'''simple docstring'''

from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
						from .data.data_collator import DataCollator
						from .modeling_utils import PreTrainedModel
						from .tokenization_utils_base import PreTrainedTokenizerBase
						from .trainer_callback import TrainerCallback
						from .trainer_utils import EvalPrediction, PredictionOutput
						from .training_args import TrainingArguments


A_	   = logging.get_logger(__name__)




class 		SCREAMING_SNAKE_CASE_					(_UpperCAmelCase      ):



			"""simple docstring"""






			def __init__(							self				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = (None, None)				,			snake_case_ = None				,			):
									super().__init__(
									    model=snake_case_				,			args=snake_case_				,			data_collator=snake_case_				,			train_dataset=snake_case_				,			eval_dataset=snake_case_				,			tokenizer=snake_case_				,			model_init=snake_case_				,			compute_metrics=snake_case_				,			callbacks=snake_case_				,			optimizers=snake_case_				,			preprocess_logits_for_metrics=snake_case_				,			)

									# Override self.model.generation_config if a GenerationConfig is specified in args.
									# Priority: args.generation_config > model.generation_config > default GenerationConfig.
									if self.args.generation_config is not None:
															snake_case :       Tuple				=    self.load_generation_config(self.args.generation_config					)
															snake_case :       Tuple				=    gen_config



			@staticmethod
			def   lowerCamelCase (							snake_case_					):

									# GenerationConfig provided, nothing to do
									if isinstance(snake_case_				,			snake_case_					):
															return deepcopy(snake_case_					)

									# str or Path
									snake_case :       Optional[int]				=    Path(snake_case_					) if isinstance(snake_case_				,			snake_case_					) else gen_config_arg
									snake_case :       Tuple				=    None

									# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
									# This step is required in order to determine config_file_name
									if pretrained_model_name.is_file():
															snake_case :       Any				=    pretrained_model_name.name
															snake_case :       Any				=    pretrained_model_name.parent
									# dir path
									elif pretrained_model_name.is_dir():
															pass
									# model id or URL
									else:
															snake_case :       Union[str, Any]				=    gen_config_arg

									snake_case :       Optional[int]				=    GenerationConfig.from_pretrained(snake_case_				,			snake_case_					)
									return gen_config



			def   lowerCamelCase (							self				,			snake_case_ = None				,			snake_case_ = None				,			snake_case_ = "eval"				,			**snake_case_				,			):
									snake_case :       List[Any]				=    gen_kwargs.copy()
									if gen_kwargs.get("max_length"					) is None and gen_kwargs.get("max_new_tokens"					) is None:
															snake_case :       Tuple				=    self.args.generation_max_length
									snake_case :       Optional[Any]				=    (
									    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"					) is not None else self.args.generation_num_beams
									)
									snake_case :       Optional[Any]				=    gen_kwargs

									return super().evaluate(snake_case_				,			ignore_keys=snake_case_				,			metric_key_prefix=snake_case_					)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_ = None				,			snake_case_ = "test"				,			**snake_case_				,			):
									snake_case :       Optional[int]				=    gen_kwargs.copy()
									if gen_kwargs.get("max_length"					) is None and gen_kwargs.get("max_new_tokens"					) is None:
															snake_case :       Dict				=    self.args.generation_max_length
									snake_case :       List[str]				=    (
									    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"					) is not None else self.args.generation_num_beams
									)
									snake_case :       Any				=    gen_kwargs

									return super().predict(snake_case_				,			ignore_keys=snake_case_				,			metric_key_prefix=snake_case_					)



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_ = None				,			**snake_case_				,			):

									if not self.args.predict_with_generate or prediction_loss_only:
															return super().prediction_step(
															    snake_case_				,			snake_case_				,			prediction_loss_only=snake_case_				,			ignore_keys=snake_case_					)

									snake_case :       str				=    "labels" in inputs
									snake_case :       Union[str, Any]				=    self._prepare_inputs(snake_case_					)

									# XXX: adapt synced_gpus for fairscale as well
									# Priority (handled in generate):
									# gen_kwargs > model.generation_config > default GenerationConfig()

									if len(snake_case_					) == 0 and hasattr(self				,			"_gen_kwargs"					):
															snake_case :       Optional[Any]				=    self._gen_kwargs.copy()

									if gen_kwargs.get("max_length"					) is None and gen_kwargs.get("max_new_tokens"					) is None:
															snake_case :       Optional[int]				=    self.model.config.max_length
									snake_case :       Optional[int]				=    (
									    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"					) is not None else self.model.config.num_beams
									)
									snake_case :       Optional[Any]				=    True if is_deepspeed_zeroa_enabled() else False
									snake_case :       Union[str, Any]				=    (
									    gen_kwargs["synced_gpus"] if gen_kwargs.get("synced_gpus"					) is not None else default_synced_gpus
									)

									# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
									# (otherwise, it would continue generating from the padded `decoder_input_ids`)
									if (
									    "labels" in inputs
									    and "decoder_input_ids" in inputs
									    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
									):
															snake_case :       Union[str, Any]				=    {k: v for k, v in inputs.items() if k != "decoder_input_ids"}
									snake_case :       Tuple				=    self.model.generate(**snake_case_				,			**snake_case_					)

									# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
									# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
									# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
									if self.model.generation_config._from_model_config:
															snake_case :       Optional[Any]				=    False

