

"""simple docstring"""


import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__A						   =			{"LayoutLMv2Config", "LayoutLMv3Config"}





@is_pipeline_test
class     snake_case	(				unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_   :      Tuple         =  MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
				SCREAMING_SNAKE_CASE_   :      Union[str, Any]         =  TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

				if model_mapping is not None:
								SCREAMING_SNAKE_CASE_   :      Tuple         =  {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
				if tf_model_mapping is not None:
								SCREAMING_SNAKE_CASE_   :      str         =  {
								    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
								}

				def 							lowercase_     (	self							:				List[Any]     ,  UpperCamelCase__							:				Optional[int]     ,  UpperCamelCase__							:				Union[str, Any]     ,  UpperCamelCase__							:				List[str])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			ZeroShotClassificationPipeline(
						    model=lowerCAmelCase__     ,  tokenizer=lowerCAmelCase__     ,  candidate_labels=["polics", "health"])
						return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]

				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				List[str]     ,  UpperCamelCase__							:				Dict)->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Dict          =			classifier("Who are you voting for in 2020?"     ,  candidate_labels="politics")
						self.assertEqual(lowerCAmelCase__     ,  {"sequence": ANY(lowerCAmelCase__), "labels": [ANY(lowerCAmelCase__)], "scores": [ANY(lowerCAmelCase__)]})

						# No kwarg
						__lowerCAmelCase:       int          =			classifier("Who are you voting for in 2020?"     ,  ["politics"])
						self.assertEqual(lowerCAmelCase__     ,  {"sequence": ANY(lowerCAmelCase__), "labels": [ANY(lowerCAmelCase__)], "scores": [ANY(lowerCAmelCase__)]})

						__lowerCAmelCase:       List[str]          =			classifier("Who are you voting for in 2020?"     ,  candidate_labels=["politics"])
						self.assertEqual(lowerCAmelCase__     ,  {"sequence": ANY(lowerCAmelCase__), "labels": [ANY(lowerCAmelCase__)], "scores": [ANY(lowerCAmelCase__)]})

						__lowerCAmelCase:       Optional[Any]          =			classifier("Who are you voting for in 2020?"     ,  candidate_labels="politics, public health")
						self.assertEqual(
						    lowerCAmelCase__     ,  {"sequence": ANY(lowerCAmelCase__), "labels": [ANY(lowerCAmelCase__), ANY(lowerCAmelCase__)], "scores": [ANY(lowerCAmelCase__), ANY(lowerCAmelCase__)]})
						self.assertAlmostEqual(sum(nested_simplify(outputs["scores"]))     ,  1.0)

						__lowerCAmelCase:       List[str]          =			classifier("Who are you voting for in 2020?"     ,  candidate_labels=["politics", "public health"])
						self.assertEqual(
						    lowerCAmelCase__     ,  {"sequence": ANY(lowerCAmelCase__), "labels": [ANY(lowerCAmelCase__), ANY(lowerCAmelCase__)], "scores": [ANY(lowerCAmelCase__), ANY(lowerCAmelCase__)]})
						self.assertAlmostEqual(sum(nested_simplify(outputs["scores"]))     ,  1.0)

						__lowerCAmelCase:       Union[str, Any]          =			classifier(
						    "Who are you voting for in 2020?"     ,  candidate_labels="politics"     ,  hypothesis_template="This text is about {}")
						self.assertEqual(lowerCAmelCase__     ,  {"sequence": ANY(lowerCAmelCase__), "labels": [ANY(lowerCAmelCase__)], "scores": [ANY(lowerCAmelCase__)]})

						# https://github.com/huggingface/transformers/issues/13846
						__lowerCAmelCase:       str          =			classifier(["I am happy"]     ,  ["positive", "negative"])
						self.assertEqual(
						    lowerCAmelCase__     ,  [
						        {"sequence": ANY(lowerCAmelCase__), "labels": [ANY(lowerCAmelCase__), ANY(lowerCAmelCase__)], "scores": [ANY(lowerCAmelCase__), ANY(lowerCAmelCase__)]}
						        for i in range(1)
						    ]     ,  )
						__lowerCAmelCase:       Dict          =			classifier(["I am happy", "I am sad"]     ,  ["positive", "negative"])
						self.assertEqual(
						    lowerCAmelCase__     ,  [
						        {"sequence": ANY(lowerCAmelCase__), "labels": [ANY(lowerCAmelCase__), ANY(lowerCAmelCase__)], "scores": [ANY(lowerCAmelCase__), ANY(lowerCAmelCase__)]}
						        for i in range(2)
						    ]     ,  )

						with self.assertRaises(lowerCAmelCase__):
								classifier(""     ,  candidate_labels="politics")

						with self.assertRaises(lowerCAmelCase__):
								classifier(lowerCAmelCase__     ,  candidate_labels="politics")

