






from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import (
					    TFRoFormerForCausalLM,
					    TFRoFormerForMaskedLM,
					    TFRoFormerForMultipleChoice,
					    TFRoFormerForQuestionAnswering,
					    TFRoFormerForSequenceClassification,
					    TFRoFormerForTokenClassification,
					    TFRoFormerModel,
					)
					from transformers.models.roformer.modeling_tf_roformer import (
					    TFRoFormerSelfAttention,
					    TFRoFormerSinusoidalPositionalEmbedding,
					)
class    _UpperCAmelCase      :

				"""simple docstring"""




				def __init__(  self		:					Dict,						lowerCamelCase		:					Dict,						lowerCamelCase		:					Any=13,						lowerCamelCase		:					Union[str, Any]=7,						lowerCamelCase		:					Dict=True,						lowerCamelCase		:					Dict=True,						lowerCamelCase		:					int=True,						lowerCamelCase		:					int=True,						lowerCamelCase		:					Any=99,						lowerCamelCase		:					Union[str, Any]=32,						lowerCamelCase		:					Any=2,						lowerCamelCase		:					Any=4,						lowerCamelCase		:					Any=37,						lowerCamelCase		:					Tuple="gelu",						lowerCamelCase		:					Optional[int]=0.1,						lowerCamelCase		:					Optional[Any]=0.1,						lowerCamelCase		:					Optional[int]=512,						lowerCamelCase		:					int=16,						lowerCamelCase		:					Union[str, Any]=2,						lowerCamelCase		:					Optional[Any]=0.02,						lowerCamelCase		:					int=3,						lowerCamelCase		:					Any=4,						lowerCamelCase		:					str=None,						):

								'''simple docstring'''
								lowercase__     				=    parent
								lowercase__     				=    13
								lowercase__     				=    7
								lowercase__     				=    True
								lowercase__     				=    True
								lowercase__     				=    True
								lowercase__     				=    True
								lowercase__     				=    99
								lowercase__     				=    32
								lowercase__     				=    2
								lowercase__     				=    4
								lowercase__     				=    37
								lowercase__     				=    '''gelu'''
								lowercase__     				=    0.1
								lowercase__     				=    0.1
								lowercase__     				=    512
								lowercase__     				=    16
								lowercase__     				=    2
								lowercase__     				=    0.02
								lowercase__     				=    3
								lowercase__     				=    4
								lowercase__     				=    None




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    ids_tensor([self.batch_size, self.seq_length],						self.vocab_size						)

								lowercase__     				=    None
								if self.use_input_mask:
												lowercase__     				=    random_attention_mask([self.batch_size, self.seq_length]						)

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

								lowercase__     				=    None
								lowercase__     				=    None
								lowercase__     				=    None
								if self.use_labels:
												lowercase__     				=    ids_tensor([self.batch_size],						self.type_sequence_label_size						)
												lowercase__     				=    ids_tensor([self.batch_size, self.seq_length],						self.num_labels						)
												lowercase__     				=    ids_tensor([self.batch_size],						self.num_choices						)

								lowercase__     				=    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,						initializer_range=self.initializer_range,						return_dict=lowerCamelCase,						)

								return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					Any,						lowerCamelCase		:					str,						lowerCamelCase		:					int,						lowerCamelCase		:					Dict,						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    TFRoFormerModel(config=lowerCamelCase						)
								lowercase__     				=    {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

								lowercase__     				=    [input_ids, input_mask]
								lowercase__     				=    model(lowerCamelCase						)

								lowercase__     				=    model(lowerCamelCase						)

								self.parent.assertEqual(result.last_hidden_state.shape,						(self.batch_size, self.seq_length, self.hidden_size)						)




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Any,						lowerCamelCase		:					int,						lowerCamelCase		:					int,						lowerCamelCase		:					List[Any],						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    True
								lowercase__     				=    TFRoFormerForCausalLM(config=lowerCamelCase						)
								lowercase__     				=    {
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								    '''token_type_ids''': token_type_ids,
								}
								lowercase__     				=    model(lowerCamelCase						)['''logits''']
								self.parent.assertListEqual(
								    list(prediction_scores.numpy().shape						),						[self.batch_size, self.seq_length, self.vocab_size]						)




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					Any,						lowerCamelCase		:					Any,						lowerCamelCase		:					List[str],						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					List[Any],						lowerCamelCase		:					Tuple,						lowerCamelCase		:					Any						):

