

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 					lowercase							(			unittest.TestCase     ):




				def __init__(   self ,  snake_case ,  snake_case=13 ,  snake_case=7 ,  snake_case=True ,  snake_case=True ,  snake_case=True ,  snake_case=True ,  snake_case=99 ,  snake_case=32 ,  snake_case=5 ,  snake_case=4 ,  snake_case=37 ,  snake_case="gelu" ,  snake_case=0.1 ,  snake_case=0.1 ,  snake_case=512 ,  snake_case=16 ,  snake_case=2 ,  snake_case=0.02 ,  snake_case=4 ,  ):
											snake_case_     							=       parent
											snake_case_     							=       batch_size
											snake_case_     							=       seq_length
											snake_case_     							=       is_training
											snake_case_     							=       use_attention_mask
											snake_case_     							=       use_token_type_ids
											snake_case_     							=       use_labels
											snake_case_     							=       vocab_size
											snake_case_     							=       hidden_size
											snake_case_     							=       num_hidden_layers
											snake_case_     							=       num_attention_heads
											snake_case_     							=       intermediate_size
											snake_case_     							=       hidden_act
											snake_case_     							=       hidden_dropout_prob
											snake_case_     							=       attention_probs_dropout_prob
											snake_case_     							=       max_position_embeddings
											snake_case_     							=       type_vocab_size
											snake_case_     							=       type_sequence_label_size
											snake_case_     							=       initializer_range
											snake_case_     							=       num_choices




				def        a				(   self			):
											snake_case_     							=       ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size			)

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

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

											snake_case_     							=       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=snake_case ,  initializer_range=self.initializer_range ,  )

											return config, input_ids, token_type_ids, attention_mask




				def        a				(   self			):
											snake_case_     							=       self.prepare_config_and_inputs()
											snake_case_							,       snake_case_							,       snake_case_							,       snake_case_     							=       config_and_inputs
											snake_case_     							=       {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
											return config, inputs_dict




				def        a				(   self			):
											snake_case_     							=       self.prepare_config_and_inputs()
											snake_case_							,       snake_case_							,       snake_case_							,       snake_case_     							=       config_and_inputs

											snake_case_     							=       True
											snake_case_     							=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
											snake_case_     							=       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 					lowercase							(			lowercase_      ,    unittest.TestCase     ):
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     True

				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     (
				    (
				        FlaxRobertaPreLayerNormModel,
				        FlaxRobertaPreLayerNormForCausalLM,
				        FlaxRobertaPreLayerNormForMaskedLM,
				        FlaxRobertaPreLayerNormForSequenceClassification,
				        FlaxRobertaPreLayerNormForTokenClassification,
				        FlaxRobertaPreLayerNormForMultipleChoice,
				        FlaxRobertaPreLayerNormForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)




				def        a				(   self			):
											snake_case_     							=       FlaxRobertaPreLayerNormModelTester(self			)




				@slow
				def        a				(   self			):
											for model_class_name in self.all_model_classes:
																		snake_case_     							=       model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40' ,  from_pt=snake_case			)
																		snake_case_     							=       model(np.ones((1, 1)			)			)
																		self.assertIsNotNone(snake_case			)







@require_flax
class 					lowercase							(			unittest.TestCase     ):




				@slow
				def        a				(   self			):
											snake_case_     							=       FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40' ,  from_pt=snake_case			)

											snake_case_     							=       np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] ,  dtype=jnp.intaa			)
											snake_case_     							=       model(snake_case			)[0]
											snake_case_     							=       [1, 11, 5_0265]
											self.assertEqual(list(output.shape			) ,  snake_case			)
											# compare the actual values for a slice.
											snake_case_     							=       np.array(
											    [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]] ,  dtype=np.floataa			)
											self.assertTrue(np.allclose(output[:, :3, :3] ,  snake_case ,  atol=1e-4			)			)




				@slow
				def        a				(   self			):
											snake_case_     							=       FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40' ,  from_pt=snake_case			)

											snake_case_     							=       np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] ,  dtype=jnp.intaa			)
											snake_case_     							=       model(snake_case			)[0]
											# compare the actual values for a slice.
											snake_case_     							=       np.array(
											    [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]] ,  dtype=np.floataa			)
											self.assertTrue(np.allclose(output[:, :3, :3] ,  snake_case ,  atol=1e-4			)			)







from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					Union[str, Any]			     =     ['''image_processor''', '''tokenizer''']
				__SCREAMING_SNAKE_CASE   :					Tuple			     =     '''AutoImageProcessor'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     '''AutoTokenizer'''




				def __init__(   self ,  snake_case ,  snake_case			):
											super().__init__(snake_case ,  snake_case			)
											snake_case_     							=       self.image_processor




				def __call__(   self ,  snake_case=None ,  snake_case=None ,  snake_case=None ,  **snake_case			):

											if text is None and images is None:
																		raise ValueError('You have to specify either text or images. Both cannot be none.'			)

											if text is not None:
																		snake_case_     							=       self.tokenizer(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if images is not None:
																		snake_case_     							=       self.image_processor(snake_case ,  return_tensors=snake_case ,  **snake_case			)

											if text is not None and images is not None:
																		snake_case_     							=       image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**snake_case			) ,  tensor_type=snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.batch_decode(*snake_case ,  **snake_case			)




				def        a				(   self ,  *snake_case ,  **snake_case			):
											return self.tokenizer.decode(*snake_case ,  **snake_case			)




				@property
				def        a				(   self			):
											return ["input_ids", "attention_mask", "pixel_values"]




