




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    RemBertConfig,
			    RemBertForCausalLM,
			    RemBertForMaskedLM,
			    RemBertForMultipleChoice,
			    RemBertForQuestionAnswering,
			    RemBertForSequenceClassification,
			    RemBertForTokenClassification,
			    RemBertModel,
			)
			from transformers.models.rembert.modeling_rembert import REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST






class _UpperCamelCase							:
			'''simple docstring'''







			def __init__(     self							:	List[Any] , a							:	int , a							:	List[Any]=13 , a							:	Union[str, Any]=7 , a							:	Union[str, Any]=True , a							:	Optional[int]=True , a							:	Optional[int]=True , a							:	int=True , a							:	Dict=99 , a							:	Union[str, Any]=32 , a							:	Any=18 , a							:	Any=43 , a							:	Any=5 , a							:	Tuple=4 , a							:	Tuple=37 , a							:	Union[str, Any]="gelu" , a							:	Union[str, Any]=0.1 , a							:	List[str]=0.1 , a							:	Dict=512 , a							:	Optional[int]=16 , a							:	Any=2 , a							:	Any=0.02 , a							:	Union[str, Any]=3 , a							:	Any=4 , a							:	List[Any]=None , )							->    Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    parent
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    batch_size
				SCREAMING_SNAKE_CASE				: Tuple			       =    seq_length
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    is_training
				SCREAMING_SNAKE_CASE				: List[Any]			       =    use_input_mask
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    use_token_type_ids
				SCREAMING_SNAKE_CASE				: List[str]			       =    use_labels
				SCREAMING_SNAKE_CASE				: str			       =    vocab_size
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    hidden_size
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    input_embedding_size
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    output_embedding_size
				SCREAMING_SNAKE_CASE				: List[str]			       =    num_hidden_layers
				SCREAMING_SNAKE_CASE				: Tuple			       =    num_attention_heads
				SCREAMING_SNAKE_CASE				: List[str]			       =    intermediate_size
				SCREAMING_SNAKE_CASE				: int			       =    hidden_act
				SCREAMING_SNAKE_CASE				: str			       =    hidden_dropout_prob
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    max_position_embeddings
				SCREAMING_SNAKE_CASE				: int			       =    type_vocab_size
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    type_sequence_label_size
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    initializer_range
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    num_labels
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    num_choices
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    scope
			def 							__UpperCamelCase	(     self							:	Dict		)							->    str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Any			       =    ids_tensor([self.batch_size, self.seq_length] , self.vocab_size		)

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

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

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

				SCREAMING_SNAKE_CASE				: int			       =    RemBertConfig(
				    vocab_size=self.vocab_size , hidden_size=self.hidden_size , input_embedding_size=self.input_embedding_size , output_embedding_size=self.output_embedding_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=a , initializer_range=self.initializer_range , )

				return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
			def 							__UpperCamelCase	(     self							:	Any		)							->    Optional[int]:

				"""simple docstring"""

				(
				    (
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,   
				)				: Optional[int]			       =    self.prepare_config_and_inputs()

				SCREAMING_SNAKE_CASE				: Dict			       =    True
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
				SCREAMING_SNAKE_CASE				: Any			       =    ids_tensor([self.batch_size, self.seq_length] , vocab_size=2		)

