






"""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,
)
snake_case   :   Optional[Any]    		=		logging.getLogger(__name__)

snake_case   :   Dict    		=		int(1_0_0_0_0)  # Hardcoded max length to avoid infinite loop

snake_case   :   Optional[int]    		=		{
    """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   :   List[Any]    		=		"""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	(							__snake_case:   Union[str, Any]     )						->		str:

       """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	(							__snake_case:   Tuple   ,    __snake_case:   Dict   ,    __snake_case:   Dict   ,    __snake_case:   List[str]     )						->		str:

       """simple docstring"""





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

       __magic_name__										=						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	(							__snake_case:   Union[str, Any]   ,    __snake_case:   Union[str, Any]   ,    __snake_case:   Union[str, Any]   ,    __snake_case:   Tuple     )						->		Any:

       """simple docstring"""





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

              __magic_name__										=						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	(							__snake_case:   List[str]   ,    __snake_case:   int   ,    __snake_case:   Optional[int]   ,    __snake_case:   List[Any]     )						->		List[str]:

       """simple docstring"""





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


def        A	(							__snake_case:   Union[str, Any]   ,    __snake_case:   Union[str, Any]   ,    __snake_case:   int   ,    __snake_case:   int     )						->		Tuple:

       """simple docstring"""





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


snake_case   :   int    		=		{
    """ctrl""": prepare_ctrl_input,
    """xlm""": prepare_xlm_input,
    """xlnet""": prepare_xlnet_input,
    """transfo-xl""": prepare_transfoxl_input,
}


def        A	(							__snake_case:   List[Any]   ,    __snake_case:   Optional[int]     )						->		Optional[int]:

       """simple docstring"""





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


def        A	(							__snake_case:   Union[str, Any]     )						->		Tuple:

       """simple docstring"""





       __magic_name__										=						None
       if hasattr(UpperCAmelCase_   ,    'hidden_size'     ):
              __magic_name__										=						model_config.hidden_size
       elif hasattr(UpperCAmelCase_   ,    'n_embed'     ):
              __magic_name__										=						model_config.n_embed
       elif hasattr(UpperCAmelCase_   ,    'n_embd'     ):
              __magic_name__										=						model_config.n_embd

       __magic_name__										=						None
       if hasattr(UpperCAmelCase_   ,    'num_attention_heads'     ):
              __magic_name__										=						model_config.num_attention_heads
       elif hasattr(UpperCAmelCase_   ,    'n_head'     ):
              __magic_name__										=						model_config.n_head

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

       __magic_name__										=						int(embedding_size / num_head     )
       if hasattr(UpperCAmelCase_   ,    'n_layer'     ):
              __magic_name__										=						model_config.n_layer
       elif hasattr(UpperCAmelCase_   ,    'num_hidden_layers'     ):
              __magic_name__										=						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	(							__snake_case:   Union[str, Any]   ,    __snake_case:   Optional[int]   ,    __snake_case:   List[Any]     )						->		List[Any]:

       """simple docstring"""





       __magic_name__										=						sparse_model_config(model.config     )
       if model.config.model_type == "bloom":
              __magic_name__										=						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:
              __magic_name__										=						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	(							__snake_case:   Union[str, Any]   ,    __snake_case:   Dict   ,    __snake_case:   Union[str, Any]     )						->		List[Any]:

       """simple docstring"""





       __magic_name__										=						len(UpperCAmelCase_     )
       __magic_name__										=						tokenizer.batch_encode_plus(UpperCAmelCase_   ,    return_tensors='pt'     )
       __magic_name__										=						dummy_input.to(model.device     )
       if model.config.use_cache:
              __magic_name__										=						generate_past_key_values(UpperCAmelCase_   ,    UpperCAmelCase_   ,    1     )
       __magic_name__										=						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__   (							A_):







    """simple docstring"""
    __UpperCAmelCase											=   ("""_optimized""", """_default""")







    def __init__(		self   :  int							,						UpperCamelCase_   :  Union[str, Any]							,						UpperCamelCase_   :  List[str]   ):





           '''simple docstring'''

           __magic_name__										=						optimized
           __magic_name__										=						default







    def __call__(		self   :  Optional[Any]							,						*UpperCamelCase_   :  Dict							,						**UpperCamelCase_   :  str   ):





           '''simple docstring'''

           if kwargs["past_key_values"] is None and self._default.config.use_cache:
                  __magic_name__										=						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_   )
           __magic_name__										=						self._optimized(**UpperCamelCase_   )
           __magic_name__										=						outputs[0]
           __magic_name__										=						outputs[1]
           __magic_name__										=						CausalLMOutputWithPast(
               loss=UpperCamelCase_							,						logits=UpperCamelCase_							,						past_key_values=UpperCamelCase_							,						hidden_states=UpperCamelCase_							,						attentions=UpperCamelCase_							,						)
           return fixed_output







    def __getattr__(		self   :  str							,						UpperCamelCase_   :  Union[str, Any]   ):





