





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

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_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		:  Dict			       =logging.get_logger(__name__)

_lowercase		:  List[str]			       =OrderedDict(
    [
        ("""audio-spectrogram-transformer""", """ASTFeatureExtractor"""),
        ("""beit""", """BeitFeatureExtractor"""),
        ("""chinese_clip""", """ChineseCLIPFeatureExtractor"""),
        ("""clap""", """ClapFeatureExtractor"""),
        ("""clip""", """CLIPFeatureExtractor"""),
        ("""clipseg""", """ViTFeatureExtractor"""),
        ("""conditional_detr""", """ConditionalDetrFeatureExtractor"""),
        ("""convnext""", """ConvNextFeatureExtractor"""),
        ("""cvt""", """ConvNextFeatureExtractor"""),
        ("""data2vec-audio""", """Wav2Vec2FeatureExtractor"""),
        ("""data2vec-vision""", """BeitFeatureExtractor"""),
        ("""deformable_detr""", """DeformableDetrFeatureExtractor"""),
        ("""deit""", """DeiTFeatureExtractor"""),
        ("""detr""", """DetrFeatureExtractor"""),
        ("""dinat""", """ViTFeatureExtractor"""),
        ("""donut-swin""", """DonutFeatureExtractor"""),
        ("""dpt""", """DPTFeatureExtractor"""),
        ("""encodec""", """EncodecFeatureExtractor"""),
        ("""flava""", """FlavaFeatureExtractor"""),
        ("""glpn""", """GLPNFeatureExtractor"""),
        ("""groupvit""", """CLIPFeatureExtractor"""),
        ("""hubert""", """Wav2Vec2FeatureExtractor"""),
        ("""imagegpt""", """ImageGPTFeatureExtractor"""),
        ("""layoutlmv2""", """LayoutLMv2FeatureExtractor"""),
        ("""layoutlmv3""", """LayoutLMv3FeatureExtractor"""),
        ("""levit""", """LevitFeatureExtractor"""),
        ("""maskformer""", """MaskFormerFeatureExtractor"""),
        ("""mctct""", """MCTCTFeatureExtractor"""),
        ("""mobilenet_v1""", """MobileNetV1FeatureExtractor"""),
        ("""mobilenet_v2""", """MobileNetV2FeatureExtractor"""),
        ("""mobilevit""", """MobileViTFeatureExtractor"""),
        ("""nat""", """ViTFeatureExtractor"""),
        ("""owlvit""", """OwlViTFeatureExtractor"""),
        ("""perceiver""", """PerceiverFeatureExtractor"""),
        ("""poolformer""", """PoolFormerFeatureExtractor"""),
        ("""regnet""", """ConvNextFeatureExtractor"""),
        ("""resnet""", """ConvNextFeatureExtractor"""),
        ("""segformer""", """SegformerFeatureExtractor"""),
        ("""sew""", """Wav2Vec2FeatureExtractor"""),
        ("""sew-d""", """Wav2Vec2FeatureExtractor"""),
        ("""speech_to_text""", """Speech2TextFeatureExtractor"""),
        ("""speecht5""", """SpeechT5FeatureExtractor"""),
        ("""swiftformer""", """ViTFeatureExtractor"""),
        ("""swin""", """ViTFeatureExtractor"""),
        ("""swinv2""", """ViTFeatureExtractor"""),
        ("""table-transformer""", """DetrFeatureExtractor"""),
        ("""timesformer""", """VideoMAEFeatureExtractor"""),
        ("""tvlt""", """TvltFeatureExtractor"""),
        ("""unispeech""", """Wav2Vec2FeatureExtractor"""),
        ("""unispeech-sat""", """Wav2Vec2FeatureExtractor"""),
        ("""van""", """ConvNextFeatureExtractor"""),
        ("""videomae""", """VideoMAEFeatureExtractor"""),
        ("""vilt""", """ViltFeatureExtractor"""),
        ("""vit""", """ViTFeatureExtractor"""),
        ("""vit_mae""", """ViTFeatureExtractor"""),
        ("""vit_msn""", """ViTFeatureExtractor"""),
        ("""wav2vec2""", """Wav2Vec2FeatureExtractor"""),
        ("""wav2vec2-conformer""", """Wav2Vec2FeatureExtractor"""),
        ("""wavlm""", """Wav2Vec2FeatureExtractor"""),
        ("""whisper""", """WhisperFeatureExtractor"""),
        ("""xclip""", """CLIPFeatureExtractor"""),
        ("""yolos""", """YolosFeatureExtractor"""),
    ]
)

