'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import (
						    TFRoFormerForCausalLM,
						    TFRoFormerForMaskedLM,
						    TFRoFormerForMultipleChoice,
						    TFRoFormerForQuestionAnswering,
						    TFRoFormerForSequenceClassification,
						    TFRoFormerForTokenClassification,
						    TFRoFormerModel,
						)
						from transformers.models.roformer.modeling_tf_roformer import (
						    TFRoFormerSelfAttention,
						    TFRoFormerSinusoidalPositionalEmbedding,
						)





class     __magic_name__						:







			def __init__(	self				:  str				,			lowercase_				:  Optional[int]				,			lowercase_				:  List[Any]=13				,			lowercase_				:  Optional[int]=7				,			lowercase_				:  Tuple=True				,			lowercase_				:  str=True				,			lowercase_				:  Tuple=True				,			lowercase_				:  Optional[Any]=True				,			lowercase_				:  List[Any]=99				,			lowercase_				:  int=32				,			lowercase_				:  str=2				,			lowercase_				:  List[str]=4				,			lowercase_				:  Optional[Any]=37				,			lowercase_				:  str="gelu"				,			lowercase_				:  List[Any]=0.1				,			lowercase_				:  List[str]=0.1				,			lowercase_				:  int=512				,			lowercase_				:  List[str]=16				,			lowercase_				:  str=2				,			lowercase_				:  List[str]=0.02				,			lowercase_				:  str=3				,			lowercase_				:  Tuple=4				,			lowercase_				:  Any=None				,			):
										lowercase_					:			int										=       parent
										lowercase_					:			Any										=       13
										lowercase_					:			List[Any]										=       7
										lowercase_					:			int										=       True
										lowercase_					:			Tuple										=       True
										lowercase_					:			Optional[Any]										=       True
										lowercase_					:			Optional[int]										=       True
										lowercase_					:			Dict										=       99
										lowercase_					:			str										=       32
										lowercase_					:			str										=       2
										lowercase_					:			Tuple										=       4
										lowercase_					:			Tuple										=       37
										lowercase_					:			Union[str, Any]										=       """gelu"""
										lowercase_					:			int										=       0.1
										lowercase_					:			Dict										=       0.1
										lowercase_					:			Optional[Any]										=       512
										lowercase_					:			Any										=       16
										lowercase_					:			Dict										=       2
										lowercase_					:			Dict										=       0.02
										lowercase_					:			Optional[Any]										=       3
										lowercase_					:			int										=       4
										lowercase_					:			str										=       None







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										lowercase_					:			Tuple										=       ids_tensor([self.batch_size, self.seq_length]				,			self.vocab_size    )

										lowercase_					:			List[str]										=       None
										if self.use_input_mask:
																	lowercase_					:			Optional[int]										=       random_attention_mask([self.batch_size, self.seq_length]    )

										lowercase_					:			Union[str, Any]										=       None
										if self.use_token_type_ids:
																	lowercase_					:			Dict										=       ids_tensor([self.batch_size, self.seq_length]				,			self.type_vocab_size    )

										lowercase_					:			Optional[Any]										=       None
										lowercase_					:			Optional[int]										=       None
										lowercase_					:			Dict										=       None
										if self.use_labels:
																	lowercase_					:			int										=       ids_tensor([self.batch_size]				,			self.type_sequence_label_size    )
																	lowercase_					:			List[str]										=       ids_tensor([self.batch_size, self.seq_length]				,			self.num_labels    )
																	lowercase_					:			Optional[Any]										=       ids_tensor([self.batch_size]				,			self.num_choices    )

										lowercase_					:			Union[str, Any]										=       RoFormerConfig(
										    vocab_size=self.vocab_size				,			hidden_size=self.hidden_size				,			num_hidden_layers=self.num_hidden_layers				,			num_attention_heads=self.num_attention_heads				,			intermediate_size=self.intermediate_size				,			hidden_act=self.hidden_act				,			hidden_dropout_prob=self.hidden_dropout_prob				,			attention_probs_dropout_prob=self.attention_probs_dropout_prob				,			max_position_embeddings=self.max_position_embeddings				,			type_vocab_size=self.type_vocab_size				,			initializer_range=self.initializer_range				,			return_dict=lowercase_				,			)

										return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  Optional[int]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  List[str]				,			lowercase_				:  List[str]				,			lowercase_				:  Any				,			lowercase_				:  str				,			lowercase_				:  Union[str, Any]    ):
										lowercase_					:			int										=       TFRoFormerModel(config=lowercase_    )
										lowercase_					:			Any										=       {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

