code: 
  
  
  
  
  
  
  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,
  )
  __snake_case							      =						logging.getLogger(__name__)
  
  __snake_case							      =						int(1_00_00)  # Hardcoded max length to avoid infinite loop
  
  __snake_case							      =						{
      """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
  __snake_case							      =						"""In 1991, the remains of Russian Tsar Nicholas II and his family\n(except for Alexei and Maria) are discovered.\nThe voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the\nremainder of the story. 1883 Western Siberia,\na young Grigori Rasputin is asked by his father and a group of men to perform magic.\nRasputin has a vision and denounces one of the men as a horse thief. Although his\nfather initially slaps him for making such an accusation, Rasputin watches as the\nman is chased outside and beaten. Twenty years later, Rasputin sees a vision of\nthe Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,\nwith people, even a bishop, begging for his blessing. <eod> </s> <eos>"""
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			List[Any] ):
  				np.random.seed(args.seed )
  				torch.manual_seed(args.seed )
  				if args.n_gpu > 0:
  								torch.cuda.manual_seed_all(args.seed )
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Optional[int]  ,						SCREAMING_SNAKE_CASE__						:			Union[str, Any]  ,						SCREAMING_SNAKE_CASE__						:			Tuple  ,						SCREAMING_SNAKE_CASE__						:			str ):
  				if args.temperature > 0.7:
  								logger.info('''CTRL typically works better with lower temperatures (and lower top_k).''' )
  
  				UpperCamelCase :int										=     tokenizer.encode(a_  ,						add_special_tokens=a_ )
  				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 						_A     (       SCREAMING_SNAKE_CASE__						:			Any  ,						SCREAMING_SNAKE_CASE__						:			Tuple  ,						SCREAMING_SNAKE_CASE__						:			Dict  ,						SCREAMING_SNAKE_CASE__						:			List[Any] ):
  				# kwargs = {"language": None, "mask_token_id": None}
  
  				# Set the language
  				UpperCamelCase :int										=     hasattr(model.config  ,						'''use_lang_emb''' ) and model.config.use_lang_emb
  				if hasattr(model.config  ,						'''lang2id''' ) and use_lang_emb:
  								UpperCamelCase :int										=     model.config.langaid.keys()
  								if args.xlm_language in available_languages:
  												UpperCamelCase :Optional[Any]										=     args.xlm_language
  								else:
  												UpperCamelCase :int										=     None
  												while language not in available_languages:
  																UpperCamelCase :Union[str, Any]										=     input('''Using XLM. Select language in ''' + str(list(a_ ) ) + ''' >>> ''' )
  
  								UpperCamelCase :Tuple										=     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 						_A     (       SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			Union[str, Any]  ,						SCREAMING_SNAKE_CASE__						:			Any  ,						SCREAMING_SNAKE_CASE__						:			int ):
  				UpperCamelCase :Any										=     args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
  				UpperCamelCase :Tuple										=     prefix + prompt_text
  				return prompt_text
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Optional[Any]  ,						SCREAMING_SNAKE_CASE__						:			Dict  ,						SCREAMING_SNAKE_CASE__						:			Union[str, Any]  ,						SCREAMING_SNAKE_CASE__						:			Union[str, Any] ):
  				UpperCamelCase :str										=     args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
  				UpperCamelCase :List[str]										=     prefix + prompt_text
  				return prompt_text
  
  
  __snake_case							      =						{
      """ctrl""": prepare_ctrl_input,
      """xlm""": prepare_xlm_input,
      """xlnet""": prepare_xlnet_input,
      """transfo-xl""": prepare_transfoxl_input,
  }
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Union[str, Any]  ,						SCREAMING_SNAKE_CASE__						:			Tuple ):
  				if length < 0 and max_sequence_length > 0:
  								UpperCamelCase :Dict										=     max_sequence_length
  				elif 0 < max_sequence_length < length:
  								UpperCamelCase :List[str]										=     max_sequence_length  # No generation bigger than model size
  				elif length < 0:
  								UpperCamelCase :str										=     MAX_LENGTH  # avoid infinite loop
  				return length
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			List[str] ):
  				UpperCamelCase :int										=     None
  				if hasattr(a_  ,						'''hidden_size''' ):
  								UpperCamelCase :Dict										=     model_config.hidden_size
  				elif hasattr(a_  ,						'''n_embed''' ):
  								UpperCamelCase :Dict										=     model_config.n_embed
  				elif hasattr(a_  ,						'''n_embd''' ):
  								UpperCamelCase :Any										=     model_config.n_embd
  
