




'''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,
)
lowerCAmelCase_    	=							logging.getLogger(__name__)

lowerCAmelCase_    	=							int(1_0_0_0_0)  # Hardcoded max length to avoid infinite loop

lowerCAmelCase_    	=							{
    '''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
lowerCAmelCase_    	=							'''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 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   np.random.seed(args.seed	)
   torch.manual_seed(args.seed	)
   if args.n_gpu > 0:
      torch.cuda.manual_seed_all(args.seed	)







def 	_A			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase	):






   '''simple docstring'''







   if args.temperature > 0.7:
      logger.info('CTRL typically works better with lower temperatures (and lower top_k).'	)

   A__         =    tokenizer.encode(UpperCAmelCase			,add_special_tokens=UpperCAmelCase	)
   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			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase	):






   '''simple docstring'''







   A__         =    hasattr(model.config			,'use_lang_emb'	) and model.config.use_lang_emb
   if hasattr(model.config			,'lang2id'	) and use_lang_emb:
      A__         =    model.config.langaid.keys()
      if args.xlm_language in available_languages:
         A__         =    args.xlm_language
      else:
         A__         =    None
         while language not in available_languages:
            A__         =    input('Using XLM. Select language in ' + str(list(UpperCAmelCase	)	) + ' >>> '	)

      A__         =    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			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase	):






   '''simple docstring'''







   A__         =    args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
   A__         =    prefix + prompt_text
   return prompt_text







def 	_A			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase	):






   '''simple docstring'''







   A__         =    args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
   A__         =    prefix + prompt_text
   return prompt_text


lowerCAmelCase_    	=							{
    '''ctrl''': prepare_ctrl_input,
    '''xlm''': prepare_xlm_input,
    '''xlnet''': prepare_xlnet_input,
    '''transfo-xl''': prepare_transfoxl_input,
}







def 	_A			(   UpperCAmelCase			,UpperCAmelCase	):






   '''simple docstring'''







   if length < 0 and max_sequence_length > 0:
      A__         =    max_sequence_length
   elif 0 < max_sequence_length < length:
      A__         =    max_sequence_length  # No generation bigger than model size
   elif length < 0:
      A__         =    MAX_LENGTH  # avoid infinite loop
   return length







def 	_A			(   UpperCAmelCase	):






   '''simple docstring'''







   A__         =    None
   if hasattr(UpperCAmelCase			,'hidden_size'	):
      A__         =    model_config.hidden_size
   elif hasattr(UpperCAmelCase			,'n_embed'	):
      A__         =    model_config.n_embed
   elif hasattr(UpperCAmelCase			,'n_embd'	):
      A__         =    model_config.n_embd

   A__         =    None
   if hasattr(UpperCAmelCase			,'num_attention_heads'	):
      A__         =    model_config.num_attention_heads
   elif hasattr(UpperCAmelCase			,'n_head'	):
      A__         =    model_config.n_head

   if embedding_size is None or num_head is None or num_head == 0:
      raise ValueError('Check the model config'	)

   A__         =    int(embedding_size / num_head	)
   if hasattr(UpperCAmelCase			,'n_layer'	):
      A__         =    model_config.n_layer
   elif hasattr(UpperCAmelCase			,'num_hidden_layers'	):
      A__         =    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			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase	):






   '''simple docstring'''







   A__     , A__     , A__         =    sparse_model_config(model.config	)
   if model.config.model_type == "bloom":
      A__         =    tuple(
          (
              torch.empty(int(num_attention_heads * batch_size	)			,UpperCAmelCase			,UpperCAmelCase	)
              .to(model.dtype	)
              .to(model.device	),
              torch.empty(int(num_attention_heads * batch_size	)			,UpperCAmelCase			,UpperCAmelCase	)
              .to(model.dtype	)
              .to(model.device	),
          )
          for _ in range(UpperCAmelCase	)	)
   else:
      A__         =    tuple(
          (
              torch.empty(UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase	)
              .to(model.dtype	)
              .to(model.device	),
              torch.empty(UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase	)
              .to(model.dtype	)
              .to(model.device	),
          )
          for _ in range(UpperCAmelCase	)	)
   return past_key_values







def 	_A			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase	):






   '''simple docstring'''







   A__         =    len(UpperCAmelCase	)
   A__         =    tokenizer.batch_encode_plus(UpperCAmelCase			,return_tensors='pt'	)
   A__         =    dummy_input.to(model.device	)
   if model.config.use_cache:
      A__         =    generate_past_key_values(UpperCAmelCase			,UpperCAmelCase			,1	)
   A__         =    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   _snake_case( UpperCAmelCase			):
 __snake_case:       Tuple									=				('''_optimized''', '''_default''')






 def __init__(self					:     str						,		a					:     Tuple						,		a					:     int						)					->      Tuple:






    """simple docstring"""


    A__         =    optimized
    A__         =    default






 def __call__(self					:     List[str]						,		*a					:     Dict						,		**a					:     Union[str, Any]						)					->      Optional[int]:






    """simple docstring"""


    if kwargs["past_key_values"] is None and self._default.config.use_cache:
       A__         =    generate_past_key_values(self._default						,		kwargs['input_ids'].shape[0]						,		0						)
    kwargs.pop('position_ids'						,		a						)
    for k in list(kwargs.keys()						):
       if kwargs[k] is None or isinstance(kwargs[k]						,		a						):
          kwargs.pop(a						)
    A__         =    self._optimized(**a						)
    A__         =    outputs[0]
    A__         =    outputs[1]
    A__         =    CausalLMOutputWithPast(
        loss=a						,		logits=a						,		past_key_values=a						,		hidden_states=a						,		attentions=a						,		)
    return fixed_output






 def __getattr__(self					:     str						,		a					:     Union[str, Any]						)					->      str:






    """simple docstring"""


    return getattr(self._default						,		a						)






 def 	_UpperCamelCase   (self					:     List[str]						,		a					:     str						,		a					:     str=None						,		a					:     List[Any]=None						,		a					:     List[Any]=None						,		**a					:     Optional[Any]						)					->      Dict:






    """simple docstring"""


    return self._default.prepare_inputs_for_generation(
        a						,		past_key_values=a						,		inputs_embeds=a						,		use_cache=a						,		**a						)






 def 	_UpperCamelCase   (self					:     Union[str, Any]						,		a					:     Tuple[Tuple[torch.Tensor]]						,		a					:     torch.Tensor						)					->      Tuple[Tuple[torch.Tensor]]:






    """simple docstring"""


    return self._default._reorder_cache(a						,		a						)







def 	_A			(   ):






   '''simple docstring'''







   A__         =    argparse.ArgumentParser()
   parser.add_argument(
       '--model_type'			,default=UpperCAmelCase			,type=UpperCAmelCase			,required=UpperCAmelCase			,help='Model type selected in the list: ' + ', '.join(MODEL_CLASSES.keys()	)			,)
   parser.add_argument(
       '--model_name_or_path'			,default=UpperCAmelCase			,type=UpperCAmelCase			,required=UpperCAmelCase			,help='Path to pre-trained model or shortcut name selected in the list: ' + ', '.join(MODEL_CLASSES.keys()	)			,)

   parser.add_argument('--prompt'			,type=UpperCAmelCase			,default=''	)
   parser.add_argument('--length'			,type=UpperCAmelCase			,default=20	)
   parser.add_argument('--stop_token'			,type=UpperCAmelCase			,default=UpperCAmelCase			,help='Token at which text generation is stopped'	)

   parser.add_argument(
       '--temperature'			,type=UpperCAmelCase			,default=1.0			,help='temperature of 1.0 has no effect, lower tend toward greedy sampling'			,)
   parser.add_argument(
       '--repetition_penalty'			,type=UpperCAmelCase			,default=1.0			,help='primarily useful for CTRL model; in that case, use 1.2'	)
   parser.add_argument('--k'			,type=UpperCAmelCase			,default=0	)
   parser.add_argument('--p'			,type=UpperCAmelCase			,default=0.9	)

   parser.add_argument('--prefix'			,type=UpperCAmelCase			,default=''			,help='Text added prior to input.'	)
   parser.add_argument('--padding_text'			,type=UpperCAmelCase			,default=''			,help='Deprecated, the use of `--prefix` is preferred.'	)
   parser.add_argument('--xlm_language'			,type=UpperCAmelCase			,default=''			,help='Optional language when used with the XLM model.'	)

   parser.add_argument('--seed'			,type=UpperCAmelCase			,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=UpperCAmelCase			,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'	)
   A__         =    parser.parse_args()

   A__         =    torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'	)
   A__         =    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(UpperCAmelCase	)

   # Initialize the model and tokenizer
   try:
      A__         =    args.model_type.lower()
      A__     , A__         =    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 :)'	)

   A__         =    tokenizer_class.from_pretrained(args.model_name_or_path	)
   if tokenizer.pad_token is None:
      A__         =    tokenizer.eos_token
   A__         =    model_class.from_pretrained(args.model_name_or_path	)
   model.to(args.device	)

   if args.fpaa:
      model.half()
   A__         =    getattr(model.config			,'max_position_embeddings'			,0	)
   A__         =    adjust_length_to_model(args.length			,max_sequence_length=UpperCAmelCase	)
   logger.info(UpperCAmelCase	)

   A__         =    args.prompt if args.prompt else input('Model prompt >>> '	)