_lowercase		:  List[Any]			       =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__						):
		for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
				if class_name in extractors:
						lowerCamelCase_	:     Optional[Any] 						=      model_type_to_module_name(lowerCAmelCase__						)

						lowerCamelCase_	:     List[str] 						=      importlib.import_module(F".{module_name}"							,'transformers.models'						)
						try:
								return getattr(lowerCAmelCase__							,lowerCAmelCase__						)
						except AttributeError:
								continue

		for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
				if getattr(lowerCAmelCase__							,'__name__'							,lowerCAmelCase__						) == 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.
		lowerCamelCase_	:     str 						=      importlib.import_module('transformers'						)
		if hasattr(lowerCAmelCase__							,lowerCAmelCase__						):
				return getattr(lowerCAmelCase__							,lowerCAmelCase__						)

		return None


def 		_SCREAMING_SNAKE_CASE     ( lowerCAmelCase__							,lowerCAmelCase__ = None							,lowerCAmelCase__ = False							,lowerCAmelCase__ = False							,lowerCAmelCase__ = None							,lowerCAmelCase__ = None							,lowerCAmelCase__ = None							,lowerCAmelCase__ = False							,**lowerCAmelCase__							,):
		lowerCamelCase_	:     List[Any] 						=      get_file_from_repo(
		    lowerCAmelCase__							,lowerCAmelCase__							,cache_dir=lowerCAmelCase__							,force_download=lowerCAmelCase__							,resume_download=lowerCAmelCase__							,proxies=lowerCAmelCase__							,use_auth_token=lowerCAmelCase__							,revision=lowerCAmelCase__							,local_files_only=lowerCAmelCase__							,)
		if resolved_config_file is None:
				logger.info(
				    'Could not locate the feature extractor configuration file, will try to use the model config instead.'						)
				return {}

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






class 						UpperCamelCase_  :



					def __init__(		self     :     str						):
							raise EnvironmentError(
							    'AutoFeatureExtractor is designed to be instantiated '
							    'using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.'						)



					@classmethod
					@replace_list_option_in_docstrings(lowerCamelCase						)
					def   __a     (		cls     :     Optional[int]       ,      lowerCamelCase     :     Optional[int]       ,      **lowerCamelCase     :     List[Any]						):
							lowerCamelCase_	:     Union[str, Any] 						=      kwargs.pop('config'       ,      lowerCamelCase						)
							lowerCamelCase_	:     List[str] 						=      kwargs.pop('trust_remote_code'       ,      lowerCamelCase						)
							lowerCamelCase_	:     int 						=      True

							lowerCamelCase_						,      lowerCamelCase_	:     Any 						=      FeatureExtractionMixin.get_feature_extractor_dict(lowerCamelCase       ,      **lowerCamelCase						)
							lowerCamelCase_	:     Union[str, Any] 						=      config_dict.get('feature_extractor_type'       ,      lowerCamelCase						)
							lowerCamelCase_	:     List[Any] 						=      None
							if "AutoFeatureExtractor" in config_dict.get('auto_map'       ,      {}						):
									lowerCamelCase_	:     Union[str, Any] 						=      config_dict['auto_map']['AutoFeatureExtractor']

							# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
							if feature_extractor_class is None and feature_extractor_auto_map is None:
									if not isinstance(lowerCamelCase       ,      lowerCamelCase						):
											lowerCamelCase_	:     Optional[Any] 						=      AutoConfig.from_pretrained(lowerCamelCase       ,      **lowerCamelCase						)
									# It could be in `config.feature_extractor_type``
									lowerCamelCase_	:     Union[str, Any] 						=      getattr(lowerCamelCase       ,      'feature_extractor_type'       ,      lowerCamelCase						)
									if hasattr(lowerCamelCase       ,      'auto_map'						) and "AutoFeatureExtractor" in config.auto_map:
											lowerCamelCase_	:     Optional[int] 						=      config.auto_map['AutoFeatureExtractor']

							if feature_extractor_class is not None:
									lowerCamelCase_	:     Optional[int] 						=      feature_extractor_class_from_name(lowerCamelCase						)

							lowerCamelCase_	:     List[Any] 						=      feature_extractor_auto_map is not None
							lowerCamelCase_	:     Optional[Any] 						=      feature_extractor_class is not None or type(lowerCamelCase						) in FEATURE_EXTRACTOR_MAPPING
							lowerCamelCase_	:     Optional[int] 						=      resolve_trust_remote_code(
							    lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase						)