										lowercase_					:			List[Any]										=       [input_ids, input_mask]
										lowercase_					:			Any										=       model(lowercase_    )

										lowercase_					:			Any										=       model(lowercase_    )

										self.parent.assertEqual(result.last_hidden_state.shape				,			(self.batch_size, self.seq_length, self.hidden_size)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  int				,			lowercase_				:  List[Any]				,			lowercase_				:  Any				,			lowercase_				:  List[str]				,			lowercase_				:  str				,			lowercase_				:  str				,			lowercase_				:  List[str]    ):
										lowercase_					:			str										=       True
										lowercase_					:			List[Any]										=       TFRoFormerForCausalLM(config=lowercase_    )
										lowercase_					:			Union[str, Any]										=       {
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}
										lowercase_					:			Optional[Any]										=       model(lowercase_    )["""logits"""]
										self.parent.assertListEqual(
										    list(prediction_scores.numpy().shape    )				,			[self.batch_size, self.seq_length, self.vocab_size]    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  int				,			lowercase_				:  List[str]				,			lowercase_				:  Optional[Any]				,			lowercase_				:  Any				,			lowercase_				:  Optional[int]				,			lowercase_				:  Optional[Any]    ):
										lowercase_					:			List[Any]										=       TFRoFormerForMaskedLM(config=lowercase_    )
										lowercase_					:			str										=       {
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}
										lowercase_					:			Dict										=       model(lowercase_    )
										self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.vocab_size)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Optional[int]				,			lowercase_				:  Optional[int]				,			lowercase_				:  List[str]				,			lowercase_				:  Optional[int]				,			lowercase_				:  Optional[int]				,			lowercase_				:  int				,			lowercase_				:  Optional[Any]    ):
										lowercase_					:			List[Any]										=       self.num_labels
										lowercase_					:			Any										=       TFRoFormerForSequenceClassification(config=lowercase_    )
										lowercase_					:			Any										=       {
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}

										lowercase_					:			List[str]										=       model(lowercase_    )
										self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_labels)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple				,			lowercase_				:  List[Any]				,			lowercase_				:  List[str]				,			lowercase_				:  int				,			lowercase_				:  Optional[int]				,			lowercase_				:  Optional[int]				,			lowercase_				:  Optional[Any]				,			lowercase_				:  Any    ):
										lowercase_					:			Optional[int]										=       self.num_choices
										lowercase_					:			int										=       TFRoFormerForMultipleChoice(config=lowercase_    )
										lowercase_					:			str										=       tf.tile(tf.expand_dims(lowercase_				,			1    )				,			(1, self.num_choices, 1)    )
										lowercase_					:			Optional[int]										=       tf.tile(tf.expand_dims(lowercase_				,			1    )				,			(1, self.num_choices, 1)    )
										lowercase_					:			Union[str, Any]										=       tf.tile(tf.expand_dims(lowercase_				,			1    )				,			(1, self.num_choices, 1)    )
										lowercase_					:			str										=       {
										    """input_ids""": multiple_choice_inputs_ids,
										    """attention_mask""": multiple_choice_input_mask,
										    """token_type_ids""": multiple_choice_token_type_ids,
										}
										lowercase_					:			Optional[Any]										=       model(lowercase_    )
										self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.num_choices)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  str				,			lowercase_				:  int				,			lowercase_				:  int				,			lowercase_				:  List[str]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  Union[str, Any]				,			lowercase_				:  str    ):
										lowercase_					:			Tuple										=       self.num_labels
										lowercase_					:			List[Any]										=       TFRoFormerForTokenClassification(config=lowercase_    )
										lowercase_					:			List[str]										=       {
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}
										lowercase_					:			Union[str, Any]										=       model(lowercase_    )
										self.parent.assertEqual(result.logits.shape				,			(self.batch_size, self.seq_length, self.num_labels)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  int				,			lowercase_				:  List[str]				,			lowercase_				:  Optional[int]				,			lowercase_				:  List[str]				,			lowercase_				:  str				,			lowercase_				:  List[str]				,			lowercase_				:  Optional[int]    ):
										lowercase_					:			Optional[Any]										=       TFRoFormerForQuestionAnswering(config=lowercase_    )
										lowercase_					:			Union[str, Any]										=       {
										    """input_ids""": input_ids,
										    """attention_mask""": input_mask,
										    """token_type_ids""": token_type_ids,
										}

										lowercase_					:			Any										=       model(lowercase_    )
										self.parent.assertEqual(result.start_logits.shape				,			(self.batch_size, self.seq_length)    )
										self.parent.assertEqual(result.end_logits.shape				,			(self.batch_size, self.seq_length)    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_					:			List[Any]										=       self.prepare_config_and_inputs()
										(
										    (
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						(
										    lowercase_
										)						,						
										)					:			int										=       config_and_inputs
										lowercase_					:			List[str]										=       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
										return config, inputs_dict





