






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  _UpperCAmelCase   (			A__						,unittest.TestCase      ):

				"""simple docstring"""




				lowercase__											=			PriorTransformer
				lowercase__											=			"""hidden_states"""




				@property
				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     4
								lowercase__  					=     8
								lowercase__  					=     7

								lowercase__  					=     floats_tensor((batch_size, embedding_dim)    ).to(lowerCamelCase    )

								lowercase__  					=     floats_tensor((batch_size, embedding_dim)    ).to(lowerCamelCase    )
								lowercase__  					=     floats_tensor((batch_size, num_embeddings, embedding_dim)    ).to(lowerCamelCase    )

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




				def 							lowercase__  (	self		:       Tuple,    lowerCamelCase		:       int=0    ):

								'''simple docstring'''
								torch.manual_seed(lowerCamelCase    )
								lowercase__  					=     4
								lowercase__  					=     8
								lowercase__  					=     7

								lowercase__  					=     torch.randn((batch_size, embedding_dim)    ).to(lowerCamelCase    )

								lowercase__  					=     torch.randn((batch_size, embedding_dim)    ).to(lowerCamelCase    )
								lowercase__  					=     torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowerCamelCase    )

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




				@property
				def 							lowercase__  (	self		:       Optional[Any]    ):

								'''simple docstring'''
								return (4, 8)




				@property
				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								return (4, 8)




				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								lowercase__  					=     {
								    '''num_attention_heads''': 2,
								    '''attention_head_dim''': 4,
								    '''num_layers''': 2,
								    '''embedding_dim''': 8,
								    '''num_embeddings''': 7,
								    '''additional_embeddings''': 4,
								}
								lowercase__  					=     self.dummy_input
								return init_dict, inputs_dict




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__					,		lowercase__  					=     PriorTransformer.from_pretrained(
								    '''hf-internal-testing/prior-dummy''',    output_loading_info=lowerCamelCase    )
								self.assertIsNotNone(lowerCamelCase    )
								self.assertEqual(len(loading_info['''missing_keys''']    ),    0    )

								model.to(lowerCamelCase    )
								lowercase__  					=     model(**self.dummy_input    )[0]

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




				def 							lowercase__  (	self		:       Tuple    ):

								'''simple docstring'''
								lowercase__					,		lowercase__  					=     self.prepare_init_args_and_inputs_for_common()

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

								lowercase__  					=     ['''hidden_states''', '''timestep''']
								self.assertListEqual(arg_names[:2],    lowerCamelCase    )




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''    )
								lowercase__  					=     model.to(lowerCamelCase    )

								if hasattr(lowerCamelCase,    '''set_default_attn_processor'''    ):
												model.set_default_attn_processor()

								lowercase__  					=     self.get_dummy_seed_input()

								with torch.no_grad():
												lowercase__  					=     model(**lowerCamelCase    )[0]

								lowercase__  					=     output[0, :5].flatten().cpu()
								print(lowerCamelCase    )

								# 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__  					=     torch.tensor([-1.3436, -0.2870, 0.7538, 0.4368, -0.0239]    )
								self.assertTrue(torch_all_close(lowerCamelCase,    lowerCamelCase,    rtol=1E-2    )    )



@slow
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       int,    lowerCamelCase		:       List[str]=1,    lowerCamelCase		:       Dict=768,    lowerCamelCase		:       int=77,    lowerCamelCase		:       Tuple=0    ):

								'''simple docstring'''
								torch.manual_seed(lowerCamelCase    )
								lowercase__  					=     batch_size
								lowercase__  					=     embedding_dim
								lowercase__  					=     num_embeddings

								lowercase__  					=     torch.randn((batch_size, embedding_dim)    ).to(lowerCamelCase    )

								lowercase__  					=     torch.randn((batch_size, embedding_dim)    ).to(lowerCamelCase    )
								lowercase__  					=     torch.randn((batch_size, num_embeddings, embedding_dim)    ).to(lowerCamelCase    )

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




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




				@parameterized.expand(
				    [
				        # fmt: off
				        [13, [-0.5861,  0.1283, -0.0931,  0.0882,  0.4476,  0.1329, -0.0498,  0.0640]],
				        [37, [-0.4913,  0.0110, -0.0483,  0.0541,  0.4954, -0.0170,  0.0354,  0.1651]],
				        # fmt: on
				    ]    )
				def 							lowercase__  (	self		:       int,    lowerCamelCase		:       str,    lowerCamelCase		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior''',    subfolder='''prior'''    )
								model.to(lowerCamelCase    )
								lowercase__  					=     self.get_dummy_seed_input(seed=lowerCamelCase    )

								with torch.no_grad():
												lowercase__  					=     model(**lowerCamelCase    )[0]

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

								lowercase__  					=     sample[0, :8].flatten().cpu()
								print(lowerCamelCase    )
								lowercase__  					=     torch.tensor(lowerCamelCase    )

