



'''simple docstring'''







import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

_UpperCamelCase  						=			logging.getLogger()
_UpperCamelCase  						=			torch.cuda.is_available()
_UpperCamelCase  						=			{
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1e-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Optional[Any]					, UpperCAmelCase_      :	List[str]						) ->							Union[str, Any]:
							lowerCAmelCase__  =   """\n""".join(_SCREAMING_SNAKE_CASE						)
							Path(_SCREAMING_SNAKE_CASE						).open("""w"""						).writelines(_SCREAMING_SNAKE_CASE						)


_UpperCamelCase  						=			[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
_UpperCamelCase  						=			["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
_UpperCamelCase  						=			"""patrickvonplaten/t5-tiny-random"""
_UpperCamelCase  						=			"""sshleifer/t5-tinier-random"""
_UpperCamelCase  						=			"""sshleifer/bart-tiny-random"""
_UpperCamelCase  						=			"""sshleifer/tiny-mbart"""
_UpperCamelCase  						=			"""sshleifer/tiny-marian-en-de"""
_UpperCamelCase  						=			"""stas/tiny-wmt19-en-de"""


_UpperCamelCase  						=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks
def 			_lowerCAmelCase(      UpperCAmelCase_      :	Optional[int]						) ->							str:


							for split in ["train", "val", "test"]:
														_dump_articles(os.path.join(_SCREAMING_SNAKE_CASE					, F'''{split}.source'''						)					, _SCREAMING_SNAKE_CASE						)
														_dump_articles(os.path.join(_SCREAMING_SNAKE_CASE					, F'''{split}.target'''						)					, _SCREAMING_SNAKE_CASE						)
							return tmp_dir

class      lowerCamelCase__     (     _lowercase				):






	'''simple docstring'''

	@classmethod
	def      lowercase__							(  cls		:  List[Any]					)     ->			Tuple:




								'''simple docstring'''





								logging.disable(logging.CRITICAL					)  # remove noisy download output from tracebacks
								return cls




	@slow
	@require_torch_gpu
	def      lowercase__							(  self		:  int					)     ->			Union[str, Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   list_models()
								lowerCAmelCase__  =   """sshleifer"""
								lowerCAmelCase__  =   [x.modelId for x in model_list if x.modelId.startswith(__a					)]
								lowerCAmelCase__  =   ["""sshleifer/blenderbot-3B""", """sshleifer/blenderbot-90M"""]
								lowerCAmelCase__  =   []
								for m in model_ids:
															if m in allowed_to_be_broken:
																						continue
															try:
																						AutoConfig.from_pretrained(__a					)
															except Exception:
																						failures.append(__a					)
								assert not failures, f'''The following models could not be loaded through AutoConfig: {failures}'''




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




								'''simple docstring'''





								lowerCAmelCase__  =   {"""student_encoder_layers""": 2, """student_decoder_layers""": 1, """no_teacher""": True}
								self._test_distiller_cli(__a					)




	def      lowercase__							(  self		:  Any					)     ->			str:




								'''simple docstring'''





								lowerCAmelCase__  =   {
								    """student_encoder_layers""": 2,
								    """student_decoder_layers""": 1,
								    """max_epochs""": 4,
								    """val_check_interval""": 0.2_5,
								    """alpha_hid""": 2.0,
								    """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
								}
								lowerCAmelCase__  =   self._test_distiller_cli(__a   ,		check_contents=__a					)

								lowerCAmelCase__  =   list(Path(model.output_dir					).glob("""*.ckpt"""					)					)
								self.assertEqual(1   ,		len(__a					)					)
								lowerCAmelCase__  =   list(Path(model.output_dir					).glob("""**/*.bin"""					)					)
								self.assertEqual(len(__a					)   ,		2					)
								lowerCAmelCase__  =   lmap(str.strip   ,		Path(model.hparams.data_dir					).joinpath("""test.source"""					).open().readlines()					)
								lowerCAmelCase__  =   tempfile.mktemp()  # XXX: not being cleaned up
								generate_summaries_or_translations(__a   ,		__a   ,		str(model.output_dir / """best_tfmr"""					)					)
								self.assertTrue(Path(__a					).exists()					)

								lowerCAmelCase__  =   self.get_auto_remove_tmp_dir()
								convert_pl_to_hf(ckpts[0]   ,		transformer_ckpts[0].parent   ,		__a					)
								assert os.path.exists(os.path.join(__a   ,		"""pytorch_model.bin"""					)					)




	def      lowercase__							(  self		:  List[str]					)     ->			Any:




