import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

lowerCAmelCase__					:       List[Any]		=       logging.get_logger(__name__)
def 			UpperCamelCase__						(    A__      ,			A__ = True   )       -> Optional[int]:
				snake_case__ :     List[Any] 							=				{}
				for name in model.attn_processors.keys():
								snake_case__ :     int 							=				name.endswith('attn2.processor'   ) and not (
								    name.startswith('transformer_in'   ) or 'temp_attentions' in name.split('.'   )
								)
								snake_case__ :     int 							=				model.config.cross_attention_dim if has_cross_attention else None
								if name.startswith('mid_block'   ):
												snake_case__ :     int 							=				model.config.block_out_channels[-1]
								elif name.startswith('up_blocks'   ):
												snake_case__ :     List[Any] 							=				int(name[len('up_blocks.'   )]   )
												snake_case__ :     List[Any] 							=				list(reversed(model.config.block_out_channels   )   )[block_id]
								elif name.startswith('down_blocks'   ):
												snake_case__ :     int 							=				int(name[len('down_blocks.'   )]   )
												snake_case__ :     Union[str, Any] 							=				model.config.block_out_channels[block_id]
								elif name.startswith('transformer_in'   ):
												# Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
												snake_case__ :     Tuple 							=				8 * model.config.attention_head_dim

								snake_case__ :     int 							=				LoRAAttnProcessor(hidden_size=A__      ,			cross_attention_dim=A__   )
								snake_case__ :     Any 							=				lora_attn_procs[name].to(model.device   )

								if mock_weights:
												# add 1 to weights to mock trained weights
												with torch.no_grad():
																lora_attn_procs[name].to_q_lora.up.weight += 1
																lora_attn_procs[name].to_k_lora.up.weight += 1
																lora_attn_procs[name].to_v_lora.up.weight += 1
																lora_attn_procs[name].to_out_lora.up.weight += 1

				return lora_attn_procs





@skip_mps
class 			__snake_case  (      _lowerCamelCase   ,_lowerCamelCase   ,unittest.TestCase     ):
					__lowerCamelCase	 =       UNetaDConditionModel
					__lowerCamelCase	 =       """sample"""




					@property
					def       __a      (       self		)					->  Any:



									'''simple docstring'''







									snake_case__ :     int 							=				4
									snake_case__ :     Dict 							=				4
									snake_case__ :     Union[str, Any] 							=				4
									snake_case__ :     List[str] 							=				(32, 32)

									snake_case__ :     List[Any] 							=				floats_tensor((batch_size, num_channels, num_frames) + sizes		).to(__UpperCamelCase		)
									snake_case__ :     int 							=				torch.tensor([10]		).to(__UpperCamelCase		)
									snake_case__ :     Optional[Any] 							=				floats_tensor((batch_size, 4, 32)		).to(__UpperCamelCase		)

									return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}




					@property
					def       __a      (       self		)					->  str:



									'''simple docstring'''







									return (4, 4, 32, 32)




					@property
					def       __a      (       self		)					->  Optional[Any]:



									'''simple docstring'''







									return (4, 4, 32, 32)




					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									snake_case__ :     int 							=				{
									    'block_out_channels': (32, 64),
									    'down_block_types': (
									        'CrossAttnDownBlock3D',
									        'DownBlock3D',
									    ),
									    'up_block_types': ('UpBlock3D', 'CrossAttnUpBlock3D'),
									    'cross_attention_dim': 32,
									    'attention_head_dim': 8,
									    'out_channels': 4,
									    'in_channels': 4,
									    'layers_per_block': 1,
									    'sample_size': 32,
									}
									snake_case__ :     List[Any] 							=				self.dummy_input
									return init_dict, inputs_dict




					@unittest.skipIf(
					    torch_device != 'cuda' or not is_xformers_available()   ,	reason='XFormers attention is only available with CUDA and `xformers` installed'   ,	)
					def       __a      (       self		)					->  Union[str, Any]:



									'''simple docstring'''







									snake_case__							,  snake_case__ :     str 							=				self.prepare_init_args_and_inputs_for_common()
									snake_case__ :     Any 							=				self.model_class(**__UpperCamelCase		)

									model.enable_xformers_memory_efficient_attention()

									assert (
									    model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
									    == "XFormersAttnProcessor"
									), "xformers is not enabled"




					def       __a      (       self		)					->  Optional[Any]:



									'''simple docstring'''







									snake_case__							,  snake_case__ :     List[Any] 							=				self.prepare_init_args_and_inputs_for_common()

									snake_case__ :     Optional[int] 							=				32

									snake_case__ :     Optional[Any] 							=				self.model_class(**__UpperCamelCase		)
									model.to(__UpperCamelCase		)
									model.eval()

									with torch.no_grad():
													snake_case__ :     Union[str, Any] 							=				model(**__UpperCamelCase		)

													if isinstance(__UpperCamelCase   ,	__UpperCamelCase		):
																	snake_case__ :     Optional[int] 							=				output.sample

									self.assertIsNotNone(__UpperCamelCase		)
									snake_case__ :     Optional[Any] 							=				inputs_dict['sample'].shape
									self.assertEqual(output.shape   ,	__UpperCamelCase   ,	'Input and output shapes do not match'		)




					def       __a      (       self		)					->  Union[str, Any]:



									'''simple docstring'''







									snake_case__							,  snake_case__ :     Tuple 							=				self.prepare_init_args_and_inputs_for_common()
									snake_case__ :     Dict 							=				self.model_class(**__UpperCamelCase		)
									model.to(__UpperCamelCase		)
									model.eval()

									with torch.no_grad():
													# Warmup pass when using mps (see #372)
													if torch_device == "mps" and isinstance(__UpperCamelCase   ,	__UpperCamelCase		):
																	model(**self.dummy_input		)

													snake_case__ :     Optional[int] 							=				model(**__UpperCamelCase		)
													if isinstance(__UpperCamelCase   ,	__UpperCamelCase		):
																	snake_case__ :     Tuple 							=				first.sample

													snake_case__ :     Optional[int] 							=				model(**__UpperCamelCase		)
													if isinstance(__UpperCamelCase   ,	__UpperCamelCase		):
																	snake_case__ :     Any 							=				second.sample

									snake_case__ :     Tuple 							=				first.cpu().numpy()
									snake_case__ :     Union[str, Any] 							=				second.cpu().numpy()
									snake_case__ :     Tuple 							=				out_a[~np.isnan(__UpperCamelCase		)]
									snake_case__ :     Tuple 							=				out_a[~np.isnan(__UpperCamelCase		)]
									snake_case__ :     str 							=				np.amax(np.abs(out_a - out_a		)		)
									self.assertLessEqual(__UpperCamelCase   ,	1E-5		)




					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									snake_case__							,  snake_case__ :     int 							=				self.prepare_init_args_and_inputs_for_common()

									snake_case__ :     Tuple 							=				8

									snake_case__ :     List[str] 							=				self.model_class(**__UpperCamelCase		)
									model.to(__UpperCamelCase		)
									model.eval()

									model.set_attention_slice('auto'		)
									with torch.no_grad():
													snake_case__ :     int 							=				model(**__UpperCamelCase		)
									assert output is not None

									model.set_attention_slice('max'		)
									with torch.no_grad():
													snake_case__ :     List[Any] 							=				model(**__UpperCamelCase		)
									assert output is not None

									model.set_attention_slice(2		)
									with torch.no_grad():
													snake_case__ :     List[str] 							=				model(**__UpperCamelCase		)
									assert output is not None




					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									snake_case__							,  snake_case__ :     Dict 							=				self.prepare_init_args_and_inputs_for_common()

									snake_case__ :     List[str] 							=				8

									snake_case__ :     int 							=				self.model_class(**__UpperCamelCase		)
									model.to(__UpperCamelCase		)

									with torch.no_grad():
													snake_case__ :     int 							=				model(**__UpperCamelCase		).sample

									snake_case__ :     List[str] 							=				create_lora_layers(__UpperCamelCase		)

