
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 __snake_case		(				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		,):



											'''simple docstring'''







											lowercase							:					str	     =				parent
											lowercase							:					Optional[int]	     =				batch_size
											lowercase							:					Optional[int]	     =				seq_length
											lowercase							:					List[Any]	     =				is_training
											lowercase							:					Tuple	     =				use_attention_mask
											lowercase							:					List[str]	     =				use_token_type_ids
											lowercase							:					Dict	     =				use_labels
											lowercase							:					List[Any]	     =				vocab_size
											lowercase							:					List[Any]	     =				hidden_size
											lowercase							:					Union[str, Any]	     =				num_hidden_layers
											lowercase							:					str	     =				num_attention_heads
											lowercase							:					Any	     =				intermediate_size
											lowercase							:					Optional[int]	     =				hidden_act
											lowercase							:					int	     =				hidden_dropout_prob
											lowercase							:					Optional[Any]	     =				attention_probs_dropout_prob
											lowercase							:					Any	     =				max_position_embeddings
											lowercase							:					Tuple	     =				type_vocab_size
											lowercase							:					Union[str, Any]	     =				type_sequence_label_size
											lowercase							:					List[Any]	     =				initializer_range
											lowercase							:					Tuple	     =				num_choices
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					List[Any]	     =				ids_tensor([self.batch_size, self.seq_length]		,self.vocab_size				)

											lowercase							:					Dict	     =				None
											if self.use_attention_mask:
																		lowercase							:					Union[str, Any]	     =				random_attention_mask([self.batch_size, self.seq_length]				)

											lowercase							:					List[Any]	     =				None
											if self.use_token_type_ids:
																		lowercase							:					Optional[int]	     =				ids_tensor([self.batch_size, self.seq_length]		,self.type_vocab_size				)

											lowercase							:					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=snake_case		,initializer_range=self.initializer_range		,)

											return config, input_ids, token_type_ids, attention_mask
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					int	     =				self.prepare_config_and_inputs()
											lowercase							:					List[str]	     =				config_and_inputs
											lowercase							:					Dict	     =				{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
											return config, inputs_dict
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					Optional[int]	     =				self.prepare_config_and_inputs()
											lowercase							:					Optional[Any]	     =				config_and_inputs

											lowercase							:					List[str]	     =				True
											lowercase							:					Any	     =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
											lowercase							:					Optional[int]	     =				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 __snake_case		(				lowerCAmelCase ,     unittest.TestCase					):
				_a		:						Optional[int]=    True

				_a		:						Optional[int]=    (
				    (
				        FlaxRobertaPreLayerNormModel,
				        FlaxRobertaPreLayerNormForCausalLM,
				        FlaxRobertaPreLayerNormForMaskedLM,
				        FlaxRobertaPreLayerNormForSequenceClassification,
				        FlaxRobertaPreLayerNormForTokenClassification,
				        FlaxRobertaPreLayerNormForMultipleChoice,
				        FlaxRobertaPreLayerNormForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					List[Any]	     =				FlaxRobertaPreLayerNormModelTester(self				)
				@slow
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											for model_class_name in self.all_model_classes:
																		lowercase							:					Optional[Any]	     =				model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""		,from_pt=snake_case				)
																		lowercase							:					int	     =				model(np.ones((1, 1)				)				)
																		self.assertIsNotNone(snake_case				)






@require_flax
class __snake_case		(				unittest.TestCase					):
				@slow
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					Optional[Any]	     =				FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""		,from_pt=snake_case				)

											lowercase							:					List[Any]	     =				np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		,dtype=jnp.intaa				)
											lowercase							:					Optional[int]	     =				model(snake_case				)[0]
											lowercase							:					List[Any]	     =				[1, 11, 50265]
											self.assertEqual(list(output.shape				)		,snake_case				)
											# compare the actual values for a slice.
											lowercase							:					Any	     =				np.array(
											    [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]		,dtype=np.floataa				)
											self.assertTrue(np.allclose(output[:, :3, :3]		,snake_case		,atol=1e-4				)				)
				@slow
				def 				_SCREAMING_SNAKE_CASE	(  self				):



											'''simple docstring'''







											lowercase							:					List[str]	     =				FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""		,from_pt=snake_case				)

											lowercase							:					Tuple	     =				np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		,dtype=jnp.intaa				)
											lowercase							:					Optional[int]	     =				model(snake_case				)[0]
											# compare the actual values for a slice.
											lowercase							:					List[str]	     =				np.array(
											    [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]		,dtype=np.floataa				)
											self.assertTrue(np.allclose(output[:, :3, :3]		,snake_case		,atol=1e-4				)				)


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase  : List[Any]						   =							logging.get_logger(__name__)

lowercase  : List[Any]						   =							{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}

class __snake_case		(				lowerCAmelCase					):
    _a		:						Dict=    "mobilenet_v1"
    def __init__(  self		,snake_case=3		,snake_case=224		,snake_case=1.0		,snake_case=8		,snake_case="relu6"		,snake_case=True		,snake_case=0.999		,snake_case=0.02		,snake_case=0.001		,**snake_case		,):



           '''simple docstring'''







           super().__init__(**snake_case				)

           if depth_multiplier <= 0:
                  raise ValueError("""depth_multiplier must be greater than zero."""				)

           lowercase							:					int	     =				num_channels
           lowercase							:					Union[str, Any]	     =				image_size
           lowercase							:					int	     =				depth_multiplier
           lowercase							:					Tuple	     =				min_depth
           lowercase							:					Dict	     =				hidden_act
           lowercase							:					Dict	     =				tf_padding
           lowercase							:					Dict	     =				classifier_dropout_prob
           lowercase							:					int	     =				initializer_range
           lowercase							:					List[str]	     =				layer_norm_eps






class __snake_case		(				lowerCAmelCase					):
    _a		:						int=    version.parse("1.11"					)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return OrderedDict([("""pixel_values""", {0: """batch"""})]				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           if self.task == "image-classification":
                  return OrderedDict([("""logits""", {0: """batch"""})]				)
           else:
                  return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})]				)
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 1e-4