								'''simple docstring'''





								lowerCAmelCase__  =   AutoModelForSeqaSeqLM.from_pretrained(__a					)
								lowerCAmelCase__  ,lowerCAmelCase__  =   model.dummy_inputs["""input_ids"""], model.dummy_inputs["""attention_mask"""]
								lowerCAmelCase__  =   torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]   ,		dtype=torch.long   ,		device=model.device					)
								lowerCAmelCase__  =   target_ids[:, :-1].contiguous()  # Why this line?
								lowerCAmelCase__  =   target_ids[:, 1:].clone()  # why clone?
								lowerCAmelCase__  =   model(
								    __a   ,		attention_mask=__a   ,		decoder_input_ids=__a   ,		labels=__a   ,		use_cache=__a					).loss

								lowerCAmelCase__  =   model(__a   ,		attention_mask=__a   ,		decoder_input_ids=__a   ,		use_cache=__a					).logits

								lowerCAmelCase__  =   nn.functional.log_softmax(__a   ,		dim=-1					)
								lowerCAmelCase__  ,lowerCAmelCase__  =   label_smoothed_nll_loss(
								    __a   ,		__a   ,		0.1   ,		ignore_index=model.config.pad_token_id					)
								with self.assertRaises(__a					):
															# TODO: understand why this breaks
															self.assertEqual(__a   ,		__a					)




	def      lowercase__							(  self		:  List[str]					)     ->			List[Any]:




								'''simple docstring'''





								lowerCAmelCase__  =   {
								    """student_encoder_layers""": 2,
								    """student_decoder_layers""": 1,
								    """num_train_epochs""": 4,
								    """val_check_interval""": 0.2_5,
								    """alpha_hid""": 2.0,
								    """task""": """translation""",
								    """model_name_or_path""": """IGNORE_THIS_IT_DOESNT_GET_USED""",
								    """tokenizer_name""": MBART_TINY,
								    """teacher""": MBART_TINY,
								    """src_lang""": """en_XX""",
								    """tgt_lang""": """ro_RO""",
								}
								lowerCAmelCase__  =   self._test_distiller_cli(__a   ,		check_contents=__a					)
								assert model.model.config.model_type == "mbart"

								lowerCAmelCase__  =   list(Path(model.output_dir					).glob("""*.ckpt"""					)					)
								self.assertEqual(1   ,		len(__a					)					)
								lowerCAmelCase__  =   list(Path(model.output_dir					).glob("""**/*.bin"""					)					)
								lowerCAmelCase__  =   list(Path(model.output_dir					).glob("""best_tfmr/*"""					)					)
								assert len(__a					) > 2
								self.assertEqual(len(__a					)   ,		2					)




	def      lowercase__							(  self		:  str					)     ->			Tuple:




								'''simple docstring'''





								lowerCAmelCase__  =   {
								    """student_encoder_layers""": 1,
								    """student_decoder_layers""": 1,
								    """alpha_hid""": 2.0,
								    """teacher""": T5_TINY,
								    """model_name_or_path""": T5_TINY,
								    """tokenizer_name""": T5_TINY,
								}
								self._test_distiller_cli(__a					)




	def      lowercase__							(  self		:  Dict					)     ->			str:




								'''simple docstring'''





								lowerCAmelCase__  =   {
								    """teacher""": T5_TINY,
								    """student""": T5_TINIER,
								    """model_name_or_path""": T5_TINIER,
								    """tokenizer_name""": T5_TINIER,
								}
								self._test_distiller_cli(__a					)




	def      lowercase__							(  self		:  Optional[Any]   ,		__A		:  Tuple   ,		__A		:  str=True					)     ->			Any:




								'''simple docstring'''





								lowerCAmelCase__  =   {
								    """label_smoothing""": 0.0,
								    """early_stopping_patience""": -1,
								    """train_batch_size""": 1,
								    """eval_batch_size""": 2,
								    """max_epochs""": 2,
								    """alpha_mlm""": 0.2,
								    """alpha_ce""": 0.8,
								    """do_predict""": True,
								    """model_name_or_path""": """sshleifer/tinier_bart""",
								    """teacher""": CHEAP_ARGS["""model_name_or_path"""],
								    """val_check_interval""": 0.5,
								}
								default_updates.update(__a					)
								lowerCAmelCase__  =   CHEAP_ARGS.copy()
								lowerCAmelCase__  =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
								lowerCAmelCase__  =   self.get_auto_remove_tmp_dir()