									# make sure we can set a list of attention processors
									model.set_attn_processor(__UpperCamelCase		)
									model.to(__UpperCamelCase		)

									# test that attn processors can be set to itself
									model.set_attn_processor(model.attn_processors		)

									with torch.no_grad():
													snake_case__ :     int 							=				model(**__UpperCamelCase   ,	cross_attention_kwargs={'scale': 0.0}		).sample
													snake_case__ :     Dict 							=				model(**__UpperCamelCase   ,	cross_attention_kwargs={'scale': 0.5}		).sample
													snake_case__ :     List[Any] 							=				model(**__UpperCamelCase   ,	cross_attention_kwargs={'scale': 0.5}		).sample

									assert (samplea - samplea).abs().max() < 3E-3
									assert (samplea - samplea).abs().max() < 3E-3

									# sample 2 and sample 3 should be different
									assert (samplea - samplea).abs().max() > 3E-3




					def       __a      (       self		)					->  Tuple:



									'''simple docstring'''







									snake_case__							,  snake_case__ :     Tuple 							=				self.prepare_init_args_and_inputs_for_common()

									snake_case__ :     List[Any] 							=				8

									torch.manual_seed(0		)
									snake_case__ :     int 							=				self.model_class(**__UpperCamelCase		)
									model.to(__UpperCamelCase		)

									with torch.no_grad():
													snake_case__ :     str 							=				model(**__UpperCamelCase		).sample

									snake_case__ :     List[Any] 							=				create_lora_layers(__UpperCamelCase		)
									model.set_attn_processor(__UpperCamelCase		)

									with torch.no_grad():
													snake_case__ :     Optional[Any] 							=				model(**__UpperCamelCase   ,	cross_attention_kwargs={'scale': 0.5}		).sample

									with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_attn_procs(__UpperCamelCase		)
													self.assertTrue(os.path.isfile(os.path.join(__UpperCamelCase   ,	'pytorch_lora_weights.bin'		)		)		)
													torch.manual_seed(0		)
													snake_case__ :     List[Any] 							=				self.model_class(**__UpperCamelCase		)
													new_model.to(__UpperCamelCase		)
													new_model.load_attn_procs(__UpperCamelCase		)

									with torch.no_grad():
													snake_case__ :     Tuple 							=				new_model(**__UpperCamelCase   ,	cross_attention_kwargs={'scale': 0.5}		).sample

									assert (sample - new_sample).abs().max() < 1E-3

									# LoRA and no LoRA should NOT be the same
									assert (sample - old_sample).abs().max() > 1E-4




					def       __a      (       self		)					->  Any:



									'''simple docstring'''







									snake_case__							,  snake_case__ :     Tuple 							=				self.prepare_init_args_and_inputs_for_common()

									snake_case__ :     Union[str, Any] 							=				8

									torch.manual_seed(0		)
									snake_case__ :     Optional[int] 							=				self.model_class(**__UpperCamelCase		)
									model.to(__UpperCamelCase		)

									with torch.no_grad():
													snake_case__ :     List[Any] 							=				model(**__UpperCamelCase		).sample

									snake_case__ :     Dict 							=				create_lora_layers(__UpperCamelCase		)
									model.set_attn_processor(__UpperCamelCase		)

									with torch.no_grad():
													snake_case__ :     Any 							=				model(**__UpperCamelCase   ,	cross_attention_kwargs={'scale': 0.5}		).sample

									with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_attn_procs(__UpperCamelCase   ,	safe_serialization=__UpperCamelCase		)
													self.assertTrue(os.path.isfile(os.path.join(__UpperCamelCase   ,	'pytorch_lora_weights.safetensors'		)		)		)
													torch.manual_seed(0		)
													snake_case__ :     Any 							=				self.model_class(**__UpperCamelCase		)
													new_model.to(__UpperCamelCase		)
													new_model.load_attn_procs(__UpperCamelCase		)

									with torch.no_grad():
													snake_case__ :     Dict 							=				new_model(**__UpperCamelCase   ,	cross_attention_kwargs={'scale': 0.5}		).sample

									assert (sample - new_sample).abs().max() < 3E-3

									# LoRA and no LoRA should NOT be the same
									assert (sample - old_sample).abs().max() > 1E-4




					def       __a      (       self		)					->  Any:



