



"""simple docstring"""






import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


_lowercase			:			Dict										=						HfApi()

_lowercase			:			List[Any]										=						{}
# fmt: off
_lowercase			:			List[Any]										=						torch.tensor([
    -0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347, 1.3_433, -1.1_743, -3.7_467,
    1.2_342, -2.2_485, 0.4_636, 0.8_076, -0.7_991, 0.3_969, 0.8_498, 0.9_189,
    -1.8_887, -3.3_522, 0.7_639, 0.2_040, 0.6_271, -2.7_148, -1.6_316, 3.0_839,
    0.3_186, 0.2_721, -0.9_759, -1.2_461, 2.6_257, 1.3_557
])
_lowercase			:			Tuple										=						torch.tensor([
    -2.3_639, -2.5_344, 0.0_054, -0.6_674, 1.5_990, 1.0_158, 0.3_124, -2.1_436,
    1.8_795, -2.5_429, -0.1_566, -0.3_973, 1.2_490, 2.6_447, 1.2_283, -0.5_208,
    -2.8_154, -3.5_119, 2.3_838, 1.2_033, 1.7_201, -2.1_256, -1.4_576, 2.7_948,
    2.4_204, -0.9_752, -1.2_546, 0.8_027, 3.2_758, 3.1_365
])
_lowercase			:			List[Any]										=						torch.tensor([
    -0.6_531, -0.6_891, -0.3_172, -0.5_375, -0.9_140, -0.5_367, -0.1_175, -0.7_869,
    -0.3_808, -0.4_513, -0.2_098, -0.0_083, 0.3_183, 0.5_140, 0.2_247, -0.1_304,
    -0.1_302, -0.2_802, -0.2_084, -0.2_025, -0.4_967, -0.4_873, -0.0_861, 0.6_925,
    0.0_250, 0.1_290, -0.1_543, 0.6_316, 1.0_460, 1.4_943
])
_lowercase			:			List[Any]										=						torch.tensor([
    0.0_911, 0.1_107, 0.0_182, 0.0_435, -0.0_805, -0.0_608, 0.0_381, 0.2_172,
    -0.0_280, 0.1_327, -0.0_299, -0.0_255, -0.0_050, -0.1_170, -0.1_046, 0.0_309,
    0.1_367, 0.1_728, -0.0_533, -0.0_748, -0.0_534, 0.1_624, 0.0_384, -0.1_805,
    -0.0_707, 0.0_642, 0.0_220, -0.0_134, -0.1_333, -0.1_505
])
_lowercase			:			Optional[int]										=						torch.tensor([
    0.1_321, 0.1_337, 0.0_440, 0.0_622, -0.0_591, -0.0_370, 0.0_503, 0.2_133,
    -0.0_177, 0.1_415, -0.0_116, -0.0_112, 0.0_044, -0.0_980, -0.0_789, 0.0_395,
    0.1_502, 0.1_785, -0.0_488, -0.0_514, -0.0_404, 0.1_539, 0.0_454, -0.1_559,
    -0.0_665, 0.0_659, 0.0_383, -0.0_005, -0.1_266, -0.1_386
])
_lowercase			:			Tuple										=						torch.tensor([
    0.1_154, 0.1_218, 0.0_307, 0.0_526, -0.0_711, -0.0_541, 0.0_366, 0.2_078,
    -0.0_267, 0.1_317, -0.0_226, -0.0_193, -0.0_014, -0.1_055, -0.0_902, 0.0_330,
    0.1_391, 0.1_709, -0.0_562, -0.0_693, -0.0_560, 0.1_482, 0.0_381, -0.1_683,
    -0.0_681, 0.0_661, 0.0_331, -0.0_046, -0.1_268, -0.1_431
])
_lowercase			:			Tuple										=						torch.tensor([
    0.1_192, 0.1_240, 0.0_414, 0.0_606, -0.0_557, -0.0_412, 0.0_430, 0.2_042,
    -0.0_200, 0.1_385, -0.0_115, -0.0_132, 0.0_017, -0.0_965, -0.0_802, 0.0_398,
    0.1_433, 0.1_747, -0.0_458, -0.0_533, -0.0_407, 0.1_545, 0.0_419, -0.1_574,
    -0.0_645, 0.0_626, 0.0_341, -0.0_010, -0.1_199, -0.1_390
])
_lowercase			:			Optional[Any]										=						torch.tensor([