								args_d.update(data_dir=__a   ,		output_dir=__a   ,		**__a					)
								lowerCAmelCase__  =   distill_main(argparse.Namespace(**__a					)					)
								if not check_contents:
															return model
								lowerCAmelCase__  =   os.listdir(__a					)
								lowerCAmelCase__  =   {os.path.basename(__a					) for p in contents}
								lowerCAmelCase__  =   [p for p in contents if p.endswith("""ckpt"""					)]
								assert len(__a					) > 0

								self.assertIn("""test_generations.txt"""   ,		__a					)
								self.assertIn("""test_results.txt"""   ,		__a					)

								lowerCAmelCase__  =   load_json(model.metrics_save_path					)
								lowerCAmelCase__  =   metrics["""val"""][-1]
								self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""]   ,		0.0_1					)
								self.assertGreaterEqual(1.0   ,		last_step_stats["""val_avg_gen_time"""]					)
								self.assertIsInstance(last_step_stats[f'''val_avg_{model.val_metric}''']   ,		__a					)
								lowerCAmelCase__  =   int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) + 1					)
								self.assertEqual(len(metrics["""val"""]					)   ,		__a					)
								self.assertEqual(len(metrics["""test"""]					)   ,		1					)
								return model





class      lowerCamelCase__     (     _lowercase				):






	'''simple docstring'''

	@parameterized.expand(
	    [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]   ,		)
	def      lowercase__							(  self		:  Union[str, Any]   ,		__A		:  Dict					)     ->			int:




								'''simple docstring'''





								lowerCAmelCase__  =   CHEAP_ARGS.copy()
								lowerCAmelCase__  =   """translation""" if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else """summarization"""
								lowerCAmelCase__  =   0.1 if task == """translation""" else 0

								lowerCAmelCase__  =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
								lowerCAmelCase__  =   self.get_auto_remove_tmp_dir()
								args_d.update(
								    data_dir=__a   ,		model_name_or_path=__a   ,		tokenizer_name=__a   ,		train_batch_size=2   ,		eval_batch_size=2   ,		output_dir=__a   ,		do_predict=__a   ,		task=__a   ,		src_lang="""en_XX"""   ,		tgt_lang="""ro_RO"""   ,		freeze_encoder=__a   ,		freeze_embeds=__a   ,		)
								assert "n_train" in args_d
								lowerCAmelCase__  =   argparse.Namespace(**__a					)
								lowerCAmelCase__  =   main(__a					)

								lowerCAmelCase__  =   module.model.get_input_embeddings()
								assert not input_embeds.weight.requires_grad
								if model == T5_TINY:
															lowerCAmelCase__  =   module.model.lm_head
															assert not lm_head.weight.requires_grad
															assert (lm_head.weight == input_embeds.weight).all().item()
								elif model == FSMT_TINY:
															lowerCAmelCase__  =   module.model.model
															lowerCAmelCase__  =   fsmt.decoder.embed_positions
															assert not embed_pos.weight.requires_grad
															assert not fsmt.decoder.embed_tokens.weight.requires_grad
															# check that embeds are not the same
															assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
								else:
															lowerCAmelCase__  =   module.model.model
															lowerCAmelCase__  =   bart.decoder.embed_positions
															assert not embed_pos.weight.requires_grad
															assert not bart.shared.weight.requires_grad
															# check that embeds are the same
															assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
															assert bart.decoder.embed_tokens == bart.shared

								lowerCAmelCase__  =   load_json(module.output_dir / """text_batch.json"""					)
								assert isinstance(__a   ,		__a					)
								assert len(__a					) >= 4




	def      lowercase__							(  self		:  Tuple					)     ->			Optional[int]:




								'''simple docstring'''





								lowerCAmelCase__  =   CHEAP_ARGS.copy()

								lowerCAmelCase__  =   """summarization"""
								lowerCAmelCase__  =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)

								args_d.update(
								    data_dir=__a   ,		tokenizer_name=__a   ,		train_batch_size=2   ,		eval_batch_size=2   ,		do_predict=__a   ,		task=__a   ,		src_lang="""en_XX"""   ,		tgt_lang="""ro_RO"""   ,		freeze_encoder=__a   ,		freeze_embeds=__a   ,		)

								# test models whose config includes the extra_model_args
								lowerCAmelCase__  =   BART_TINY
								lowerCAmelCase__  =   self.get_auto_remove_tmp_dir()
								lowerCAmelCase__  =   args_d.copy()
								args_da.update(
								    model_name_or_path=__a   ,		output_dir=__a   ,		)
								lowerCAmelCase__  =   ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
								for p in extra_model_params:
															lowerCAmelCase__  =   0.5
								lowerCAmelCase__  =   argparse.Namespace(**__a					)
								lowerCAmelCase__  =   main(__a					)
								for p in extra_model_params:
															assert getattr(model.config   ,		__a					) == 0.5, f'''failed to override the model config for param {p}'''

