


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      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase=1_3 ,						__lowerCamelCase=7 ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=9_9 ,						__lowerCamelCase=3_2 ,						__lowerCamelCase=5 ,						__lowerCamelCase=4 ,						__lowerCamelCase=3_7 ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=5_1_2 ,						__lowerCamelCase=1_6 ,						__lowerCamelCase=2 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=4 ,						)			->				str:
            _A      :       Any				    = parent
            _A      :       Dict				    = batch_size
            _A      :       Dict				    = seq_length
            _A      :       Union[str, Any]				    = is_training
            _A      :       List[Any]				    = use_attention_mask
            _A      :       str				    = use_token_type_ids
            _A      :       Dict				    = use_labels
            _A      :       List[str]				    = vocab_size
            _A      :       Dict				    = hidden_size
            _A      :       List[Any]				    = num_hidden_layers
            _A      :       int				    = num_attention_heads
            _A      :       List[Any]				    = intermediate_size
            _A      :       int				    = hidden_act
            _A      :       List[str]				    = hidden_dropout_prob
            _A      :       List[str]				    = attention_probs_dropout_prob
            _A      :       Optional[int]				    = max_position_embeddings
            _A      :       List[str]				    = type_vocab_size
            _A      :       Dict				    = type_sequence_label_size
            _A      :       List[str]				    = initializer_range
            _A      :       Dict				    = num_choices


     def  _lowerCamelCase (						self)			->				str:
            _A      :       Optional[Any]				    = ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size)

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

            _A      :       str				    = None
            if self.use_token_type_ids:
                   _A      :       List[Any]				    = ids_tensor([self.batch_size, self.seq_length] ,						self.type_vocab_size)

            _A      :       Any				    = 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  _lowerCamelCase (						self)			->				List[Any]:
            _A      :       Optional[Any]				    = self.prepare_config_and_inputs()
            _A    ,			_A    ,			_A    ,			_A      :       Any				    = config_and_inputs
            _A      :       Dict				    = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
            return config, inputs_dict


     def  _lowerCamelCase (						self)			->				str:
            _A      :       Dict				    = self.prepare_config_and_inputs()
            _A    ,			_A    ,			_A    ,			_A      :       Optional[Any]				    = config_and_inputs

            _A      :       Any				    = True
            _A      :       Union[str, Any]				    = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
            _A      :       Optional[Any]				    = 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      lowerCAmelCase__       (				a     ,    unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= True

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


     def  _lowerCamelCase (						self)			->				Tuple:
            _A      :       Union[str, Any]				    = FlaxRobertaPreLayerNormModelTester(self)


     @slow
     def  _lowerCamelCase (						self)			->				Optional[int]:
            for model_class_name in self.all_model_classes:
                   _A      :       List[str]				    = model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40" ,						from_pt=__lowerCamelCase)
                   _A      :       int				    = model(np.ones((1, 1)))
                   self.assertIsNotNone(__lowerCamelCase)







@require_flax
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     @slow
     def  _lowerCamelCase (						self)			->				Optional[int]:
            _A      :       str				    = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40" ,						from_pt=__lowerCamelCase)

            _A      :       List[Any]				    = np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] ,						dtype=jnp.intaa)
            _A      :       Optional[int]				    = model(__lowerCamelCase)[0]
            _A      :       Union[str, Any]				    = [1, 1_1, 5_0_2_6_5]
            self.assertEqual(list(output.shape) ,						__lowerCamelCase)
            # compare the actual values for a slice.
            _A      :       Dict				    = np.array(
                [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]] ,						dtype=np.floataa)
            self.assertTrue(np.allclose(output[:, :3, :3] ,						__lowerCamelCase ,						atol=1e-4))


     @slow
     def  _lowerCamelCase (						self)			->				int:
            _A      :       Optional[Any]				    = FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40" ,						from_pt=__lowerCamelCase)

            _A      :       str				    = np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]] ,						dtype=jnp.intaa)
            _A      :       Dict				    = model(__lowerCamelCase)[0]
            # compare the actual values for a slice.
            _A      :       List[str]				    = np.array(
                [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]] ,						dtype=np.floataa)
            self.assertTrue(np.allclose(output[:, :3, :3] ,						__lowerCamelCase ,						atol=1e-4))




import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()







class      lowerCAmelCase__       (				a     ,    a     ,    unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= VQModel
     __SCREAMING_SNAKE_CASE  			= "sample"


     @property
     def  _lowerCamelCase (						self ,						__lowerCamelCase=(3_2, 3_2))			->				Optional[Any]:
            _A      :       Optional[int]				    = 4
            _A      :       Tuple				    = 3

            _A      :       List[Any]				    = floats_tensor((batch_size, num_channels) + sizes).to(__lowerCamelCase)

            return {"sample": image}


     @property
     def  _lowerCamelCase (						self)			->				int:
            return (3, 3_2, 3_2)


     @property
     def  _lowerCamelCase (						self)			->				List[Any]:
            return (3, 3_2, 3_2)


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       List[Any]				    = {
                "block_out_channels": [3_2, 6_4],
                "in_channels": 3,
                "out_channels": 3,
                "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
                "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
                "latent_channels": 3,
            }
            _A      :       int				    = self.dummy_input
            return init_dict, inputs_dict


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            pass


     def  _lowerCamelCase (						self)			->				Any:
            pass


     def  _lowerCamelCase (						self)			->				Any:
            _A    ,			_A      :       List[Any]				    = VQModel.from_pretrained("fusing/vqgan-dummy" ,						output_loading_info=__lowerCamelCase)
            self.assertIsNotNone(__lowerCamelCase)
            self.assertEqual(len(loading_info["missing_keys"]) ,						0)

            model.to(__lowerCamelCase)
            _A      :       str				    = model(**self.dummy_input)

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


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Optional[Any]				    = VQModel.from_pretrained("fusing/vqgan-dummy")
            model.to(__lowerCamelCase).eval()

            torch.manual_seed(0)
            if torch.cuda.is_available():
                   torch.cuda.manual_seed_all(0)

            _A      :       Tuple				    = torch.randn(1 ,						model.config.in_channels ,						model.config.sample_size ,						model.config.sample_size)
            _A      :       Optional[int]				    = image.to(__lowerCamelCase)
            with torch.no_grad():
                   _A      :       List[str]				    = model(__lowerCamelCase).sample

            _A      :       int				    = output[0, -1, -3:, -3:].flatten().cpu()
            # fmt: off
            _A      :       Optional[Any]				    = torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3])
            # fmt: on
            self.assertTrue(torch.allclose(__lowerCamelCase ,						__lowerCamelCase ,						atol=1e-3))