    0.1_075, 0.1_074, 0.0_205, 0.0_431, -0.0_774, -0.0_607, 0.0_298, 0.2_042,
    -0.0_320, 0.1_267, -0.0_281, -0.0_250, -0.0_064, -0.1_091, -0.0_946, 0.0_290,
    0.1_328, 0.1_650, -0.0_580, -0.0_738, -0.0_586, 0.1_440, 0.0_337, -0.1_746,
    -0.0_712, 0.0_605, 0.0_250, -0.0_099, -0.1_316, -0.1_473
])
_lowercase			:			Union[str, Any]										=						torch.tensor([
    -1.4_572, -2.0_481, -0.0_414, -0.6_005, 1.4_136, 0.5_848, 0.4_028, -2.7_330,
    1.2_212, -2.1_228, 0.2_155, 0.4_039, 0.7_662, 2.0_535, 0.7_477, -0.3_243,
    -2.1_758, -2.7_648, 1.6_947, 0.7_026, 1.2_338, -1.6_078, -0.8_682, 2.2_810,
    1.8_574, -0.5_718, -0.5_586, -0.0_186, 2.3_415, 2.1_251])
_lowercase			:			Dict										=						torch.tensor([
    -1.3_690, -1.9_720, -0.4_090, -0.6_966, 1.4_660, 0.9_938, -0.1_385, -2.7_324,
    0.7_736, -1.8_917, 0.2_923, 0.4_293, 0.1_693, 1.4_112, 1.1_887, -0.3_181,
    -2.2_160, -2.6_381, 1.3_170, 0.8_163, 0.9_240, -1.6_544, -0.6_099, 2.5_259,
    1.6_430, -0.9_090, -0.9_392, -0.0_126, 2.4_268, 2.3_266
])
_lowercase			:			Tuple										=						torch.tensor([
    -1.3_525, -1.9_628, -0.3_956, -0.6_860, 1.4_664, 1.0_014, -0.1_259, -2.7_212,
    0.7_772, -1.8_811, 0.2_996, 0.4_388, 0.1_704, 1.4_029, 1.1_701, -0.3_027,
    -2.2_053, -2.6_287, 1.3_350, 0.8_131, 0.9_274, -1.6_292, -0.6_098, 2.5_131,
    1.6_505, -0.8_958, -0.9_298, -0.0_151, 2.4_257, 2.3_355
])
_lowercase			:			List[str]										=						torch.tensor([
    -2.0_585, -2.7_897, -0.2_850, -0.8_940, 1.9_052, 0.5_702, 0.6_345, -3.8_959,
    1.5_932, -3.2_319, 0.1_974, 0.0_287, 1.7_566, 2.6_543, 0.8_387, -0.5_351,
    -3.2_736, -4.3_375, 2.9_029, 1.6_390, 1.4_640, -2.1_701, -1.9_013, 2.9_341,
    3.4_981, -0.6_255, -1.1_644, -0.1_591, 3.7_097, 3.2_066
])
_lowercase			:			Optional[Any]										=						torch.tensor([
    -2.3_139, -2.5_594, -0.0_197, -0.6_785, 1.7_001, 1.1_606, 0.3_075, -2.1_740,
    1.8_071, -2.5_630, -0.0_926, -0.3_811, 1.2_116, 2.6_246, 1.2_731, -0.5_398,
    -2.8_153, -3.6_140, 2.3_893, 1.3_262, 1.6_258, -2.1_856, -1.3_267, 2.8_395,
    2.3_779, -1.0_623, -1.2_468, 0.8_959, 3.3_367, 3.2_243
])
_lowercase			:			str										=						torch.tensor([
    -2.0_628, -2.7_667, -0.2_089, -0.8_263, 2.0_539, 0.5_992, 0.6_495, -3.8_336,
    1.6_025, -3.2_817, 0.1_721, -0.0_633, 1.7_516, 2.7_039, 0.8_100, -0.5_908,
    -3.2_113, -4.4_343, 2.9_257, 1.3_632, 1.5_562, -2.1_489, -1.9_894, 3.0_560,
    3.3_396, -0.7_328, -1.0_417, 0.0_383, 3.7_093, 3.2_343
])
_lowercase			:			List[str]										=						torch.tensor([
    -1.4_574, -2.0_569, -0.0_473, -0.6_117, 1.4_018, 0.5_769, 0.4_129, -2.7_344,
    1.2_241, -2.1_397, 0.2_000, 0.3_937, 0.7_616, 2.0_453, 0.7_324, -0.3_391,
    -2.1_746, -2.7_744, 1.6_963, 0.6_921, 1.2_187, -1.6_172, -0.8_877, 2.2_439,
    1.8_471, -0.5_839, -0.5_605, -0.0_464, 2.3_250, 2.1_219
])
# fmt: on

