





"""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


lowerCAmelCase__        =      logging.get_logger(__name__)





class 							_lowerCamelCase   (							_lowercase			):


						def __init__(self ,				__a = None ,				__a = None ,				__a = None ,				__a = None ,				__a = None ,				__a = None ,				__a = None ,				__a = None ,				__a = None ,				__a = (None, None) ,				__a = None ,				)			-> List[Any]:
								super().__init__(
								    model=__a ,				args=__a ,				data_collator=__a ,				train_dataset=__a ,				eval_dataset=__a ,				tokenizer=__a ,				model_init=__a ,				compute_metrics=__a ,				callbacks=__a ,				optimizers=__a ,				preprocess_logits_for_metrics=__a ,				)

								# 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:
										UpperCamelCase =						self.load_generation_config(self.args.generation_config		)
										UpperCamelCase =						gen_config


						@staticmethod
						def      snake_case_ (__a		)			-> GenerationConfig:

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

								# str or Path
								UpperCamelCase =						Path(__a		) if isinstance(__a ,				__a		) else gen_config_arg
								UpperCamelCase =						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():
										UpperCamelCase =						pretrained_model_name.name
										UpperCamelCase =						pretrained_model_name.parent
								# dir path
								elif pretrained_model_name.is_dir():
										pass
								# model id or URL
								else:
										UpperCamelCase =						gen_config_arg

								UpperCamelCase =						GenerationConfig.from_pretrained(__a ,				__a		)
								return gen_config


						def      snake_case_ (self ,				__a = None ,				__a = None ,				__a = "eval" ,				**__a ,				)			-> Dict[str, float]:
								UpperCamelCase =						gen_kwargs.copy()
								if gen_kwargs.get("max_length"		) is None and gen_kwargs.get("max_new_tokens"		) is None:
										UpperCamelCase =						self.args.generation_max_length
								UpperCamelCase =						(
								    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"		) is not None else self.args.generation_num_beams
								)
								UpperCamelCase =						gen_kwargs

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


						def      snake_case_ (self ,				__a ,				__a = None ,				__a = "test" ,				**__a ,				)			-> "PredictionOutput":
								UpperCamelCase =						gen_kwargs.copy()
								if gen_kwargs.get("max_length"		) is None and gen_kwargs.get("max_new_tokens"		) is None:
										UpperCamelCase =						self.args.generation_max_length
								UpperCamelCase =						(
								    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"		) is not None else self.args.generation_num_beams
								)
								UpperCamelCase =						gen_kwargs

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


						def      snake_case_ (self ,				__a ,				__a ,				__a ,				__a = None ,				**__a ,				)			-> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:

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

								UpperCamelCase =						"labels" in inputs
								UpperCamelCase =						self._prepare_inputs(__a		)

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

								if len(__a		) == 0 and hasattr(self ,				"_gen_kwargs"		):
										UpperCamelCase =						self._gen_kwargs.copy()

								if gen_kwargs.get("max_length"		) is None and gen_kwargs.get("max_new_tokens"		) is None:
										UpperCamelCase =						self.model.config.max_length
								UpperCamelCase =						(
								    gen_kwargs["num_beams"] if gen_kwargs.get("num_beams"		) is not None else self.model.config.num_beams
								)
								UpperCamelCase =						True if is_deepspeed_zeroa_enabled() else False
								UpperCamelCase =						(
								    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
								):
										UpperCamelCase =						{k: v for k, v in inputs.items() if k != "decoder_input_ids"}
								UpperCamelCase =						self.model.generate(**__a ,				**__a		)

								# 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:
										UpperCamelCase =						False

								# Retrieves GenerationConfig from model.generation_config
								UpperCamelCase =						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:
										UpperCamelCase =						self._pad_tensors_to_max_len(__a ,				gen_config.max_length		)
								elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
										UpperCamelCase =						self._pad_tensors_to_max_len(__a ,				gen_config.max_new_tokens + 1		)

								with torch.no_grad():
										if has_labels:
												with self.compute_loss_context_manager():
														UpperCamelCase =						model(**__a		)
												if self.label_smoother is not None:
														UpperCamelCase =						self.label_smoother(__a ,				inputs["labels"]		).mean().detach()
												else:
														UpperCamelCase =						(outputs["loss"] if isinstance(__a ,				__a		) else outputs[0]).mean().detach()
										else:
												UpperCamelCase =						None

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

								if has_labels:
										UpperCamelCase =						inputs["labels"]
										if labels.shape[-1] < gen_config.max_length:
												UpperCamelCase =						self._pad_tensors_to_max_len(__a ,				gen_config.max_length		)
										elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
												UpperCamelCase =						self._pad_tensors_to_max_len(__a ,				gen_config.max_new_tokens + 1		)
								else:
										UpperCamelCase =						None

								return loss, generated_tokens, labels




						def      snake_case_ (self ,				__a ,				__a		)			-> str:
								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
										UpperCamelCase =						(
										    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:
												UpperCamelCase =						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"		)

