






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,
)
UpperCAmelCase__            =	logging.getLogger(__name__)

UpperCAmelCase__            =	int(10000)  # Hardcoded max length to avoid infinite loop

UpperCAmelCase__            =	{
    "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
UpperCAmelCase__            =	"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					(   a				:List[Any]				)							->    int:
 np.random.seed(args.seed				)
 torch.manual_seed(args.seed				)
 if args.n_gpu > 0:
  torch.cuda.manual_seed_all(args.seed				)
def 		_a					(   a				:str			,							a				:List[Any]			,							a				:Union[str, Any]			,							a				:Union[str, Any]				)							->    List[Any]:
 if args.temperature > 0.7:
  logger.info('''CTRL typically works better with lower temperatures (and lower top_k).'''				)

 a							=   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					(   a				:int			,							a				:int			,							a				:Optional[Any]			,							a				:Dict				)							->    Any:
 # kwargs = {"language": None, "mask_token_id": None}

 # Set the language
 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(a				)				) + ''' >>> '''				)

  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					(   a				:int			,							a				:Union[str, Any]			,							a				:Any			,							a				:Optional[int]				)							->    Optional[Any]:
 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					(   a				:Dict			,							a				:List[Any]			,							a				:str			,							a				:Optional[Any]				)							->    List[str]:
 a							=   args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
 a							=   prefix + prompt_text
 return prompt_text


UpperCAmelCase__            =	{
    "ctrl": prepare_ctrl_input,
    "xlm": prepare_xlm_input,
    "xlnet": prepare_xlnet_input,
    "transfo-xl": prepare_transfoxl_input,
}
def 		_a					(   a				:Tuple			,							a				:Dict				)							->    List[str]:
 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					(   a				:Any				)							->    Any:
 a							=   None
 if hasattr(a			,							'''hidden_size'''				):
  a							=   model_config.hidden_size
 elif hasattr(a			,							'''n_embed'''				):
  a							=   model_config.n_embed
 elif hasattr(a			,							'''n_embd'''				):
  a							=   model_config.n_embd

 a							=   None
 if hasattr(a			,							'''num_attention_heads'''				):
  a							=   model_config.num_attention_heads
 elif hasattr(a			,							'''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(a			,							'''n_layer'''				):
  a							=   model_config.n_layer
 elif hasattr(a			,							'''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					(   a				:List[str]			,							a				:Optional[int]			,							a				:int				)							->    Tuple:
 a						,  a						,  a							=   sparse_model_config(model.config				)
 if model.config.model_type == "bloom":
  a							=   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:
  a							=   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					(   a				:Dict			,							a				:Tuple			,							a				:List[str]				)							->    Union[str, Any]:
 a							=   len(a				)
 a							=   tokenizer.batch_encode_plus(a			,							return_tensors='''pt'''				)
 a							=   dummy_input.to(model.device				)
 if model.config.use_cache:
  a							=   generate_past_key_values(a			,							a			,							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 	lowercase_      (			lowercase     ):






      '''simple docstring'''




      __snake_case  						=						('''_optimized''', '''_default''')







      def __init__(   self   :		Union[str, Any]				,      __UpperCAmelCase   :		Tuple				,      __UpperCAmelCase   :		Optional[Any]						)					->str:




       """simple docstring"""


       a							=   optimized
       a							=   default







      def __call__(   self   :		List[Any]				,      *__UpperCAmelCase   :		List[Any]				,      **__UpperCAmelCase   :		Optional[Any]						)					->Tuple:




       """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'''				,      __UpperCAmelCase						)
       for k in list(kwargs.keys()						):
        if kwargs[k] is None or isinstance(kwargs[k]				,      __UpperCAmelCase						):
         kwargs.pop(__UpperCAmelCase						)
       a							=   self._optimized(**__UpperCAmelCase						)
       a							=   outputs[0]
       a							=   outputs[1]
       a							=   CausalLMOutputWithPast(
           loss=__UpperCAmelCase				,      logits=__UpperCAmelCase				,      past_key_values=__UpperCAmelCase				,      hidden_states=__UpperCAmelCase				,      attentions=__UpperCAmelCase				,      )
       return fixed_output







      def __getattr__(   self   :		List[Any]				,      __UpperCAmelCase   :		str						)					->List[str]:




       """simple docstring"""


       return getattr(self._default				,      __UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		str				,      __UpperCAmelCase   :		Optional[int]				,      __UpperCAmelCase   :		Any=None				,      __UpperCAmelCase   :		Any=None				,      __UpperCAmelCase   :		List[Any]=None				,      **__UpperCAmelCase   :		Optional[int]						)					->List[Any]:




       """simple docstring"""


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







      def    __lowerCAmelCase      (   self   :		List[Any]				,      __UpperCAmelCase   :		Tuple[Tuple[torch.Tensor]]				,      __UpperCAmelCase   :		torch.Tensor						)					->Tuple[Tuple[torch.Tensor]]:




       """simple docstring"""


       return self._default._reorder_cache(__UpperCAmelCase				,      __UpperCAmelCase						)
def 		_a					(   )							->    Optional[int]:
 a							=   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'''				)
 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(a				)

 # 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=a				)
 logger.info(a				)

 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(a			,							a			,							a			,							a				)

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

  a							=   tokenizer.encode(
      a			,							add_special_tokens=a			,							return_tensors='''pt'''			,							**a				)
 else:
  a							=   args.prefix if args.prefix else args.padding_text
  a							=   tokenizer.encode(prefix + prompt_text			,							add_special_tokens=a			,							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(a			,							a			,							a				)
  torch._C._jit_set_texpr_fuser_enabled(a				)
  a							=   False
  if hasattr(a			,							'''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(a			,							a				) is not None				)
  a							=   torch.jit.trace(a			,							a			,							strict=a				)
  a							=   torch.jit.freeze(traced_model.eval()				)
  traced_model(*a				)
  traced_model(*a				)

  a							=   _ModelFallbackWrapper(a			,							a				)

 a							=   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_()

 a							=   []

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

  # Decode text
  a							=   tokenizer.decode(a			,							clean_up_tokenization_spaces=a				)

  # 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=a				)				) :]
  )

  generated_sequences.append(a				)
  print(a				)

 return generated_sequences


if __name__ == "__main__":
     main()










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__            =	{"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__            =	[
         "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "VisualBertForMultipleChoice",
         "VisualBertForPreTraining",
         "VisualBertForQuestionAnswering",
         "VisualBertForRegionToPhraseAlignment",
         "VisualBertForVisualReasoning",
         "VisualBertLayer",
         "VisualBertModel",
         "VisualBertPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_visual_bert import (
              VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              VisualBertForMultipleChoice,
              VisualBertForPreTraining,
              VisualBertForQuestionAnswering,
              VisualBertForRegionToPhraseAlignment,
              VisualBertForVisualReasoning,
              VisualBertLayer,
              VisualBertModel,
              VisualBertPreTrainedModel,
          )


else:
     import sys

     UpperCAmelCase__            =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