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

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

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

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

				_lowercase			:			int										=						torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
				_lowercase			:			Optional[Any]										=						torch.tensor([1_0] * noise.shape[0])
				with torch.no_grad():
						_lowercase			:			Tuple										=						model(noise, time_step).sample

				assert torch.allclose(
				    logits[0, 0, 0, :3_0], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1E-3
				)
				print(f'{mod.modelId} has passed successfully!!!')









"""simple docstring"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings


_lowercase			:			Tuple										=						r"\n    [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n    can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n    Args:\n        title_sep (`str`, *optional*, defaults to  `\" / \"`):\n            Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n        doc_sep (`str`, *optional*, defaults to  `\" // \"`):\n            Separator inserted between the text of the retrieved document and the original input when calling\n            [`RagRetriever`].\n        n_docs (`int`, *optional*, defaults to 5):\n            Number of documents to retrieve.\n        max_combined_length (`int`, *optional*, defaults to 300):\n            Max length of contextualized input returned by [`~RagRetriever.__call__`].\n        retrieval_vector_size (`int`, *optional*, defaults to 768):\n            Dimensionality of the document embeddings indexed by [`RagRetriever`].\n        retrieval_batch_size (`int`, *optional*, defaults to 8):\n            Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n            [`RagRetriever`].\n        dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n            A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n            using `datasets.list_datasets()`).\n        dataset_split (`str`, *optional*, defaults to `\"train\"`)\n            Which split of the `dataset` to load.\n        index_name (`str`, *optional*, defaults to `\"compressed\"`)\n            The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n            `\"compressed\"`.\n        index_path (`str`, *optional*)\n            The path to the serialized faiss index on disk.\n        passages_path (`str`, *optional*):\n            A path to text passages compatible with the faiss index. Required if using\n            [`~models.rag.retrieval_rag.LegacyIndex`]\n        use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n            Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n        label_smoothing (`float`, *optional*, defaults to 0.0):\n            Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n            in the loss calculation. If set to 0, no label smoothing is performed.\n        do_marginalize (`bool`, *optional*, defaults to `False`):\n            If `True`, the logits are marginalized over all documents by making use of\n            `torch.nn.functional.log_softmax`.\n        reduce_loss (`bool`, *optional*, defaults to `False`):\n            Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n        do_deduplication (`bool`, *optional*, defaults to `True`):\n            Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n            set to `False` if used while training with distributed backend.\n        exclude_bos_score (`bool`, *optional*, defaults to `False`):\n            Whether or not to disregard the BOS token when computing the loss.\n        output_retrieved(`bool`, *optional*, defaults to `False`):\n            If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n            `context_attention_mask` are returned. See returned tensors for more detail.\n        use_cache (`bool`, *optional*, defaults to `True`):\n            Whether or not the model should return the last key/values attentions (not used by all models).\n        forced_eos_token_id (`int`, *optional*):\n            The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n            `eos_token_id`.\n"

@add_start_docstrings(lowerCAmelCase_       )
class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							'rag'
					_a             =							True