  				UpperCamelCase :Optional[int]										=     None
  				if hasattr(a_  ,						'''num_attention_heads''' ):
  								UpperCamelCase :List[str]										=     model_config.num_attention_heads
  				elif hasattr(a_  ,						'''n_head''' ):
  								UpperCamelCase :Optional[Any]										=     model_config.n_head
  
  				if embedding_size is None or num_head is None or num_head == 0:
  								raise ValueError('''Check the model config''' )
  
  				UpperCamelCase :Any										=     int(embedding_size / num_head )
  				if hasattr(a_  ,						'''n_layer''' ):
  								UpperCamelCase :Union[str, Any]										=     model_config.n_layer
  				elif hasattr(a_  ,						'''num_hidden_layers''' ):
  								UpperCamelCase :List[str]										=     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 						_A     (       SCREAMING_SNAKE_CASE__						:			Union[str, Any]  ,						SCREAMING_SNAKE_CASE__						:			List[Any]  ,						SCREAMING_SNAKE_CASE__						:			Optional[Any] ):
  				UpperCamelCase							,     UpperCamelCase							,     UpperCamelCase :int										=     sparse_model_config(model.config )
  				if model.config.model_type == "bloom":
  								UpperCamelCase :List[Any]										=     tuple(
  								    (
  								        torch.empty(int(num_attention_heads * batch_size )  ,						a_  ,						a_ )
  								        .to(model.dtype )
  								        .to(model.device ),
  								        torch.empty(int(num_attention_heads * batch_size )  ,						a_  ,						a_ )
  								        .to(model.dtype )
  								        .to(model.device ),
  								    )
  								    for _ in range(a_ ) )
  				else:
  								UpperCamelCase :Optional[int]										=     tuple(
  								    (
  								        torch.empty(a_  ,						a_  ,						a_  ,						a_ )
  								        .to(model.dtype )
  								        .to(model.device ),
  								        torch.empty(a_  ,						a_  ,						a_  ,						a_ )
  								        .to(model.dtype )
  								        .to(model.device ),
  								    )
  								    for _ in range(a_ ) )
  				return past_key_values
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			List[Any]  ,						SCREAMING_SNAKE_CASE__						:			Union[str, Any]  ,						SCREAMING_SNAKE_CASE__						:			Optional[Any] ):
  				UpperCamelCase :Optional[Any]										=     len(a_ )
  				UpperCamelCase :Any										=     tokenizer.batch_encode_plus(a_  ,						return_tensors='''pt''' )
  				UpperCamelCase :Tuple										=     dummy_input.to(model.device )
  				if model.config.use_cache:
  								UpperCamelCase :List[str]										=     generate_past_key_values(a_  ,						a_  ,						1 )
  				UpperCamelCase :Dict										=     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_				):
  
  
  
  						"""simple docstring"""
  						UpperCamelCase_       :      str     =('_optimized', '_default')
  						def __init__(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   Tuple:
  										UpperCamelCase :str										=     optimized
  										UpperCamelCase :str										=     default
  						def __call__(       self					,       *SCREAMING_SNAKE_CASE_					,       **SCREAMING_SNAKE_CASE_				)       ->   Optional[Any]:
  
  										if kwargs["past_key_values"] is None and self._default.config.use_cache:
  														UpperCamelCase :str										=     generate_past_key_values(self._default					,       kwargs['''input_ids'''].shape[0]					,       0				)
  										kwargs.pop('''position_ids'''					,       lowerCamelCase_				)
  										for k in list(kwargs.keys()				):
  														if kwargs[k] is None or isinstance(kwargs[k]					,       lowerCamelCase_				):
  																		kwargs.pop(lowerCamelCase_				)
  										UpperCamelCase :Optional[Any]										=     self._optimized(**lowerCamelCase_				)
  										UpperCamelCase :Any										=     outputs[0]
  										UpperCamelCase :Any										=     outputs[1]
  										UpperCamelCase :Tuple										=     CausalLMOutputWithPast(
  										    loss=lowerCamelCase_					,       logits=lowerCamelCase_					,       past_key_values=lowerCamelCase_					,       hidden_states=lowerCamelCase_					,       attentions=lowerCamelCase_					,       )
  										return fixed_output
  						def __getattr__(       self					,       SCREAMING_SNAKE_CASE_				)       ->   List[str]:
  										return getattr(self._default					,       lowerCamelCase_				)
  						def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=None					,       SCREAMING_SNAKE_CASE_=None					,       **SCREAMING_SNAKE_CASE_				)       ->   Dict:
  										return self._default.prepare_inputs_for_generation(
  										    lowerCamelCase_					,       past_key_values=lowerCamelCase_					,       inputs_embeds=lowerCamelCase_					,       use_cache=lowerCamelCase_					,       **lowerCamelCase_				)
  