									'''simple docstring'''







									snake_case__							,  snake_case__ :     Any 							=				self.prepare_init_args_and_inputs_for_common()

									snake_case__ :     Optional[Any] 							=				8

									torch.manual_seed(0		)
									snake_case__ :     List[str] 							=				self.model_class(**__UpperCamelCase		)
									model.to(__UpperCamelCase		)

									snake_case__ :     Optional[int] 							=				create_lora_layers(__UpperCamelCase   ,	mock_weights=__UpperCamelCase		)
									model.set_attn_processor(__UpperCamelCase		)
									# Saving as torch, properly reloads with directly filename
									with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_attn_procs(__UpperCamelCase		)
													self.assertTrue(os.path.isfile(os.path.join(__UpperCamelCase   ,	'pytorch_lora_weights.bin'		)		)		)
													torch.manual_seed(0		)
													snake_case__ :     Tuple 							=				self.model_class(**__UpperCamelCase		)
													new_model.to(__UpperCamelCase		)
													new_model.load_attn_procs(__UpperCamelCase   ,	weight_name='pytorch_lora_weights.bin'		)




					def       __a      (       self		)					->  Tuple:



									'''simple docstring'''







									snake_case__							,  snake_case__ :     Any 							=				self.prepare_init_args_and_inputs_for_common()

									snake_case__ :     str 							=				8

									torch.manual_seed(0		)
									snake_case__ :     List[str] 							=				self.model_class(**__UpperCamelCase		)
									model.to(__UpperCamelCase		)

									snake_case__ :     Dict 							=				create_lora_layers(__UpperCamelCase   ,	mock_weights=__UpperCamelCase		)
									model.set_attn_processor(__UpperCamelCase		)
									# Saving as torch, properly reloads with directly filename
									with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_attn_procs(__UpperCamelCase		)
													self.assertTrue(os.path.isfile(os.path.join(__UpperCamelCase   ,	'pytorch_lora_weights.bin'		)		)		)
													torch.manual_seed(0		)
													snake_case__ :     Union[str, Any] 							=				self.model_class(**__UpperCamelCase		)
													new_model.to(__UpperCamelCase		)
													with self.assertRaises(__UpperCamelCase		) as e:
																	new_model.load_attn_procs(__UpperCamelCase   ,	use_safetensors=__UpperCamelCase		)
													self.assertIn('Error no file named pytorch_lora_weights.safetensors'   ,	str(e.exception		)		)




					def       __a      (       self		)					->  int:



									'''simple docstring'''







									snake_case__							,  snake_case__ :     Union[str, Any] 							=				self.prepare_init_args_and_inputs_for_common()

									snake_case__ :     Union[str, Any] 							=				8

									torch.manual_seed(0		)
									snake_case__ :     Optional[Any] 							=				self.model_class(**__UpperCamelCase		)
									model.to(__UpperCamelCase		)

									with torch.no_grad():
													snake_case__ :     Optional[Any] 							=				model(**__UpperCamelCase		).sample

									snake_case__ :     int 							=				create_lora_layers(__UpperCamelCase		)
									model.set_attn_processor(__UpperCamelCase		)

									with torch.no_grad():
													snake_case__ :     Optional[Any] 							=				model(**__UpperCamelCase   ,	cross_attention_kwargs={'scale': 0.0}		).sample

									model.set_attn_processor(AttnProcessor()		)

									with torch.no_grad():
													snake_case__ :     List[Any] 							=				model(**__UpperCamelCase		).sample

									assert (sample - new_sample).abs().max() < 1E-4
									assert (sample - old_sample).abs().max() < 3E-3




