






import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs


class 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase       ):
     '''simple docstring'''


     def __init__(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Optional[int]       ,			UpperCAmelCase_							:						Dict=None       ,			UpperCAmelCase_							:						Dict=True       ,			UpperCAmelCase_							:						List[Any]=None       ,			**UpperCAmelCase_							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   Dict						    =     parent
       SCREAMING_SNAKE_CASE							:   Dict						    =     config_class
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     has_text_modality
       SCREAMING_SNAKE_CASE							:   List[str]						    =     kwargs
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     common_properties


     def 					_A							(					self							:						Union[str, Any]   ):
       SCREAMING_SNAKE_CASE							:   int						    =     self.config_class(**self.inputs_dict   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     (
           ["hidden_size", "num_attention_heads", "num_hidden_layers"]
           if self.common_properties is None
           else self.common_properties
       )

       # Add common fields for text models
       if self.has_text_modality:
         common_properties.extend(["vocab_size"]   )

       # Test that config has the common properties as getters
       for prop in common_properties:
         self.parent.assertTrue(hasattr(UpperCAmelCase_       ,			UpperCAmelCase_   )       ,			msg=f'''`{prop}` does not exist'''   )

       # Test that config has the common properties as setter
       for idx, name in enumerate(UpperCAmelCase_   ):
         try:
           setattr(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )
           self.parent.assertEqual(
               getattr(UpperCAmelCase_       ,			UpperCAmelCase_   )       ,			UpperCAmelCase_       ,			msg=f'''`{name} value {idx} expected, but was {getattr(UpperCAmelCase_       ,			UpperCAmelCase_   )}'''   )
         except NotImplementedError:
           # Some models might not be able to implement setters for common_properties
           # In that case, a NotImplementedError is raised
           pass

        # Test if config class can be called with Config(prop_name=..)
       for idx, name in enumerate(UpperCAmelCase_   ):
         try:
           SCREAMING_SNAKE_CASE							:   List[str]						    =     self.config_class(**{name: idx}   )
           self.parent.assertEqual(
               getattr(UpperCAmelCase_       ,			UpperCAmelCase_   )       ,			UpperCAmelCase_       ,			msg=f'''`{name} value {idx} expected, but was {getattr(UpperCAmelCase_       ,			UpperCAmelCase_   )}'''   )
         except NotImplementedError:
           # Some models might not be able to implement setters for common_properties
           # In that case, a NotImplementedError is raised
           pass


     def 					_A							(					self							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.config_class(**self.inputs_dict   )
       SCREAMING_SNAKE_CASE							:   Any						    =     json.loads(config.to_json_string()   )
       for key, value in self.inputs_dict.items():
         self.parent.assertEqual(obj[key]       ,			UpperCAmelCase_   )


     def 					_A							(					self							:						Tuple   ):
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     self.config_class(**self.inputs_dict   )

       with tempfile.TemporaryDirectory() as tmpdirname:
         SCREAMING_SNAKE_CASE							:   Dict						    =     os.path.join(UpperCAmelCase_       ,			"config.json"   )
         config_first.to_json_file(UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   Tuple						    =     self.config_class.from_json_file(UpperCAmelCase_   )

       self.parent.assertEqual(config_second.to_dict()       ,			config_first.to_dict()   )


     def 					_A							(					self							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   Tuple						    =     self.config_class(**self.inputs_dict   )

       with tempfile.TemporaryDirectory() as tmpdirname:
         config_first.save_pretrained(UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.config_class.from_pretrained(UpperCAmelCase_   )

       self.parent.assertEqual(config_second.to_dict()       ,			config_first.to_dict()   )


     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.config_class(**self.inputs_dict   )