@require_tf
class     __magic_name__						(    _UpperCAmelCase,     _UpperCAmelCase,     unittest.TestCase):
			UpperCamelCase__				=       (
			    (
			        TFRoFormerModel,
			        TFRoFormerForCausalLM,
			        TFRoFormerForMaskedLM,
			        TFRoFormerForQuestionAnswering,
			        TFRoFormerForSequenceClassification,
			        TFRoFormerForTokenClassification,
			        TFRoFormerForMultipleChoice,
			    )
			    if is_tf_available()
			    else ()
			)
			UpperCamelCase__				=       (
			    {
			        '''feature-extraction''': TFRoFormerModel,
			        '''fill-mask''': TFRoFormerForMaskedLM,
			        '''question-answering''': TFRoFormerForQuestionAnswering,
			        '''text-classification''': TFRoFormerForSequenceClassification,
			        '''text-generation''': TFRoFormerForCausalLM,
			        '''token-classification''': TFRoFormerForTokenClassification,
			        '''zero-shot''': TFRoFormerForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)

			UpperCamelCase__				=       False
			UpperCamelCase__				=       False







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]				,			lowercase_				:  Optional[int]				,			lowercase_				:  str				,			lowercase_				:  Optional[Any]				,			lowercase_				:  Tuple				,			lowercase_				:  List[Any]    ):
										if pipeline_test_casse_name == "TextGenerationPipelineTests":
																	return True

										return False







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										lowercase_					:			List[Any]										=       TFRoFormerModelTester(self    )
										lowercase_					:			Optional[Any]										=       ConfigTester(self				,			config_class=lowercase_				,			hidden_size=37    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										self.config_tester.run_common_tests()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			List[str]										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]    ):
										lowercase_					:			Dict										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_lm(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										lowercase_					:			Optional[Any]										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_					:			str										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_multiple_choice(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										lowercase_					:			Union[str, Any]										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										lowercase_					:			Dict										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										lowercase_					:			Any										=       self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*lowercase_    )







			@slow
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										lowercase_					:			Any										=       TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""    )
										self.assertIsNotNone(lowercase_    )





@require_tf
class     __magic_name__						(    unittest.TestCase):







			@slow
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[int]    ):
										lowercase_					:			Optional[int]										=       TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""    )
										lowercase_					:			Union[str, Any]										=       tf.constant([[0, 1, 2, 3, 4, 5]]    )
										lowercase_					:			str										=       model(lowercase_    )[0]

										# TODO Replace vocab size
										lowercase_					:			Optional[Any]										=       50000

										lowercase_					:			List[str]										=       [1, 6, vocab_size]
										self.assertEqual(output.shape				,			lowercase_    )

										print(output[:, :3, :3]    )

										# TODO Replace values below with what was printed above.
										lowercase_					:			Any										=       tf.constant(
										    [
										        [
										            [-0.12_05_33_41, -1.0_26_49_01, 0.29_22_19_46],
										            [-1.5_13_37_83, 0.19_74_33, 0.15_19_06_07],
										            [-5.0_13_54_03, -3.90_02_56, -0.84_03_87_64],
										        ]
										    ]    )
										tf.debugging.assert_near(output[:, :3, :3]				,			lowercase_				,			atol=1E-4    )





@require_tf
class     __magic_name__						(    unittest.TestCase):
			UpperCamelCase__				=       1e-4







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										lowercase_					:			Union[str, Any]										=       tf.constant([[4, 10]]    )
										lowercase_					:			List[Any]										=       TFRoFormerSinusoidalPositionalEmbedding(num_positions=6				,			embedding_dim=6    )

										lowercase_					:			Union[str, Any]										=       emba(input_ids.shape    )
										lowercase_					:			Dict										=       tf.constant(
										    [[0.00_00, 0.00_00, 0.00_00, 1.00_00, 1.00_00, 1.00_00], [0.84_15, 0.04_64, 0.00_22, 0.54_03, 0.99_89, 1.00_00]]    )

										tf.debugging.assert_near(lowercase_				,			lowercase_				,			atol=self.tolerance    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			int										=       tf.constant(
										    [
										        [0.00_00, 0.00_00, 0.00_00, 0.00_00, 0.00_00],
										        [0.84_15, 0.82_19, 0.80_20, 0.78_19, 0.76_17],
										        [0.90_93, 0.93_64, 0.95_81, 0.97_49, 0.98_70],
										    ]    )
										lowercase_					:			Optional[int]										=       TFRoFormerSinusoidalPositionalEmbedding(num_positions=512				,			embedding_dim=512    )
										emba([2, 16, 512]    )
										lowercase_					:			Tuple										=       emba.weight[:3, :5]

										tf.debugging.assert_near(lowercase_				,			lowercase_				,			atol=self.tolerance    )





