



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


UpperCAmelCase__  =			logging.get_logger(__name__)







class      __lowerCAmelCase       (     A		):



			def __init__(		self  :    Union[str, Any]					,						A  :    Union["PreTrainedModel", nn.Module] = None					,						A  :    "TrainingArguments" = None					,						A  :    Optional["DataCollator"] = None					,						A  :    Optional[Dataset] = None					,						A  :    Optional[Union[Dataset, Dict[str, Dataset]]] = None					,						A  :    Optional["PreTrainedTokenizerBase"] = None					,						A  :    Optional[Callable[[], "PreTrainedModel"]] = None					,						A  :    Optional[Callable[["EvalPrediction"], Dict]] = None					,						A  :    Optional[List["TrainerCallback"]] = None					,						A  :    Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None)					,						A  :    Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None					,						)  ->       Any:



						"""simple docstring"""






						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        _lowerCamelCase			(		A  :    Union[str, GenerationConfig])  ->       GenerationConfig:



						"""simple docstring"""






						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        _lowerCamelCase			(		self  :    Dict					,						A  :    Optional[Dataset] = None					,						A  :    Optional[List[str]] = None					,						A  :    str = "eval"					,						**A  :    Optional[int]					,						)  ->       Dict[str, float]:



						"""simple docstring"""






						_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        _lowerCamelCase			(		self  :    str					,						A  :    Dataset					,						A  :    Optional[List[str]] = None					,						A  :    str = "test"					,						**A  :    Tuple					,						)  ->       "PredictionOutput":



						"""simple docstring"""






						_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        _lowerCamelCase			(		self  :    Optional[Any]					,						A  :    nn.Module					,						A  :    Dict[str, Union[torch.Tensor, Any]]					,						A  :    bool					,						A  :    Optional[List[str]] = None					,						**A  :    Union[str, Any]					,						)  ->       Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:



						"""simple docstring"""






						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        _lowerCamelCase			(		self  :    List[str]					,						A  :    str					,						A  :    Optional[Any])  ->       List[str]:



						"""simple docstring"""






						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





import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 				A      (	_UpperCAmelCase							:  Tuple				,  _UpperCAmelCase							:  Any				,  _UpperCAmelCase							:  str       )							->					Union[str, Any]:







			'''simple docstring'''
			# Construct configuration depending on size
			_UpperCAmelCase									=  {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  MODELS[model_size]  # set model hyperparameters
			_UpperCAmelCase									=  ImageGPTConfig(n_embd=_UpperCAmelCase				,  n_layer=_UpperCAmelCase				,  n_head=_UpperCAmelCase       )
			_UpperCAmelCase									=  ImageGPTForCausalLM(_UpperCAmelCase       )

			# Load weights from numpy
			load_tf_weights_in_imagegpt(_UpperCAmelCase				,  _UpperCAmelCase				,  _UpperCAmelCase       )

			# Save pytorch-model
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + WEIGHTS_NAME
			_UpperCAmelCase									=  pytorch_dump_folder_path + '/' + CONFIG_NAME
			print(F"Save PyTorch model to {pytorch_weights_dump_path}"       )
			torch.save(model.state_dict()				,  _UpperCAmelCase       )
			print(F"Save configuration file to {pytorch_config_dump_path}"       )
			with open(_UpperCAmelCase				,  'w'				,  encoding='utf-8'       ) as f:
						f.write(config.to_json_string()       )


if __name__ == "__main__":
							UpperCAmelCase__  =			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--imagegpt_checkpoint_path",
							    default=None,
							    type=str,
							    required=True,
							    help="Path to the TensorFlow checkpoint path.",
							)
							parser.add_argument(
							    "--model_size",
							    default=None,
							    type=str,
							    required=True,
							    help="Size of the model (can be either 'small', 'medium' or 'large').",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							UpperCAmelCase__  =			parser.parse_args()
							convert_imagegpt_checkpoint_to_pytorch(
							    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
							)
