


'''simple docstring'''



import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


_lowercase      :      Optional[Any]            =					getLogger(__name__)


def 				lowerCamelCase__				(	A       :      List[Any]				,       A       :      str				,       A       :      str				,       A       :      int = 8				,       A       :      int = 10_24				,       A       :      Optional[int]="val"				,       A       :      int=None				,       A       :      Union[str, Any]=False				,       A       :      Any="summarization"				,       A       :      Dict=None				,       A       :      Dict=1				,       A       :      Dict = None				,       A       :      str=""				,       **A       :      int				,       ):




						'''simple docstring'''






						UpperCAmelCase					    =     str(A					)
						assert local_rank is not None
						torch.distributed.init_process_group(backend='''nccl'''				,       rank=A					)

						UpperCAmelCase					    =     Path(A					)
						UpperCAmelCase					    =     save_dir.joinpath(f"""rank_{local_rank}_output.json"""					)
						torch.cuda.set_device(A					)
						UpperCAmelCase					    =     AutoModelForSeqaSeqLM.from_pretrained(A					).cuda()
						if fpaa:
												UpperCAmelCase					    =     model.half()
						# determine if we need to increase num_beams
						use_task_specific_params(A				,       A					)  # update config with task specific params
						UpperCAmelCase					    =     generate_kwargs.pop('''num_beams'''				,       model.config.num_beams					)  # AttributeError risk?
						if num_return_sequences > num_beams:
												UpperCAmelCase					    =     num_return_sequences

						UpperCAmelCase					    =     AutoTokenizer.from_pretrained(A					)
						logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""					)  # if this is wrong, check config.model_type.

						if max_source_length is None:
												UpperCAmelCase					    =     tokenizer.model_max_length
						if prefix is None:
												UpperCAmelCase					    =     prefix or getattr(model.config				,       '''prefix'''				,       ''''''					) or ''''''
						UpperCAmelCase					    =     SeqaSeqDataset(
						    A				,       A				,       A				,       max_target_length=10_24				,       type_path=A				,       n_obs=A				,       prefix=A				,       **A				,       )
						# I set shuffle=True for a more accurate progress bar.
						# If all the longest samples are first, the prog bar estimate is too high at the beginning.
						UpperCAmelCase					    =     ds.make_sortish_sampler(A				,       distributed=A				,       add_extra_examples=A				,       shuffle=A					)
						UpperCAmelCase					    =     DataLoader(A				,       sampler=A				,       batch_size=A				,       collate_fn=ds.collate_fn					)
						UpperCAmelCase					    =     []
						for batch in tqdm(A					):
												UpperCAmelCase					    =     model.generate(
												    input_ids=batch['''input_ids'''].to(model.device					)				,       attention_mask=batch['''attention_mask'''].to(model.device					)				,       num_return_sequences=A				,       num_beams=A				,       **A				,       )
												UpperCAmelCase					    =     tokenizer.batch_decode(A				,       skip_special_tokens=A				,       clean_up_tokenization_spaces=A					)
												UpperCAmelCase					    =     batch['''ids''']
												if num_return_sequences > 1:
																		UpperCAmelCase					    =     chunks(A				,       A					)  # batch size chunks, each of size num_return_seq
												for i, pred in enumerate(A					):
																		results.append({'''pred''': pred, '''id''': ids[i].item()}					)
						save_json(A				,       A					)
						return results, sampler.num_replicas


def 				lowerCamelCase__				(	):




						'''simple docstring'''