									# Retrieves GenerationConfig from model.generation_config
									snake_case :       List[Any]				=    self.model.generation_config
									# in case the batch is shorter than max length, the output should be padded
									if generated_tokens.shape[-1] < gen_config.max_length:
															snake_case :       int				=    self._pad_tensors_to_max_len(snake_case_				,			gen_config.max_length					)
									elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
															snake_case :       int				=    self._pad_tensors_to_max_len(snake_case_				,			gen_config.max_new_tokens + 1					)

									with torch.no_grad():
															if has_labels:
																					with self.compute_loss_context_manager():
																											snake_case :       str				=    model(**snake_case_					)
																					if self.label_smoother is not None:
																											snake_case :       List[str]				=    self.label_smoother(snake_case_				,			inputs["labels"]					).mean().detach()
																					else:
																											snake_case :       Optional[int]				=    (outputs["loss"] if isinstance(snake_case_				,			snake_case_					) else outputs[0]).mean().detach()
															else:
																					snake_case :       str				=    None

									if self.args.prediction_loss_only:
															return loss, None, None

									if has_labels:
															snake_case :       Dict				=    inputs["labels"]
															if labels.shape[-1] < gen_config.max_length:
																					snake_case :       Dict				=    self._pad_tensors_to_max_len(snake_case_				,			gen_config.max_length					)
															elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
																					snake_case :       int				=    self._pad_tensors_to_max_len(snake_case_				,			gen_config.max_new_tokens + 1					)
									else:
															snake_case :       List[str]				=    None

									return loss, generated_tokens, labels






			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_					):
									if self.tokenizer is not None and hasattr(self.tokenizer				,			"pad_token_id"					):
															# If PAD token is not defined at least EOS token has to be defined
															snake_case :       str				=    (
															    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
															)
									else:
															if self.model.config.pad_token_id is not None:
																					snake_case :       Any				=    self.model.config.pad_token_id
															else:
																					raise ValueError("Pad_token_id must be set in the configuration of the model, in order to pad tensors"					)

									snake_case :       Tuple				=    pad_token_id * torch.ones(
									    (tensor.shape[0], max_length)				,			dtype=tensor.dtype				,			device=tensor.device					)
									snake_case :       List[Any]				=    tensor
									return padded_tensor






'''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 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_				,			snake_case_=sys.maxsize					):
									snake_case :       Any				=    "bilinear"
									snake_case :       Dict				=    max_size
									snake_case :       Dict				=    short_edge_length






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

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

									return img_augs







class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_					):
									snake_case :       Union[str, Any]				=    ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST]				,			cfg.INPUT.MAX_SIZE_TEST					)
									snake_case :       Any				=    cfg.INPUT.FORMAT
									snake_case :       Optional[int]				=    cfg.SIZE_DIVISIBILITY
									snake_case :       Optional[int]				=    cfg.PAD_VALUE
									snake_case :       int				=    cfg.INPUT.MAX_SIZE_TEST
									snake_case :       Optional[int]				=    cfg.MODEL.DEVICE
									snake_case :       int				=    torch.tensor(cfg.MODEL.PIXEL_STD					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)				,			1				,			1					)
									snake_case :       List[Any]				=    torch.tensor(cfg.MODEL.PIXEL_MEAN					).to(self.device					).view(len(cfg.MODEL.PIXEL_STD					)				,			1				,			1					)
									snake_case :       Dict				=    lambda snake_case_					: (x - self.pixel_mean) / self.pixel_std



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

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






			def __call__(							self				,			snake_case_				,			snake_case_=False					):
									with torch.no_grad():
															if not isinstance(snake_case_				,			snake_case_					):
																					snake_case :       int				=    [images]
															if single_image:
																					assert len(snake_case_					) == 1
															for i in range(len(snake_case_					)					):
																					if isinstance(images[i]				,			torch.Tensor					):
																											images.insert(snake_case_				,			images.pop(snake_case_					).to(self.device					).float()					)
																					elif not isinstance(images[i]				,			torch.Tensor					):
																											images.insert(
																											    snake_case_				,			torch.as_tensor(img_tensorize(images.pop(snake_case_					)				,			input_format=self.input_format					)					)
																											    .to(self.device					)
																											    .float()				,			)
            # resize smallest edge
															snake_case :       List[Any]				=    torch.tensor([im.shape[:2] for im in images]					)
															snake_case :       Any				=    self.aug(snake_case_					)
															# 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
															snake_case :       str				=    [self.normalizer(snake_case_					) for x in images]
															# now pad them to do the following operations
															snake_case				,					snake_case :       int				=    self.pad(snake_case_					)
															# Normalize

															if self.size_divisibility > 0:
																					raise NotImplementedError()
															# pad
															snake_case :       Union[str, Any]				=    torch.true_divide(snake_case_				,			snake_case_					)
															if single_image:
																					return images[0], sizes[0], scales_yx[0]
															else:
																					return images, sizes, scales_yx







def        UpperCAmelCase__   (				_lowerCAmelCase    :			Any,      _lowerCAmelCase    :			Optional[int])    ->		int:



						"""simple docstring"""

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



def        UpperCAmelCase__   (				_lowerCAmelCase    :			Union[str, Any],      _lowerCAmelCase    :			Tuple[int, int])    ->		List[str]:



						"""simple docstring"""

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