				return (
				    config,
				    input_ids,
				    token_type_ids,
				    input_mask,
				    sequence_labels,
				    token_labels,
				    choice_labels,
				    encoder_hidden_states,
				    encoder_attention_mask,
				)
			def 							__UpperCamelCase	(     self							:	Optional[Any] , a							:	Union[str, Any] , a							:	Any , a							:	Union[str, Any] , a							:	Optional[int] , a							:	Optional[int] , a							:	Tuple , a							:	Optional[Any]		)							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Tuple			       =    RemBertModel(config=a		)
				model.to(a		)
				model.eval()
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    model(a , attention_mask=a , token_type_ids=a		)
				SCREAMING_SNAKE_CASE				: Dict			       =    model(a , token_type_ids=a		)
				SCREAMING_SNAKE_CASE				: str			       =    model(a		)
				self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size)		)
			def 							__UpperCamelCase	(     self							:	Optional[int] , a							:	Tuple , a							:	List[str] , a							:	Tuple , a							:	Optional[int] , a							:	Any , a							:	int , a							:	Optional[Any] , a							:	Optional[int] , a							:	Union[str, Any] , )							->    Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: int			       =    True
				SCREAMING_SNAKE_CASE				: List[Any]			       =    RemBertModel(a		)
				model.to(a		)
				model.eval()
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    model(
				    a , attention_mask=a , token_type_ids=a , encoder_hidden_states=a , encoder_attention_mask=a , )
				SCREAMING_SNAKE_CASE				: Any			       =    model(
				    a , attention_mask=a , token_type_ids=a , encoder_hidden_states=a , )
				SCREAMING_SNAKE_CASE				: List[str]			       =    model(a , attention_mask=a , token_type_ids=a		)
				self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size)		)
			def 							__UpperCamelCase	(     self							:	int , a							:	Union[str, Any] , a							:	int , a							:	str , a							:	Any , a							:	Any , a							:	Tuple , a							:	Dict , a							:	str , a							:	Optional[Any] , )							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    RemBertForCausalLM(config=a		)
				model.to(a		)
				model.eval()
				SCREAMING_SNAKE_CASE				: str			       =    model(a , attention_mask=a , token_type_ids=a , labels=a		)
				self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size)		)
			def 							__UpperCamelCase	(     self							:	List[str] , a							:	Dict , a							:	Dict , a							:	List[str] , a							:	Optional[int] , a							:	Union[str, Any] , a							:	Dict , a							:	List[str]		)							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    RemBertForMaskedLM(config=a		)
				model.to(a		)
				model.eval()
				SCREAMING_SNAKE_CASE				: List[str]			       =    model(a , attention_mask=a , token_type_ids=a , labels=a		)
				self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size)		)
			def 							__UpperCamelCase	(     self							:	List[Any] , a							:	Optional[int] , a							:	List[Any] , a							:	Optional[Any] , a							:	Any , a							:	List[str] , a							:	Dict , a							:	int , a							:	int , a							:	Optional[int] , )							->    List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Any			       =    True
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    True
				SCREAMING_SNAKE_CASE				: int			       =    RemBertForCausalLM(config=a		)
				model.to(a		)
				model.eval()

				# first forward pass
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    model(
				    a , attention_mask=a , encoder_hidden_states=a , encoder_attention_mask=a , use_cache=a , )
				SCREAMING_SNAKE_CASE				: Tuple			       =    outputs.past_key_values

				# create hypothetical multiple next token and extent to next_input_ids
				SCREAMING_SNAKE_CASE				: Any			       =    ids_tensor((self.batch_size, 3) , config.vocab_size		)
				SCREAMING_SNAKE_CASE				: str			       =    ids_tensor((self.batch_size, 3) , vocab_size=2		)

				# append to next input_ids and
				SCREAMING_SNAKE_CASE				: Any			       =    torch.cat([input_ids, next_tokens] , dim=-1		)
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    torch.cat([input_mask, next_mask] , dim=-1		)

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    model(
				    a , attention_mask=a , encoder_hidden_states=a , encoder_attention_mask=a , output_hidden_states=a , )["hidden_states"][0]
				SCREAMING_SNAKE_CASE				: int			       =    model(
				    a , attention_mask=a , encoder_hidden_states=a , encoder_attention_mask=a , past_key_values=a , output_hidden_states=a , )["hidden_states"][0]

				# select random slice
				SCREAMING_SNAKE_CASE				: List[Any]			       =    ids_tensor((1,) , output_from_past.shape[-1]		).item()
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    output_from_no_past[:, -3:, random_slice_idx].detach()
				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    output_from_past[:, :, random_slice_idx].detach()