								# test models whose config doesn't include the extra_model_args
								lowerCAmelCase__  =   T5_TINY
								lowerCAmelCase__  =   self.get_auto_remove_tmp_dir()
								lowerCAmelCase__  =   args_d.copy()
								args_da.update(
								    model_name_or_path=__a   ,		output_dir=__a   ,		)
								lowerCAmelCase__  =   """encoder_layerdrop"""
								lowerCAmelCase__  =   0.5
								lowerCAmelCase__  =   argparse.Namespace(**__a					)
								with pytest.raises(__a					) as excinfo:
															lowerCAmelCase__  =   main(__a					)
								assert str(excinfo.value					) == f'''model config doesn\'t have a `{unsupported_param}` attribute'''




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




								'''simple docstring'''





								lowerCAmelCase__  =   CHEAP_ARGS.copy()

								lowerCAmelCase__  =   """summarization"""
								lowerCAmelCase__  =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)

								lowerCAmelCase__  =   BART_TINY
								lowerCAmelCase__  =   self.get_auto_remove_tmp_dir()

								args_d.update(
								    data_dir=__a   ,		model_name_or_path=__a   ,		output_dir=__a   ,		tokenizer_name=__a   ,		train_batch_size=2   ,		eval_batch_size=2   ,		do_predict=__a   ,		task=__a   ,		src_lang="""en_XX"""   ,		tgt_lang="""ro_RO"""   ,		freeze_encoder=__a   ,		freeze_embeds=__a   ,		)

								# emulate finetune.py
								lowerCAmelCase__  =   argparse.ArgumentParser()
								lowerCAmelCase__  =   pl.Trainer.add_argparse_args(__a					)
								lowerCAmelCase__  =   SummarizationModule.add_model_specific_args(__a   ,		os.getcwd()					)
								lowerCAmelCase__  =   {"""--help""": True}

								# --help test
								with pytest.raises(__a					) as excinfo:
															with CaptureStdout() as cs:
																						lowerCAmelCase__  =   parser.parse_args(__a					)
															assert False, "--help is expected to sys.exit"
								assert excinfo.type == SystemExit
								lowerCAmelCase__  =   lightning_base.arg_to_scheduler_metavar
								assert expected in cs.out, "--help is expected to list the supported schedulers"

								# --lr_scheduler=non_existing_scheduler test
								lowerCAmelCase__  =   """non_existing_scheduler"""
								lowerCAmelCase__  =   {f'''--lr_scheduler={unsupported_param}'''}
								with pytest.raises(__a					) as excinfo:
															with CaptureStderr() as cs:
																						lowerCAmelCase__  =   parser.parse_args(__a					)
															assert False, "invalid argument is expected to sys.exit"
								assert excinfo.type == SystemExit
								lowerCAmelCase__  =   f'''invalid choice: \'{unsupported_param}\''''
								assert expected in cs.err, f'''should have bailed on invalid choice of scheduler {unsupported_param}'''

								# --lr_scheduler=existing_scheduler test
								lowerCAmelCase__  =   """cosine"""
								lowerCAmelCase__  =   args_d.copy()
								lowerCAmelCase__  =   supported_param
								lowerCAmelCase__  =   argparse.Namespace(**__a					)
								lowerCAmelCase__  =   main(__a					)
								assert (
								    getattr(model.hparams   ,		"""lr_scheduler"""					) == supported_param
								), f'''lr_scheduler={supported_param} shouldn\'t fail'''












"""simple docstring"""





import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize





class 							_lowerCamelCase   :


						def __init__(self ,				__a ,				__a=sys.maxsize		)			-> Tuple:
								UpperCamelCase =						"bilinear"
								UpperCamelCase =						max_size
								UpperCamelCase =						short_edge_length




						def __call__(self ,				__a		)			-> Dict:
								UpperCamelCase =						[]
								for img in imgs:
										UpperCamelCase ,					UpperCamelCase =						img.shape[:2]
										# later: provide list and randomly choose index for resize
										UpperCamelCase =						np.random.randint(self.short_edge_length[0] ,				self.short_edge_length[1] + 1		)
										if size == 0:
												return img
										UpperCamelCase =						size * 1.0 / min(__a ,				__a		)
										if h < w:
												UpperCamelCase ,					UpperCamelCase =						size, scale * w
										else:
												UpperCamelCase ,					UpperCamelCase =						scale * h, size
										if max(__a ,				__a		) > self.max_size:
												UpperCamelCase =						self.max_size * 1.0 / max(__a ,				__a		)
												UpperCamelCase =						newh * scale
												UpperCamelCase =						neww * scale
										UpperCamelCase =						int(neww + 0.5		)
										UpperCamelCase =						int(newh + 0.5		)

