






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 							SCREAMING_SNAKE_CASE__						(    unittest.TestCase						):





					"""simple docstring"""






					def __init__( self					:  List[Any]							,		__A					:  str							,		__A					:  List[Any]=1_3							,		__A					:  Optional[int]=7							,		__A					:  Tuple=True							,		__A					:  Tuple=True							,		__A					:  str=True							,		__A					:  Optional[int]=True							,		__A					:  List[str]=9_9							,		__A					:  Union[str, Any]=3_2							,		__A					:  Union[str, Any]=5							,		__A					:  Tuple=4							,		__A					:  List[Any]=3_7							,		__A					:  Tuple="gelu"							,		__A					:  Tuple=0.1							,		__A					:  List[str]=0.1							,		__A					:  Optional[int]=5_1_2							,		__A					:  Union[str, Any]=1_6							,		__A					:  Optional[Any]=2							,		__A					:  Optional[Any]=0.0_2							,		__A					:  Tuple=4							,		):
						snake_case__			:				str       =  parent
						snake_case__			:				Union[str, Any]       =  batch_size
						snake_case__			:				Union[str, Any]       =  seq_length
						snake_case__			:				str       =  is_training
						snake_case__			:				Optional[Any]       =  use_attention_mask
						snake_case__			:				List[Any]       =  use_token_type_ids
						snake_case__			:				List[str]       =  use_labels
						snake_case__			:				Dict       =  vocab_size
						snake_case__			:				Tuple       =  hidden_size
						snake_case__			:				List[Any]       =  num_hidden_layers
						snake_case__			:				Union[str, Any]       =  num_attention_heads
						snake_case__			:				Optional[Any]       =  intermediate_size
						snake_case__			:				Optional[Any]       =  hidden_act
						snake_case__			:				Optional[int]       =  hidden_dropout_prob
						snake_case__			:				Any       =  attention_probs_dropout_prob
						snake_case__			:				Tuple       =  max_position_embeddings
						snake_case__			:				List[Any]       =  type_vocab_size
						snake_case__			:				str       =  type_sequence_label_size
						snake_case__			:				List[Any]       =  initializer_range
						snake_case__			:				Dict       =  num_choices



					def 	_lowercase					( self					:  Tuple	):
						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__			:				List[Any]       =  random_attention_mask([self.batch_size, self.seq_length]	)

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

						snake_case__			:				Dict       =  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=__A							,		initializer_range=self.initializer_range							,		)

						return config, input_ids, token_type_ids, attention_mask






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



@require_flax
class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_			,       unittest.TestCase						):





					"""simple docstring"""






					a_     =		True

					a_     =		(
					    (
					        FlaxRoFormerModel,
					        FlaxRoFormerForMaskedLM,
					        FlaxRoFormerForSequenceClassification,
					        FlaxRoFormerForTokenClassification,
					        FlaxRoFormerForMultipleChoice,
					        FlaxRoFormerForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)



					def 	_lowercase					( self					:  List[Any]	):
						snake_case__			:				Dict       =  FlaxRoFormerModelTester(self	)






					@slow
					def 	_lowercase					( self					:  Any	):
						for model_class_name in self.all_model_classes:
							snake_case__			:				Union[str, Any]       =  model_class_name.from_pretrained("junnyu/roformer_chinese_small"							,		from_pt=__A	)
							snake_case__			:				Optional[int]       =  model(np.ones((1, 1)	)	)
							self.assertIsNotNone(__A	)



@require_flax
class 							SCREAMING_SNAKE_CASE__						(    unittest.TestCase						):





					"""simple docstring"""






					@slow
					def 	_lowercase					( self					:  List[str]	):
						snake_case__			:				Dict       =  FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"	)
						snake_case__			:				List[str]       =  jnp.array([[0, 1, 2, 3, 4, 5]]	)
						snake_case__			:				Any       =  model(__A	)[0]

						snake_case__			:				Tuple       =  5_0_0_0_0

						snake_case__			:				str       =  (1, 6, vocab_size)
						self.assertEqual(output.shape							,		__A	)

						snake_case__			:				Tuple       =  jnp.array(
						    [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]	)

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











# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)



