




import inspect
import unittest

from transformers import ResNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
					import torch
					from torch import nn

					from transformers import ResNetBackbone, ResNetForImageClassification, ResNetModel
					from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor
class      a__       :






						def __init__(  self						,     _A						,     _A=3						,     _A=3_2						,     _A=3						,     _A=1_0						,     _A=[1_0, 2_0, 3_0, 4_0]						,     _A=[1, 1, 2, 1]						,     _A=True						,     _A=True						,     _A="relu"						,     _A=3						,     _A=None						,     _A=["stage2", "stage3", "stage4"]						,     _A=[2, 3, 4]						,     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			parent
											__lowerCAmelCase							  =			batch_size
											__lowerCAmelCase							  =			image_size
											__lowerCAmelCase							  =			num_channels
											__lowerCAmelCase							  =			embeddings_size
											__lowerCAmelCase							  =			hidden_sizes
											__lowerCAmelCase							  =			depths
											__lowerCAmelCase							  =			is_training
											__lowerCAmelCase							  =			use_labels
											__lowerCAmelCase							  =			hidden_act
											__lowerCAmelCase							  =			num_labels
											__lowerCAmelCase							  =			scope
											__lowerCAmelCase							  =			len(_A     )
											__lowerCAmelCase							  =			out_features
											__lowerCAmelCase							  =			out_indices






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

											__lowerCAmelCase							  =			None
											if self.use_labels:
																__lowerCAmelCase							  =			ids_tensor([self.batch_size]						,     self.num_labels     )

											__lowerCAmelCase							  =			self.get_config()

											return config, pixel_values, labels






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return ResNetConfig(
											    num_channels=self.num_channels						,     embeddings_size=self.embeddings_size						,     hidden_sizes=self.hidden_sizes						,     depths=self.depths						,     hidden_act=self.hidden_act						,     num_labels=self.num_labels						,     out_features=self.out_features						,     out_indices=self.out_indices						,     )






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			ResNetModel(config=_A     )
											model.to(_A     )
											model.eval()
											__lowerCAmelCase							  =			model(_A     )
											# expected last hidden states: B, C, H // 32, W // 32
											self.parent.assertEqual(
											    result.last_hidden_state.shape						,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)						,     )






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.num_labels
											__lowerCAmelCase							  =			ResNetForImageClassification(_A     )
											model.to(_A     )
											model.eval()
											__lowerCAmelCase							  =			model(_A						,     labels=_A     )
											self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.num_labels)     )






						def 							__SCREAMING_SNAKE_CASE(  self						,     _A						,     _A						,     _A     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			ResNetBackbone(config=_A     )
											model.to(_A     )
											model.eval()
											__lowerCAmelCase							  =			model(_A     )

											# verify feature maps
											self.parent.assertEqual(len(result.feature_maps     )						,     len(config.out_features     )     )
											self.parent.assertListEqual(list(result.feature_maps[0].shape     )						,     [self.batch_size, self.hidden_sizes[1], 4, 4]     )

											# verify channels
											self.parent.assertEqual(len(model.channels     )						,     len(config.out_features     )     )
											self.parent.assertListEqual(model.channels						,     config.hidden_sizes[1:]     )

											# verify backbone works with out_features=None
											__lowerCAmelCase							  =			None
											__lowerCAmelCase							  =			ResNetBackbone(config=_A     )
											model.to(_A     )
											model.eval()
											__lowerCAmelCase							  =			model(_A     )

											# verify feature maps
											self.parent.assertEqual(len(result.feature_maps     )						,     1     )
											self.parent.assertListEqual(list(result.feature_maps[0].shape     )						,     [self.batch_size, self.hidden_sizes[-1], 1, 1]     )

											# verify channels
											self.parent.assertEqual(len(model.channels     )						,     1     )
											self.parent.assertListEqual(model.channels						,     [config.hidden_sizes[-1]]     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.prepare_config_and_inputs()
											__lowerCAmelCase  ,				__lowerCAmelCase  ,				__lowerCAmelCase							  =			config_and_inputs
											__lowerCAmelCase							  =			{"pixel_values": pixel_values}
											return config, inputs_dict






