

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 				SCREAMING_SNAKE_CASE__ :





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





    def 				snake_case_    (				self):
       lowercase__   :       Any				=	ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size)

       lowercase__   :       Optional[int]				=	None
       if self.use_input_mask:
          lowercase__   :       Union[str, Any]				=	random_attention_mask([self.batch_size, self.seq_length])

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

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

       lowercase__   :       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						,       initializer_range=self.initializer_range						,       return_dict=a						,       )

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





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
       lowercase__   :       Any				=	TFRoFormerModel(config=a)
       lowercase__   :       Tuple				=	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

       lowercase__   :       int				=	[input_ids, input_mask]
       lowercase__   :       List[str]				=	model(a)

       lowercase__   :       List[str]				=	model(a)

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





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
       lowercase__   :       str				=	True
       lowercase__   :       Any				=	TFRoFormerForCausalLM(config=a)
       lowercase__   :       Optional[Any]				=	{
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }
       lowercase__   :       Any				=	model(a)['logits']
       self.parent.assertListEqual(
           list(prediction_scores.numpy().shape)						,       [self.batch_size, self.seq_length, self.vocab_size])





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
       lowercase__   :       Any				=	TFRoFormerForMaskedLM(config=a)
       lowercase__   :       Optional[Any]				=	{
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }
       lowercase__   :       List[str]				=	model(a)
       self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.seq_length, self.vocab_size))





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
       lowercase__   :       Union[str, Any]				=	self.num_labels
       lowercase__   :       Tuple				=	TFRoFormerForSequenceClassification(config=a)
       lowercase__   :       str				=	{
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }

       lowercase__   :       List[Any]				=	model(a)
       self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.num_labels))





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
       lowercase__   :       int				=	self.num_choices
       lowercase__   :       int				=	TFRoFormerForMultipleChoice(config=a)
       lowercase__   :       int				=	tf.tile(tf.expand_dims(a						,       1)						,       (1, self.num_choices, 1))
       lowercase__   :       List[Any]				=	tf.tile(tf.expand_dims(a						,       1)						,       (1, self.num_choices, 1))
       lowercase__   :       Dict				=	tf.tile(tf.expand_dims(a						,       1)						,       (1, self.num_choices, 1))
       lowercase__   :       Optional[Any]				=	{
           'input_ids': multiple_choice_inputs_ids,
           'attention_mask': multiple_choice_input_mask,
           'token_type_ids': multiple_choice_token_type_ids,
       }
       lowercase__   :       List[str]				=	model(a)
       self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.num_choices))





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
       lowercase__   :       Dict				=	self.num_labels
       lowercase__   :       Any				=	TFRoFormerForTokenClassification(config=a)
       lowercase__   :       Any				=	{
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }
       lowercase__   :       Optional[int]				=	model(a)
       self.parent.assertEqual(result.logits.shape						,       (self.batch_size, self.seq_length, self.num_labels))





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a						,       a						,       a):
       lowercase__   :       Tuple				=	TFRoFormerForQuestionAnswering(config=a)
       lowercase__   :       Any				=	{
           'input_ids': input_ids,
           'attention_mask': input_mask,
           'token_type_ids': token_type_ids,
       }

       lowercase__   :       Union[str, Any]				=	model(a)
       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 				snake_case_    (				self):
       lowercase__   :       Union[str, Any]				=	self.prepare_config_and_inputs()
       (
           lowercase__			
       )   :       Union[str, Any]				=	config_and_inputs
       lowercase__   :       Any				=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
       return config, inputs_dict



@require_tf
class 				SCREAMING_SNAKE_CASE__ (__snake_case		,	__snake_case		,	unittest.TestCase					):
    __lowerCamelCase						:	str														=					(
        (
            TFRoFormerModel,
            TFRoFormerForCausalLM,
            TFRoFormerForMaskedLM,
            TFRoFormerForQuestionAnswering,
            TFRoFormerForSequenceClassification,
            TFRoFormerForTokenClassification,
            TFRoFormerForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    __lowerCamelCase						:	Any														=					(
        {
            """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 {}
    )

