





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					):






			def __init__(self ,							__magic_name__ ,							__magic_name__=13 ,							__magic_name__=7 ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=True ,							__magic_name__=99 ,							__magic_name__=32 ,							__magic_name__=5 ,							__magic_name__=4 ,							__magic_name__=37 ,							__magic_name__="gelu" ,							__magic_name__=0.1 ,							__magic_name__=0.1 ,							__magic_name__=512 ,							__magic_name__=16 ,							__magic_name__=2 ,							__magic_name__=0.02 ,							__magic_name__=4 ,							)					->       Union[str, Any]:


							'''simple docstring'''



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






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



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

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

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

							snake_case_						:   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=__magic_name__ ,							initializer_range=self.initializer_range ,							)

							return config, input_ids, token_type_ids, attention_mask






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   List[str]           =  self.prepare_config_and_inputs()
							snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_						:   Optional[int]           =  config_and_inputs
							snake_case_						:   int           =  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
							return config, inputs_dict






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  self.prepare_config_and_inputs()
							snake_case_      ,							snake_case_      ,							snake_case_      ,							snake_case_						:   Tuple           =  config_and_inputs

							snake_case_						:   List[Any]           =  True
							snake_case_						:   List[Any]           =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
							snake_case_						:   List[str]           =  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					):
			lowerCamelCase_					:					int	      =				True

			lowerCamelCase_					:					Optional[int]	      =				(
			    (
			        FlaxRobertaPreLayerNormModel,
			        FlaxRobertaPreLayerNormForCausalLM,
			        FlaxRobertaPreLayerNormForMaskedLM,
			        FlaxRobertaPreLayerNormForSequenceClassification,
			        FlaxRobertaPreLayerNormForTokenClassification,
			        FlaxRobertaPreLayerNormForMultipleChoice,
			        FlaxRobertaPreLayerNormForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)






			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  FlaxRobertaPreLayerNormModelTester(self   )






			@slow
			def       lowerCamelCase  (self   )					->       Optional[Any]:


							'''simple docstring'''



							for model_class_name in self.all_model_classes:
											snake_case_						:   str           =  model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' ,							from_pt=__magic_name__   )
											snake_case_						:   int           =  model(np.ones((1, 1)   )   )
											self.assertIsNotNone(__magic_name__   )




@require_flax
class  __lowerCAmelCase	(      unittest.TestCase					):






			@slow
			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' ,							from_pt=__magic_name__   )

							snake_case_						:   List[str]           =  np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] ,							dtype=jnp.intaa   )
							snake_case_						:   int           =  model(__magic_name__   )[0]
							snake_case_						:   int           =  [1, 11, 5_0265]
							self.assertEqual(list(output.shape   ) ,							__magic_name__   )
							# compare the actual values for a slice.
							snake_case_						:   str           =  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] ,							__magic_name__ ,							atol=1e-4   )   )






			@slow
			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



							snake_case_						:   Tuple           =  FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' ,							from_pt=__magic_name__   )

							snake_case_						:   Optional[int]           =  np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] ,							dtype=jnp.intaa   )
							snake_case_						:   Any           =  model(__magic_name__   )[0]
							# compare the actual values for a slice.
							snake_case_						:   int           =  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] ,							__magic_name__ ,							atol=1e-4   )   )







import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


lowerCAmelCase_    =    '''__DUMMY_TRANSFORMERS_USER__'''
lowerCAmelCase_    =    '''Dummy User'''
lowerCAmelCase_    =    '''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'''

lowerCAmelCase_    =    '''https://hub-ci.huggingface.co'''
lowerCAmelCase_    =    CI_HUB_ENDPOINT + '''/datasets/{repo_id}/resolve/{revision}/{path}'''
lowerCAmelCase_    =    CI_HUB_ENDPOINT + '''/{repo_id}/resolve/{revision}/{filename}'''
lowerCAmelCase_    =    Path('''~/.huggingface/hub_ci_token''').expanduser()






@pytest.fixture
def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


				monkeypatch.setattr(
				    '''huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE'''			,    _UpperCamelCase			)






@pytest.fixture
def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Optional[int]:






				"""simple docstring"""


				monkeypatch.setattr('''datasets.config.HF_ENDPOINT'''			,    _UpperCamelCase			)
				monkeypatch.setattr('''datasets.config.HUB_DATASETS_URL'''			,    _UpperCamelCase			)