  
  
  
  						def 					UpperCAmelCase		(       self					,       SCREAMING_SNAKE_CASE_					,       SCREAMING_SNAKE_CASE_				)       ->   Optional[Any]:
  										return self._default._reorder_cache(lowerCamelCase_					,       lowerCamelCase_				)
  
  
  
  
  
  
  
  def 						_A     (       ):
  				UpperCamelCase :List[str]										=     argparse.ArgumentParser()
  				parser.add_argument(
  				    '''--model_type'''  ,						default=a_  ,						type=a_  ,						required=a_  ,						help='''Model type selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys() )  ,						)
  				parser.add_argument(
  				    '''--model_name_or_path'''  ,						default=a_  ,						type=a_  ,						required=a_  ,						help='''Path to pre-trained model or shortcut name selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys() )  ,						)
  
  				parser.add_argument('''--prompt'''  ,						type=a_  ,						default='''''' )
  				parser.add_argument('''--length'''  ,						type=a_  ,						default=20 )
  				parser.add_argument('''--stop_token'''  ,						type=a_  ,						default=a_  ,						help='''Token at which text generation is stopped''' )
  
  				parser.add_argument(
  				    '''--temperature'''  ,						type=a_  ,						default=1.0  ,						help='''temperature of 1.0 has no effect, lower tend toward greedy sampling'''  ,						)
  				parser.add_argument(
  				    '''--repetition_penalty'''  ,						type=a_  ,						default=1.0  ,						help='''primarily useful for CTRL model; in that case, use 1.2''' )
  				parser.add_argument('''--k'''  ,						type=a_  ,						default=0 )
  				parser.add_argument('''--p'''  ,						type=a_  ,						default=0.9 )
  
  				parser.add_argument('''--prefix'''  ,						type=a_  ,						default=''''''  ,						help='''Text added prior to input.''' )
  				parser.add_argument('''--padding_text'''  ,						type=a_  ,						default=''''''  ,						help='''Deprecated, the use of `--prefix` is preferred.''' )
  				parser.add_argument('''--xlm_language'''  ,						type=a_  ,						default=''''''  ,						help='''Optional language when used with the XLM model.''' )
  
  				parser.add_argument('''--seed'''  ,						type=a_  ,						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=a_  ,						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''' )
  				UpperCamelCase :int										=     parser.parse_args()
  
  				UpperCamelCase :Any										=     torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu''' )
  				UpperCamelCase :Tuple										=     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(a_ )
  
  				# Initialize the model and tokenizer
  				try:
  								UpperCamelCase :int										=     args.model_type.lower()
  								UpperCamelCase							,     UpperCamelCase :List[Any]										=     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 :)''' )
  
  				UpperCamelCase :Optional[int]										=     tokenizer_class.from_pretrained(args.model_name_or_path )
  				if tokenizer.pad_token is None:
  								UpperCamelCase :List[Any]										=     tokenizer.eos_token
  				UpperCamelCase :Dict										=     model_class.from_pretrained(args.model_name_or_path )
  				model.to(args.device )
  
  				if args.fpaa:
  								model.half()
  				UpperCamelCase :Dict										=     getattr(model.config  ,						'''max_position_embeddings'''  ,						0 )
  				UpperCamelCase :str										=     adjust_length_to_model(args.length  ,						max_sequence_length=a_ )
  				logger.info(a_ )
  
  				UpperCamelCase :List[Any]										=     args.prompt if args.prompt else input('''Model prompt >>> ''' )
  
  				# Different models need different input formatting and/or extra arguments
  				UpperCamelCase :Optional[int]										=     args.model_type in PREPROCESSING_FUNCTIONS.keys()
  				if requires_preprocessing:
  								UpperCamelCase :Union[str, Any]										=     PREPROCESSING_FUNCTIONS.get(args.model_type )
  								UpperCamelCase :int										=     prepare_input(a_  ,						a_  ,						a_  ,						a_ )
  
  								if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
  												UpperCamelCase :Optional[Any]										=     {'''add_space_before_punct_symbol''': True}
  								else:
  												UpperCamelCase :List[Any]										=     {}
  