    __lowerCamelCase						:	List[str]														=					False
    __lowerCamelCase						:	Tuple														=					False





    def 				snake_case_    (				self						,       a						,       a						,       a						,       a						,       a):
       if pipeline_test_casse_name == "TextGenerationPipelineTests":
          return True

       return False





    def 				snake_case_    (				self):
       lowercase__   :       int				=	TFRoFormerModelTester(self)
       lowercase__   :       str				=	ConfigTester(self						,       config_class=a						,       hidden_size=37)





    def 				snake_case_    (				self):
       self.config_tester.run_common_tests()





    def 				snake_case_    (				self):
       lowercase__   :       Any				=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a)





    def 				snake_case_    (				self):
       lowercase__   :       Tuple				=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*a)





    def 				snake_case_    (				self):
       lowercase__   :       Tuple				=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_lm_head(*a)





    def 				snake_case_    (				self):
       lowercase__   :       Optional[Any]				=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_multiple_choice(*a)





    def 				snake_case_    (				self):
       lowercase__   :       List[str]				=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*a)





    def 				snake_case_    (				self):
       lowercase__   :       int				=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*a)





    def 				snake_case_    (				self):
       lowercase__   :       Dict				=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*a)





    @slow
    def 				snake_case_    (				self):
       lowercase__   :       str				=	TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base')
       self.assertIsNotNone(a)



@require_tf
class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):





    @slow
    def 				snake_case_    (				self):
       lowercase__   :       Optional[Any]				=	TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base')
       lowercase__   :       List[Any]				=	tf.constant([[0, 1, 2, 3, 4, 5]])
       lowercase__   :       Optional[Any]				=	model(a)[0]

       # TODO Replace vocab size
       lowercase__   :       Dict				=	5_0000

       lowercase__   :       Tuple				=	[1, 6, vocab_size]
       self.assertEqual(output.shape						,       a)

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

       # TODO Replace values below with what was printed above.
       lowercase__   :       Dict				=	tf.constant(
           [
               [
                   [-0.12_053_341, -1.0_264_901, 0.29_221_946],
                   [-1.5_133_783, 0.197_433, 0.15_190_607],
                   [-5.0_135_403, -3.900_256, -0.84_038_764],
               ]
           ])
       tf.debugging.assert_near(output[:, :3, :3]						,       a						,       atol=1e-4)



@require_tf
class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):
    __lowerCamelCase						:	Dict														=					1e-4





    def 				snake_case_    (				self):
       lowercase__   :       Optional[Any]				=	tf.constant([[4, 10]])
       lowercase__   :       Optional[Any]				=	TFRoFormerSinusoidalPositionalEmbedding(num_positions=6						,       embedding_dim=6)

       lowercase__   :       int				=	emba(input_ids.shape)
       lowercase__   :       List[Any]				=	tf.constant(
           [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]])

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





    def 				snake_case_    (				self):
       lowercase__   :       Any				=	tf.constant(
           [
               [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
               [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
               [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
           ])
       lowercase__   :       Optional[int]				=	TFRoFormerSinusoidalPositionalEmbedding(num_positions=512						,       embedding_dim=512)
       emba([2, 16, 512])
       lowercase__   :       str				=	emba.weight[:3, :5]

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



@require_tf
class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):
    __lowerCamelCase						:	Union[str, Any]														=					1e-4





    def 				snake_case_    (				self):
       # 2,12,16,64
       lowercase__   :       Union[str, Any]				=	tf.reshape(tf.range(2 * 12 * 16 * 64						,       dtype=tf.floataa)						,       shape=(2, 12, 16, 64)) / 100

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

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

       lowercase__   :       Optional[Any]				=	TFRoFormerSelfAttention.apply_rotary_position_embeddings(
           a						,       a						,       a)

       lowercase__   :       int				=	tf.constant(
           [
               [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
               [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
               [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
               [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
               [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
               [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
           ])
       lowercase__   :       Optional[int]				=	tf.constant(
           [
               [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
               [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
               [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
               [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
               [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
               [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
           ])

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




from typing import TYPE_CHECKING

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


snake_case_        =			{'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''EncoderDecoderModel''']

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''TFEncoderDecoderModel''']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''FlaxEncoderDecoderModel''']

if TYPE_CHECKING:
       from .configuration_encoder_decoder import EncoderDecoderConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_encoder_decoder import EncoderDecoderModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
       import sys

       snake_case_        =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