					@unittest.skipIf(
					    torch_device != 'cuda' or not is_xformers_available()   ,	reason='XFormers attention is only available with CUDA and `xformers` installed'   ,	)
					def       __a      (       self		)					->  List[str]:



									'''simple docstring'''







									snake_case__							,  snake_case__ :     Dict 							=				self.prepare_init_args_and_inputs_for_common()

									snake_case__ :     Tuple 							=				4

									torch.manual_seed(0		)
									snake_case__ :     Tuple 							=				self.model_class(**__UpperCamelCase		)
									model.to(__UpperCamelCase		)
									snake_case__ :     List[str] 							=				create_lora_layers(__UpperCamelCase		)
									model.set_attn_processor(__UpperCamelCase		)

									# default
									with torch.no_grad():
													snake_case__ :     Dict 							=				model(**__UpperCamelCase		).sample

													model.enable_xformers_memory_efficient_attention()
													snake_case__ :     Optional[Any] 							=				model(**__UpperCamelCase		).sample

													model.disable_xformers_memory_efficient_attention()
													snake_case__ :     Dict 							=				model(**__UpperCamelCase		).sample

									assert (sample - on_sample).abs().max() < 1E-4
									assert (sample - off_sample).abs().max() < 1E-4




					def       __a      (       self		)					->  Optional[Any]:



									'''simple docstring'''







									snake_case__							,  snake_case__ :     Union[str, Any] 							=				self.prepare_init_args_and_inputs_for_common()
									snake_case__ :     str 							=				32

									snake_case__ :     Optional[int] 							=				self.model_class(**__UpperCamelCase		)
									model.to(__UpperCamelCase		)
									model.eval()

									with torch.no_grad():
													snake_case__ :     Optional[int] 							=				model(**__UpperCamelCase		)[0]

									model.enable_forward_chunking()
									with torch.no_grad():
													snake_case__ :     int 							=				model(**__UpperCamelCase		)[0]

									self.assertEqual(output.shape   ,	output_a.shape   ,	'Shape doesn\'t match'		)
									assert np.abs(output.cpu() - output_a.cpu()		).max() < 1E-2


# (todo: sayakpaul) implement SLOW tests.



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,
)


lowerCAmelCase__					:       List[Any]		=       logging.getLogger(__name__)
logging.basicConfig(stream=sys.stdout, level=logging.INFO)


lowerCAmelCase__					:       Union[str, Any]		=       namedtuple('''Batch''', ['''document_names''', '''batch_size''', '''src''', '''segs''', '''mask_src''', '''tgt_str'''])
def 			UpperCamelCase__						(    A__   )       -> Dict:
				snake_case__ :     List[str] 							=				BertTokenizer.from_pretrained('bert-base-uncased'      ,			do_lower_case=A__   )
				snake_case__ :     Optional[int] 							=				BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization'   )
				model.to(args.device   )
				model.eval()

				snake_case__ :     Optional[int] 							=				{
				    'BOS': tokenizer.vocab['[unused0]'],
				    'EOS': tokenizer.vocab['[unused1]'],
				    'PAD': tokenizer.vocab['[PAD]'],
				}

				if args.compute_rouge:
								snake_case__ :     Tuple 							=				[]
								snake_case__ :     Union[str, Any] 							=				[]

								import nltk
								import rouge

								nltk.download('punkt'   )
								snake_case__ :     Optional[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.
				snake_case__ :     int 							=				''
				snake_case__ :     Any 							=				''

