code: 
  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
  
  
  lowercase__						=			logging.get_logger(__name__)
  
  
  
  
  
  
  class   snake_case__					(			UpperCamelCase_				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: Union[str, Any]     ,  UpperCamelCase__							: int = None     ,  UpperCamelCase__							: Union[str, Any] = None     ,  UpperCamelCase__							: List[str] = None     ,  UpperCamelCase__							: Optional[int] = None     ,  UpperCamelCase__							: Optional[Any] = None     ,  UpperCamelCase__							: Optional[Any] = None     ,  UpperCamelCase__							: List[Any] = None     ,  UpperCamelCase__							: Dict = None     ,  UpperCamelCase__							: str = None     ,  UpperCamelCase__							: Any = (None, None)     ,  UpperCamelCase__							: Optional[int] = None     ,  )	-> Any:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				super().__init__(
  				    model=__SCREAMING_SNAKE_CASE     ,  args=__SCREAMING_SNAKE_CASE     ,  data_collator=__SCREAMING_SNAKE_CASE     ,  train_dataset=__SCREAMING_SNAKE_CASE     ,  eval_dataset=__SCREAMING_SNAKE_CASE     ,  tokenizer=__SCREAMING_SNAKE_CASE     ,  model_init=__SCREAMING_SNAKE_CASE     ,  compute_metrics=__SCREAMING_SNAKE_CASE     ,  callbacks=__SCREAMING_SNAKE_CASE     ,  optimizers=__SCREAMING_SNAKE_CASE     ,  preprocess_logits_for_metrics=__SCREAMING_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     :					str     =	self.load_generation_config(self.args.generation_config			)
  						snake_case     :					Any     =	gen_config
  
  
  
  		@staticmethod
  		def 			lowerCAmelCase							(			UpperCamelCase__							: Union[str, Any]			)	-> GenerationConfig:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if isinstance(__SCREAMING_SNAKE_CASE     ,  __SCREAMING_SNAKE_CASE			):
  						return deepcopy(__SCREAMING_SNAKE_CASE			)
  
  				# str or Path
  				snake_case     :					Union[str, Any]     =	Path(__SCREAMING_SNAKE_CASE			) if isinstance(__SCREAMING_SNAKE_CASE     ,  __SCREAMING_SNAKE_CASE			) else gen_config_arg
  				snake_case     :					Dict     =	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     :					Dict     =	pretrained_model_name.name
  						snake_case     :					List[Any]     =	pretrained_model_name.parent
  				# dir path
  				elif pretrained_model_name.is_dir():
  						pass
  				# model id or URL
  				else:
  						snake_case     :					Optional[int]     =	gen_config_arg
  
  				snake_case     :					Tuple     =	GenerationConfig.from_pretrained(__SCREAMING_SNAKE_CASE     ,  __SCREAMING_SNAKE_CASE			)
  				return gen_config
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict     ,  UpperCamelCase__							: List[Any] = None     ,  UpperCamelCase__							: Dict = None     ,  UpperCamelCase__							: List[Any] = "eval"     ,  **UpperCamelCase__							: str     ,  )	-> Dict[str, float]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, 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     :					Any     =	(
  				    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''			) is not None else self.args.generation_num_beams
  				)
  				snake_case     :					Optional[int]     =	gen_kwargs
  
  				return super().evaluate(__SCREAMING_SNAKE_CASE     ,  ignore_keys=__SCREAMING_SNAKE_CASE     ,  metric_key_prefix=__SCREAMING_SNAKE_CASE			)
  
  
  
  		def 			lowerCAmelCase							(			self							: List[Any]     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: List[Any] = None     ,  UpperCamelCase__							: List[str] = "test"     ,  **UpperCamelCase__							: Tuple     ,  )	-> "PredictionOutput":
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[str]     =	gen_kwargs.copy()
  				if gen_kwargs.get('''max_length'''			) is None and gen_kwargs.get('''max_new_tokens'''			) is None:
  						snake_case     :					str     =	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     :					str     =	gen_kwargs
  
  				return super().predict(__SCREAMING_SNAKE_CASE     ,  ignore_keys=__SCREAMING_SNAKE_CASE     ,  metric_key_prefix=__SCREAMING_SNAKE_CASE			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: Optional[int] = None     ,  **UpperCamelCase__							: List[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(
  						    __SCREAMING_SNAKE_CASE     ,  __SCREAMING_SNAKE_CASE     ,  prediction_loss_only=__SCREAMING_SNAKE_CASE     ,  ignore_keys=__SCREAMING_SNAKE_CASE			)
  
  				snake_case     :					Any     =	'''labels''' in inputs
  				snake_case     :					List[Any]     =	self._prepare_inputs(__SCREAMING_SNAKE_CASE			)
  