										if img.dtype == np.uinta:
												UpperCamelCase =						Image.fromarray(__a		)
												UpperCamelCase =						pil_image.resize((neww, newh) ,				PILImageResampling.BILINEAR		)
												UpperCamelCase =						np.asarray(__a		)
										else:
												UpperCamelCase =						img.permute(2 ,				0 ,				1		).unsqueeze(0		)  # 3, 0, 1)  # hw(c) -> nchw
												UpperCamelCase =						nn.functional.interpolate(
												    __a ,				(newh, neww) ,				mode=self.interp_method ,				align_corners=__a		).squeeze(0		)
										img_augs.append(__a		)

								return img_augs





class 							_lowerCamelCase   :


						def __init__(self ,				__a		)			-> Any:
								UpperCamelCase =						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] ,				cfg.INPUT.MAX_SIZE_TEST		)
								UpperCamelCase =						cfg.INPUT.FORMAT
								UpperCamelCase =						cfg.SIZE_DIVISIBILITY
								UpperCamelCase =						cfg.PAD_VALUE
								UpperCamelCase =						cfg.INPUT.MAX_SIZE_TEST
								UpperCamelCase =						cfg.MODEL.DEVICE
								UpperCamelCase =						torch.tensor(cfg.MODEL.PIXEL_STD		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		) ,				1 ,				1		)
								UpperCamelCase =						torch.tensor(cfg.MODEL.PIXEL_MEAN		).to(self.device		).view(len(cfg.MODEL.PIXEL_STD		) ,				1 ,				1		)
								UpperCamelCase =						lambda __a		: (x - self.pixel_mean) / self.pixel_std


						def      snake_case_ (self ,				__a		)			-> List[Any]:
								UpperCamelCase =						tuple(max(__a		) for s in zip(*[img.shape for img in images]		)		)
								UpperCamelCase =						[im.shape[-2:] for im in images]
								UpperCamelCase =						[
								    nn.functional.pad(
								        __a ,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] ,				value=self.pad_value ,				)
								    for size, im in zip(__a ,				__a		)
								]

								return torch.stack(__a		), torch.tensor(__a		)




						def __call__(self ,				__a ,				__a=False		)			-> Optional[int]:
								with torch.no_grad():
										if not isinstance(__a ,				__a		):
												UpperCamelCase =						[images]
										if single_image:
												assert len(__a		) == 1
										for i in range(len(__a		)		):
												if isinstance(images[i] ,				torch.Tensor		):
														images.insert(__a ,				images.pop(__a		).to(self.device		).float()		)
												elif not isinstance(images[i] ,				torch.Tensor		):
														images.insert(
														    __a ,				torch.as_tensor(img_tensorize(images.pop(__a		) ,				input_format=self.input_format		)		)
														    .to(self.device		)
														    .float() ,				)
            # resize smallest edge
										UpperCamelCase =						torch.tensor([im.shape[:2] for im in images]		)
										UpperCamelCase =						self.aug(__a		)
										# transpose images and convert to torch tensors
										# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
										# now normalize before pad to avoid useless arithmetic
										UpperCamelCase =						[self.normalizer(__a		) for x in images]
										# now pad them to do the following operations
										UpperCamelCase ,					UpperCamelCase =						self.pad(__a		)
										# Normalize

										if self.size_divisibility > 0:
												raise NotImplementedError()
										# pad
										UpperCamelCase =						torch.true_divide(__a ,				__a		)
										if single_image:
												return images[0], sizes[0], scales_yx[0]
										else:
												return images, sizes, scales_yx



def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		boxes[:, 0::2] *= scale_yx[:, 1]
		boxes[:, 1::2] *= scale_yx[:, 0]
		return boxes






def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		assert torch.isfinite(_SCREAMING_SNAKE_CASE     ).all(), "Box tensor contains infinite or NaN!"
		UpperCamelCase ,					UpperCamelCase =						box_size
		tensor[:, 0].clamp_(min=0	,						max=_SCREAMING_SNAKE_CASE     )
		tensor[:, 1].clamp_(min=0	,						max=_SCREAMING_SNAKE_CASE     )
		tensor[:, 2].clamp_(min=0	,						max=_SCREAMING_SNAKE_CASE     )
		tensor[:, 3].clamp_(min=0	,						max=_SCREAMING_SNAKE_CASE     )