								'''simple docstring'''
								lowercase__     				=    TFRoFormerForMaskedLM(config=lowerCamelCase						)
								lowercase__     				=    {
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								    '''token_type_ids''': token_type_ids,
								}
								lowercase__     				=    model(lowerCamelCase						)
								self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.seq_length, self.vocab_size)						)




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					str,						lowerCamelCase		:					int,						lowerCamelCase		:					Any,						lowerCamelCase		:					int,						lowerCamelCase		:					List[str],						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.num_labels
								lowercase__     				=    TFRoFormerForSequenceClassification(config=lowerCamelCase						)
								lowercase__     				=    {
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								    '''token_type_ids''': token_type_ids,
								}

								lowercase__     				=    model(lowerCamelCase						)
								self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.num_labels)						)




				def        lowercase__     (  self		:					Dict,						lowerCamelCase		:					Tuple,						lowerCamelCase		:					List[str],						lowerCamelCase		:					Any,						lowerCamelCase		:					str,						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					int,						lowerCamelCase		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.num_choices
								lowercase__     				=    TFRoFormerForMultipleChoice(config=lowerCamelCase						)
								lowercase__     				=    tf.tile(tf.expand_dims(lowerCamelCase,						1						),						(1, self.num_choices, 1)						)
								lowercase__     				=    tf.tile(tf.expand_dims(lowerCamelCase,						1						),						(1, self.num_choices, 1)						)
								lowercase__     				=    tf.tile(tf.expand_dims(lowerCamelCase,						1						),						(1, self.num_choices, 1)						)
								lowercase__     				=    {
								    '''input_ids''': multiple_choice_inputs_ids,
								    '''attention_mask''': multiple_choice_input_mask,
								    '''token_type_ids''': multiple_choice_token_type_ids,
								}
								lowercase__     				=    model(lowerCamelCase						)
								self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.num_choices)						)




				def        lowercase__     (  self		:					Optional[Any],						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					int,						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Dict,						lowerCamelCase		:					Optional[Any],						lowerCamelCase		:					Dict,						lowerCamelCase		:					Dict						):

								'''simple docstring'''
								lowercase__     				=    self.num_labels
								lowercase__     				=    TFRoFormerForTokenClassification(config=lowerCamelCase						)
								lowercase__     				=    {
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								    '''token_type_ids''': token_type_ids,
								}
								lowercase__     				=    model(lowerCamelCase						)
								self.parent.assertEqual(result.logits.shape,						(self.batch_size, self.seq_length, self.num_labels)						)




				def        lowercase__     (  self		:					Tuple,						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					str,						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Dict,						lowerCamelCase		:					int,						lowerCamelCase		:					List[Any],						lowerCamelCase		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    TFRoFormerForQuestionAnswering(config=lowerCamelCase						)
								lowercase__     				=    {
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								    '''token_type_ids''': token_type_ids,
								}

								lowercase__     				=    model(lowerCamelCase						)
								self.parent.assertEqual(result.start_logits.shape,						(self.batch_size, self.seq_length)						)
								self.parent.assertEqual(result.end_logits.shape,						(self.batch_size, self.seq_length)						)




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.prepare_config_and_inputs()
								(
								    (
								    lowercase__		
								)				,		(
								    lowercase__		
								)				,		(
								    lowercase__		
								)				,		(
								    lowercase__		
								)				,		(
								    lowercase__		
								)				,		(
								    lowercase__		
								)				,		(
								    lowercase__		
								)				,				
								)     				=    config_and_inputs
								lowercase__     				=    {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
								return config, inputs_dict



@require_tf
class    _UpperCAmelCase      ( A__	,A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= (
				    (
				        TFRoFormerModel,
				        TFRoFormerForCausalLM,
				        TFRoFormerForMaskedLM,
				        TFRoFormerForQuestionAnswering,
				        TFRoFormerForSequenceClassification,
				        TFRoFormerForTokenClassification,
				        TFRoFormerForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				lowercase__       							= (
				    {
				        """feature-extraction""": TFRoFormerModel,
				        """fill-mask""": TFRoFormerForMaskedLM,
				        """question-answering""": TFRoFormerForQuestionAnswering,
				        """text-classification""": TFRoFormerForSequenceClassification,
				        """text-generation""": TFRoFormerForCausalLM,
				        """token-classification""": TFRoFormerForTokenClassification,
				        """zero-shot""": TFRoFormerForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				lowercase__       							= False
				lowercase__       							= False




				def        lowercase__     (  self		:					str,						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Optional[int],						lowerCamelCase		:					Tuple,						lowerCamelCase		:					Union[str, Any],						lowerCamelCase		:					Optional[Any]						):