@pytest.fixture
def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				monkeypatch.setattr('''huggingface_hub.hf_api.HfFolder.path_token'''			,    _UpperCamelCase			)






@pytest.fixture
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				HfFolder.save_token(_UpperCamelCase			)
				yield
				HfFolder.delete_token()






@pytest.fixture(scope='''session'''			)
def 					lowerCamelCase_						(    )		->						List[str]:






				"""simple docstring"""


				return HfApi(endpoint=_UpperCamelCase			)






@pytest.fixture(scope='''session'''			)
def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				snake_case_						:   Union[str, Any]           =  HfFolder.get_token()
				HfFolder.save_token(_UpperCamelCase			)
				yield CI_HUB_USER_TOKEN
				if previous_token is not None:
								HfFolder.save_token(_UpperCamelCase			)






@pytest.fixture
def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


				def _cleanup_repo(_UpperCamelCase			):
								hf_api.delete_repo(_UpperCamelCase			,    token=_UpperCamelCase			,    repo_type='''dataset'''			)

				return _cleanup_repo






@pytest.fixture
def 					lowerCamelCase_						(    _UpperCamelCase			)		->						List[str]:






				"""simple docstring"""


				@contextmanager
				def _temporary_repo(_UpperCamelCase			):
								try:
												yield repo_id
								finally:
												cleanup_repo(_UpperCamelCase			)

				return _temporary_repo






@pytest.fixture(scope='''session'''			)
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				snake_case_						:   Optional[int]           =  f'''repo_txt_data-{int(time.time() * 10E3			)}'''
				snake_case_						:   Any           =  f'''{CI_HUB_USER}/{repo_name}'''
				hf_api.create_repo(_UpperCamelCase			,    token=_UpperCamelCase			,    repo_type='''dataset'''			,    private=_UpperCamelCase			)
				hf_api.upload_file(
				    token=_UpperCamelCase			,    path_or_fileobj=str(_UpperCamelCase			)			,    path_in_repo='''data/text_data.txt'''			,    repo_id=_UpperCamelCase			,    repo_type='''dataset'''			,    )
				yield repo_id
				try:
								hf_api.delete_repo(_UpperCamelCase			,    token=_UpperCamelCase			,    repo_type='''dataset'''			)
				except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
								pass






@pytest.fixture()
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				return hf_private_dataset_repo_txt_data_






@pytest.fixture(scope='''session'''			)
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:   int           =  f'''repo_zipped_txt_data-{int(time.time() * 10E3			)}'''
				snake_case_						:   Tuple           =  f'''{CI_HUB_USER}/{repo_name}'''
				hf_api.create_repo(_UpperCamelCase			,    token=_UpperCamelCase			,    repo_type='''dataset'''			,    private=_UpperCamelCase			)
				hf_api.upload_file(
				    token=_UpperCamelCase			,    path_or_fileobj=str(_UpperCamelCase			)			,    path_in_repo='''data.zip'''			,    repo_id=_UpperCamelCase			,    repo_type='''dataset'''			,    )
				yield repo_id
				try:
								hf_api.delete_repo(_UpperCamelCase			,    token=_UpperCamelCase			,    repo_type='''dataset'''			)
				except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
								pass






@pytest.fixture()
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Dict:






				"""simple docstring"""


				return hf_private_dataset_repo_zipped_txt_data_






@pytest.fixture(scope='''session'''			)
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:   List[str]           =  f'''repo_zipped_img_data-{int(time.time() * 10E3			)}'''
				snake_case_						:   str           =  f'''{CI_HUB_USER}/{repo_name}'''
				hf_api.create_repo(_UpperCamelCase			,    token=_UpperCamelCase			,    repo_type='''dataset'''			,    private=_UpperCamelCase			)
				hf_api.upload_file(
				    token=_UpperCamelCase			,    path_or_fileobj=str(_UpperCamelCase			)			,    path_in_repo='''data.zip'''			,    repo_id=_UpperCamelCase			,    repo_type='''dataset'''			,    )
				yield repo_id
				try:
								hf_api.delete_repo(_UpperCamelCase			,    token=_UpperCamelCase			,    repo_type='''dataset'''			)
				except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
								pass






@pytest.fixture()
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						int:






				"""simple docstring"""


				return hf_private_dataset_repo_zipped_img_data_