				self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

				# test that outputs are equal for slice
				self.parent.assertTrue(torch.allclose(a , a , atol=1e-3		)		)
			def 							__UpperCamelCase	(     self							:	str , a							:	str , a							:	str , a							:	Tuple , a							:	List[Any] , a							:	Optional[Any] , a							:	Dict , a							:	List[Any]		)							->    List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[Any]			       =    RemBertForQuestionAnswering(config=a		)
				model.to(a		)
				model.eval()
				SCREAMING_SNAKE_CASE				: int			       =    model(
				    a , attention_mask=a , token_type_ids=a , start_positions=a , end_positions=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							:	str , a							:	List[Any] , a							:	List[Any] , a							:	Union[str, Any] , a							:	str , a							:	Dict , a							:	Optional[int] , a							:	Any		)							->    Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: str			       =    self.num_labels
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    RemBertForSequenceClassification(a		)
				model.to(a		)
				model.eval()
				SCREAMING_SNAKE_CASE				: List[str]			       =    model(a , attention_mask=a , token_type_ids=a , labels=a		)
				self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels)		)
			def 							__UpperCamelCase	(     self							:	int , a							:	Optional[Any] , a							:	List[Any] , a							:	Union[str, Any] , a							:	int , a							:	Tuple , a							:	Optional[Any] , a							:	str		)							->    List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Any			       =    self.num_labels
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    RemBertForTokenClassification(config=a		)
				model.to(a		)
				model.eval()
				SCREAMING_SNAKE_CASE				: Tuple			       =    model(a , attention_mask=a , token_type_ids=a , labels=a		)
				self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels)		)
			def 							__UpperCamelCase	(     self							:	Optional[int] , a							:	Any , a							:	Tuple , a							:	int , a							:	Optional[int] , a							:	Any , a							:	Dict , a							:	str		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    self.num_choices
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    RemBertForMultipleChoice(config=a		)
				model.to(a		)
				model.eval()
				SCREAMING_SNAKE_CASE				: Optional[int]			       =    input_ids.unsqueeze(1		).expand(-1 , self.num_choices , -1		).contiguous()
				SCREAMING_SNAKE_CASE				: str			       =    token_type_ids.unsqueeze(1		).expand(-1 , self.num_choices , -1		).contiguous()
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    input_mask.unsqueeze(1		).expand(-1 , self.num_choices , -1		).contiguous()
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    model(
				    a , attention_mask=a , token_type_ids=a , labels=a , )
				self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices)		)






			def 							__UpperCamelCase	(     self							:	str		)							->    Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.prepare_config_and_inputs()
				(
				    (
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,   
				)				: List[Any]			       =    config_and_inputs
				SCREAMING_SNAKE_CASE				: Tuple			       =    {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
				return config, inputs_dict






@require_torch
class _UpperCamelCase							(							__A							,		__A							,		unittest.TestCase				):
			'''simple docstring'''







			lowerCamelCase__          =(
			    (
			        RemBertModel,
			        RemBertForMaskedLM,
			        RemBertForCausalLM,
			        RemBertForMultipleChoice,
			        RemBertForQuestionAnswering,
			        RemBertForSequenceClassification,
			        RemBertForTokenClassification,
			    )
			    if is_torch_available()
			    else ()
			)
			lowerCamelCase__          =(RemBertForCausalLM,) if is_torch_available() else ()
			lowerCamelCase__          =(
			    {
			        'feature-extraction': RemBertModel,
			        'fill-mask': RemBertForMaskedLM,
			        'question-answering': RemBertForQuestionAnswering,
			        'text-classification': RemBertForSequenceClassification,
			        'text-generation': RemBertForCausalLM,
			        'token-classification': RemBertForTokenClassification,
			        'zero-shot': RemBertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)
			def 							__UpperCamelCase	(     self							:	str		)							->    Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Tuple			       =    RemBertModelTester(self		)
				SCREAMING_SNAKE_CASE				: int			       =    ConfigTester(self , config_class=a , hidden_size=37		)
			def 							__UpperCamelCase	(     self							:	Dict		)							->    Union[str, Any]:

				"""simple docstring"""

				self.config_tester.run_common_tests()
			def 							__UpperCamelCase	(     self							:	str		)							->    Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Tuple			       =    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a		)
			def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Tuple			       =    self.model_tester.prepare_config_and_inputs()
				for type in ["absolute", "relative_key", "relative_key_query"]:
					SCREAMING_SNAKE_CASE				: Tuple			       =    type
					self.model_tester.create_and_check_model(*a		)
			def 							__UpperCamelCase	(     self							:	str		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Dict			       =    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_lm(*a		)
			def 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: str			       =    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_multiple_choice(*a		)
			def 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: int			       =    self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_decoder_model_past_large_inputs(*a		)
			def 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: int			       =    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_question_answering(*a		)
			def 							__UpperCamelCase	(     self							:	Optional[Any]		)							->    str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: List[str]			       =    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_sequence_classification(*a		)
			def 							__UpperCamelCase	(     self							:	List[str]		)							->    Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Tuple			       =    self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_token_classification(*a		)
			def 							__UpperCamelCase	(     self							:	List[str]		)							->    int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.model_tester.prepare_config_and_inputs_for_decoder()
				self.model_tester.create_and_check_model_as_decoder(*a		)
			def 							__UpperCamelCase	(     self							:	List[str]		)							->    int:

				"""simple docstring"""