				snake_case__ :     Dict 							=				build_data_iterator(A__      ,			A__   )
				snake_case__ :     List[Any] 							=				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__   ):
								snake_case__ :     Dict 							=				predictor.translate_batch(A__   )
								snake_case__ :     List[str] 							=				predictor.from_batch(A__   )
								snake_case__ :     List[Any] 							=				[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:
								snake_case__ :     str 							=				rouge_evaluator.get_scores(A__      ,			A__   )
								snake_case__ :     Tuple 							=				format_rouge_scores(A__   )
								save_rouge_scores(A__   )
								print(A__   )
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__   )       -> Optional[int]:
				for summary, document_name in zip(A__      ,			A__   ):
								# Prepare the summary file's name
								if "." in document_name:
												snake_case__ :     Optional[int] 							=				'.'.join(document_name.split('.'   )[:-1]   )
												snake_case__ :     List[str] 							=				document_name.split('.'   )[-1]
												snake_case__ :     Optional[Any] 							=				bare_document_name + '_summary.' + extension
								else:
												snake_case__ :     Optional[Any] 							=				document_name + '_summary'

								snake_case__ :     Optional[int] 							=				os.path.join(A__      ,			A__   )
								with open(A__      ,			'w'   ) as output:
												output.write(A__   )
def 			UpperCamelCase__						(    A__   )       -> Dict:
				snake_case__							,  snake_case__							,  snake_case__ :     str 							=				translation
				snake_case__ :     str 							=				(
				    raw_summary.replace('[unused0]'      ,			''   )
				    .replace('[unused3]'      ,			''   )
				    .replace('[PAD]'      ,			''   )
				    .replace('[unused1]'      ,			''   )
				    .replace(r' +'      ,			' '   )
				    .replace(' [unused2] '      ,			'. '   )
				    .replace('[unused2]'      ,			''   )
				    .strip()
				)

				return summary
def 			UpperCamelCase__						(    A__   )       -> Dict:
				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 			UpperCamelCase__						(    A__   )       -> str:
				with open('rouge_scores.txt'      ,			'w'   ) as output:
								output.write(A__   )
def 			UpperCamelCase__						(    A__      ,			A__   )       -> str:
				snake_case__ :     Optional[int] 							=				load_and_cache_examples(A__      ,			A__   )
				snake_case__ :     str 							=				SequentialSampler(A__   )

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

				snake_case__ :     List[str] 							=				DataLoader(
				    A__      ,			sampler=A__      ,			batch_size=args.batch_size      ,			collate_fn=A__      ,			)

				return iterator
def 			UpperCamelCase__						(    A__      ,			A__   )       -> Optional[int]:
				snake_case__ :     List[Any] 							=				CNNDMDataset(args.documents_dir   )
				return dataset
def 			UpperCamelCase__						(    A__      ,			A__      ,			A__      ,			A__   )       -> List[Any]:
				snake_case__ :     List[str] 							=				[x for x in data if not len(x[1]   ) == 0]  # remove empty_files
				snake_case__ :     Tuple 							=				[name for name, _, _ in data]
				snake_case__ :     Any 							=				[' '.join(A__   ) for _, _, summary_list in data]

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

				snake_case__ :     Optional[Any] 							=				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 			UpperCamelCase__						(    A__      ,			A__   )       -> List[str]:
				snake_case__ :     Optional[int] 							=				summary_tokens.to('cpu'   ).numpy()
				snake_case__ :     Dict 							=				tokenizer.decode(A__   )
				snake_case__ :     Tuple 							=				summary.split('.'   )
				snake_case__ :     int 							=				[s + '.' for s in sentences]
				return sentences
def 			UpperCamelCase__						(    )       -> Union[str, Any]:
				snake_case__ :     List[Any] 							=				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=0.9_5      ,			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.'      ,			)
				snake_case__ :     int 							=				parser.parse_args()

				# Select device (distibuted not available)
				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:
								snake_case__ :     List[str] 							=				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 			UpperCamelCase__						(    A__   )       -> Dict:
				if not os.path.exists(A__   ):
								return False

				snake_case__ :     List[str] 							=				os.listdir(A__   )
				if len(A__   ) == 0:
								return False

				return True


if __name__ == "__main__":
				main()