						with self.assertRaises(lowerCAmelCase__):
								classifier("Who are you voting for in 2020?"     ,  candidate_labels="")

						with self.assertRaises(lowerCAmelCase__):
								classifier("Who are you voting for in 2020?"     ,  candidate_labels=lowerCAmelCase__)

						with self.assertRaises(lowerCAmelCase__):
								classifier(
								    "Who are you voting for in 2020?"     ,  candidate_labels="politics"     ,  hypothesis_template="Not formatting template"     ,  )

						with self.assertRaises(lowerCAmelCase__):
								classifier(
								    "Who are you voting for in 2020?"     ,  candidate_labels="politics"     ,  hypothesis_template=lowerCAmelCase__     ,  )

						self.run_entailment_id(lowerCAmelCase__)

				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				int)->   Any:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			zero_shot_classifier.model.config
						__lowerCAmelCase:       Optional[Any]          =			config.labelaid
						__lowerCAmelCase:       Tuple          =			zero_shot_classifier.entailment_id

						__lowerCAmelCase:       Dict          =			{"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
						self.assertEqual(zero_shot_classifier.entailment_id     ,  -1)

						__lowerCAmelCase:       Optional[int]          =			{"entailment": 0, "neutral": 1, "contradiction": 2}
						self.assertEqual(zero_shot_classifier.entailment_id     ,  0)

						__lowerCAmelCase:       str          =			{"ENTAIL": 0, "NON-ENTAIL": 1}
						self.assertEqual(zero_shot_classifier.entailment_id     ,  0)

						__lowerCAmelCase:       List[str]          =			{"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
						self.assertEqual(zero_shot_classifier.entailment_id     ,  2)

						__lowerCAmelCase:       Any          =			original_labelaid
						self.assertEqual(lowerCAmelCase__     ,  zero_shot_classifier.entailment_id)

				@require_torch
				def 							lowercase_     (	self							:				Optional[int])->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			pipeline(
						    "zero-shot-classification"     ,  model="sshleifer/tiny-distilbert-base-cased-distilled-squad"     ,  framework="pt"     ,  )
						# There was a regression in 4.10 for this
						# Adding a test so we don't make the mistake again.
						# https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
						zero_shot_classifier(
						    "Who are you voting for in 2020?" * 1_0_0     ,  candidate_labels=["politics", "public health", "science"])

				@require_torch
				def 							lowercase_     (	self							:				Union[str, Any])->   Union[str, Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			pipeline(
						    "zero-shot-classification"     ,  model="sshleifer/tiny-distilbert-base-cased-distilled-squad"     ,  framework="pt"     ,  )
						__lowerCAmelCase:       Any          =			zero_shot_classifier(
						    "Who are you voting for in 2020?"     ,  candidate_labels=["politics", "public health", "science"])

						self.assertEqual(
						    nested_simplify(lowerCAmelCase__)     ,  {
						        "sequence": "Who are you voting for in 2020?",
						        "labels": ["science", "public health", "politics"],
						        "scores": [0.333, 0.333, 0.333],
						    }     ,  )

				@require_tf
				def 							lowercase_     (	self							:				Union[str, Any])->   Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			pipeline(
						    "zero-shot-classification"     ,  model="sshleifer/tiny-distilbert-base-cased-distilled-squad"     ,  framework="tf"     ,  )
						__lowerCAmelCase:       List[str]          =			zero_shot_classifier(
						    "Who are you voting for in 2020?"     ,  candidate_labels=["politics", "public health", "science"])

						self.assertEqual(
						    nested_simplify(lowerCAmelCase__)     ,  {
						        "sequence": "Who are you voting for in 2020?",
						        "labels": ["science", "public health", "politics"],
						        "scores": [0.333, 0.333, 0.333],
						    }     ,  )

				@slow
				@require_torch
				def 							lowercase_     (	self							:				Any)->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			pipeline("zero-shot-classification"     ,  model="roberta-large-mnli"     ,  framework="pt")
						__lowerCAmelCase:       Any          =			zero_shot_classifier(
						    "Who are you voting for in 2020?"     ,  candidate_labels=["politics", "public health", "science"])

