**lowercase_		)
            A__   				=       kwargs.pop('code_revision',lowercase_		)
            if os.path.isdir(lowercase_		):
                image_processor_class.register_for_auto_class()
            return image_processor_class.from_dict(lowercase_,**lowercase_		)
        elif image_processor_class is not None:
            return image_processor_class.from_dict(lowercase_,**lowercase_		)
        # Last try: we use the IMAGE_PROCESSOR_MAPPING.
        elif type(lowercase_		) in IMAGE_PROCESSOR_MAPPING:
            A__   				=       IMAGE_PROCESSOR_MAPPING[type(lowercase_		)]
            return image_processor_class.from_dict(lowercase_,**lowercase_		)

        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__					(				lowercase_			:	List[str],lowercase_			:	Tuple		)->   str:





        '''simple docstring'''







        IMAGE_PROCESSOR_MAPPING.register(lowercase_,lowercase_		)




# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


lowercase_			=  Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

lowercase_			=  {"base": "patrickvonplaten/wav2vec2_tiny_random", "robust": "patrickvonplaten/wav2vec2_tiny_random_robust"}

lowercase_			=  "zero2"
lowercase_			=  "zero3"
lowercase_			=  [ZEROa, ZEROa]
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     List[Any]				)							->				Dict:





    '''simple docstring'''






    A__   				=       parameterized.to_safe_name('_'.join(str(SCREAMING_SNAKE_CASE__				) for x in param.args				)				)
    return f'{func.__name__}_{param_based_name}'


# Cartesian-product of zero stages with models to test
lowercase_			=  list(itertools.product(stages, models.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	int,lowercase_			:	str,lowercase_			:	Any		)->   Optional[int]:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @require_torch_multi_gpu
    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	Optional[Any],lowercase_			:	List[Any]		)->   int:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	List[str],lowercase_			:	List[str],lowercase_			:	List[Any]		)->   Any:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    @require_torch_multi_gpu
    @parameterized.expand(lowercase_,name_func=lowercase_		)
    def 						snake_case__					(				self			:	Dict,lowercase_			:	Optional[Any],lowercase_			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







        self.run_and_check(
            stage=lowercase_,model=lowercase_,distributed=lowercase_,fpaa=lowercase_,)


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	Any		)->   Union[str, Any]:





        '''simple docstring'''







        pass


    def 						snake_case__					(				self			:	int,lowercase_			:	str,lowercase_			:	str,lowercase_			:	int = 1_0,lowercase_			:	bool = True,lowercase_			:	bool = True,lowercase_			:	bool = True,)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       models[model]

        A__   				=       self.run_trainer(
            stage=lowercase_,model_name=lowercase_,eval_steps=lowercase_,num_train_epochs=1,distributed=lowercase_,fpaa=lowercase_,)

        self.do_checks(lowercase_		)

        return output_dir


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	str,lowercase_			:	str,lowercase_			:	int = 1_0,lowercase_			:	int = 1,lowercase_			:	bool = True,lowercase_			:	bool = True,)->   Any:





        '''simple docstring'''







        A__   				=       self.get_auto_remove_tmp_dir('./xxx',after=lowercase_		)
        A__   				=       F'\n            --model_name_or_path {model_name}\n            --dataset_name hf-internal-testing/librispeech_asr_dummy\n            --dataset_config_name clean\n            --train_split_name validation\n            --validation_split_name validation\n            --output_dir {output_dir}\n            --num_train_epochs {str(lowercase_		)}\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 2\n            --evaluation_strategy steps\n            --learning_rate 5e-4\n            --warmup_steps 8\n            --orthography timit\n            --preprocessing_num_workers 1\n            --group_by_length\n            --freeze_feature_extractor\n            --report_to none\n            --save_steps 0\n            --eval_steps {eval_steps}\n            --report_to none\n        '.split()

        if fpaa:
            args.extend(['--fp16']		)

        # currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
        # hence the separate config files
        A__   				=       F'--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json'.split()
        A__   				=       [F'{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py']
        A__   				=       self.get_launcher(lowercase_		)

        A__   				=       launcher + script + args + ds_args
        # keep for quick debug
        # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
        execute_subprocess_async(lowercase_,env=self.get_env()		)

        return output_dir


    def 						snake_case__					(				self			:	Any,lowercase_			:	int=False		)->   Tuple:





        '''simple docstring'''







        A__   				=       min(2,get_gpu_count()		) if distributed else 1
        return F'deepspeed --num_nodes 1 --num_gpus {num_gpus}'.split()