@require_torch
class      a__       (   snake_case__					,							snake_case__					,							unittest.TestCase ):
						_a				:       Optional[int]   =       (
						    (
						        ResNetModel,
						        ResNetForImageClassification,
						        ResNetBackbone,
						    )
						    if is_torch_available()
						    else ()
						)
						_a				:       Optional[Any]   =       (
						    {"""feature-extraction""": ResNetModel, """image-classification""": ResNetForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_a				:       Dict   =       True
						_a				:       List[str]   =       False
						_a				:       List[Any]   =       False
						_a				:       Optional[Any]   =       False
						_a				:       Union[str, Any]   =       False






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			ResNetModelTester(self     )
											__lowerCAmelCase							  =			ConfigTester(self						,     config_class=_A						,     has_text_modality=_A     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											self.create_and_test_config_common_properties()
											self.config_tester.create_and_test_config_to_json_string()
											self.config_tester.create_and_test_config_to_json_file()
											self.config_tester.create_and_test_config_from_and_save_pretrained()
											self.config_tester.create_and_test_config_with_num_labels()
											self.config_tester.check_config_can_be_init_without_params()
											self.config_tester.check_config_arguments_init()






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return






						@unittest.skip(reason="ResNet does not use inputs_embeds"     )
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											pass






						@unittest.skip(reason="ResNet does not support input and output embeddings"     )
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											pass






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase  ,				__lowerCAmelCase							  =			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																__lowerCAmelCase							  =			model_class(_A     )
																__lowerCAmelCase							  =			inspect.signature(model.forward     )
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																__lowerCAmelCase							  =			[*signature.parameters.keys()]

																__lowerCAmelCase							  =			["pixel_values"]
																self.assertListEqual(arg_names[:1]						,     _A     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_A     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*_A     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase  ,				__lowerCAmelCase							  =			self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																__lowerCAmelCase							  =			model_class(config=_A     )
																for name, module in model.named_modules():
																					if isinstance(_A						,     (nn.BatchNormad, nn.GroupNorm)     ):
																										self.assertTrue(
																										    torch.all(module.weight == 1     )						,     msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""						,     )
																										self.assertTrue(
																										    torch.all(module.bias == 0     )						,     msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""						,     )






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											def check_hidden_states_output(_A						,     _A						,     _A     ):
																__lowerCAmelCase							  =			model_class(_A     )
																model.to(_A     )
																model.eval()

																with torch.no_grad():
																					__lowerCAmelCase							  =			model(**self._prepare_for_class(_A						,     _A     )     )

																__lowerCAmelCase							  =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																__lowerCAmelCase							  =			self.model_tester.num_stages
																self.assertEqual(len(_A     )						,     expected_num_stages + 1     )

																# ResNet's feature maps are of shape (batch_size, num_channels, height, width)
																self.assertListEqual(
																    list(hidden_states[0].shape[-2:]     )						,     [self.model_tester.image_size // 4, self.model_tester.image_size // 4]						,     )

											__lowerCAmelCase  ,				__lowerCAmelCase							  =			self.model_tester.prepare_config_and_inputs_for_common()
											__lowerCAmelCase							  =			["basic", "bottleneck"]
											for model_class in self.all_model_classes:
																for layer_type in layers_type:
																					__lowerCAmelCase							  =			layer_type
																					__lowerCAmelCase							  =			True
																					check_hidden_states_output(_A						,     _A						,     _A     )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					__lowerCAmelCase							  =			True

																					check_hidden_states_output(_A						,     _A						,     _A     )






						@unittest.skip(reason="ResNet does not use feedforward chunking"     )
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											pass






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_A     )






						@slow
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase							  =			ResNetModel.from_pretrained(_A     )
																self.assertIsNotNone(_A     )







def       _a (		):
					__lowerCAmelCase							  =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
					return image
@require_torch
@require_vision
class      a__       (   unittest.TestCase ):






						@cached_property
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											return (
											    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     )
											    if is_vision_available()
											    else None
											)






						@slow
						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(_A     )

											__lowerCAmelCase							  =			self.default_image_processor
											__lowerCAmelCase							  =			prepare_img()
											__lowerCAmelCase							  =			image_processor(images=_A						,     return_tensors="pt"     ).to(_A     )

											# forward pass
											with torch.no_grad():
																__lowerCAmelCase							  =			model(**_A     )

											# verify the logits
											__lowerCAmelCase							  =			torch.Size((1, 1_0_0_0)     )
											self.assertEqual(outputs.logits.shape						,     _A     )

