code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import importlib
  import importlib.metadata
  import os
  import warnings
  from distutils.util import strtobool
  from functools import lru_cache
  
  import torch
  from packaging import version
  from packaging.version import parse
  
  from .environment import parse_flag_from_env
  from .versions import compare_versions, is_torch_version
  
  
  try:
  	import torch_xla.core.xla_model as xm  # noqa: F401
  
  	_a      :				Any											=					True
  except ImportError:
  	_a      :				Optional[int]											=					False
  
  
  # Cache this result has it's a C FFI call which can be pretty time-consuming
  _a      :				int											=					torch.distributed.is_available()
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase			)      ->      Optional[int]:
  			# Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
  			__lowerCAmelCase      		=       importlib.util.find_spec(lowercase			) is not None
  			if package_exists:
  						try:
  									__lowerCAmelCase      		=       importlib.metadata.metadata(lowercase			)
  									return True
  						except importlib.metadata.PackageNotFoundError:
  									return False
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      bool:
  			return _torch_distributed_available
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      Union[str, Any]:
  			try:
  						pass
  			except ImportError:
  						print(
  						    """Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not"""
  						    """ detected. If you see \"ValueError: Invalid backend: 'ccl'\" error, please install Intel(R) oneCCL"""
  						    """ Bindings for PyTorch*."""			)
  			return (
  			    importlib.util.find_spec("""torch_ccl"""			) is not None
  			    or importlib.util.find_spec("""oneccl_bindings_for_pytorch"""			) is not None
  			)
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      str:
  			return importlib.metadata.version("""oneccl_bind_pt"""			)
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      Tuple:
  			return _is_package_available("""transformer_engine"""			)
  
  
  
  
  
  @lru_cache
  def      _lowerCAmelCase		(		lowercase=True			)      ->      str:
  			# Due to bugs on the amp series GPUs, we disable torch-xla on them
  			if torch.cuda.is_available():
  						return False
  			if _tpu_available and check_device:
  						try:
  									# Will raise a RuntimeError if no XLA configuration is found
  									__lowerCAmelCase      		=       xm.xla_device()
  									return True
  						except RuntimeError:
  									return False
  			return _tpu_available
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      int:
  			return _is_package_available("""deepspeed"""			)
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase=False			)      ->      Any:
  			if is_tpu_available():
  						return not ignore_tpu
  			if torch.cuda.is_available():
  						return torch.cuda.is_bfaa_supported()
  			if is_npu_available():
  						return False
  			return True
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      int:
  			__lowerCAmelCase      		=       _is_package_available("""bitsandbytes"""			)
  			if package_exists:
  						__lowerCAmelCase      		=       version.parse(importlib.metadata.version("""bitsandbytes"""			)			)
  						return compare_versions(lowercase      ,			""">="""      ,			"""0.39.0"""			)
  			return False
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      Union[str, Any]:
  			__lowerCAmelCase      		=       _is_package_available("""bitsandbytes"""			)
  			if package_exists:
  						__lowerCAmelCase      		=       version.parse(importlib.metadata.version("""bitsandbytes"""			)			)
  						return compare_versions(lowercase      ,			""">="""      ,			"""0.37.2"""			)
  			return False
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      Dict:
  			return _is_package_available("""bitsandbytes"""			)
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      Any:
  			if strtobool(os.environ.get("""ACCELERATE_USE_MEGATRON_LM"""      ,			"""False"""			)			) == 1:
  						__lowerCAmelCase      		=       importlib.util.find_spec("""megatron"""			) is not None
  						if package_exists:
  									try:
  												__lowerCAmelCase      		=       parse(importlib.metadata.version("""megatron-lm"""			)			)
  												return compare_versions(lowercase      ,			""">="""      ,			"""2.2.0"""			)
  									except Exception as e:
  												warnings.warn(f'Parse Megatron version failed. Exception:{e}'			)
  												return False
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      str:
  			return _is_package_available("""safetensors"""			)
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      Optional[int]:
  			return _is_package_available("""transformers"""			)
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      Optional[int]:
  			return _is_package_available("""datasets"""			)
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      str:
  			return _is_package_available("""aim"""			)
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      str:
  			return _is_package_available("""tensorboard"""			) or _is_package_available("""tensorboardX"""			)
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      List[str]:
  			return _is_package_available("""wandb"""			)
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      Optional[Any]:
  			return _is_package_available("""comet_ml"""			)
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      Tuple:
  			return _is_package_available("""boto3"""			)
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      Any:
  			if _is_package_available("""rich"""			):
  						if "ACCELERATE_DISABLE_RICH" in os.environ:
  									warnings.warn(
  									    """`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`."""			)
  									return not parse_flag_from_env("""ACCELERATE_DISABLE_RICH"""      ,			lowercase			)
  						return parse_flag_from_env("""ACCELERATE_ENABLE_RICH"""      ,			lowercase			)
  			return False
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      str:
  			return _is_package_available("""sagemaker"""			)
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      Dict:
  			return _is_package_available("""tqdm"""			)
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      Any:
  			return _is_package_available("""mlflow"""			)
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      int:
  			return is_torch_version(""">="""      ,			"""1.12"""			) and torch.backends.mps.is_available() and torch.backends.mps.is_built()
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      Union[str, Any]:
  			def get_major_and_minor_from_version(lowercase			):
  						return str(version.parse(lowercase			).major			) + "." + str(version.parse(lowercase			).minor			)
  
