
'''simple docstring'''






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     __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,    ):

									"""simple docstring"""


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

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= ids_tensor([self.batch_size, self.seq_length],    self.vocab_size      )

									lowerCamelCase					:			Any									= None
									if self.use_input_mask:
													lowerCamelCase					:			List[str]									= random_attention_mask([self.batch_size, self.seq_length]      )

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

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

									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,    initializer_range=self.initializer_range,    return_dict=A,    )

									return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= TFRoFormerModel(config=A      )
									lowerCamelCase					:			Dict									= {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

									lowerCamelCase					:			Any									= [input_ids, input_mask]
									lowerCamelCase					:			Any									= model(A      )

									lowerCamelCase					:			List[Any]									= model(A      )

									self.parent.assertEqual(result.last_hidden_state.shape,    (self.batch_size, self.seq_length, self.hidden_size)      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A,    A,    A      ):

									"""simple docstring"""


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

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= TFRoFormerForMaskedLM(config=A      )
									lowerCamelCase					:			Tuple									= {
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									lowerCamelCase					:			Tuple									= model(A      )
									self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.seq_length, self.vocab_size)      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= self.num_labels
									lowerCamelCase					:			List[Any]									= TFRoFormerForSequenceClassification(config=A      )
									lowerCamelCase					:			str									= {
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									lowerCamelCase					:			Dict									= model(A      )
									self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.num_labels)      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A,    A,    A      ):

									"""simple docstring"""


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

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= self.num_labels
									lowerCamelCase					:			str									= TFRoFormerForTokenClassification(config=A      )
									lowerCamelCase					:			Tuple									= {
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}
									lowerCamelCase					:			Optional[int]									= model(A      )
									self.parent.assertEqual(result.logits.shape,    (self.batch_size, self.seq_length, self.num_labels)      )
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A,    A,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= TFRoFormerForQuestionAnswering(config=A      )
									lowerCamelCase					:			Optional[Any]									= {
									    'input_ids': input_ids,
									    'attention_mask': input_mask,
									    'token_type_ids': token_type_ids,
									}

									lowerCamelCase					:			List[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     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= self.prepare_config_and_inputs()
									(
									    lowerCamelCase 
									)					:			Dict									= config_and_inputs
									lowerCamelCase					:			Optional[Any]									= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
									return config, inputs_dict





@require_tf
class     __snake_case		(   a__    ,   a__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	(
					    (
					        TFRoFormerModel,
					        TFRoFormerForCausalLM,
					        TFRoFormerForMaskedLM,
					        TFRoFormerForQuestionAnswering,
					        TFRoFormerForSequenceClassification,
					        TFRoFormerForTokenClassification,
					        TFRoFormerForMultipleChoice,
					    )
					    if is_tf_available()
					    else ()
					)
					_lowerCAmelCase			     =	(
					    {
					        '''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			     =	False
					_lowerCAmelCase			     =	False
					def     UpperCAmelCase_							(     self,    A,    A,    A,    A,    A      ):

									"""simple docstring"""


									if pipeline_test_casse_name == "TextGenerationPipelineTests":
													return True

									return False
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= TFRoFormerModelTester(self      )
									lowerCamelCase					:			Tuple									= ConfigTester(self,    config_class=A,    hidden_size=37      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


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

									"""simple docstring"""


									lowerCamelCase					:			str									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_lm_head(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_multiple_choice(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_question_answering(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_sequence_classification(*A      )
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_token_classification(*A      )




					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'      )
									self.assertIsNotNone(A      )





@require_tf
class     __snake_case		(   unittest.TestCase):
					@slow
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'      )
									lowerCamelCase					:			Optional[int]									= tf.constant([[0, 1, 2, 3, 4, 5]]      )
									lowerCamelCase					:			List[Any]									= model(A      )[0]

									# TODO Replace vocab size
									lowerCamelCase					:			List[str]									= 5_0000

									lowerCamelCase					:			Dict									= [1, 6, vocab_size]
									self.assertEqual(output.shape,    A      )

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

									# TODO Replace values below with what was printed above.
									lowerCamelCase					:			Optional[Any]									= tf.constant(
									    [
									        [
									            [-0.1205_3341, -1.026_4901, 0.2922_1946],
									            [-1.513_3783, 0.19_7433, 0.1519_0607],
									            [-5.013_5403, -3.90_0256, -0.8403_8764],
									        ]
									    ]      )
									tf.debugging.assert_near(output[:, :3, :3],    A,    atol=1e-4      )