				(
				    (
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,(
				    SCREAMING_SNAKE_CASE   
				)       ,   
				)				: Optional[Any]			       =    self.model_tester.prepare_config_and_inputs_for_decoder()

				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    None

				self.model_tester.create_and_check_model_as_decoder(
				    a , a , a , a , a , a , a , a , a , )






			@slow
			def 							__UpperCamelCase	(     self							:	Dict		)							->    List[Any]:

				"""simple docstring"""

				for model_name in REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE				: Optional[Any]			       =    RemBertModel.from_pretrained(a		)
					self.assertIsNotNone(a		)






@require_torch
class _UpperCamelCase							(							unittest.TestCase				):
			'''simple docstring'''







			@slow
			def 							__UpperCamelCase	(     self							:	Optional[int]		)							->    List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: str			       =    RemBertModel.from_pretrained("google/rembert"		)
				SCREAMING_SNAKE_CASE				: Any			       =    torch.tensor([[312, 5_6498, 313, 2125, 313]]		)
				SCREAMING_SNAKE_CASE				: int			       =    torch.tensor([[0, 0, 0, 1, 1]]		)
				with torch.no_grad():
					SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    model(a , token_type_ids=a , output_hidden_states=a		)

				SCREAMING_SNAKE_CASE				: Optional[Any]			       =    1152

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    torch.Size((1, 5, hidden_size)		)
				self.assertEqual(output["last_hidden_state"].shape , a		)

				SCREAMING_SNAKE_CASE				: Any			       =    torch.tensor(
				    [
				        [
				            [0.0754, -0.2022, 0.1904],
				            [-0.3354, -0.3692, -0.4791],
				            [-0.2314, -0.6729, -0.0749],
				            [-0.0396, -0.3105, -0.4234],
				            [-0.1571, -0.0525, 0.5353],
				        ]
				    ]		)

				# Running on the original tf implementation gives slightly different results here.
				# Not clear why this variations is present
				# TODO: Find reason for discrepancy
				# expected_original_implementation = [[
				#     [0.07630594074726105, -0.20146065950393677, 0.19107051193714142],
				#     [-0.3405614495277405, -0.36971670389175415, -0.4808273911476135],
				#     [-0.22587086260318756, -0.6656315922737122, -0.07844287157058716],
				#     [-0.04145475849509239, -0.3077218234539032, -0.42316967248916626],
				#     [-0.15887849032878876, -0.054529931396245956, 0.5356100797653198]
				# ]]

				self.assertTrue(torch.allclose(output["last_hidden_state"][:, :, :3] , a , atol=1e-4		)		)


import argparse
import logging
import os
import sys
from collections import namedtuple

import torch
from modeling_bertabs import BertAbs, build_predictor
from torch.utils.data import DataLoader, SequentialSampler
from tqdm import tqdm

from transformers import BertTokenizer

from .utils_summarization import (
    CNNDMDataset,
    build_mask,
    compute_token_type_ids,
    encode_for_summarization,
    truncate_or_pad,
)


a_         =      logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


a_         =      namedtuple('Batch', ['document_names', 'batch_size', 'src', 'segs', 'mask_src', 'tgt_str'])

def 	lowerCamelCase__	(   _a):
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    BertTokenizer.from_pretrained("bert-base-uncased"							,     do_lower_case=_a)
	SCREAMING_SNAKE_CASE				: int			       =    BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization")
	model.to(args.device)
	model.eval()

	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    {
	    "BOS": tokenizer.vocab["[unused0]"],
	    "EOS": tokenizer.vocab["[unused1]"],
	    "PAD": tokenizer.vocab["[PAD]"],
	}

	if args.compute_rouge:
		SCREAMING_SNAKE_CASE				: Optional[int]			       =    []
		SCREAMING_SNAKE_CASE				: int			       =    []

		import nltk
		import rouge

		nltk.download("punkt")
		SCREAMING_SNAKE_CASE				: List[Any]			       =    rouge.Rouge(
		    metrics=["rouge-n", "rouge-l"]							,     max_n=2							,     limit_length=_a							,     length_limit=args.beam_size							,     length_limit_type="words"							,     apply_avg=_a							,     apply_best=_a							,     alpha=0.5							,     weight_factor=1.2							,     stemming=_a							,     )

	# these (unused) arguments are defined to keep the compatibility
	# with the legacy code and will be deleted in a next iteration.
	SCREAMING_SNAKE_CASE				: int			       =    ""
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    ""