   # Different models need different input formatting and/or extra arguments
   A__         =    args.model_type in PREPROCESSING_FUNCTIONS.keys()
   if requires_preprocessing:
      A__         =    PREPROCESSING_FUNCTIONS.get(args.model_type	)
      A__         =    prepare_input(UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase	)

      if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
         A__         =    {'add_space_before_punct_symbol': True}
      else:
         A__         =    {}

      A__         =    tokenizer.encode(
          UpperCAmelCase			,add_special_tokens=UpperCAmelCase			,return_tensors='pt'			,**UpperCAmelCase	)
   else:
      A__         =    args.prefix if args.prefix else args.padding_text
      A__         =    tokenizer.encode(prefix + prompt_text			,add_special_tokens=UpperCAmelCase			,return_tensors='pt'	)
   A__         =    encoded_prompt.to(args.device	)

   if encoded_prompt.size()[-1] == 0:
      A__         =    None
   else:
      A__         =    encoded_prompt

   if args.jit:
      A__         =    ['enable jit']
      A__         =    prepare_jit_inputs(UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase	)
      torch._C._jit_set_texpr_fuser_enabled(UpperCAmelCase	)
      A__         =    False
      if hasattr(UpperCAmelCase			,'forward'	):
         A__         =    inspect.signature(model.forward	)
      else:
         A__         =    inspect.signature(model.__call__	)
      A__         =    tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(UpperCAmelCase			,UpperCAmelCase	) is not None	)
      A__         =    torch.jit.trace(UpperCAmelCase			,UpperCAmelCase			,strict=UpperCAmelCase	)
      A__         =    torch.jit.freeze(traced_model.eval()	)
      traced_model(*UpperCAmelCase	)
      traced_model(*UpperCAmelCase	)

      A__         =    _ModelFallbackWrapper(UpperCAmelCase			,UpperCAmelCase	)

   A__         =    model.generate(
       input_ids=UpperCAmelCase			,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=UpperCAmelCase			,num_return_sequences=args.num_return_sequences			,)

   # Remove the batch dimension when returning multiple sequences
   if len(output_sequences.shape	) > 2:
      output_sequences.squeeze_()

   A__         =    []

   for generated_sequence_idx, generated_sequence in enumerate(UpperCAmelCase	):
      print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""	)
      A__         =    generated_sequence.tolist()

      # Decode text
      A__         =    tokenizer.decode(UpperCAmelCase			,clean_up_tokenization_spaces=UpperCAmelCase	)

      # Remove all text after the stop token
      A__         =    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
      A__         =    (
          prompt_text + text[len(tokenizer.decode(encoded_prompt[0]			,clean_up_tokenization_spaces=UpperCAmelCase	)	) :]
      )

      generated_sequences.append(UpperCAmelCase	)
      print(UpperCAmelCase	)

   return generated_sequences


if __name__ == "__main__":
    main()








'''simple docstring'''







import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


lowerCAmelCase_    	=							2 << 2_0  # 2mb
lowerCAmelCase_    	=							get_logger(__name__)

class   _snake_case( UpperCAmelCase			):






 def 	_UpperCamelCase   (self					:     Optional[int]						)					->      Optional[Any]:






    """simple docstring"""


    A__         =    self._options.get_all_options().get('runner'						)
    return runner in [None, "DirectRunner", "PortableRunner"]







def 	_A			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase=False	):






   '''simple docstring'''







   A__         =    FileSystems
   if fs.exists(UpperCAmelCase	):
      if force_upload:
         logger.info(F"""Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True."""	)
      else:
         logger.info(F"""Remote path already exist: {remote_file_path}. Skipping it as force_upload=False."""	)
         return
   with fs.create(UpperCAmelCase	) as remote_file:
      with open(UpperCAmelCase			,'rb'	) as local_file:
         A__         =    local_file.read(UpperCAmelCase	)
         while chunk:
            remote_file.write(UpperCAmelCase	)
            A__         =    local_file.read(UpperCAmelCase	)







def 	_A			(   UpperCAmelCase			,UpperCAmelCase			,UpperCAmelCase=False	):






   '''simple docstring'''







   A__         =    FileSystems
   if os.path.exists(UpperCAmelCase	):
      if force_download:
         logger.info(F"""Local path already exist: {remote_file_path}. Overwriting it as force_upload=True."""	)
      else:
         logger.info(F"""Local path already exist: {remote_file_path}. Skipping it as force_upload=False."""	)
         return
   with fs.open(UpperCAmelCase	) as remote_file:
      with open(UpperCAmelCase			,'wb'	) as local_file:
         A__         =    remote_file.read(UpperCAmelCase	)
         while chunk:
            local_file.write(UpperCAmelCase	)
            A__         =    remote_file.read(UpperCAmelCase	)