  			__lowerCAmelCase      		=       importlib.metadata.version("""torch"""			)
  			if importlib.util.find_spec("""intel_extension_for_pytorch"""			) is None:
  						return False
  			__lowerCAmelCase      		=       """N/A"""
  			try:
  						__lowerCAmelCase      		=       importlib.metadata.version("""intel_extension_for_pytorch"""			)
  			except importlib.metadata.PackageNotFoundError:
  						return False
  			__lowerCAmelCase      		=       get_major_and_minor_from_version(_torch_version			)
  			__lowerCAmelCase      		=       get_major_and_minor_from_version(_ipex_version			)
  			if torch_major_and_minor != ipex_major_and_minor:
  						warnings.warn(
  						    f'Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,'
  						    f' but PyTorch {_torch_version} is found. Please switch to the matching version and run again.'			)
  						return False
  			return True
  
  
  
  
  
  @lru_cache
  def      _lowerCAmelCase		(		lowercase=False			)      ->      List[str]:
  			if importlib.util.find_spec("""torch"""			) is None or importlib.util.find_spec("""torch_npu"""			) is None:
  						return False
  
  			import torch
  			import torch_npu  # noqa: F401
  
  			if check_device:
  						try:
  									# Will raise a RuntimeError if no NPU is found
  									__lowerCAmelCase      		=       torch.npu.device_count()
  									return torch.npu.is_available()
  						except RuntimeError:
  									return False
  			return hasattr(lowercase      ,			"""npu"""			) and torch.npu.is_available()
  
  
  
  
  
  
  
  @lru_cache
  def      _lowerCAmelCase		(		lowercase=False			)      ->      Optional[Any]:
  			if not parse_flag_from_env("""ACCELERATE_USE_XPU"""      ,			default=lowercase			):
  						return False
  			if is_ipex_available():
  						import torch
  
  						if is_torch_version("""<="""      ,			"""1.12"""			):
  									return False
  			else:
  						return False
  
  			import intel_extension_for_pytorch  # noqa: F401
  
  			if check_device:
  						try:
  									# Will raise a RuntimeError if no XPU  is found
  									__lowerCAmelCase      		=       torch.xpu.device_count()
  									return torch.xpu.is_available()
  						except RuntimeError:
  									return False
  			return hasattr(lowercase      ,			"""xpu"""			) and torch.xpu.is_available()
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import argparse
  import inspect
  import logging
  from typing import Tuple
  
  import numpy as np
  import torch
  
  from transformers import (
      AutoTokenizer,
      BloomForCausalLM,
      BloomTokenizerFast,
      CTRLLMHeadModel,
      CTRLTokenizer,
      GenerationMixin,
      GPTaLMHeadModel,
      GPTaTokenizer,
      GPTJForCausalLM,
      LlamaForCausalLM,
      LlamaTokenizer,
      OpenAIGPTLMHeadModel,
      OpenAIGPTTokenizer,
      OPTForCausalLM,
      TransfoXLLMHeadModel,
      TransfoXLTokenizer,
      XLMTokenizer,
      XLMWithLMHeadModel,
      XLNetLMHeadModel,
      XLNetTokenizer,
  )
  from transformers.modeling_outputs import CausalLMOutputWithPast
  
  
  logging.basicConfig(
      format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
      datefmt="""%m/%d/%Y %H:%M:%S""",
      level=logging.INFO,
  )
  _a      :				Optional[int]											=					logging.getLogger(__name__)
  
