




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					,UpperCamelCase					,UpperCamelCase=13					,UpperCamelCase=7					,UpperCamelCase=True					,UpperCamelCase=True					,UpperCamelCase=True					,UpperCamelCase=True					,UpperCamelCase=99					,UpperCamelCase=32					,UpperCamelCase=5					,UpperCamelCase=4					,UpperCamelCase=37					,UpperCamelCase="gelu"					,UpperCamelCase=0.1					,UpperCamelCase=0.1					,UpperCamelCase=512					,UpperCamelCase=16					,UpperCamelCase=2					,UpperCamelCase=0.02					,UpperCamelCase=4					,)						->    Dict:
					snake_case__      :Union[str, Any]         =   parent
					snake_case__      :Optional[int]         =   batch_size
					snake_case__      :str         =   seq_length
					snake_case__      :Tuple         =   is_training
					snake_case__      :Optional[Any]         =   use_attention_mask
					snake_case__      :str         =   use_token_type_ids
					snake_case__      :Optional[Any]         =   use_labels
					snake_case__      :Dict         =   vocab_size
					snake_case__      :Union[str, Any]         =   hidden_size
					snake_case__      :str         =   num_hidden_layers
					snake_case__      :str         =   num_attention_heads
					snake_case__      :int         =   intermediate_size
					snake_case__      :Union[str, Any]         =   hidden_act
					snake_case__      :Optional[int]         =   hidden_dropout_prob
					snake_case__      :Optional[Any]         =   attention_probs_dropout_prob
					snake_case__      :Optional[int]         =   max_position_embeddings
					snake_case__      :List[str]         =   type_vocab_size
					snake_case__      :Union[str, Any]         =   type_sequence_label_size
					snake_case__      :Tuple         =   initializer_range
					snake_case__      :Any         =   num_choices






	def    lowerCAmelCase_							(		self       )						->    Tuple:
					snake_case__      :Optional[int]         =   ids_tensor([self.batch_size, self.seq_length]					,self.vocab_size       )

					snake_case__      :Optional[Any]         =   None
					if self.use_attention_mask:
									snake_case__      :int         =   random_attention_mask([self.batch_size, self.seq_length]       )

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

					snake_case__      :List[str]         =   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       )						->    Tuple:
					snake_case__      :List[str]         =   self.prepare_config_and_inputs()
					snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__      :Tuple         =   config_and_inputs
					snake_case__      :Union[str, Any]         =   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
					return config, inputs_dict






	def    lowerCAmelCase_							(		self       )						->    int:
					snake_case__      :Tuple         =   self.prepare_config_and_inputs()
					snake_case__    ,						snake_case__    ,						snake_case__    ,						snake_case__      :Optional[Any]         =   config_and_inputs

					snake_case__      :Tuple         =   True
					snake_case__      :Optional[Any]         =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
					snake_case__      :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 					_snake_case						(			_lowerCAmelCase       ,    unittest.TestCase ):
	_A          =   True

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






	def    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :List[str]         =   FlaxRobertaPreLayerNormModelTester(self       )






	@slow
	def    lowerCAmelCase_							(		self       )						->    str:







					for model_class_name in self.all_model_classes:
									snake_case__      :Optional[Any]         =   model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"					,from_pt=_lowerCAmelCase       )
									snake_case__      :Union[str, Any]         =   model(np.ones((1, 1)       )       )
									self.assertIsNotNone(_lowerCAmelCase       )




@require_flax
class 					_snake_case						(			unittest.TestCase ):






	@slow
	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :Dict         =   FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"					,from_pt=_lowerCAmelCase       )

					snake_case__      :Tuple         =   np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]					,dtype=jnp.intaa       )
					snake_case__      :Union[str, Any]         =   model(_lowerCAmelCase       )[0]
					snake_case__      :List[Any]         =   [1, 11, 50_265]
					self.assertEqual(list(output.shape       )					,_lowerCAmelCase       )
					# compare the actual values for a slice.
					snake_case__      :Union[str, Any]         =   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    lowerCAmelCase_							(		self       )						->    Dict:
					snake_case__      :Dict         =   FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"					,from_pt=_lowerCAmelCase       )

					snake_case__      :str         =   np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]					,dtype=jnp.intaa       )
					snake_case__      :List[Any]         =   model(_lowerCAmelCase       )[0]
					# compare the actual values for a slice.
					snake_case__      :List[Any]         =   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       )       )







import os
import sys
import unittest


__UpperCAmelCase							:   str							=  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase							:   Tuple							=  os.path.join(git_repo_path, "src", "diffusers")


class 					_snake_case						(			unittest.TestCase ):






	def    lowerCAmelCase_							(		self       )						->    Union[str, Any]:
					snake_case__      :Tuple         =   find_backend("    if not is_torch_available():"       )
					self.assertEqual(UpperCamelCase					,"torch"       )

					# backend_with_underscore = find_backend("    if not is_tensorflow_text_available():")
					# self.assertEqual(backend_with_underscore, "tensorflow_text")

					snake_case__      :Tuple         =   find_backend("    if not (is_torch_available() and is_transformers_available()):"       )
					self.assertEqual(UpperCamelCase					,"torch_and_transformers"       )

					# double_backend_with_underscore = find_backend(
					#    "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
					# )
					# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")

					snake_case__      :str         =   find_backend(
					    "    if not (is_torch_available() and is_transformers_available() and is_onnx_available()):"       )
					self.assertEqual(UpperCamelCase					,"torch_and_transformers_and_onnx"       )






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :int         =   read_init()
					# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
					self.assertIn("torch"					,UpperCamelCase       )
					self.assertIn("torch_and_transformers"					,UpperCamelCase       )
					self.assertIn("flax_and_transformers"					,UpperCamelCase       )
					self.assertIn("torch_and_transformers_and_onnx"					,UpperCamelCase       )

					# Likewise, we can't assert on the exact content of a key
					self.assertIn("UNet2DModel"					,objects["torch"]       )
					self.assertIn("FlaxUNet2DConditionModel"					,objects["flax"]       )
					self.assertIn("StableDiffusionPipeline"					,objects["torch_and_transformers"]       )
					self.assertIn("FlaxStableDiffusionPipeline"					,objects["flax_and_transformers"]       )
					self.assertIn("LMSDiscreteScheduler"					,objects["torch_and_scipy"]       )
					self.assertIn("OnnxStableDiffusionPipeline"					,objects["torch_and_transformers_and_onnx"]       )






	def    lowerCAmelCase_							(		self       )						->    Any:
					snake_case__      :Union[str, Any]         =   create_dummy_object("CONSTANT"					,"'torch'"       )
					self.assertEqual(UpperCamelCase					,"\nCONSTANT = None\n"       )

					snake_case__      :Optional[Any]         =   create_dummy_object("function"					,"'torch'"       )
					self.assertEqual(
					    UpperCamelCase					,"\ndef function(*args, **kwargs):\n    requires_backends(function, 'torch')\n"       )

					snake_case__      :str         =   "\nclass FakeClass(metaclass=DummyObject):\n    _backends = 'torch'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, 'torch')\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, 'torch')\n"
					snake_case__      :List[str]         =   create_dummy_object("FakeClass"					,"'torch'"       )
					self.assertEqual(UpperCamelCase					,UpperCamelCase       )






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :Tuple         =   "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = [\"torch\"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, [\"torch\"])\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, [\"torch\"])\n"
					snake_case__      :int         =   create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}       )
					self.assertEqual(dummy_files["torch"]					,UpperCamelCase       )