						UpperCAmelCase					    =     argparse.ArgumentParser(
						    epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'''					)
						parser.add_argument('''--data_dir'''				,       type=A				,       help='''like cnn_dm/test.source'''					)
						parser.add_argument(
						    '''--model_name'''				,       type=A				,       help='''like facebook/bart-large-cnn,t5-base, etc.'''				,       default='''sshleifer/distilbart-xsum-12-3'''				,       )
						parser.add_argument('''--save_dir'''				,       type=A				,       help='''where to save'''				,       default='''tmp_gen'''					)
						parser.add_argument('''--max_source_length'''				,       type=A				,       default=A					)
						parser.add_argument(
						    '''--type_path'''				,       type=A				,       default='''test'''				,       help='''which subset to evaluate typically train/val/test'''					)
						parser.add_argument('''--task'''				,       type=A				,       default='''summarization'''				,       help='''used for task_specific_params + metrics'''					)
						parser.add_argument('''--bs'''				,       type=A				,       default=8				,       required=A				,       help='''batch size'''					)
						parser.add_argument(
						    '''--local_rank'''				,       type=A				,       default=-1				,       required=A				,       help='''should be passed by distributed.launch'''					)

						parser.add_argument(
						    '''--n_obs'''				,       type=A				,       default=A				,       required=A				,       help='''How many observations. Defaults to all.'''					)
						parser.add_argument(
						    '''--num_return_sequences'''				,       type=A				,       default=1				,       required=A				,       help='''How many sequences to return'''					)
						parser.add_argument(
						    '''--sync_timeout'''				,       type=A				,       default=6_00				,       required=A				,       help='''How long should master process wait for other processes to finish.'''				,       )
						parser.add_argument('''--src_lang'''				,       type=A				,       default=A				,       required=A					)
						parser.add_argument('''--tgt_lang'''				,       type=A				,       default=A				,       required=A					)
						parser.add_argument(
						    '''--prefix'''				,       type=A				,       required=A				,       default=A				,       help='''will be added to the begininng of src examples'''					)
						parser.add_argument('''--fp16'''				,       action='''store_true'''					)
						parser.add_argument('''--debug'''				,       action='''store_true'''					)
						UpperCAmelCase					    =     time.time()
						UpperCAmelCase    ,    UpperCAmelCase					    =     parser.parse_known_args()
						UpperCAmelCase					    =     parse_numeric_n_bool_cl_kwargs(A					)
						if generate_kwargs and args.local_rank <= 0:
												print(f"""parsed the following generate kwargs: {generate_kwargs}"""					)
						UpperCAmelCase					    =     Path(args.save_dir + '''_tmp'''					)
						Path(A					).mkdir(exist_ok=A					)  # this handles locking.
						UpperCAmelCase					    =     list(json_save_dir.glob('''rank_*.json'''					)					)
						if intermediate_files:
												raise ValueError(f"""Found files at {json_save_dir} please move or remove them."""					)
												# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
						UpperCAmelCase					    =     {}
						if args.src_lang is not None:
												UpperCAmelCase					    =     args.src_lang
						if args.tgt_lang is not None:
												UpperCAmelCase					    =     args.tgt_lang

						Path(args.save_dir					).mkdir(exist_ok=A					)
						UpperCAmelCase    ,    UpperCAmelCase					    =     eval_data_dir(
						    args.data_dir				,       A				,       args.model_name				,       type_path=args.type_path				,       bs=args.bs				,       fpaa=args.fpaa				,       task=args.task				,       local_rank=args.local_rank				,       n_obs=args.n_obs				,       max_source_length=args.max_source_length				,       num_return_sequences=args.num_return_sequences				,       prefix=args.prefix				,       dataset_kwargs=A				,       **A				,       )

						if args.local_rank <= 0:
												UpperCAmelCase					    =     Path(args.save_dir					)
												save_dir.mkdir(exist_ok=A					)
												UpperCAmelCase					    =     gather_results_from_each_node(A				,       A				,       args.sync_timeout					)
												UpperCAmelCase					    =     combine_partial_results(A					)
												if args.num_return_sequences > 1:
																		UpperCAmelCase					    =     save_dir.joinpath('''pseudolabel_results.json'''					)
																		print(f"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"""					)
																		save_json(A				,       A					)
																		return
												UpperCAmelCase					    =     Path(args.data_dir					).joinpath(args.type_path + '''.target'''					)
												with open(A					) as f:
																		UpperCAmelCase					    =     [x.rstrip() for x in f.readlines()][: len(A					)]

