






"""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        UpperCamelCase			(	unittest.TestCase      ):


 def __init__(     self		:						Dict							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						Optional[Any]=13							,						UpperCAmelCase__		:						Any=7							,						UpperCAmelCase__		:						List[str]=True							,						UpperCAmelCase__		:						Any=True							,						UpperCAmelCase__		:						Optional[int]=True							,						UpperCAmelCase__		:						Tuple=True							,						UpperCAmelCase__		:						str=99							,						UpperCAmelCase__		:						str=32							,						UpperCAmelCase__		:						List[str]=5							,						UpperCAmelCase__		:						Any=4							,						UpperCAmelCase__		:						List[Any]=37							,						UpperCAmelCase__		:						Dict="gelu"							,						UpperCAmelCase__		:						int=0.1							,						UpperCAmelCase__		:						List[Any]=0.1							,						UpperCAmelCase__		:						List[str]=512							,						UpperCAmelCase__		:						Union[str, Any]=16							,						UpperCAmelCase__		:						Dict=2							,						UpperCAmelCase__		:						Optional[int]=0.0_2							,						UpperCAmelCase__		:						Dict=4							,						)					->       Optional[int]:
   _a  :       Optional[int]             =	parent
   _a  :       Any             =	batch_size
   _a  :       Tuple             =	seq_length
   _a  :       List[Any]             =	is_training
   _a  :       Dict             =	use_attention_mask
   _a  :       Optional[int]             =	use_token_type_ids
   _a  :       Tuple             =	use_labels
   _a  :       Optional[Any]             =	vocab_size
   _a  :       List[str]             =	hidden_size
   _a  :       Optional[int]             =	num_hidden_layers
   _a  :       Optional[Any]             =	num_attention_heads
   _a  :       str             =	intermediate_size
   _a  :       List[Any]             =	hidden_act
   _a  :       str             =	hidden_dropout_prob
   _a  :       str             =	attention_probs_dropout_prob
   _a  :       List[Any]             =	max_position_embeddings
   _a  :       str             =	type_vocab_size
   _a  :       Union[str, Any]             =	type_sequence_label_size
   _a  :       Tuple             =	initializer_range
   _a  :       Optional[Any]             =	num_choices


 def _lowercase	(     self		:						Tuple							)					->       Optional[Any]:
   _a  :       Optional[Any]             =	ids_tensor([self.batch_size, self.seq_length]							,						self.vocab_size							)

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

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

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







@require_flax
class        UpperCamelCase			(	lowerCAmelCase__		,     unittest.TestCase      ):
 UpperCamelCase					:       Union[str, Any]			=					True

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


 def _lowercase	(     self		:						List[str]							)					->       Optional[int]:
   _a  :       List[Any]             =	FlaxRoFormerModelTester(self							)







 @slow
 def _lowercase	(     self		:						Optional[Any]							)					->       Dict:
   for model_class_name in self.all_model_classes:
     _a  :       List[str]             =	model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""							,						from_pt=__a							)
     _a  :       Optional[int]             =	model(np.ones((1, 1)							)							)
     self.assertIsNotNone(__a							)







@require_flax
class        UpperCamelCase			(	unittest.TestCase      ):


 @slow
 def _lowercase	(     self		:						str							)					->       int:
   _a  :       Union[str, Any]             =	FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""							)
   _a  :       Tuple             =	jnp.array([[0, 1, 2, 3, 4, 5]]							)
   _a  :       List[str]             =	model(__a							)[0]

   _a  :       Union[str, Any]             =	50000

   _a  :       Union[str, Any]             =	(1, 6, vocab_size)
   self.assertEqual(output.shape							,						__a							)

   _a  :       Dict             =	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							)							)





"""simple docstring"""


A     :   int			    =			{
    "Pillow": "Pillow",
    "accelerate": "accelerate>=0.11.0",
    "compel": "compel==0.1.8",
    "black": "black~=23.1",
    "datasets": "datasets",
    "filelock": "filelock",
    "flax": "flax>=0.4.1",
    "hf-doc-builder": "hf-doc-builder>=0.3.0",
    "huggingface-hub": "huggingface-hub>=0.13.2",
    "requests-mock": "requests-mock==1.10.0",
    "importlib_metadata": "importlib_metadata",
    "invisible-watermark": "invisible-watermark",
    "isort": "isort>=5.5.4",
    "jax": "jax>=0.2.8,!=0.3.2",
    "jaxlib": "jaxlib>=0.1.65",
    "Jinja2": "Jinja2",
    "k-diffusion": "k-diffusion>=0.0.12",
    "torchsde": "torchsde",
    "note_seq": "note_seq",
    "librosa": "librosa",
    "numpy": "numpy",
    "omegaconf": "omegaconf",
    "parameterized": "parameterized",
    "protobuf": "protobuf>=3.20.3,<4",
    "pytest": "pytest",
    "pytest-timeout": "pytest-timeout",
    "pytest-xdist": "pytest-xdist",
    "ruff": "ruff>=0.0.241",
    "safetensors": "safetensors",
    "sentencepiece": "sentencepiece>=0.1.91,!=0.1.92",
    "scipy": "scipy",
    "onnx": "onnx",
    "regex": "regex!=2019.12.17",
    "requests": "requests",
    "tensorboard": "tensorboard",
    "torch": "torch>=1.4",
    "torchvision": "torchvision",
    "transformers": "transformers>=4.25.1",
    "urllib3": "urllib3<=2.0.0",
}