@require_tf
class     __magic_name__						(    unittest.TestCase):
			UpperCamelCase__				=       1e-4







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										# 2,12,16,64
										lowercase_					:			Optional[int]										=       tf.reshape(tf.range(2 * 12 * 16 * 64				,			dtype=tf.floataa    )				,			shape=(2, 12, 16, 64)    ) / 100

										lowercase_					:			Dict										=       -tf.reshape(tf.range(2 * 12 * 16 * 64				,			dtype=tf.floataa    )				,			shape=(2, 12, 16, 64)    ) / 100

										lowercase_					:			Optional[Any]										=       TFRoFormerSinusoidalPositionalEmbedding(num_positions=32				,			embedding_dim=64    )
										lowercase_					:			List[Any]										=       embed_positions([2, 16, 768]    )[None, None, :, :]

										lowercase_						,						lowercase_					:			Dict										=       TFRoFormerSelfAttention.apply_rotary_position_embeddings(
										    lowercase_				,			lowercase_				,			lowercase_    )

										lowercase_					:			List[str]										=       tf.constant(
										    [
										        [0.00_00, 0.01_00, 0.02_00, 0.03_00, 0.04_00, 0.05_00, 0.06_00, 0.07_00],
										        [-0.20_12, 0.88_97, 0.02_63, 0.94_01, 0.20_74, 0.94_63, 0.34_81, 0.93_43],
										        [-1.70_57, 0.62_71, -1.21_45, 1.38_97, -0.63_03, 1.76_47, -0.11_73, 1.89_85],
										        [-2.17_31, -1.63_97, -2.73_58, 0.28_54, -2.18_40, 1.71_83, -1.30_18, 2.48_71],
										        [0.27_17, -3.61_73, -2.92_06, -2.19_88, -3.66_38, 0.38_58, -2.91_55, 2.29_80],
										        [3.98_59, -2.15_80, -0.79_84, -4.49_04, -4.11_81, -2.02_52, -4.47_82, 1.12_53],
										    ]    )
										lowercase_					:			List[str]										=       tf.constant(
										    [
										        [0.00_00, -0.01_00, -0.02_00, -0.03_00, -0.04_00, -0.05_00, -0.06_00, -0.07_00],
										        [0.20_12, -0.88_97, -0.02_63, -0.94_01, -0.20_74, -0.94_63, -0.34_81, -0.93_43],
										        [1.70_57, -0.62_71, 1.21_45, -1.38_97, 0.63_03, -1.76_47, 0.11_73, -1.89_85],
										        [2.17_31, 1.63_97, 2.73_58, -0.28_54, 2.18_40, -1.71_83, 1.30_18, -2.48_71],
										        [-0.27_17, 3.61_73, 2.92_06, 2.19_88, 3.66_38, -0.38_58, 2.91_55, -2.29_80],
										        [-3.98_59, 2.15_80, 0.79_84, 4.49_04, 4.11_81, 2.02_52, 4.47_82, -1.12_53],
										    ]    )

										tf.debugging.assert_near(query_layer[0, 0, :6, :8]				,			lowercase_				,			atol=self.tolerance    )
										tf.debugging.assert_near(key_layer[0, 0, :6, :8]				,			lowercase_				,			atol=self.tolerance    )






'''simple docstring'''




import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()





class     __magic_name__						(    _UpperCAmelCase,     unittest.TestCase):
			UpperCamelCase__				=       PriorTransformer
			UpperCamelCase__				=       '''hidden_states'''







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int    ):
										lowercase_					:			Optional[Any]										=       4
										lowercase_					:			int										=       8
										lowercase_					:			Union[str, Any]										=       7

										lowercase_					:			List[str]										=       floats_tensor((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Tuple										=       floats_tensor((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			List[str]										=       floats_tensor((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]				,			lowercase_				:  Optional[Any]=0    ):
										torch.manual_seed(lowercase_    )
										lowercase_					:			int										=       4
										lowercase_					:			Any										=       8
										lowercase_					:			Tuple										=       7

										lowercase_					:			Union[str, Any]										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Tuple										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			Union[str, Any]										=       torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										return (4, 8)