  _a      :				int											=					int(1_0_0_0_0)  # Hardcoded max length to avoid infinite loop
  
  _a      :				List[str]											=					{
      """gpt2""": (GPTaLMHeadModel, GPTaTokenizer),
      """ctrl""": (CTRLLMHeadModel, CTRLTokenizer),
      """openai-gpt""": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
      """xlnet""": (XLNetLMHeadModel, XLNetTokenizer),
      """transfo-xl""": (TransfoXLLMHeadModel, TransfoXLTokenizer),
      """xlm""": (XLMWithLMHeadModel, XLMTokenizer),
      """gptj""": (GPTJForCausalLM, AutoTokenizer),
      """bloom""": (BloomForCausalLM, BloomTokenizerFast),
      """llama""": (LlamaForCausalLM, LlamaTokenizer),
      """opt""": (OPTForCausalLM, GPTaTokenizer),
  }
  
  # Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
  # in https://github.com/rusiaaman/XLNet-gen#methodology
  # and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
  _a      :				List[Any]											=					"""In 1991, the remains of Russian Tsar Nicholas II and his family
  (except for Alexei and Maria) are discovered.
  The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
  remainder of the story. 1883 Western Siberia,
  a young Grigori Rasputin is asked by his father and a group of men to perform magic.
  Rasputin has a vision and denounces one of the men as a horse thief. Although his
  father initially slaps him for making such an accusation, Rasputin watches as the
  man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
  the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
  with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase			)      ->      Optional[int]:
  			np.random.seed(args.seed			)
  			torch.manual_seed(args.seed			)
  			if args.n_gpu > 0:
  						torch.cuda.manual_seed_all(args.seed			)
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)      ->      Tuple:
  			if args.temperature > 0.7:
  						logger.info("""CTRL typically works better with lower temperatures (and lower top_k)."""			)
  
  			__lowerCAmelCase      		=       tokenizer.encode(lowercase      ,			add_special_tokens=lowercase			)
  			if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()			):
  						logger.info("""WARNING! You are not starting your generation from a control code so you won't get good results"""			)
  			return prompt_text
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)      ->      List[str]:
  			# kwargs = {"language": None, "mask_token_id": None}
  
  			# Set the language
  			__lowerCAmelCase      		=       hasattr(model.config      ,			"""use_lang_emb"""			) and model.config.use_lang_emb
  			if hasattr(model.config      ,			"""lang2id"""			) and use_lang_emb:
  						__lowerCAmelCase      		=       model.config.langaid.keys()
  						if args.xlm_language in available_languages:
  									__lowerCAmelCase      		=       args.xlm_language
  						else:
  									__lowerCAmelCase      		=       None
  									while language not in available_languages:
  												__lowerCAmelCase      		=       input("""Using XLM. Select language in """ + str(list(lowercase			)			) + """ >>> """			)
  
  						__lowerCAmelCase      		=       model.config.langaid[language]
  						# kwargs["language"] = tokenizer.lang2id[language]
  
  			# TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
  			# XLM masked-language modeling (MLM) models need masked token
  			# is_xlm_mlm = "mlm" in args.model_name_or_path
  			# if is_xlm_mlm:
  			#     kwargs["mask_token_id"] = tokenizer.mask_token_id
  
  			return prompt_text
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)      ->      Dict:
  			__lowerCAmelCase      		=       args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
  			__lowerCAmelCase      		=       prefix + prompt_text
  			return prompt_text
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)      ->      List[str]:
  			__lowerCAmelCase      		=       args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
  			__lowerCAmelCase      		=       prefix + prompt_text
  			return prompt_text
  
  
  _a      :				List[Any]											=					{
      """ctrl""": prepare_ctrl_input,
      """xlm""": prepare_xlm_input,
      """xlnet""": prepare_xlnet_input,
      """transfo-xl""": prepare_transfoxl_input,
  }
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      Union[str, Any]:
  			if length < 0 and max_sequence_length > 0:
  						__lowerCAmelCase      		=       max_sequence_length
  			elif 0 < max_sequence_length < length:
  						__lowerCAmelCase      		=       max_sequence_length  # No generation bigger than model size
  			elif length < 0:
  						__lowerCAmelCase      		=       MAX_LENGTH  # avoid infinite loop
  			return length
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase			)      ->      Dict:
  			__lowerCAmelCase      		=       None
  			if hasattr(lowercase      ,			"""hidden_size"""			):
  						__lowerCAmelCase      		=       model_config.hidden_size
  			elif hasattr(lowercase      ,			"""n_embed"""			):
  						__lowerCAmelCase      		=       model_config.n_embed
  			elif hasattr(lowercase      ,			"""n_embd"""			):
  						__lowerCAmelCase      		=       model_config.n_embd
  
