
'''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
									)			, (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, (
									    lowerCamelCase
									)			, (
									    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			, 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 os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A			      =      logging.get_logger(__name__)


A			      =      '▁'

A			      =      {'vocab_file': 'spiece.model'}

A			      =      {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

A			      =      {
    'google/reformer-crime-and-punishment': 52_4288,
}




class     __snake_case		(   a__):
					_lowerCAmelCase			     =	VOCAB_FILES_NAMES
					_lowerCAmelCase			     =	PRETRAINED_VOCAB_FILES_MAP
					_lowerCAmelCase			     =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_lowerCAmelCase			     =	['''input_ids''', '''attention_mask''']
					def __init__(     self,    A,    A="</s>",    A="<unk>",    A=[],    A = None,    **A,    ):

									"""simple docstring"""


									lowerCamelCase					:			str									= {} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    eos_token=A,    unk_token=A,    additional_special_tokens=A,    sp_model_kwargs=self.sp_model_kwargs,    **A,    )

									lowerCamelCase					:			Any									= vocab_file
									lowerCamelCase					:			List[Any]									= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(A      )
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return self.sp_model.get_piece_size()
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= {self.convert_ids_to_tokens(A      ): i for i in range(self.vocab_size      )}
									vocab.update(self.added_tokens_encoder      )
									return vocab
					def __getstate__(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= self.__dict__.copy()
									lowerCamelCase					:			List[Any]									= None
									return state
					def __setstate__(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= d

									# for backward compatibility
									if not hasattr(self,    'sp_model_kwargs'      ):
													lowerCamelCase					:			List[str]									= {}

									lowerCamelCase					:			Optional[int]									= spm.SentencePieceProcessor(**self.sp_model_kwargs      )
									self.sp_model.Load(self.vocab_file      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return self.sp_model.encode(A,    out_type=A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									return self.sp_model.piece_to_id(A      )
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									if index < self.sp_model.get_piece_size():
													lowerCamelCase					:			Union[str, Any]									= self.sp_model.IdToPiece(A      )
									return token
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= []
									lowerCamelCase					:			Optional[int]									= ''
									for token in tokens:
													# make sure that special tokens are not decoded using sentencepiece model
													if token in self.all_special_tokens:
																	out_string += self.sp_model.decode(A      ) + token
																	lowerCamelCase					:			Any									= []
													else:
																	current_sub_tokens.append(A      )
									out_string += self.sp_model.decode(A      )
									return out_string.strip()




					def     UpperCAmelCase_							(     self,    A,    A = None      ):

									"""simple docstring"""


									if not os.path.isdir(A      ):
													logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
													return
									lowerCamelCase					:			str									= os.path.join(
									    A,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

									if os.path.abspath(self.vocab_file      ) != os.path.abspath(A      ) and os.path.isfile(self.vocab_file      ):
													copyfile(self.vocab_file,    A      )
									elif not os.path.isfile(self.vocab_file      ):
													with open(A,    'wb'      ) as fi:
																	lowerCamelCase					:			Optional[Any]									= self.sp_model.serialized_model_proto()
																	fi.write(A      )

									return (out_vocab_file,)