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  Union[str, Any]    ):
										return (4, 8)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										lowercase_					:			Optional[Any]										=       {
										    """num_attention_heads""": 2,
										    """attention_head_dim""": 4,
										    """num_layers""": 2,
										    """embedding_dim""": 8,
										    """num_embeddings""": 7,
										    """additional_embeddings""": 4,
										}
										lowercase_					:			List[Any]										=       self.dummy_input
										return init_dict, inputs_dict







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_						,						lowercase_					:			Tuple										=       PriorTransformer.from_pretrained(
										    """hf-internal-testing/prior-dummy"""				,			output_loading_info=lowercase_    )
										self.assertIsNotNone(lowercase_    )
										self.assertEqual(len(loading_info["""missing_keys"""]    )				,			0    )

										model.to(lowercase_    )
										lowercase_					:			Optional[int]										=       model(**self.dummy_input    )[0]

										assert hidden_states is not None, "Make sure output is not None"







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										lowercase_						,						lowercase_					:			str										=       self.prepare_init_args_and_inputs_for_common()

										lowercase_					:			List[Any]										=       self.model_class(**lowercase_    )
										lowercase_					:			Any										=       inspect.signature(model.forward    )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										lowercase_					:			Any										=       [*signature.parameters.keys()]

										lowercase_					:			Optional[Any]										=       ["""hidden_states""", """timestep"""]
										self.assertListEqual(arg_names[:2]				,			lowercase_    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_					:			Optional[Any]										=       PriorTransformer.from_pretrained("""hf-internal-testing/prior-dummy"""    )
										lowercase_					:			Any										=       model.to(lowercase_    )

										if hasattr(lowercase_				,			"""set_default_attn_processor"""    ):
																	model.set_default_attn_processor()

										lowercase_					:			Any										=       self.get_dummy_seed_input()

										with torch.no_grad():
																	lowercase_					:			List[str]										=       model(**lowercase_    )[0]

										lowercase_					:			Tuple										=       output[0, :5].flatten().cpu()
										print(lowercase_    )

										# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
										# the expected output slices are not the same for CPU and GPU.
										lowercase_					:			Dict										=       torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]    )
										self.assertTrue(torch_all_close(lowercase_				,			lowercase_				,			rtol=1E-2    )    )





@slow
class     __magic_name__						(    unittest.TestCase):







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[Any]				,			lowercase_				:  Union[str, Any]=1				,			lowercase_				:  Optional[Any]=768				,			lowercase_				:  Tuple=77				,			lowercase_				:  Optional[int]=0    ):
										torch.manual_seed(lowercase_    )
										lowercase_					:			Optional[Any]										=       batch_size
										lowercase_					:			int										=       embedding_dim
										lowercase_					:			int										=       num_embeddings

										lowercase_					:			Dict										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )

										lowercase_					:			Any										=       torch.randn((batch_size, embedding_dim)    ).to(lowercase_    )
										lowercase_					:			List[Any]										=       torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowercase_    )

										return {
										    "hidden_states": hidden_states,
										    "timestep": 2,
										    "proj_embedding": proj_embedding,
										    "encoder_hidden_states": encoder_hidden_states,
										}







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Any    ):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







			@parameterized.expand(
			    [
			        # fmt: off
			        [13, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
			        [37, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
			        # fmt: on
			    ]    )
			def 	SCREAMING_SNAKE_CASE_     (	self				:  int				,			lowercase_				:  Any				,			lowercase_				:  List[str]    ):
										lowercase_					:			List[Any]										=       PriorTransformer.from_pretrained("""kandinsky-community/kandinsky-2-1-prior"""				,			subfolder="""prior"""    )
										model.to(lowercase_    )
										lowercase_					:			Optional[Any]										=       self.get_dummy_seed_input(seed=lowercase_    )

										with torch.no_grad():
																	lowercase_					:			Tuple										=       model(**lowercase_    )[0]

										assert list(sample.shape    ) == [1, 768]

										lowercase_					:			Union[str, Any]										=       sample[0, :8].flatten().cpu()
										print(lowercase_    )
										lowercase_					:			Optional[Any]										=       torch.tensor(lowercase_    )

										assert torch_all_close(lowercase_				,			lowercase_				,			atol=1E-3    )