						self.assertEqual(
						    nested_simplify(lowerCAmelCase__)     ,  {
						        "sequence": "Who are you voting for in 2020?",
						        "labels": ["politics", "public health", "science"],
						        "scores": [0.976, 0.015, 0.009],
						    }     ,  )
						__lowerCAmelCase:       List[str]          =			zero_shot_classifier(
						    "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
						    " in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
						    " through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
						    " solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
						    " machine translation tasks show these models to be superior in quality while being more parallelizable"
						    " and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
						    " English-to-German translation task, improving over the existing best results, including ensembles by"
						    " over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
						    " single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
						    " fraction of the training costs of the best models from the literature. We show that the Transformer"
						    " generalizes well to other tasks by applying it successfully to English constituency parsing both with"
						    " large and limited training data."     ,  candidate_labels=["machine learning", "statistics", "translation", "vision"]     ,  multi_label=lowerCAmelCase__     ,  )
						self.assertEqual(
						    nested_simplify(lowerCAmelCase__)     ,  {
						        "sequence": (
						            "The dominant sequence transduction models are based on complex recurrent or convolutional neural"
						            " networks in an encoder-decoder configuration. The best performing models also connect the"
						            " encoder and decoder through an attention mechanism. We propose a new simple network"
						            " architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
						            " and convolutions entirely. Experiments on two machine translation tasks show these models to be"
						            " superior in quality while being more parallelizable and requiring significantly less time to"
						            " train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
						            " improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
						            " English-to-French translation task, our model establishes a new single-model state-of-the-art"
						            " BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
						            " costs of the best models from the literature. We show that the Transformer generalizes well to"
						            " other tasks by applying it successfully to English constituency parsing both with large and"
						            " limited training data."
						        ),
						        "labels": ["translation", "machine learning", "vision", "statistics"],
						        "scores": [0.817, 0.713, 0.018, 0.018],
						    }     ,  )




				@slow
				@require_tf
				def 							lowercase_     (	self							:				Optional[Any])->   Dict:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			pipeline("zero-shot-classification"     ,  model="roberta-large-mnli"     ,  framework="tf")
						__lowerCAmelCase:       Optional[int]          =			zero_shot_classifier(
						    "Who are you voting for in 2020?"     ,  candidate_labels=["politics", "public health", "science"])

						self.assertEqual(
						    nested_simplify(lowerCAmelCase__)     ,  {
						        "sequence": "Who are you voting for in 2020?",
						        "labels": ["politics", "public health", "science"],
						        "scores": [0.976, 0.015, 0.009],
						    }     ,  )
						__lowerCAmelCase:       int          =			zero_shot_classifier(
						    "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
						    " in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
						    " through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
						    " solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
						    " machine translation tasks show these models to be superior in quality while being more parallelizable"
						    " and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
						    " English-to-German translation task, improving over the existing best results, including ensembles by"
						    " over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
						    " single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
						    " fraction of the training costs of the best models from the literature. We show that the Transformer"
						    " generalizes well to other tasks by applying it successfully to English constituency parsing both with"
						    " large and limited training data."     ,  candidate_labels=["machine learning", "statistics", "translation", "vision"]     ,  multi_label=lowerCAmelCase__     ,  )
						self.assertEqual(
						    nested_simplify(lowerCAmelCase__)     ,  {
						        "sequence": (
						            "The dominant sequence transduction models are based on complex recurrent or convolutional neural"
						            " networks in an encoder-decoder configuration. The best performing models also connect the"
						            " encoder and decoder through an attention mechanism. We propose a new simple network"
						            " architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
						            " and convolutions entirely. Experiments on two machine translation tasks show these models to be"
						            " superior in quality while being more parallelizable and requiring significantly less time to"
						            " train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
						            " improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
						            " English-to-French translation task, our model establishes a new single-model state-of-the-art"
						            " BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
						            " costs of the best models from the literature. We show that the Transformer generalizes well to"
						            " other tasks by applying it successfully to English constituency parsing both with large and"
						            " limited training data."
						        ),
						        "labels": ["translation", "machine learning", "vision", "statistics"],
						        "scores": [0.817, 0.713, 0.018, 0.018],
						    }     ,  )






"""simple docstring"""




import random

import torch
from huggingface_hub import HfApi

from diffusers import UNetaDModel


__UpperCamelCase						=      HfApi()

__UpperCamelCase						=      {}
# fmt: off
__UpperCamelCase						=      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
])
__UpperCamelCase						=      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
])
__UpperCamelCase						=      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
])
__UpperCamelCase						=      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
])
__UpperCamelCase						=      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
])
__UpperCamelCase						=      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
])
__UpperCamelCase						=      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
])
__UpperCamelCase						=      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
])
__UpperCamelCase						=      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])
__UpperCamelCase						=      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
])
__UpperCamelCase						=      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
])
__UpperCamelCase						=      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
])
__UpperCamelCase						=      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
])
__UpperCamelCase						=      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
])
__UpperCamelCase						=      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

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

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

		if mod.modelId.startswith('''CompVis'''):
			__UpperCamelCase						=      UNetaDModel.from_pretrained(local_checkpoint, subfolder='''unet''')
		else:
			__UpperCamelCase						=      UNetaDModel.from_pretrained(local_checkpoint)

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

		__UpperCamelCase						=      torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
		__UpperCamelCase						=      torch.tensor([10] * noise.shape[0])
		with torch.no_grad():
			__UpperCamelCase						=      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!!!''')


