






"""simple docstring"""
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   SCREAMING_SNAKE_CASE	(					unittest.TestCase    ):





	"""simple docstring"""




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





	def  lowerCamelCase(self				):
		A_		:    Union[str, Any]						     = ids_tensor([self.batch_size, self.seq_length]  ,  self.vocab_size				)

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

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

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





	def  lowerCamelCase(self				):
		A_		:    Tuple						     = self.prepare_config_and_inputs()
		A_     ,  A_     ,  A_     ,  A_		:    Optional[int]						     = config_and_inputs

		A_		:    List[Any]						     = True
		A_		:    Dict						     = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
		A_		:    Tuple						     = 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   SCREAMING_SNAKE_CASE	(					_SCREAMING_SNAKE_CASE							,							unittest.TestCase    ):





	"""simple docstring"""




	_A							:					int 			=					True

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





	def  lowerCamelCase(self				):
		A_		:    Optional[int]						     = FlaxRobertaPreLayerNormModelTester(self				)





	@slow
	def  lowerCamelCase(self				):
		for model_class_name in self.all_model_classes:
			A_		:    Optional[int]						     = model_class_name.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,  from_pt=lowerCAmelCase_				)
			A_		:    Optional[int]						     = model(np.ones((1, 1)				)				)
			self.assertIsNotNone(lowerCAmelCase_				)






@require_flax
class   SCREAMING_SNAKE_CASE	(					unittest.TestCase    ):





	"""simple docstring"""




	@slow
	def  lowerCamelCase(self				):
		A_		:    Dict						     = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,  from_pt=lowerCAmelCase_				)

		A_		:    str						     = np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  ,  dtype=jnp.intaa				)
		A_		:    Union[str, Any]						     = model(lowerCAmelCase_				)[0]
		A_		:    Any						     = [1, 11, 50265]
		self.assertEqual(list(output.shape				)  ,  lowerCAmelCase_				)
		# compare the actual values for a slice.
		A_		:    Optional[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				):
		A_		:    int						     = FlaxRobertaPreLayerNormModel.from_pretrained("""andreasmadsen/efficient_mlm_m0.40"""  ,  from_pt=lowerCAmelCase_				)

		A_		:    List[str]						     = np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]  ,  dtype=jnp.intaa				)
		A_		:    Optional[int]						     = model(lowerCAmelCase_				)[0]
		# compare the actual values for a slice.
		A_		:    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				)				)









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase       =				{
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowerCAmelCase       =				[
							    "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TimesformerModel",
							    "TimesformerForVideoClassification",
							    "TimesformerPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_timesformer import (
														    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TimesformerForVideoClassification,
														    TimesformerModel,
														    TimesformerPreTrainedModel,
														)

else:
							import sys

							_lowerCAmelCase       =				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

