
"""simple docstring"""




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,
)


lowerCAmelCase__				=					logging.get_logger(__name__)

lowerCAmelCase__				=					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'),
    ]
)

lowerCAmelCase__				=					_LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)



def 							_lowerCamelCase			(			__a     ):
		for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
				if class_name in extractors:
						SCREAMING_SNAKE_CASE_    			=			model_type_to_module_name(__a     )

						SCREAMING_SNAKE_CASE_    			=			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.
		SCREAMING_SNAKE_CASE_    			=			importlib.import_module('''transformers'''     )
		if hasattr(__a,				__a     ):
				return getattr(__a,				__a     )

		return None



def 							_lowerCamelCase			(			__a,				__a = None,				__a = False,				__a = False,				__a = None,				__a = None,				__a = None,				__a = False,				**__a,				):
		SCREAMING_SNAKE_CASE_    			=			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 			snake_case  :

							def __init__(self     ):

									"""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(SCREAMING_SNAKE_CASE_     )
							def _lowercase       (cls							,							SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			kwargs.pop('''config'''							,							SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			kwargs.pop('''trust_remote_code'''							,							SCREAMING_SNAKE_CASE_     )
									SCREAMING_SNAKE_CASE_    			=			True

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

									if image_processor_class is not None:
											SCREAMING_SNAKE_CASE_    			=			image_processor_class_from_name(SCREAMING_SNAKE_CASE_     )

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

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

									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 _lowercase       (SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									IMAGE_PROCESSOR_MAPPING.register(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     )

"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
						import numpy as np
						import tensorflow as tf

						from transformers import TFCamembertModel





@require_tf
@require_sentencepiece
@require_tokenizers
class 			snake_case  (				unittest.TestCase ):

							@slow
							def _lowercase       (self     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''     )

									SCREAMING_SNAKE_CASE_    			=			tf.convert_to_tensor(
									    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]							,							dtype=tf.intaa							,							)  # J'aime le camembert !"

									SCREAMING_SNAKE_CASE_    			=			model(SCREAMING_SNAKE_CASE_     )['''last_hidden_state''']
									SCREAMING_SNAKE_CASE_    			=			tf.TensorShape((1, 10, 7_68)     )
									self.assertEqual(output.shape							,							SCREAMING_SNAKE_CASE_     )
									# compare the actual values for a slice.
									SCREAMING_SNAKE_CASE_    			=			tf.convert_to_tensor(
									    [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]							,							dtype=tf.floataa							,							)
									# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
									# camembert.eval()
									# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

									self.assertTrue(np.allclose(output[:, :3, :3].numpy()							,							expected_slice.numpy()							,							atol=1e-4     )     )