@require_tf
class     __snake_case		(   unittest.TestCase):
					_lowerCAmelCase			     =	1E-4
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= tf.constant([[4, 10]]      )
									lowerCamelCase					:			Tuple									= TFRoFormerSinusoidalPositionalEmbedding(num_positions=6,    embedding_dim=6      )

									lowerCamelCase					:			List[str]									= emba(input_ids.shape      )
									lowerCamelCase					:			Tuple									= 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(A,    A,    atol=self.tolerance      )




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= 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],
									    ]      )
									lowerCamelCase					:			int									= TFRoFormerSinusoidalPositionalEmbedding(num_positions=512,    embedding_dim=512      )
									emba([2, 16, 512]      )
									lowerCamelCase					:			Optional[Any]									= emba.weight[:3, :5]

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





@require_tf
class     __snake_case		(   unittest.TestCase):
					_lowerCAmelCase			     =	1E-4
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Dict									= tf.reshape(tf.range(2 * 12 * 16 * 64,    dtype=tf.floataa      ),    shape=(2, 12, 16, 64)      ) / 100

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

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

									lowerCamelCase					:			List[str]									= TFRoFormerSelfAttention.apply_rotary_position_embeddings(
									    A,    A,    A      )

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





'''simple docstring'''






import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


A			      =      {
    '<': operator.lt,
    '<=': operator.le,
    '==': operator.eq,
    '!=': operator.ne,
    '>=': operator.ge,
    '>': operator.gt,
}





def    UpperCAmelCase							(  UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							Optional[Any]       ,							UpperCAmelCase__							:							Union[str, Any]       ,							UpperCAmelCase__							:							List[Any]       ,							UpperCAmelCase__							:							Dict       ,							UpperCAmelCase__							:							Optional[int]):
				if got_ver is None or want_ver is None:
								raise ValueError(
								    F'''Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'''
								    F''' reinstalling {pkg}.''')
				if not ops[op](version.parse(UpperCAmelCase__)       ,							version.parse(UpperCAmelCase__)):
								raise ImportError(
								    F'''{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}''')





def    UpperCAmelCase							(  UpperCAmelCase__							:							str       ,							UpperCAmelCase__							:							Optional[str] = None):
				lowerCamelCase					:			List[Any]									= F'''\n{hint}''' if hint is not None else ''

				# non-versioned check
				if re.match(R'^[\w_\-\d]+$'       ,							UpperCAmelCase__):
								lowerCamelCase			, lowerCamelCase			, lowerCamelCase					:			Optional[Any]									= requirement, None, None
				else:
								lowerCamelCase					:			Optional[Any]									= re.findall(R'^([^!=<>\s]+)([\s!=<>]{1,2}.+)'       ,							UpperCAmelCase__)
								if not match:
												raise ValueError(
												    'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but'
												    F''' got {requirement}''')
								lowerCamelCase			, lowerCamelCase					:			Dict									= match[0]
								lowerCamelCase					:			Dict									= want_full.split(',')  # there could be multiple requirements
								lowerCamelCase					:			Union[str, Any]									= {}
								for w in want_range:
												lowerCamelCase					:			int									= re.findall(R'^([\s!=<>]{1,2})(.+)'       ,							UpperCAmelCase__)
												if not match:
																raise ValueError(
																    'requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,'
																    F''' but got {requirement}''')
												lowerCamelCase			, lowerCamelCase					:			List[Any]									= match[0]
												lowerCamelCase					:			Optional[int]									= want_ver
												if op not in ops:
																raise ValueError(F'''{requirement}: need one of {list(ops.keys())}, but got {op}''')

    # special case
				if pkg == "python":
								lowerCamelCase					:			Optional[int]									= '.'.join([str(UpperCAmelCase__) for x in sys.version_info[:3]])
								for op, want_ver in wanted.items():
												_compare_versions(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)
								return

				# check if any version is installed
				try:
								lowerCamelCase					:			Any									= importlib.metadata.version(UpperCAmelCase__)
				except importlib.metadata.PackageNotFoundError:
								raise importlib.metadata.PackageNotFoundError(
								    F'''The \'{requirement}\' distribution was not found and is required by this application. {hint}''')

				# check that the right version is installed if version number or a range was provided
				if want_ver is not None:
								for op, want_ver in wanted.items():
												_compare_versions(UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__       ,							UpperCAmelCase__)





def    UpperCAmelCase							(  UpperCAmelCase__							:							str):
				lowerCamelCase					:			List[str]									= 'Try: pip install transformers -U or pip install -e \'.[dev]\' if you\'re working with git main'
				return require_version(UpperCAmelCase__       ,							UpperCAmelCase__)