  				# XXX: adapt synced_gpus for fairscale as well
  				# Priority (handled in generate):
  				# gen_kwargs > model.generation_config > default GenerationConfig()
  
  				if len(__SCREAMING_SNAKE_CASE			) == 0 and hasattr(self     ,  '''_gen_kwargs'''			):
  						snake_case     :					Any     =	self._gen_kwargs.copy()
  
  				if gen_kwargs.get('''max_length'''			) is None and gen_kwargs.get('''max_new_tokens'''			) is None:
  						snake_case     :					Tuple     =	self.model.config.max_length
  				snake_case     :					Tuple     =	(
  				    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''			) is not None else self.model.config.num_beams
  				)
  				snake_case     :					Union[str, 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     :					int     =	{k: v for k, v in inputs.items() if k != '''decoder_input_ids'''}
  				snake_case     :					Tuple     =	self.model.generate(**__SCREAMING_SNAKE_CASE     ,  **__SCREAMING_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     :					Any     =	False
  
  				# Retrieves GenerationConfig from model.generation_config
  				snake_case     :					Union[str, 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     :					Any     =	self._pad_tensors_to_max_len(__SCREAMING_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     :					Optional[Any]     =	self._pad_tensors_to_max_len(__SCREAMING_SNAKE_CASE     ,  gen_config.max_new_tokens + 1			)
  
  				with torch.no_grad():
  						if has_labels:
  								with self.compute_loss_context_manager():
  										snake_case     :					Optional[int]     =	model(**__SCREAMING_SNAKE_CASE			)
  								if self.label_smoother is not None:
  										snake_case     :					Dict     =	self.label_smoother(__SCREAMING_SNAKE_CASE     ,  inputs['''labels''']			).mean().detach()
  								else:
  										snake_case     :					Union[str, Any]     =	(outputs['''loss'''] if isinstance(__SCREAMING_SNAKE_CASE     ,  __SCREAMING_SNAKE_CASE			) else outputs[0]).mean().detach()
  						else:
  								snake_case     :					Optional[int]     =	None
  
  				if self.args.prediction_loss_only:
  						return loss, None, None
  
  				if has_labels:
  						snake_case     :					Optional[int]     =	inputs['''labels''']
  						if labels.shape[-1] < gen_config.max_length:
  								snake_case     :					Tuple     =	self._pad_tensors_to_max_len(__SCREAMING_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     :					Optional[Any]     =	self._pad_tensors_to_max_len(__SCREAMING_SNAKE_CASE     ,  gen_config.max_new_tokens + 1			)
  				else:
  						snake_case     :					Dict     =	None
  
  				return loss, generated_tokens, labels
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: Optional[int]			)	-> Optional[int]:
  
  
  
  
  
  
  				"""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
  						snake_case     :					List[Any]     =	(
  						    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     :					Tuple     =	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     :					Union[str, Any]     =	pad_token_id * torch.ones(
  				    (tensor.shape[0], max_length)     ,  dtype=tensor.dtype     ,  device=tensor.device			)
  				snake_case     :					Optional[Any]     =	tensor
  				return padded_tensor
  
  
  
style_context: 
  '''simple docstring'''
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:
  
  
  		'''simple docstring'''
  
  
  		snake_case     :					Tuple     =	json.load(open(SCREAMING_SNAKE_CASE__   ,					'''r''' ) )
  		snake_case     :					Any     =	GPTNeoConfig(
  		    hidden_size=config_json['''n_embd''']   ,					num_layers=config_json['''n_layer''']   ,					num_heads=config_json['''n_head''']   ,					attention_types=config_json['''attention_types''']   ,					max_position_embeddings=config_json['''n_positions''']   ,					resid_dropout=config_json['''res_dropout''']   ,					embed_dropout=config_json['''embed_dropout''']   ,					attention_dropout=config_json['''attn_dropout''']   ,					)
  		print(F'Building PyTorch model from configuration: {config}' )
  		snake_case     :					Union[str, Any]     =	GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__ )
  
  		# Load weights from tf checkpoint
  		load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
  
  		# Save pytorch-model
  		print(F'Save PyTorch model to {pytorch_dump_path}' )
  		model.save_pretrained(SCREAMING_SNAKE_CASE__ )
  
  
  if __name__ == "__main__":
  						lowercase__						=			argparse.ArgumentParser()
  						# Required parameters
  						parser.add_argument(
  						    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
  						)
  						parser.add_argument(
  						    "--config_file",
  						    default=None,
  						    type=str,
  						    required=True,
  						    help=(
  						        "The config json file corresponding to the pre-trained mesh-tf model. \n"
  						        "This specifies the model architecture."
  						    ),
  						)
  						parser.add_argument(
  						    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  						)
  						lowercase__						=			parser.parse_args()
  						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  
  