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












import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
							import jax.numpy as jnp

							from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
							    FlaxRobertaPreLayerNormForCausalLM,
							    FlaxRobertaPreLayerNormForMaskedLM,
							    FlaxRobertaPreLayerNormForMultipleChoice,
							    FlaxRobertaPreLayerNormForQuestionAnswering,
							    FlaxRobertaPreLayerNormForSequenceClassification,
							    FlaxRobertaPreLayerNormForTokenClassification,
							    FlaxRobertaPreLayerNormModel,
							)
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				def __init__(	self		:       List[Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       Dict=13,    lowerCamelCase		:       Optional[int]=7,    lowerCamelCase		:       Optional[Any]=True,    lowerCamelCase		:       Union[str, Any]=True,    lowerCamelCase		:       Optional[Any]=True,    lowerCamelCase		:       Optional[Any]=True,    lowerCamelCase		:       List[Any]=99,    lowerCamelCase		:       Any=32,    lowerCamelCase		:       List[str]=5,    lowerCamelCase		:       Union[str, Any]=4,    lowerCamelCase		:       Union[str, Any]=37,    lowerCamelCase		:       Optional[int]="gelu",    lowerCamelCase		:       Tuple=0.1,    lowerCamelCase		:       List[str]=0.1,    lowerCamelCase		:       Optional[Any]=512,    lowerCamelCase		:       int=16,    lowerCamelCase		:       str=2,    lowerCamelCase		:       int=0.02,    lowerCamelCase		:       int=4,    ):

								'''simple docstring'''
								lowercase__  					=     parent
								lowercase__  					=     batch_size
								lowercase__  					=     seq_length
								lowercase__  					=     is_training
								lowercase__  					=     use_attention_mask
								lowercase__  					=     use_token_type_ids
								lowercase__  					=     use_labels
								lowercase__  					=     vocab_size
								lowercase__  					=     hidden_size
								lowercase__  					=     num_hidden_layers
								lowercase__  					=     num_attention_heads
								lowercase__  					=     intermediate_size
								lowercase__  					=     hidden_act
								lowercase__  					=     hidden_dropout_prob
								lowercase__  					=     attention_probs_dropout_prob
								lowercase__  					=     max_position_embeddings
								lowercase__  					=     type_vocab_size
								lowercase__  					=     type_sequence_label_size
								lowercase__  					=     initializer_range
								lowercase__  					=     num_choices




				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     ids_tensor([self.batch_size, self.seq_length],    self.vocab_size    )

								lowercase__  					=     None
								if self.use_attention_mask:
												lowercase__  					=     random_attention_mask([self.batch_size, self.seq_length]    )

								lowercase__  					=     None
								if self.use_token_type_ids:
												lowercase__  					=     ids_tensor([self.batch_size, self.seq_length],    self.type_vocab_size    )

								lowercase__  					=     RobertaPreLayerNormConfig(
								    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,    is_decoder=lowerCamelCase,    initializer_range=self.initializer_range,    )

								return config, input_ids, token_type_ids, attention_mask




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.prepare_config_and_inputs()
								lowercase__					,		lowercase__					,		lowercase__					,		lowercase__  					=     config_and_inputs
								lowercase__  					=     {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
								return config, inputs_dict




				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     self.prepare_config_and_inputs()
								lowercase__					,		lowercase__					,		lowercase__					,		lowercase__  					=     config_and_inputs

								lowercase__  					=     True
								lowercase__  					=     floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
								lowercase__  					=     ids_tensor([self.batch_size, self.seq_length],    vocab_size=2    )

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)



@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class  _UpperCAmelCase   (			A__						,unittest.TestCase      ):

				"""simple docstring"""




				lowercase__											=			True

				lowercase__											=			(
				    (
				        FlaxRobertaPreLayerNormModel,
				        FlaxRobertaPreLayerNormForCausalLM,
				        FlaxRobertaPreLayerNormForMaskedLM,
				        FlaxRobertaPreLayerNormForSequenceClassification,
				        FlaxRobertaPreLayerNormForTokenClassification,
				        FlaxRobertaPreLayerNormForMultipleChoice,
				        FlaxRobertaPreLayerNormForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)




				def 							lowercase__  (	self		:       int    ):

								'''simple docstring'''
								lowercase__  					=     FlaxRobertaPreLayerNormModelTester(self    )




				@slow
				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								for model_class_name in self.all_model_classes:
												lowercase__  					=     model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''',    from_pt=lowerCamelCase    )
												lowercase__  					=     model(np.ones((1, 1)    )    )
												self.assertIsNotNone(lowerCamelCase    )



@require_flax
class  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				@slow
				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								lowercase__  					=     FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''',    from_pt=lowerCamelCase    )

								lowercase__  					=     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]],    dtype=jnp.intaa    )
								lowercase__  					=     model(lowerCamelCase    )[0]
								lowercase__  					=     [1, 11, 50_265]
								self.assertEqual(list(output.shape    ),    lowerCamelCase    )
								# compare the actual values for a slice.
								lowercase__  					=     np.array(
								    [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]],    dtype=np.floataa    )
								self.assertTrue(np.allclose(output[:, :3, :3],    lowerCamelCase,    atol=1E-4    )    )




				@slow
				def 							lowercase__  (	self		:       Any    ):

								'''simple docstring'''
								lowercase__  					=     FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''',    from_pt=lowerCamelCase    )

								lowercase__  					=     np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]],    dtype=jnp.intaa    )
								lowercase__  					=     model(lowerCamelCase    )[0]
								# compare the actual values for a slice.
								lowercase__  					=     np.array(
								    [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]],    dtype=np.floataa    )
								self.assertTrue(np.allclose(output[:, :3, :3],    lowerCamelCase,    atol=1E-4    )    )