  			__lowerCAmelCase      		=       None
  			if hasattr(lowercase      ,			"""num_attention_heads"""			):
  						__lowerCAmelCase      		=       model_config.num_attention_heads
  			elif hasattr(lowercase      ,			"""n_head"""			):
  						__lowerCAmelCase      		=       model_config.n_head
  
  			if embedding_size is None or num_head is None or num_head == 0:
  						raise ValueError("""Check the model config"""			)
  
  			__lowerCAmelCase      		=       int(embedding_size / num_head			)
  			if hasattr(lowercase      ,			"""n_layer"""			):
  						__lowerCAmelCase      		=       model_config.n_layer
  			elif hasattr(lowercase      ,			"""num_hidden_layers"""			):
  						__lowerCAmelCase      		=       model_config.num_hidden_layers
  			else:
  						raise ValueError("""Number of hidden layers couldn't be determined from the model config"""			)
  
  			return num_layer, num_head, num_embedding_size_per_head
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase			)      ->      Optional[Any]:
  			__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       sparse_model_config(model.config			)
  			if model.config.model_type == "bloom":
  						__lowerCAmelCase      		=       tuple(
  						    (
  						        torch.empty(int(num_attention_heads * batch_size			)      ,			lowercase      ,			lowercase			)
  						        .to(model.dtype			)
  						        .to(model.device			),
  						        torch.empty(int(num_attention_heads * batch_size			)      ,			lowercase      ,			lowercase			)
  						        .to(model.dtype			)
  						        .to(model.device			),
  						    )
  						    for _ in range(lowercase			)			)
  			else:
  						__lowerCAmelCase      		=       tuple(
  						    (
  						        torch.empty(lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)
  						        .to(model.dtype			)
  						        .to(model.device			),
  						        torch.empty(lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)
  						        .to(model.dtype			)
  						        .to(model.device			),
  						    )
  						    for _ in range(lowercase			)			)
  			return past_key_values
  
  
  
  
  
  def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase			)      ->      Any:
  			__lowerCAmelCase      		=       len(lowercase			)
  			__lowerCAmelCase      		=       tokenizer.batch_encode_plus(lowercase      ,			return_tensors="""pt"""			)
  			__lowerCAmelCase      		=       dummy_input.to(model.device			)
  			if model.config.use_cache:
  						__lowerCAmelCase      		=       generate_past_key_values(lowercase      ,			lowercase      ,			1			)
  			__lowerCAmelCase      		=       torch.cat(
  			    [
  			        torch.zeros(dummy_input["""attention_mask"""].shape[0]      ,			1			)
  			        .to(dummy_input["""attention_mask"""].dtype			)
  			        .to(model.device			),
  			        dummy_input["""attention_mask"""],
  			    ]      ,			-1      ,			)
  			return dummy_input
  
  
  
  
  
  class 					_UpperCAmelCase							(		lowerCAmelCase_     ):
  		a     :     Optional[Any]     						=("""_optimized""", """_default""")
  		def __init__(						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					__lowerCAmelCase      		=       optimized
  					__lowerCAmelCase      		=       default
  		def __call__(						self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					if kwargs["past_key_values"] is None and self._default.config.use_cache:
  								__lowerCAmelCase      		=       generate_past_key_values(self._default,kwargs["""input_ids"""].shape[0],0    )
  					kwargs.pop("""position_ids""",__SCREAMING_SNAKE_CASE    )
  					for k in list(kwargs.keys()    ):
  								if kwargs[k] is None or isinstance(kwargs[k],__SCREAMING_SNAKE_CASE    ):
  											kwargs.pop(__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       self._optimized(**__SCREAMING_SNAKE_CASE    )
  					__lowerCAmelCase      		=       outputs[0]
  					__lowerCAmelCase      		=       outputs[1]
  					__lowerCAmelCase      		=       CausalLMOutputWithPast(
  					    loss=__SCREAMING_SNAKE_CASE,logits=__SCREAMING_SNAKE_CASE,past_key_values=__SCREAMING_SNAKE_CASE,hidden_states=__SCREAMING_SNAKE_CASE,attentions=__SCREAMING_SNAKE_CASE,)
  					return fixed_output
  		def __getattr__(						self,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					return getattr(self._default,__SCREAMING_SNAKE_CASE    )
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,**__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					return self._default.prepare_inputs_for_generation(
  					    __SCREAMING_SNAKE_CASE,past_key_values=__SCREAMING_SNAKE_CASE,inputs_embeds=__SCREAMING_SNAKE_CASE,use_cache=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )
  