												# Calculate metrics, save metrics,  and save _generations.txt
												UpperCAmelCase					    =     '''translation''' in args.task
												UpperCAmelCase					    =     calculate_bleu if calc_bleu else calculate_rouge
												UpperCAmelCase					    =     '''bleu''' if calc_bleu else '''rouge'''
												UpperCAmelCase					    =     score_fn(A				,       A					)
												UpperCAmelCase					    =     len(A					)
												UpperCAmelCase					    =     time.time() - start_time
												UpperCAmelCase					    =     round(runtime / metrics['''n_obs''']				,       4					)
												UpperCAmelCase					    =     num_replicas
												# TODO(@stas00): add whatever metadata to metrics
												UpperCAmelCase					    =     save_dir.joinpath(f"""{args.type_path}_{metric_name}.json"""					)
												save_json(A				,       A				,       indent=A					)
												print(A					)
												write_txt_file(A				,       save_dir.joinpath(f"""{args.type_path}_generations.txt"""					)					)
												if args.debug:
																		write_txt_file(A				,       save_dir.joinpath(f"""{args.type_path}.target"""					)					)
												else:
																		shutil.rmtree(A					)


def 				lowerCamelCase__				(	A       :      Optional[Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     []
						for partial_result in partial_results:
												records.extend(A					)
						UpperCAmelCase					    =     sorted(A				,       key=lambda A					: x["id"]					)
						UpperCAmelCase					    =     [x['''pred'''] for x in records]
						return preds


def 				lowerCamelCase__				(	A       :      Tuple				,       A       :      Tuple				,       A       :      int					):




						'''simple docstring'''






						UpperCAmelCase					    =     time.time()
						logger.info('''waiting for all nodes to finish'''					)
						UpperCAmelCase					    =     None
						while (time.time() - start_wait) < timeout:
												UpperCAmelCase					    =     list(save_dir.glob('''rank_*.json'''					)					)
												if len(A					) < num_replicas:
																		continue
												try:
																		# make sure all json files are fully saved
																		UpperCAmelCase					    =     lmap(A				,       A					)
																		return json_data
												except JSONDecodeError:
																		continue
						else:
												raise TimeoutError('''Rank 0 gave up on waiting for other processes'''					)
						# Unreachable


if __name__ == "__main__":
	# Usage for MT:
	run_generate()









'''simple docstring'''



import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
	import torch

	from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin






class 	UpperCamelCase__:




				def __init__(		self      :			List[str]				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			Optional[int]=None				,   lowerCAmelCase      :			Optional[int]=None				,   lowerCAmelCase      :			List[str]=None				,   lowerCAmelCase      :			int="resnet50"				,   lowerCAmelCase      :			Optional[int]=3				,   lowerCAmelCase      :			Union[str, Any]=32				,   lowerCAmelCase      :			List[Any]=3				,   lowerCAmelCase      :			Tuple=True				,   lowerCAmelCase      :			str=True				,   )->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     parent
										UpperCAmelCase					    =     out_indices if out_indices is not None else [4]
										UpperCAmelCase					    =     stage_names
										UpperCAmelCase					    =     out_features
										UpperCAmelCase					    =     backbone
										UpperCAmelCase					    =     batch_size
										UpperCAmelCase					    =     image_size
										UpperCAmelCase					    =     num_channels
										UpperCAmelCase					    =     use_pretrained_backbone
										UpperCAmelCase					    =     is_training




				def 					a__(		self      :			Optional[Any]				)->					Optional[int]:

										"""simple docstring"""




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

										return config, pixel_values




				def 					a__(		self      :			int				)->					Optional[Any]:

										"""simple docstring"""




										return TimmBackboneConfig(
										    image_size=self.image_size				,   num_channels=self.num_channels				,   out_features=self.out_features				,   out_indices=self.out_indices				,   stage_names=self.stage_names				,   use_pretrained_backbone=self.use_pretrained_backbone				,   backbone=self.backbone				,   )