											__lowerCAmelCase							  =			torch.tensor([-11.10_69, -9.78_77, -8.37_77]     ).to(_A     )

											self.assertTrue(torch.allclose(outputs.logits[0, :3]						,     _A						,     atol=1E-4     )     )






@require_torch
class      a__       (   snake_case__					,							unittest.TestCase ):
						_a				:       Dict   =       (ResNetBackbone,) if is_torch_available() else ()
						_a				:       List[Any]   =       False
						_a				:       Any   =       ResNetConfig






						def 							__SCREAMING_SNAKE_CASE(  self     ):




											"""simple docstring"""



											__lowerCAmelCase							  =			ResNetModelTester(self     )










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,
)


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


UpperCamelCase__            =     namedtuple("""Batch""", ["""document_names""", """batch_size""", """src""", """segs""", """mask_src""", """tgt_str"""])







def       _a (		SCREAMING_SNAKE_CASE_				:							int	):
					__lowerCAmelCase							  =			BertTokenizer.from_pretrained("bert-base-uncased" , do_lower_case=SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			BertAbs.from_pretrained("remi/bertabs-finetuned-extractive-abstractive-summarization"	)
					model.to(args.device	)
					model.eval()

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

					if args.compute_rouge:
										__lowerCAmelCase							  =			[]
										__lowerCAmelCase							  =			[]

										import nltk
										import rouge

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

					# these (unused) arguments are defined to keep the compatibility
					# with the legacy code and will be deleted in a next iteration.
					__lowerCAmelCase							  =			""
					__lowerCAmelCase							  =			""

					__lowerCAmelCase							  =			build_data_iterator(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			build_predictor(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)

					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(SCREAMING_SNAKE_CASE_	):
										__lowerCAmelCase							  =			predictor.translate_batch(SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			predictor.from_batch(SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			[format_summary(SCREAMING_SNAKE_CASE_	) for t in translations]
										save_summaries(SCREAMING_SNAKE_CASE_ , args.summaries_output_dir , batch.document_names	)

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

					if args.compute_rouge:
										__lowerCAmelCase							  =			rouge_evaluator.get_scores(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
										__lowerCAmelCase							  =			format_rouge_scores(SCREAMING_SNAKE_CASE_	)
										save_rouge_scores(SCREAMING_SNAKE_CASE_	)
										print(SCREAMING_SNAKE_CASE_	)







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

										__lowerCAmelCase							  =			os.path.join(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
										with open(SCREAMING_SNAKE_CASE_ , "w"	) as output:
															output.write(SCREAMING_SNAKE_CASE_	)







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int]	):
					__lowerCAmelCase  ,				__lowerCAmelCase  ,				__lowerCAmelCase							  =			translation
					__lowerCAmelCase							  =			(
					    raw_summary.replace("[unused0]" , ""	)
					    .replace("[unused3]" , ""	)
					    .replace("[PAD]" , ""	)
					    .replace("[unused1]" , ""	)
					    .replace(R" +" , " "	)
					    .replace(" [unused2] " , ". "	)
					    .replace("[unused2]" , ""	)
					    .strip()
					)

					return summary







def       _a (		SCREAMING_SNAKE_CASE_				:							Union[str, Any]	):
					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       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int]	):
					with open("rouge_scores.txt" , "w"	) as output:
										output.write(SCREAMING_SNAKE_CASE_	)







def       _a (		SCREAMING_SNAKE_CASE_				:							str , SCREAMING_SNAKE_CASE_				:							Optional[int]	):
					__lowerCAmelCase							  =			load_and_cache_examples(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			SequentialSampler(SCREAMING_SNAKE_CASE_	)

					def collate_fn(SCREAMING_SNAKE_CASE_				:							List[Any]	):
										return collate(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , block_size=5_12 , device=args.device	)

					__lowerCAmelCase							  =			DataLoader(
					    SCREAMING_SNAKE_CASE_ , sampler=SCREAMING_SNAKE_CASE_ , batch_size=args.batch_size , collate_fn=SCREAMING_SNAKE_CASE_ , )

					return iterator







def       _a (		SCREAMING_SNAKE_CASE_				:							Union[str, Any] , SCREAMING_SNAKE_CASE_				:							List[Any]	):
					__lowerCAmelCase							  =			CNNDMDataset(args.documents_dir	)
					return dataset