           '''simple docstring'''

           return getattr(self._default							,						UpperCamelCase_   )







    def 							a__ (		self   :  Union[str, Any]							,						UpperCamelCase_   :  Optional[int]							,						UpperCamelCase_   :  Optional[Any]=None							,						UpperCamelCase_   :  int=None							,						UpperCamelCase_   :  List[str]=None							,						**UpperCamelCase_   :  Any   ):





           '''simple docstring'''

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







    def 							a__ (		self   :  Tuple							,						UpperCamelCase_   :  Tuple[Tuple[torch.Tensor]]							,						UpperCamelCase_   :  torch.Tensor   ):





           '''simple docstring'''

           return self._default._reorder_cache(UpperCamelCase_							,						UpperCamelCase_   )


def        A	(							)						->		List[Any]:

       """simple docstring"""





       __magic_name__										=						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=2_0     )
       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=4_2   ,    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'     )
       __magic_name__										=						parser.parse_args()

       __magic_name__										=						torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'     )
       __magic_name__										=						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:
              __magic_name__										=						args.model_type.lower()
              __magic_name__										=						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 :)'     )

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

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

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

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

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

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

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

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

              __magic_name__										=						_ModelFallbackWrapper(UpperCAmelCase_   ,    UpperCAmelCase_     )

       __magic_name__										=						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_()

       __magic_name__										=						[]

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

              # Decode text
              __magic_name__										=						tokenizer.decode(UpperCAmelCase_   ,    clean_up_tokenization_spaces=UpperCAmelCase_     )

              # Remove all text after the stop token
              __magic_name__										=						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
              __magic_name__										=						(
                  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"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case   :   int    		=		logging.get_logger(__name__)

snake_case   :   List[str]    		=		{
    """uw-madison/nystromformer-512""": """https://huggingface.co/uw-madison/nystromformer-512/resolve/main/config.json""",
    # See all Nystromformer models at https://huggingface.co/models?filter=nystromformer
}



class 					UpperCamelCase__   (							a_):







    """simple docstring"""
    __UpperCAmelCase											=   """nystromformer"""







    def __init__(		self   :  Dict							,						UpperCamelCase_   :  Optional[Any]=3_0_0_0_0							,						UpperCamelCase_   :  Dict=7_6_8							,						UpperCamelCase_   :  Tuple=1_2							,						UpperCamelCase_   :  Any=1_2							,						UpperCamelCase_   :  List[str]=3_0_7_2							,						UpperCamelCase_   :  Tuple="gelu_new"							,						UpperCamelCase_   :  Optional[Any]=0.1							,						UpperCamelCase_   :  str=0.1							,						UpperCamelCase_   :  Tuple=5_1_0							,						UpperCamelCase_   :  Optional[int]=2							,						UpperCamelCase_   :  Optional[int]=6_4							,						UpperCamelCase_   :  Tuple=6_4							,						UpperCamelCase_   :  Optional[int]=6_5							,						UpperCamelCase_   :  Optional[int]=False							,						UpperCamelCase_   :  List[str]=0.02							,						UpperCamelCase_   :  List[Any]=1e-5							,						UpperCamelCase_   :  Union[str, Any]=1							,						UpperCamelCase_   :  Tuple=0							,						UpperCamelCase_   :  List[Any]=2							,						**UpperCamelCase_   :  str							,						):





           '''simple docstring'''

           __magic_name__										=						vocab_size
           __magic_name__										=						max_position_embeddings
           __magic_name__										=						hidden_size
           __magic_name__										=						num_hidden_layers
           __magic_name__										=						num_attention_heads
           __magic_name__										=						intermediate_size
           __magic_name__										=						hidden_act
           __magic_name__										=						hidden_dropout_prob
           __magic_name__										=						attention_probs_dropout_prob
           __magic_name__										=						initializer_range
           __magic_name__										=						type_vocab_size
           __magic_name__										=						segment_means_seq_len
           __magic_name__										=						num_landmarks
           __magic_name__										=						conv_kernel_size
           __magic_name__										=						inv_coeff_init_option
           __magic_name__										=						layer_norm_eps
           super().__init__(pad_token_id=UpperCamelCase_							,						bos_token_id=UpperCamelCase_							,						eos_token_id=UpperCamelCase_							,						**UpperCamelCase_   )