								UpperCamelCase =						pad_token_id * torch.ones(
								    (tensor.shape[0], max_length) ,				dtype=tensor.dtype ,				device=tensor.device		)
								UpperCamelCase =						tensor
								return padded_tensor













"""simple docstring"""





import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser


def    a__       (       _SCREAMING_SNAKE_CASE=None	,						_SCREAMING_SNAKE_CASE=None     ):




		"""simple docstring"""


		return field(default_factory=lambda: default	,						metadata=_SCREAMING_SNAKE_CASE     )





@dataclass
class 							_lowerCamelCase   :
						UpperCAmelCase_			=      field(
						    metadata={"help": "The csv file to plot."}	,      )
						UpperCAmelCase_			=      field(
						    default=_lowercase	,      metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}	,      )
						UpperCAmelCase_			=      field(
						    default=_lowercase	,      metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}	,      )
						UpperCAmelCase_			=      field(
						    default=_lowercase	,      metadata={"help": "Disable logarithmic scale when plotting"}	,      )
						UpperCAmelCase_			=      field(
						    default=_lowercase	,      metadata={
						        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
						    }	,      )
						UpperCAmelCase_			=      field(
						    default=_lowercase	,      metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}	,      )
						UpperCAmelCase_			=      list_field(
						    default=_lowercase	,      metadata={"help": "List of model names that are used instead of the ones in the csv file."}			)



def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		try:
				int(_SCREAMING_SNAKE_CASE     )
				return True
		except ValueError:
				return False


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		try:
				float(_SCREAMING_SNAKE_CASE     )
				return True
		except ValueError:
				return False





class 							_lowerCamelCase   :


						def __init__(self ,				__a		)			-> Tuple:
								UpperCamelCase =						args
								UpperCamelCase =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)

								with open(self.args.csv_file ,				newline=""		) as csv_file:
										UpperCamelCase =						csv.DictReader(__a		)
										for row in reader:
												UpperCamelCase =						row["model"]
												self.result_dict[model_name]["bsz"].append(int(row["batch_size"]		)		)
												self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]		)		)
												if can_convert_to_int(row["result"]		):
														# value is not None
														UpperCamelCase =						int(row["result"]		)
												elif can_convert_to_float(row["result"]		):
														# value is not None
														UpperCamelCase =						float(row["result"]		)




						def      snake_case_ (self		)			-> Optional[int]:
								UpperCamelCase ,					UpperCamelCase =						plt.subplots()
								UpperCamelCase =						"Time usage" if self.args.is_time else "Memory usage"
								UpperCamelCase =						title_str + " for training" if self.args.is_train else title_str + " for inference"

								if not self.args.no_log_scale:
										# set logarithm scales
										ax.set_xscale("log"		)
										ax.set_yscale("log"		)

								for axis in [ax.xaxis, ax.yaxis]:
										axis.set_major_formatter(ScalarFormatter()		)

								for model_name_idx, model_name in enumerate(self.result_dict.keys()		):
										UpperCamelCase =						sorted(set(self.result_dict[model_name]["bsz"]		)		)
										UpperCamelCase =						sorted(set(self.result_dict[model_name]["seq_len"]		)		)
										UpperCamelCase =						self.result_dict[model_name]["result"]

										((UpperCamelCase) ,					(UpperCamelCase)) =						(
										    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
										)

										UpperCamelCase =						(
										    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
										)

										for inner_loop_value in inner_loop_array:
												if self.args.plot_along_batch:
														UpperCamelCase =						np.asarray(
														    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,				dtype=__a ,				)
												else:
														UpperCamelCase =						np.asarray(
														    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,				dtype=np.floataa ,				)

												((UpperCamelCase) ,					(UpperCamelCase)) =						(
												    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
												)

												UpperCamelCase =						np.asarray(__a ,				__a		)[: len(__a		)]
												plt.scatter(
												    __a ,				__a ,				label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"		)
												plt.plot(__a ,				__a ,				"--"		)

										title_str += F" {label_model_name} vs."

								UpperCamelCase =						title_str[:-4]
								UpperCamelCase =						"Time in s" if self.args.is_time else "Memory in MB"

								# plot
								plt.title(__a		)
								plt.xlabel(__a		)
								plt.ylabel(__a		)
								plt.legend()

								if self.args.figure_png_file is not None:
										plt.savefig(self.args.figure_png_file		)
								else:
										plt.show()



def    a__       (       ):




		"""simple docstring"""


		UpperCamelCase =						HfArgumentParser(_SCREAMING_SNAKE_CASE     )
		UpperCamelCase =						parser.parse_args_into_dataclasses()[0]
		UpperCamelCase =						Plot(args=_SCREAMING_SNAKE_CASE     )
		plot.plot()


if __name__ == "__main__":
						main()