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int] , SCREAMING_SNAKE_CASE_				:							Optional[Any] , SCREAMING_SNAKE_CASE_				:							str , SCREAMING_SNAKE_CASE_				:							int	):
					__lowerCAmelCase							  =			[x for x in data if not len(x[1]	) == 0]  # remove empty_files
					__lowerCAmelCase							  =			[name for name, _, _ in data]
					__lowerCAmelCase							  =			[" ".join(SCREAMING_SNAKE_CASE_	) for _, _, summary_list in data]

					__lowerCAmelCase							  =			[encode_for_summarization(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_	) for _, story, summary in data]
					__lowerCAmelCase							  =			torch.tensor(
					    [truncate_or_pad(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , tokenizer.pad_token_id	) for story, _ in encoded_text]	)
					__lowerCAmelCase							  =			compute_token_type_ids(SCREAMING_SNAKE_CASE_ , tokenizer.cls_token_id	)
					__lowerCAmelCase							  =			build_mask(SCREAMING_SNAKE_CASE_ , tokenizer.pad_token_id	)

					__lowerCAmelCase							  =			Batch(
					    document_names=SCREAMING_SNAKE_CASE_ , batch_size=len(SCREAMING_SNAKE_CASE_	) , src=encoded_stories.to(SCREAMING_SNAKE_CASE_	) , segs=encoder_token_type_ids.to(SCREAMING_SNAKE_CASE_	) , mask_src=encoder_mask.to(SCREAMING_SNAKE_CASE_	) , tgt_str=SCREAMING_SNAKE_CASE_ , )

					return batch







def       _a (		SCREAMING_SNAKE_CASE_				:							Dict , SCREAMING_SNAKE_CASE_				:							Optional[Any]	):
					__lowerCAmelCase							  =			summary_tokens.to("cpu"	).numpy()
					__lowerCAmelCase							  =			tokenizer.decode(SCREAMING_SNAKE_CASE_	)
					__lowerCAmelCase							  =			summary.split("."	)
					__lowerCAmelCase							  =			[s + "." for s in sentences]
					return sentences







def       _a (		):
					__lowerCAmelCase							  =			argparse.ArgumentParser()
					parser.add_argument(
					    "--documents_dir" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="The folder where the documents to summarize are located." , )
					parser.add_argument(
					    "--summaries_output_dir" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="The folder in wich the summaries should be written. Defaults to the folder where the documents are" , )
					parser.add_argument(
					    "--compute_rouge" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help="Compute the ROUGE metrics during evaluation. Only available for the CNN/DailyMail dataset." , )
					# EVALUATION options
					parser.add_argument(
					    "--no_cuda" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , help="Whether to force the execution on CPU." , )
					parser.add_argument(
					    "--batch_size" , default=4 , type=SCREAMING_SNAKE_CASE_ , help="Batch size per GPU/CPU for training." , )
					# BEAM SEARCH arguments
					parser.add_argument(
					    "--min_length" , default=50 , type=SCREAMING_SNAKE_CASE_ , help="Minimum number of tokens for the summaries." , )
					parser.add_argument(
					    "--max_length" , default=2_00 , type=SCREAMING_SNAKE_CASE_ , help="Maixmum number of tokens for the summaries." , )
					parser.add_argument(
					    "--beam_size" , default=5 , type=SCREAMING_SNAKE_CASE_ , help="The number of beams to start with for each example." , )
					parser.add_argument(
					    "--alpha" , default=0.95 , type=SCREAMING_SNAKE_CASE_ , help="The value of alpha for the length penalty in the beam search." , )
					parser.add_argument(
					    "--block_trigram" , default=SCREAMING_SNAKE_CASE_ , type=SCREAMING_SNAKE_CASE_ , help="Whether to block the existence of repeating trigrams in the text generated by beam search." , )
					__lowerCAmelCase							  =			parser.parse_args()

					# Select device (distibuted not available)
					__lowerCAmelCase							  =			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:
										__lowerCAmelCase							  =			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=SCREAMING_SNAKE_CASE_	)

					evaluate(SCREAMING_SNAKE_CASE_	)







def       _a (		SCREAMING_SNAKE_CASE_				:							Optional[int]	):
					if not os.path.exists(SCREAMING_SNAKE_CASE_	):
										return False

					__lowerCAmelCase							  =			os.listdir(SCREAMING_SNAKE_CASE_	)
					if len(SCREAMING_SNAKE_CASE_	) == 0:
										return False

					return True


if __name__ == "__main__":
					main()