  
  
  
  
  
  
  		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
  
  
  
  					'''simple docstring'''
  
  					return self._default._reorder_cache(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    )
  
  
  
  
  
  def      _lowerCAmelCase		(		)      ->      int:
  			__lowerCAmelCase      		=       argparse.ArgumentParser()
  			parser.add_argument(
  			    """--model_type"""      ,			default=lowercase      ,			type=lowercase      ,			required=lowercase      ,			help="""Model type selected in the list: """ + """, """.join(MODEL_CLASSES.keys()			)      ,			)
  			parser.add_argument(
  			    """--model_name_or_path"""      ,			default=lowercase      ,			type=lowercase      ,			required=lowercase      ,			help="""Path to pre-trained model or shortcut name selected in the list: """ + """, """.join(MODEL_CLASSES.keys()			)      ,			)
  
  			parser.add_argument("""--prompt"""      ,			type=lowercase      ,			default=""""""			)
  			parser.add_argument("""--length"""      ,			type=lowercase      ,			default=20			)
  			parser.add_argument("""--stop_token"""      ,			type=lowercase      ,			default=lowercase      ,			help="""Token at which text generation is stopped"""			)
  
  			parser.add_argument(
  			    """--temperature"""      ,			type=lowercase      ,			default=1.0      ,			help="""temperature of 1.0 has no effect, lower tend toward greedy sampling"""      ,			)
  			parser.add_argument(
  			    """--repetition_penalty"""      ,			type=lowercase      ,			default=1.0      ,			help="""primarily useful for CTRL model; in that case, use 1.2"""			)
  			parser.add_argument("""--k"""      ,			type=lowercase      ,			default=0			)
  			parser.add_argument("""--p"""      ,			type=lowercase      ,			default=0.9			)
  
  			parser.add_argument("""--prefix"""      ,			type=lowercase      ,			default=""""""      ,			help="""Text added prior to input."""			)
  			parser.add_argument("""--padding_text"""      ,			type=lowercase      ,			default=""""""      ,			help="""Deprecated, the use of `--prefix` is preferred."""			)
  			parser.add_argument("""--xlm_language"""      ,			type=lowercase      ,			default=""""""      ,			help="""Optional language when used with the XLM model."""			)
  
  			parser.add_argument("""--seed"""      ,			type=lowercase      ,			default=42      ,			help="""random seed for initialization"""			)
  			parser.add_argument("""--no_cuda"""      ,			action="""store_true"""      ,			help="""Avoid using CUDA when available"""			)
  			parser.add_argument("""--num_return_sequences"""      ,			type=lowercase      ,			default=1      ,			help="""The number of samples to generate."""			)
  			parser.add_argument(
  			    """--fp16"""      ,			action="""store_true"""      ,			help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""      ,			)
  			parser.add_argument("""--jit"""      ,			action="""store_true"""      ,			help="""Whether or not to use jit trace to accelerate inference"""			)
  			__lowerCAmelCase      		=       parser.parse_args()
  
  			__lowerCAmelCase      		=       torch.device("""cuda""" if torch.cuda.is_available() and not args.no_cuda else """cpu"""			)
  			__lowerCAmelCase      		=       0 if args.no_cuda else torch.cuda.device_count()
  
  			logger.warning(f'device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'			)
  
  			set_seed(lowercase			)
  
  			# Initialize the model and tokenizer
  			try:
  						__lowerCAmelCase      		=       args.model_type.lower()
  						__lowerCAmelCase		, __lowerCAmelCase      		=       MODEL_CLASSES[args.model_type]
  			except KeyError:
  						raise KeyError("""the model {} you specified is not supported. You are welcome to add it and open a PR :)"""			)
  