								'''simple docstring'''
								if pipeline_test_casse_name == "TextGenerationPipelineTests":
												return True

								return False




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    TFRoFormerModelTester(self						)
								lowercase__     				=    ConfigTester(self,						config_class=lowerCamelCase,						hidden_size=37						)




				def        lowercase__     (  self		:					Optional[Any]						):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*lowerCamelCase						)




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_lm_head(*lowerCamelCase						)




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase						)




				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_question_answering(*lowerCamelCase						)




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase						)




				def        lowercase__     (  self		:					Tuple						):

								'''simple docstring'''
								lowercase__     				=    self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_token_classification(*lowerCamelCase						)




				@slow
				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    TFRoFormerModel.from_pretrained('''junnyu/roformer_chinese_base'''						)
								self.assertIsNotNone(lowerCamelCase						)



@require_tf
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				@slow
				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    TFRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''						)
								lowercase__     				=    tf.constant([[0, 1, 2, 3, 4, 5]]						)
								lowercase__     				=    model(lowerCamelCase						)[0]

								# TODO Replace vocab size
								lowercase__     				=    50_000

								lowercase__     				=    [1, 6, vocab_size]
								self.assertEqual(output.shape,						lowerCamelCase						)

								print(output[:, :3, :3]						)

								# TODO Replace values below with what was printed above.
								lowercase__     				=    tf.constant(
								    [
								        [
								            [-0.12053341, -1.0264901, 0.29221946],
								            [-1.5133783, 0.197433, 0.15190607],
								            [-5.0135403, -3.900256, -0.84038764],
								        ]
								    ]						)
								tf.debugging.assert_near(output[:, :3, :3],						lowerCamelCase,						atol=1E-4						)



@require_tf
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= 1E-4




				def        lowercase__     (  self		:					int						):

								'''simple docstring'''
								lowercase__     				=    tf.constant([[4, 10]]						)
								lowercase__     				=    TFRoFormerSinusoidalPositionalEmbedding(num_positions=6,						embedding_dim=6						)

								lowercase__     				=    emba(input_ids.shape						)
								lowercase__     				=    tf.constant(
								    [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]						)

								tf.debugging.assert_near(lowerCamelCase,						lowerCamelCase,						atol=self.tolerance						)




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    tf.constant(
								    [
								        [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
								        [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
								        [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
								    ]						)
								lowercase__     				=    TFRoFormerSinusoidalPositionalEmbedding(num_positions=512,						embedding_dim=512						)
								emba([2, 16, 512]						)
								lowercase__     				=    emba.weight[:3, :5]

								tf.debugging.assert_near(lowerCamelCase,						lowerCamelCase,						atol=self.tolerance						)



@require_tf
class    _UpperCAmelCase      ( unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= 1E-4




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    tf.reshape(tf.range(2 * 12 * 16 * 64,						dtype=tf.floataa						),						shape=(2, 12, 16, 64)						) / 100

								lowercase__     				=    -tf.reshape(tf.range(2 * 12 * 16 * 64,						dtype=tf.floataa						),						shape=(2, 12, 16, 64)						) / 100

								lowercase__     				=    TFRoFormerSinusoidalPositionalEmbedding(num_positions=32,						embedding_dim=64						)
								lowercase__     				=    embed_positions([2, 16, 768]						)[None, None, :, :]

								lowercase__				,		lowercase__     				=    TFRoFormerSelfAttention.apply_rotary_position_embeddings(
								    lowerCamelCase,						lowerCamelCase,						lowerCamelCase						)

								lowercase__     				=    tf.constant(
								    [
								        [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
								        [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
								        [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
								        [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
								        [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
								        [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
								    ]						)
								lowercase__     				=    tf.constant(
								    [
								        [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
								        [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
								        [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
								        [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
								        [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
								        [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
								    ]						)

								tf.debugging.assert_near(query_layer[0, 0, :6, :8],						lowerCamelCase,						atol=self.tolerance						)
								tf.debugging.assert_near(key_layer[0, 0, :6, :8],						lowerCamelCase,						atol=self.tolerance						)












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


A__			:    Any         =				{'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A__			:    Union[str, Any]         =				[
							    'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'UniSpeechForCTC',
							    'UniSpeechForPreTraining',
							    'UniSpeechForSequenceClassification',
							    'UniSpeechModel',
							    'UniSpeechPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_unispeech import (
														    UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
														    UniSpeechForCTC,
														    UniSpeechForPreTraining,
														    UniSpeechForSequenceClassification,
														    UniSpeechModel,
														    UniSpeechPreTrainedModel,
														)

else:
							import sys

							A__			:    List[Any]         =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