				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Dict				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     TimmBackbone(config=lowerCAmelCase				)
										model.to(lowerCAmelCase				)
										model.eval()
										with torch.no_grad():
																UpperCAmelCase					    =     model(lowerCAmelCase				)
										self.parent.assertEqual(
										    result.feature_map[-1].shape				,   (self.batch_size, model.channels[-1], 14, 14)				,   )





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

										"""simple docstring"""




										UpperCAmelCase					    =     self.prepare_config_and_inputs()
										UpperCAmelCase    ,    UpperCAmelCase					    =     config_and_inputs
										UpperCAmelCase					    =     {'''pixel_values''': pixel_values}
										return config, inputs_dict







@require_torch
@require_timm
class 	UpperCamelCase__(       lowerCAmelCase						,    lowerCAmelCase						,    lowerCAmelCase						,    unittest.TestCase	):
				__magic_name__    :				List[str]  		=	(TimmBackbone,) if is_torch_available() else ()
				__magic_name__    :				Any  		=	{"feature-extraction": TimmBackbone} if is_torch_available() else {}
				__magic_name__    :				Union[str, Any]  		=	False
				__magic_name__    :				int  		=	False
				__magic_name__    :				Tuple  		=	False
				__magic_name__    :				List[str]  		=	False




				def 					a__(		self      :			int				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     TimmBackboneModelTester(self				)
										UpperCAmelCase					    =     ConfigTester(self				,   config_class=lowerCAmelCase				,   has_text_modality=lowerCAmelCase				)




				def 					a__(		self      :			List[str]				)->					Dict:

										"""simple docstring"""




										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 					a__(		self      :			List[Any]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     '''resnet18'''
										UpperCAmelCase					    =     '''microsoft/resnet-18'''

										UpperCAmelCase					    =     AutoBackbone.from_pretrained(lowerCAmelCase				,   use_timm_backbone=lowerCAmelCase				)
										UpperCAmelCase					    =     AutoBackbone.from_pretrained(lowerCAmelCase				)

										self.assertEqual(len(timm_model.out_features				)				,   len(transformers_model.out_features				)				)
										self.assertEqual(len(timm_model.stage_names				)				,   len(transformers_model.stage_names				)				)
										self.assertEqual(timm_model.channels				,   transformers_model.channels				)
										# Out indices are set to the last layer by default. For timm models, we don't know
										# the number of layers in advance, so we set it to (-1,), whereas for transformers
										# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
										self.assertEqual(timm_model.out_indices				,   (-1,)				)
										self.assertEqual(transformers_model.out_indices				,   [len(timm_model.stage_names				) - 1]				)

										UpperCAmelCase					    =     AutoBackbone.from_pretrained(lowerCAmelCase				,   use_timm_backbone=lowerCAmelCase				,   out_indices=[1, 2, 3]				)
										UpperCAmelCase					    =     AutoBackbone.from_pretrained(lowerCAmelCase				,   out_indices=[1, 2, 3]				)

										self.assertEqual(timm_model.out_indices				,   transformers_model.out_indices				)
										self.assertEqual(len(timm_model.out_features				)				,   len(transformers_model.out_features				)				)
										self.assertEqual(timm_model.channels				,   transformers_model.channels				)




				@unittest.skip('''TimmBackbone doesn\'t support feed forward chunking'''				)
				def 					a__(		self      :			Union[str, Any]				)->					Tuple:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone doesn\'t have num_hidden_layers attribute'''				)
				def 					a__(		self      :			Dict				)->					Dict:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone initialization is managed on the timm side'''				)
				def 					a__(		self      :			Tuple				)->					List[Any]:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''				)
				def 					a__(		self      :			List[str]				)->					Optional[Any]:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone models doesn\'t have inputs_embeds'''				)
				def 					a__(		self      :			Optional[Any]				)->					Optional[Any]:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone model cannot be created without specifying a backbone checkpoint'''				)
				def 					a__(		self      :			Any				)->					Optional[int]:

										"""simple docstring"""




										pass




				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''				)
				def 					a__(		self      :			Optional[int]				)->					Optional[int]:

										"""simple docstring"""




										pass




				@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''				)
				def 					a__(		self      :			str				)->					str:

										"""simple docstring"""




										pass




				@unittest.skip('''model weights aren\'t tied in TimmBackbone.'''				)
				def 					a__(		self      :			Any				)->					int:

										"""simple docstring"""




										pass




				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''				)
				def 					a__(		self      :			List[Any]				)->					int:

										"""simple docstring"""




										pass




				@unittest.skip('''Only checkpoints on timm can be loaded into TimmBackbone'''				)
				def 					a__(		self      :			List[Any]				)->					List[str]:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone doesn\'t have hidden size info in its configuration.'''				)
				def 					a__(		self      :			List[str]				)->					Dict:

										"""simple docstring"""




										pass




				@unittest.skip('''TimmBackbone doesn\'t support output_attentions.'''				)
				def 					a__(		self      :			str				)->					Tuple:

										"""simple docstring"""




										pass




				@unittest.skip('''Safetensors is not supported by timm.'''				)
				def 					a__(		self      :			List[Any]				)->					List[Any]:

										"""simple docstring"""




										pass




				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
				def 					a__(		self      :			int				)->					Tuple:

										"""simple docstring"""




										pass




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()

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

																UpperCAmelCase					    =     ['''pixel_values''']
																self.assertListEqual(arg_names[:1]				,   lowerCAmelCase				)




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

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()
										UpperCAmelCase					    =     True
										UpperCAmelCase					    =     self.has_attentions

										# no need to test all models as different heads yield the same functionality
										UpperCAmelCase					    =     self.all_model_classes[0]
										UpperCAmelCase					    =     model_class(lowerCAmelCase				)
										model.to(lowerCAmelCase				)

										UpperCAmelCase					    =     self._prepare_for_class(lowerCAmelCase				,   lowerCAmelCase				)
										UpperCAmelCase					    =     model(**lowerCAmelCase				)
										UpperCAmelCase					    =     outputs[0][-1]

										# Encoder-/Decoder-only models
										UpperCAmelCase					    =     outputs.hidden_states[0]
										hidden_states.retain_grad()

										if self.has_attentions:
																UpperCAmelCase					    =     outputs.attentions[0]
																attentions.retain_grad()

										output.flatten()[0].backward(retain_graph=lowerCAmelCase				)

										self.assertIsNotNone(hidden_states.grad				)

										if self.has_attentions:
																self.assertIsNotNone(attentions.grad				)





				def 					a__(		self      :			Optional[int]				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)
																model.to(lowerCAmelCase				)
																model.eval()
																UpperCAmelCase					    =     model(**lowerCAmelCase				)

																self.assertEqual(len(result.feature_maps				)				,   len(config.out_indices				)				)
																self.assertEqual(len(model.channels				)				,   len(config.out_indices				)				)

																# Check output of last stage is taken if out_features=None, out_indices=None
																UpperCAmelCase					    =     copy.deepcopy(lowerCAmelCase				)
																UpperCAmelCase					    =     None
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)
																model.to(lowerCAmelCase				)
																model.eval()
																UpperCAmelCase					    =     model(**lowerCAmelCase				)

																self.assertEqual(len(result.feature_maps				)				,   1				)
																self.assertEqual(len(model.channels				)				,   1				)

																# Check backbone can be initialized with fresh weights
																UpperCAmelCase					    =     copy.deepcopy(lowerCAmelCase				)
																UpperCAmelCase					    =     False
																UpperCAmelCase					    =     model_class(lowerCAmelCase				)
																model.to(lowerCAmelCase				)
																model.eval()
																UpperCAmelCase					    =     model(**lowerCAmelCase				)