					def __init__(	self							:  Optional[int],  lowerCamelCase							:  Union[str, Any]=None,  lowerCamelCase							:  Union[str, Any]=True,  lowerCamelCase							:  Dict=None,  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  Any=None,  lowerCamelCase							:  List[Any]=None,  lowerCamelCase							:  Optional[Any]=None,  lowerCamelCase							:  Any=" / ",  lowerCamelCase							:  Union[str, Any]=" // ",  lowerCamelCase							:  List[Any]=5,  lowerCamelCase							:  int=300,  lowerCamelCase							:  Optional[Any]=768,  lowerCamelCase							:  Optional[Any]=8,  lowerCamelCase							:  Tuple="wiki_dpr",  lowerCamelCase							:  Tuple="train",  lowerCamelCase							:  Union[str, Any]="compressed",  lowerCamelCase							:  List[str]=None,  lowerCamelCase							:  Any=None,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  Dict=False,  lowerCamelCase							:  Optional[int]=0.0,  lowerCamelCase							:  Any=True,  lowerCamelCase							:  Dict=False,  lowerCamelCase							:  Tuple=False,  lowerCamelCase							:  List[str]=False,  lowerCamelCase							:  List[Any]=True,  lowerCamelCase							:  Optional[Any]=None,  **lowerCamelCase							:  str,  )-> List[Any]:
								super().__init__(
								    bos_token_id=lowerCamelCase,  pad_token_id=lowerCamelCase,  eos_token_id=lowerCamelCase,  decoder_start_token_id=lowerCamelCase,  forced_eos_token_id=lowerCamelCase,  is_encoder_decoder=lowerCamelCase,  prefix=lowerCamelCase,  vocab_size=lowerCamelCase,  **lowerCamelCase,  )
								assert (
								    "question_encoder" in kwargs and "generator" in kwargs
								), "Config has to be initialized with question_encoder and generator config"
								lowerCamelCase__		:		Tuple		     =kwargs.pop('''question_encoder'''					)
								lowerCamelCase__		:		int		     =question_encoder_config.pop('''model_type'''					)
								lowerCamelCase__		:		Dict		     =kwargs.pop('''generator'''					)
								lowerCamelCase__		:		Tuple		     =decoder_config.pop('''model_type'''					)

								from ..auto.configuration_auto import AutoConfig

								lowerCamelCase__		:		Tuple		     =AutoConfig.for_model(lowerCamelCase,  **lowerCamelCase					)
								lowerCamelCase__		:		Dict		     =AutoConfig.for_model(lowerCamelCase,  **lowerCamelCase					)

								lowerCamelCase__		:		Any		     =reduce_loss
								lowerCamelCase__		:		Tuple		     =label_smoothing
								lowerCamelCase__		:		List[str]		     =exclude_bos_score
								lowerCamelCase__		:		Dict		     =do_marginalize

								lowerCamelCase__		:		Union[str, Any]		     =title_sep
								lowerCamelCase__		:		Dict		     =doc_sep
								lowerCamelCase__		:		List[Any]		     =n_docs
								lowerCamelCase__		:		List[str]		     =max_combined_length

								lowerCamelCase__		:		List[Any]		     =dataset
								lowerCamelCase__		:		int		     =dataset_split
								lowerCamelCase__		:		List[Any]		     =index_name

								lowerCamelCase__		:		int		     =retrieval_vector_size
								lowerCamelCase__		:		Dict		     =retrieval_batch_size
								lowerCamelCase__		:		str		     =passages_path
								lowerCamelCase__		:		Any		     =index_path
								lowerCamelCase__		:		List[Any]		     =use_dummy_dataset

								lowerCamelCase__		:		Optional[int]		     =output_retrieved

								lowerCamelCase__		:		List[str]		     =do_deduplication

								lowerCamelCase__		:		Tuple		     =use_cache

								if self.forced_eos_token_id is None:
											lowerCamelCase__		:		int		     =getattr(self.generator,  '''forced_eos_token_id''',  lowerCamelCase					)



					@classmethod
					def 		snake_case	(	cls							:  List[str],  lowerCamelCase							:  PretrainedConfig,  lowerCamelCase							:  PretrainedConfig,  **lowerCamelCase							:  int					)-> PretrainedConfig:
								return cls(question_encoder=question_encoder_config.to_dict(),  generator=generator_config.to_dict(),  **lowerCamelCase					)




					def 		snake_case	(	self							:  Optional[int]					)-> int:
								lowerCamelCase__		:		Union[str, Any]		     =copy.deepcopy(self.__dict__					)
								lowerCamelCase__		:		Optional[int]		     =self.question_encoder.to_dict()
								lowerCamelCase__		:		Tuple		     =self.generator.to_dict()
								lowerCamelCase__		:		Optional[Any]		     =self.__class__.model_type
								return output




