

"""simple docstring"""




import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						import jax.numpy as jnp

						from transformers.models.roformer.modeling_flax_roformer import (
						    FlaxRoFormerForMaskedLM,
						    FlaxRoFormerForMultipleChoice,
						    FlaxRoFormerForQuestionAnswering,
						    FlaxRoFormerForSequenceClassification,
						    FlaxRoFormerForTokenClassification,
						    FlaxRoFormerModel,
						)





class    lowerCamelCase						(unittest.TestCase			):

			def __init__(					self				:						Dict    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						List[str]=1_3    ,				__UpperCAmelCase				:						Union[str, Any]=7    ,				__UpperCAmelCase				:						Union[str, Any]=True    ,				__UpperCAmelCase				:						Any=True    ,				__UpperCAmelCase				:						Dict=True    ,				__UpperCAmelCase				:						List[str]=True    ,				__UpperCAmelCase				:						int=9_9    ,				__UpperCAmelCase				:						Tuple=3_2    ,				__UpperCAmelCase				:						List[str]=5    ,				__UpperCAmelCase				:						Dict=4    ,				__UpperCAmelCase				:						Tuple=3_7    ,				__UpperCAmelCase				:						int="gelu"    ,				__UpperCAmelCase				:						int=0.1    ,				__UpperCAmelCase				:						Dict=0.1    ,				__UpperCAmelCase				:						Any=5_1_2    ,				__UpperCAmelCase				:						List[Any]=1_6    ,				__UpperCAmelCase				:						Optional[int]=2    ,				__UpperCAmelCase				:						Tuple=0.02    ,				__UpperCAmelCase				:						Union[str, Any]=4    ,				)   ->    Tuple:
						SCREAMING_SNAKE_CASE__								=      parent
						SCREAMING_SNAKE_CASE__								=      batch_size
						SCREAMING_SNAKE_CASE__								=      seq_length
						SCREAMING_SNAKE_CASE__								=      is_training
						SCREAMING_SNAKE_CASE__								=      use_attention_mask
						SCREAMING_SNAKE_CASE__								=      use_token_type_ids
						SCREAMING_SNAKE_CASE__								=      use_labels
						SCREAMING_SNAKE_CASE__								=      vocab_size
						SCREAMING_SNAKE_CASE__								=      hidden_size
						SCREAMING_SNAKE_CASE__								=      num_hidden_layers
						SCREAMING_SNAKE_CASE__								=      num_attention_heads
						SCREAMING_SNAKE_CASE__								=      intermediate_size
						SCREAMING_SNAKE_CASE__								=      hidden_act
						SCREAMING_SNAKE_CASE__								=      hidden_dropout_prob
						SCREAMING_SNAKE_CASE__								=      attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE__								=      max_position_embeddings
						SCREAMING_SNAKE_CASE__								=      type_vocab_size
						SCREAMING_SNAKE_CASE__								=      type_sequence_label_size
						SCREAMING_SNAKE_CASE__								=      initializer_range
						SCREAMING_SNAKE_CASE__								=      num_choices

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      ids_tensor([self.batch_size, self.seq_length]    ,				self.vocab_size       )

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

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

						SCREAMING_SNAKE_CASE__								=      RoFormerConfig(
						    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=UpperCamelCase_    ,				initializer_range=self.initializer_range    ,				)

						return config, input_ids, token_type_ids, attention_mask





			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      self.prepare_config_and_inputs()
						SCREAMING_SNAKE_CASE__								=      config_and_inputs
						SCREAMING_SNAKE_CASE__								=      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
						return config, inputs_dict





@require_flax
class    lowerCamelCase						(A__			,unittest.TestCase			):
			lowerCamelCase__   :		Optional[Any]							=							True

			lowerCamelCase__   :		List[str]							=							(
			    (
			        FlaxRoFormerModel,
			        FlaxRoFormerForMaskedLM,
			        FlaxRoFormerForSequenceClassification,
			        FlaxRoFormerForTokenClassification,
			        FlaxRoFormerForMultipleChoice,
			        FlaxRoFormerForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      FlaxRoFormerModelTester(self       )





			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Optional[int]:





						for model_class_name in self.all_model_classes:
									SCREAMING_SNAKE_CASE__								=      model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""    ,				from_pt=UpperCamelCase_       )
									SCREAMING_SNAKE_CASE__								=      model(np.ones((1, 1)       )       )
									self.assertIsNotNone(UpperCamelCase_       )





@require_flax
class    lowerCamelCase						(unittest.TestCase			):

			@slow
			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    List[Any]:
						SCREAMING_SNAKE_CASE__								=      FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""       )
						SCREAMING_SNAKE_CASE__								=      jnp.array([[0, 1, 2, 3, 4, 5]]       )
						SCREAMING_SNAKE_CASE__								=      model(UpperCamelCase_       )[0]

						SCREAMING_SNAKE_CASE__								=      5_0_0_0_0

						SCREAMING_SNAKE_CASE__								=      (1, 6, vocab_size)
						self.assertEqual(output.shape    ,				UpperCamelCase_       )

						SCREAMING_SNAKE_CASE__								=      jnp.array(
						    [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]       )

						self.assertTrue(jnp.allclose(output[:, :3, :3]    ,				UpperCamelCase_    ,				atol=1e-4       )       )







"""simple docstring"""




import pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline

@pytest.mark.integration
def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT    ):
						with pytest.raises(snake_case__    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""    )
						with pytest.raises(requests.exceptions.ConnectTimeout    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""		,				timeout=1.0    )

@pytest.mark.integration
def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.CONNECTION_FAILS    ):
						with pytest.raises(requests.exceptions.ConnectionError    ):
									requests.request("""GET"""		,				"""https://huggingface.co"""    )




def 	A       (     ):




			'''simple docstring'''


			with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1    ):
						with pytest.raises(snake_case__    ):
									http_head("""https://huggingface.co"""    )