	SCREAMING_SNAKE_CASE				: Tuple			       =    build_data_iterator(_a							,     _a)
	SCREAMING_SNAKE_CASE				: Optional[int]			       =    build_predictor(_a							,     _a							,     _a							,     _a)

	logger.info("***** Running evaluation *****")
	logger.info("  Number examples = %d"							,     len(data_iterator.dataset))
	logger.info("  Batch size = %d"							,     args.batch_size)
	logger.info("")
	logger.info("***** Beam Search parameters *****")
	logger.info("  Beam size = %d"							,     args.beam_size)
	logger.info("  Minimum length = %d"							,     args.min_length)
	logger.info("  Maximum length = %d"							,     args.max_length)
	logger.info("  Alpha (length penalty) = %.2f"							,     args.alpha)
	logger.info("  Trigrams %s be blocked"							,     ("will" if args.block_trigram else "will NOT"))

	for batch in tqdm(_a):
		SCREAMING_SNAKE_CASE				: Tuple			       =    predictor.translate_batch(_a)
		SCREAMING_SNAKE_CASE				: List[Any]			       =    predictor.from_batch(_a)
		SCREAMING_SNAKE_CASE				: List[str]			       =    [format_summary(_a) for t in translations]
		save_summaries(_a							,     args.summaries_output_dir							,     batch.document_names)

		if args.compute_rouge:
			reference_summaries += batch.tgt_str
			generated_summaries += summaries

	if args.compute_rouge:
		SCREAMING_SNAKE_CASE				: List[Any]			       =    rouge_evaluator.get_scores(_a							,     _a)
		SCREAMING_SNAKE_CASE				: Tuple			       =    format_rouge_scores(_a)
		save_rouge_scores(_a)
		print(_a)

def 	lowerCamelCase__	(   _a							,     _a							,     _a):
	for summary, document_name in zip(_a							,     _a):
		# Prepare the summary file's name
		if "." in document_name:
			SCREAMING_SNAKE_CASE				: int			       =    ".".join(document_name.split(".")[:-1])
			SCREAMING_SNAKE_CASE				: int			       =    document_name.split(".")[-1]
			SCREAMING_SNAKE_CASE				: Optional[Any]			       =    bare_document_name + "_summary." + extension
		else:
			SCREAMING_SNAKE_CASE				: List[Any]			       =    document_name + "_summary"

		SCREAMING_SNAKE_CASE				: Dict			       =    os.path.join(_a							,     _a)
		with open(_a							,     "w") as output:
			output.write(_a)

def 	lowerCamelCase__	(   _a):
	SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE				: Optional[Any]			       =    translation
	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    (
	    raw_summary.replace("[unused0]"							,     "")
	    .replace("[unused3]"							,     "")
	    .replace("[PAD]"							,     "")
	    .replace("[unused1]"							,     "")
	    .replace(r" +"							,     " ")
	    .replace(" [unused2] "							,     ". ")
	    .replace("[unused2]"							,     "")
	    .strip()
	)

	return summary

def 	lowerCamelCase__	(   _a):
	return """\n
****** ROUGE SCORES ******

** ROUGE 1
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE 2
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}

** ROUGE L
F1        >> {:.3f}
Precision >> {:.3f}
Recall    >> {:.3f}""".format(
	    scores["rouge-1"]["f"]							,     scores["rouge-1"]["p"]							,     scores["rouge-1"]["r"]							,     scores["rouge-2"]["f"]							,     scores["rouge-2"]["p"]							,     scores["rouge-2"]["r"]							,     scores["rouge-l"]["f"]							,     scores["rouge-l"]["p"]							,     scores["rouge-l"]["r"]							,     )

def 	lowerCamelCase__	(   _a):
	with open("rouge_scores.txt"							,     "w") as output:
		output.write(_a)

def 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: Any			       =    load_and_cache_examples(_a							,     _a)
	SCREAMING_SNAKE_CASE				: int			       =    SequentialSampler(_a)

	def collate_fn(_a):
		return collate(_a							,     _a							,     block_size=512							,     device=args.device)

	SCREAMING_SNAKE_CASE				: Tuple			       =    DataLoader(
	    _a							,     sampler=_a							,     batch_size=args.batch_size							,     collate_fn=_a							,     )

	return iterator

def 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: Tuple			       =    CNNDMDataset(args.documents_dir)
	return dataset

def 	lowerCamelCase__	(   _a							,     _a							,     _a							,     _a):
	SCREAMING_SNAKE_CASE				: str			       =    [x for x in data if not len(x[1]) == 0]  # remove empty_files
	SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    [name for name, _, _ in data]
	SCREAMING_SNAKE_CASE				: Dict			       =    [" ".join(_a) for _, _, summary_list in data]