  								UpperCamelCase :List[str]										=     tokenizer.encode(
  								    a_  ,						add_special_tokens=a_  ,						return_tensors='''pt'''  ,						**a_ )
  				else:
  								UpperCamelCase :Dict										=     args.prefix if args.prefix else args.padding_text
  								UpperCamelCase :Union[str, Any]										=     tokenizer.encode(prefix + prompt_text  ,						add_special_tokens=a_  ,						return_tensors='''pt''' )
  				UpperCamelCase :Union[str, Any]										=     encoded_prompt.to(args.device )
  
  				if encoded_prompt.size()[-1] == 0:
  								UpperCamelCase :List[Any]										=     None
  				else:
  								UpperCamelCase :Any										=     encoded_prompt
  
  				if args.jit:
  								UpperCamelCase :int										=     ['''enable jit''']
  								UpperCamelCase :Optional[int]										=     prepare_jit_inputs(a_  ,						a_  ,						a_ )
  								torch._C._jit_set_texpr_fuser_enabled(a_ )
  								UpperCamelCase :int										=     False
  								if hasattr(a_  ,						'''forward''' ):
  												UpperCamelCase :Tuple										=     inspect.signature(model.forward )
  								else:
  												UpperCamelCase :List[Any]										=     inspect.signature(model.__call__ )
  								UpperCamelCase :Union[str, Any]										=     tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(a_  ,						a_ ) is not None )
  								UpperCamelCase :Dict										=     torch.jit.trace(a_  ,						a_  ,						strict=a_ )
  								UpperCamelCase :List[str]										=     torch.jit.freeze(traced_model.eval() )
  								traced_model(*a_ )
  								traced_model(*a_ )
  
  								UpperCamelCase :str										=     _ModelFallbackWrapper(a_  ,						a_ )
  
  				UpperCamelCase :Union[str, Any]										=     model.generate(
  				    input_ids=a_  ,						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=a_  ,						num_return_sequences=args.num_return_sequences  ,						)
  
  				# Remove the batch dimension when returning multiple sequences
  				if len(output_sequences.shape ) > 2:
  								output_sequences.squeeze_()
  
  				UpperCamelCase :List[Any]										=     []
  
  				for generated_sequence_idx, generated_sequence in enumerate(a_ ):
  								print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===''' )
  								UpperCamelCase :Any										=     generated_sequence.tolist()
  
  								# Decode text
  								UpperCamelCase :Optional[Any]										=     tokenizer.decode(a_  ,						clean_up_tokenization_spaces=a_ )
  
  								# Remove all text after the stop token
  								UpperCamelCase :int										=     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
  								UpperCamelCase :int										=     (
  								    prompt_text + text[len(tokenizer.decode(encoded_prompt[0]  ,						clean_up_tokenization_spaces=a_ ) ) :]
  								)
  
  								generated_sequences.append(a_ )
  								print(a_ )
  
  				return generated_sequences
  
  
  if __name__ == "__main__":
  	main()
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  # as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.
  
  import os
  import sys
  
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow
  
  from .utils import load_json
  
  
  
  
  
  
  
  class     UpperCAmelCase_		(      lowercase				):
  
  
  
        """simple docstring"""
        @classmethod
        def 					UpperCAmelCase		(       cls				)       ->   List[Any]:
            return cls
  
  
  
  
        @slow
        @require_torch_gpu
        def 					UpperCAmelCase		(       self				)       ->   int:
            UpperCamelCase :int										=     self.get_auto_remove_tmp_dir()
            UpperCamelCase :List[str]										=     F'''
              --model_name Helsinki-NLP/opus-mt-en-ro
              --save_dir {output_dir}
              --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
              --num_beams 2
              --task translation
          '''.split()
  
            # we want this test to run even if there is only one GPU, but if there are more we use them all
            UpperCamelCase :List[str]										=     get_gpu_count()
            UpperCamelCase :Optional[Any]										=     F'''
              -m torch.distributed.launch
              --nproc_per_node={n_gpu}
              {self.test_file_dir}/run_distributed_eval.py
          '''.split()
            UpperCamelCase :List[Any]										=     [sys.executable] + distributed_args + args
            execute_subprocess_async(SCREAMING_SNAKE_CASE_					,       env=self.get_env()				)
  
            UpperCamelCase :Tuple										=     os.path.join(SCREAMING_SNAKE_CASE_					,       '''test_bleu.json'''				)
            UpperCamelCase :str										=     load_json(SCREAMING_SNAKE_CASE_				)
            # print(metrics)
            self.assertGreaterEqual(metrics['''bleu''']					,       25				)
  
  
  
  
  
  