  			__lowerCAmelCase      		=       tokenizer_class.from_pretrained(args.model_name_or_path			)
  			if tokenizer.pad_token is None:
  						__lowerCAmelCase      		=       tokenizer.eos_token
  			__lowerCAmelCase      		=       model_class.from_pretrained(args.model_name_or_path			)
  			model.to(args.device			)
  
  			if args.fpaa:
  						model.half()
  			__lowerCAmelCase      		=       getattr(model.config      ,			"""max_position_embeddings"""      ,			0			)
  			__lowerCAmelCase      		=       adjust_length_to_model(args.length      ,			max_sequence_length=lowercase			)
  			logger.info(lowercase			)
  
  			__lowerCAmelCase      		=       args.prompt if args.prompt else input("""Model prompt >>> """			)
  
  			# Different models need different input formatting and/or extra arguments
  			__lowerCAmelCase      		=       args.model_type in PREPROCESSING_FUNCTIONS.keys()
  			if requires_preprocessing:
  						__lowerCAmelCase      		=       PREPROCESSING_FUNCTIONS.get(args.model_type			)
  						__lowerCAmelCase      		=       prepare_input(lowercase      ,			lowercase      ,			lowercase      ,			lowercase			)
  
  						if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
  									__lowerCAmelCase      		=       {"""add_space_before_punct_symbol""": True}
  						else:
  									__lowerCAmelCase      		=       {}
  
  						__lowerCAmelCase      		=       tokenizer.encode(
  						    lowercase      ,			add_special_tokens=lowercase      ,			return_tensors="""pt"""      ,			**lowercase			)
  			else:
  						__lowerCAmelCase      		=       args.prefix if args.prefix else args.padding_text
  						__lowerCAmelCase      		=       tokenizer.encode(prefix + prompt_text      ,			add_special_tokens=lowercase      ,			return_tensors="""pt"""			)
  			__lowerCAmelCase      		=       encoded_prompt.to(args.device			)
  
  			if encoded_prompt.size()[-1] == 0:
  						__lowerCAmelCase      		=       None
  			else:
  						__lowerCAmelCase      		=       encoded_prompt
  
  			if args.jit:
  						__lowerCAmelCase      		=       ["""enable jit"""]
  						__lowerCAmelCase      		=       prepare_jit_inputs(lowercase      ,			lowercase      ,			lowercase			)
  						torch._C._jit_set_texpr_fuser_enabled(lowercase			)
  						__lowerCAmelCase      		=       False
  						if hasattr(lowercase      ,			"""forward"""			):
  									__lowerCAmelCase      		=       inspect.signature(model.forward			)
  						else:
  									__lowerCAmelCase      		=       inspect.signature(model.__call__			)
  						__lowerCAmelCase      		=       tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(lowercase      ,			lowercase			) is not None			)
  						__lowerCAmelCase      		=       torch.jit.trace(lowercase      ,			lowercase      ,			strict=lowercase			)
  						__lowerCAmelCase      		=       torch.jit.freeze(traced_model.eval()			)
  						traced_model(*lowercase			)
  						traced_model(*lowercase			)
  
  						__lowerCAmelCase      		=       _ModelFallbackWrapper(lowercase      ,			lowercase			)
  
  			__lowerCAmelCase      		=       model.generate(
  			    input_ids=lowercase      ,			max_length=args.length + len(encoded_prompt[0]			)      ,			temperature=args.temperature      ,			top_k=args.k      ,			top_p=args.p      ,			repetition_penalty=args.repetition_penalty      ,			do_sample=lowercase      ,			num_return_sequences=args.num_return_sequences      ,			)
  
  			# Remove the batch dimension when returning multiple sequences
  			if len(output_sequences.shape			) > 2:
  						output_sequences.squeeze_()
  
  			__lowerCAmelCase      		=       []
  
  			for generated_sequence_idx, generated_sequence in enumerate(lowercase			):
  						print(f'=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='			)
  						__lowerCAmelCase      		=       generated_sequence.tolist()
  
  						# Decode text
  						__lowerCAmelCase      		=       tokenizer.decode(lowercase      ,			clean_up_tokenization_spaces=lowercase			)
  
  						# Remove all text after the stop token
  						__lowerCAmelCase      		=       text[: text.find(args.stop_token			) if args.stop_token else None]
  
  						# Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
  						__lowerCAmelCase      		=       (
  						    prompt_text + text[len(tokenizer.decode(encoded_prompt[0]      ,			clean_up_tokenization_spaces=lowercase			)			) :]
  						)
  
  						generated_sequences.append(lowercase			)
  						print(lowercase			)
  
  			return generated_sequences
  
  
  if __name__ == "__main__":
  	main()
  
  