       SCREAMING_SNAKE_CASE							:   Dict						    =     "test"
       with tempfile.TemporaryDirectory() as tmpdirname:
         SCREAMING_SNAKE_CASE							:   Tuple						    =     os.path.join(UpperCAmelCase_       ,			UpperCAmelCase_   )
         config_first.save_pretrained(UpperCAmelCase_   )
         SCREAMING_SNAKE_CASE							:   List[str]						    =     self.config_class.from_pretrained(UpperCAmelCase_       ,			subfolder=UpperCAmelCase_   )

       self.parent.assertEqual(config_second.to_dict()       ,			config_first.to_dict()   )


     def 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     self.config_class(**self.inputs_dict       ,			num_labels=5   )
       self.parent.assertEqual(len(config.idalabel   )       ,			5   )
       self.parent.assertEqual(len(config.labelaid   )       ,			5   )

       SCREAMING_SNAKE_CASE							:   List[Any]						    =     3
       self.parent.assertEqual(len(config.idalabel   )       ,			3   )
       self.parent.assertEqual(len(config.labelaid   )       ,			3   )


     def 					_A							(					self							:						str   ):
       if self.config_class.is_composition:
         return
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     self.config_class()
       self.parent.assertIsNotNone(UpperCAmelCase_   )


     def 					_A							(					self							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     copy.deepcopy(UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Any						    =     self.config_class(**UpperCAmelCase_   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     []
       for key, value in config_common_kwargs.items():
         if key == "torch_dtype":
           if not is_torch_available():
             continue
           else:
             import torch

             if config.torch_dtype != torch.floataa:
               wrong_values.append(("torch_dtype", config.torch_dtype, torch.floataa)   )
         elif getattr(UpperCAmelCase_       ,			UpperCAmelCase_   ) != value:
           wrong_values.append((key, getattr(UpperCAmelCase_       ,			UpperCAmelCase_   ), value)   )

       if len(UpperCAmelCase_   ) > 0:
         SCREAMING_SNAKE_CASE							:   Any						    =     "\n".join([f'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values]   )
         raise ValueError(f'''The following keys were not properly set in the config:\n{errors}'''   )






     def 					_A							(					self							:						Tuple   ):
       self.create_and_test_config_common_properties()
       self.create_and_test_config_to_json_string()
       self.create_and_test_config_to_json_file()
       self.create_and_test_config_from_and_save_pretrained()
       self.create_and_test_config_from_and_save_pretrained_subfolder()
       self.create_and_test_config_with_num_labels()
       self.check_config_can_be_init_without_params()
       self.check_config_arguments_init()













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






							def        __a  (  self )      ->					List[Any]:
								a :						Dict      =							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 :						Dict      =							ids_tensor([self.batch_size, self.seq_length] ,   self.type_vocab_size )

								a :						Tuple      =							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=lowerCAmelCase__ ,   initializer_range=self.initializer_range ,   )

								return config, input_ids, token_type_ids, attention_mask






							def        __a  (  self )      ->					List[str]:
								a :						Dict      =							self.prepare_config_and_inputs()
								a,			a,			a,			a :						int      =							config_and_inputs
								a :						Dict      =							{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
								return config, inputs_dict


@require_flax
class 			__UpperCamelCase							(		a__						,			unittest.TestCase		):
							lowerCamelCase  :   Union[str, Any]    =True

							lowerCamelCase  :   Union[str, Any]    =(
							    (
							        FlaxRoFormerModel,
							        FlaxRoFormerForMaskedLM,
							        FlaxRoFormerForSequenceClassification,
							        FlaxRoFormerForTokenClassification,
							        FlaxRoFormerForMultipleChoice,
							        FlaxRoFormerForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)






							def        __a  (  self )      ->					List[Any]:
								a :						Dict      =							FlaxRoFormerModelTester(self )






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


@require_flax
class 			__UpperCamelCase							(		unittest.TestCase		):






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

								a :						int      =							5_0000

								a :						Any      =							(1, 6, vocab_size)
								self.assertEqual(output.shape ,   lowerCAmelCase__ )

								a :						List[Any]      =							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] ,   lowerCAmelCase__ ,   atol=1E-4 ) )


