


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 				snake_case__(							unittest.TestCase							):







						'''simple docstring'''




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




						def 						lowercase_ (				self							)					->    Any:
										lowerCAmelCase_ :      Any    				=				ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size							)

										lowerCAmelCase_ :      str    				=				None
										if self.use_attention_mask:
														lowerCAmelCase_ :      Dict    				=				random_attention_mask([self.batch_size, self.seq_length]							)

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

										lowerCAmelCase_ :      int    				=				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=__lowercase   ,						initializer_range=self.initializer_range   ,						)

										return config, input_ids, token_type_ids, attention_mask




						def 						lowercase_ (				self							)					->    str:
										lowerCAmelCase_ :      List[Any]    				=				self.prepare_config_and_inputs()
										lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ :      List[Any]    				=				config_and_inputs
										lowerCAmelCase_ :      Tuple    				=				{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
										return config, inputs_dict







@require_flax
class 				snake_case__(							UpperCAmelCase__,						unittest.TestCase							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  List[Any]					=							True

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




						def 						lowercase_ (				self							)					->    List[str]:
										lowerCAmelCase_ :      Any    				=				FlaxRoFormerModelTester(self							)




						@slow
						def 						lowercase_ (				self							)					->    Optional[Any]:
										for model_class_name in self.all_model_classes:
														lowerCAmelCase_ :      List[str]    				=				model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''   ,						from_pt=__lowercase							)
														lowerCAmelCase_ :      List[Any]    				=				model(np.ones((1, 1)							)							)
														self.assertIsNotNone(__lowercase							)







@require_flax
class 				snake_case__(							unittest.TestCase							):







						'''simple docstring'''




						@slow
						def 						lowercase_ (				self							)					->    Tuple:
										lowerCAmelCase_ :      List[str]    				=				FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''							)
										lowerCAmelCase_ :      List[str]    				=				jnp.array([[0, 1, 2, 3, 4, 5]]							)
										lowerCAmelCase_ :      Any    				=				model(__lowercase							)[0]

										lowerCAmelCase_ :      Optional[Any]    				=				5_0_0_0_0

										lowerCAmelCase_ :      Any    				=				(1, 6, vocab_size)
										self.assertEqual(output.shape   ,						__lowercase							)

										lowerCAmelCase_ :      Tuple    				=				jnp.array(
										    [[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]]							)

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



import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate







@dataclass(frozen=UpperCAmelCase__							)
class 				snake_case__(							UpperCAmelCase__							):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__      :  str					=							field(default="""automatic-speech-recognition""",						metadata={"""include_in_asdict_even_if_is_default""": True}							)
						SCREAMING_SNAKE_CASE__      :  ClassVar[Features]					=							Features({"""audio""": Audio()}							)
						SCREAMING_SNAKE_CASE__      :  ClassVar[Features]					=							Features({"""transcription""": Value("""string"""							)}							)
						SCREAMING_SNAKE_CASE__      :  str					=							"audio"
						SCREAMING_SNAKE_CASE__      :  str					=							"transcription"




						def 						lowercase_ (				self   ,						__lowercase							)					->    int:
										if self.audio_column not in features:
														raise ValueError(f"""Column {self.audio_column} is not present in features."""							)
										if not isinstance(features[self.audio_column]   ,						__lowercase							):
														raise ValueError(f"""Column {self.audio_column} is not an Audio type."""							)
										lowerCAmelCase_ :      List[str]    				=				copy.deepcopy(self							)
										lowerCAmelCase_ :      Optional[Any]    				=				self.input_schema.copy()
										lowerCAmelCase_ :      Optional[Any]    				=				features[self.audio_column]
										lowerCAmelCase_ :      List[str]    				=				input_schema
										return task_template




						@property
						def 						lowercase_ (				self							)					->    Dict[str, str]:
										return {self.audio_column: "audio", self.transcription_column: "transcription"}