	SCREAMING_SNAKE_CASE				: Optional[Any]			       =    [encode_for_summarization(_a							,     _a							,     _a) for _, story, summary in data]
	SCREAMING_SNAKE_CASE				: Dict			       =    torch.tensor(
	    [truncate_or_pad(_a							,     _a							,     tokenizer.pad_token_id) for story, _ in encoded_text])
	SCREAMING_SNAKE_CASE				: List[Any]			       =    compute_token_type_ids(_a							,     tokenizer.cls_token_id)
	SCREAMING_SNAKE_CASE				: Dict			       =    build_mask(_a							,     tokenizer.pad_token_id)

	SCREAMING_SNAKE_CASE				: Optional[int]			       =    Batch(
	    document_names=_a							,     batch_size=len(_a)							,     src=encoded_stories.to(_a)							,     segs=encoder_token_type_ids.to(_a)							,     mask_src=encoder_mask.to(_a)							,     tgt_str=_a							,     )

	return batch

def 	lowerCamelCase__	(   _a							,     _a):
	SCREAMING_SNAKE_CASE				: int			       =    summary_tokens.to("cpu").numpy()
	SCREAMING_SNAKE_CASE				: Tuple			       =    tokenizer.decode(_a)
	SCREAMING_SNAKE_CASE				: List[str]			       =    summary.split(".")
	SCREAMING_SNAKE_CASE				: int			       =    [s + "." for s in sentences]
	return sentences

def 	lowerCamelCase__	(   ):
	SCREAMING_SNAKE_CASE				: str			       =    argparse.ArgumentParser()
	parser.add_argument(
	    "--documents_dir"							,     default=_a							,     type=_a							,     required=_a							,     help="The folder where the documents to summarize are located."							,     )
	parser.add_argument(
	    "--summaries_output_dir"							,     default=_a							,     type=_a							,     required=_a							,     help="The folder in wich the summaries should be written. Defaults to the folder where the documents are"							,     )
	parser.add_argument(
	    "--compute_rouge"							,     default=_a							,     type=_a							,     required=_a							,     help="Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset."							,     )
	# EVALUATION options
	parser.add_argument(
	    "--no_cuda"							,     default=_a							,     type=_a							,     help="Whether to force the execution on CPU."							,     )
	parser.add_argument(
	    "--batch_size"							,     default=4							,     type=_a							,     help="Batch size per GPU/CPU for training."							,     )
	# BEAM SEARCH arguments
	parser.add_argument(
	    "--min_length"							,     default=50							,     type=_a							,     help="Minimum number of tokens for the summaries."							,     )
	parser.add_argument(
	    "--max_length"							,     default=200							,     type=_a							,     help="Maixmum number of tokens for the summaries."							,     )
	parser.add_argument(
	    "--beam_size"							,     default=5							,     type=_a							,     help="The number of beams to start with for each example."							,     )
	parser.add_argument(
	    "--alpha"							,     default=0.95							,     type=_a							,     help="The value of alpha for the length penalty in the beam search."							,     )
	parser.add_argument(
	    "--block_trigram"							,     default=_a							,     type=_a							,     help="Whether to block the existence of repeating trigrams in the text generated by beam search."							,     )
	SCREAMING_SNAKE_CASE				: Tuple			       =    parser.parse_args()

	# Select device (distibuted not available)
	SCREAMING_SNAKE_CASE				: Dict			       =    torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")

	# Check the existence of directories
	if not args.summaries_output_dir:
		SCREAMING_SNAKE_CASE				: Dict			       =    args.documents_dir

	if not documents_dir_is_valid(args.documents_dir):
		raise FileNotFoundError(
		    "We could not find the directory you specified for the documents to summarize, or it was empty. Please"
		    " specify a valid path.")
	os.makedirs(args.summaries_output_dir							,     exist_ok=_a)

	evaluate(_a)

def 	lowerCamelCase__	(   _a):
	if not os.path.exists(_a):
		return False

	SCREAMING_SNAKE_CASE				: Any			       =    os.listdir(_a)
	if len(_a) == 0:
		return False

	return True


if __name__ == "__main__":
			main()