							if has_remote_code and trust_remote_code:
									lowerCamelCase_	:     Union[str, Any] 						=      get_class_from_dynamic_module(
									    lowerCamelCase       ,      lowerCamelCase       ,      **lowerCamelCase						)
									lowerCamelCase_	:     Tuple 						=      kwargs.pop('code_revision'       ,      lowerCamelCase						)
									if os.path.isdir(lowerCamelCase						):
											feature_extractor_class.register_for_auto_class()
									return feature_extractor_class.from_dict(lowerCamelCase       ,      **lowerCamelCase						)
							elif feature_extractor_class is not None:
									return feature_extractor_class.from_dict(lowerCamelCase       ,      **lowerCamelCase						)
							# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
							elif type(lowerCamelCase						) in FEATURE_EXTRACTOR_MAPPING:
									lowerCamelCase_	:     List[Any] 						=      FEATURE_EXTRACTOR_MAPPING[type(lowerCamelCase						)]
									return feature_extractor_class.from_dict(lowerCamelCase       ,      **lowerCamelCase						)

							raise ValueError(
							    F"Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a "
							    F"`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following "
							    F"`model_type` keys in its {CONFIG_NAME}: {', '.join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()						)}"						)





					@staticmethod
					def   __a     (		lowerCamelCase     :     str       ,      lowerCamelCase     :     List[Any]						):
							FEATURE_EXTRACTOR_MAPPING.register(lowerCamelCase       ,      lowerCamelCase						)







import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


_lowercase		:  Dict			       =HfApi()

_lowercase		:  str			       ={}
# fmt: off
_lowercase		:  Union[str, Any]			       =torch.tensor([
    -0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
    1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
    -1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
    0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
_lowercase		:  Optional[Any]			       =torch.tensor([
    -2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
    1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
    -2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
    2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
_lowercase		:  Union[str, Any]			       =torch.tensor([
    -0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
    -0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
    -0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
    0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
_lowercase		:  Optional[Any]			       =torch.tensor([
    0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
    -0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
    0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
    -0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
_lowercase		:  Tuple			       =torch.tensor([
    0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
    -0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
    0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
    -0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
_lowercase		:  int			       =torch.tensor([
    0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
    -0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
    0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
    -0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
_lowercase		:  Tuple			       =torch.tensor([
    0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
    -0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
    0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
    -0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
_lowercase		:  Optional[int]			       =torch.tensor([
    0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
    -0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
    0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
    -0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
_lowercase		:  List[Any]			       =torch.tensor([
    -1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
    1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
    -2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
    1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
_lowercase		:  List[Any]			       =torch.tensor([
    -1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
    0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
    -2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
    1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
_lowercase		:  int			       =torch.tensor([
    -1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
    0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
    -2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
    1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
_lowercase		:  List[Any]			       =torch.tensor([
    -2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
    1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
    -3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
    3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
_lowercase		:  Any			       =torch.tensor([
    -2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
    1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
    -2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
    2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
_lowercase		:  List[str]			       =torch.tensor([
    -2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
    1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
    -3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
    3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
_lowercase		:  Tuple			       =torch.tensor([
    -1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
    1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
    -2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
    1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on

_lowercase		:  Optional[int]			       =api.list_models(filter="""diffusers""")
for mod in models:
		if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
				_lowercase		:  str			       ="""/home/patrick/google_checkpoints/""" + mod.modelId.split("""/""")[-1]

				print(F'''Started running {mod.modelId}!!!''')

				if mod.modelId.startswith("""CompVis"""):
						_lowercase		:  str			       =UNetaDModel.from_pretrained(local_checkpoint, subfolder="""unet""")
				else:
						_lowercase		:  Dict			       =UNetaDModel.from_pretrained(local_checkpoint)

				torch.manual_seed(0)
				random.seed(0)

				_lowercase		:  Optional[Any]			       =torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
				_lowercase		:  Dict			       =torch.tensor([10] * noise.shape[0])
				with torch.no_grad():
						_lowercase		:  Union[str, Any]			       =model(noise, time_step).sample

				assert torch.allclose(
				    logits[0, 0, 0, :30], results["""_""".join("""_""".join(mod.modelId.split("""/""")).split("""-"""))], atol=1e-3
				)
				print(F'''{mod.modelId} has passed successfully!!!''')
