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

__UpperCAmelCase												=   int(1_0000)  # 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        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Any							)       ->	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        SCREAMING_SNAKE_CASE_  (	snake_case_				:	int   ,     snake_case_				:	Union[str, Any]   ,     snake_case_				:	Tuple   ,     snake_case_				:	Optional[Any]							)       ->	Union[str, Any]:
 if args.temperature > 0.7:
  logger.info('CTRL typically works better with lower temperatures (and lower top_k).'							)

 SCREAMING_SNAKE_CASE  :  int								=		tokenizer.encode(snake_case_   ,     add_special_tokens=snake_case_							)
 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        SCREAMING_SNAKE_CASE_  (	snake_case_				:	str   ,     snake_case_				:	List[Any]   ,     snake_case_				:	Union[str, Any]   ,     snake_case_				:	List[str]							)       ->	str:
 # kwargs = {"language": None, "mask_token_id": None}

 # Set the language
 SCREAMING_SNAKE_CASE  :  Tuple								=		hasattr(model.config   ,     'use_lang_emb'							) and model.config.use_lang_emb
 if hasattr(model.config   ,     'lang2id'							) and use_lang_emb:
  SCREAMING_SNAKE_CASE  :  Tuple								=		model.config.langaid.keys()
  if args.xlm_language in available_languages:
   SCREAMING_SNAKE_CASE  :  Dict								=		args.xlm_language
  else:
   SCREAMING_SNAKE_CASE  :  Tuple								=		None
   while language not in available_languages:
    SCREAMING_SNAKE_CASE  :  List[str]								=		input('Using XLM. Select language in ' + str(list(snake_case_							)							) + ' >>> '							)

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



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Dict   ,     snake_case_				:	str   ,     snake_case_				:	List[Any]   ,     snake_case_				:	Any							)       ->	Dict:
 SCREAMING_SNAKE_CASE  :  Any								=		args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
 SCREAMING_SNAKE_CASE  :  List[Any]								=		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        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Optional[int]   ,     snake_case_				:	Optional[int]							)       ->	Any:
 if length < 0 and max_sequence_length > 0:
  SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		max_sequence_length
 elif 0 < max_sequence_length < length:
  SCREAMING_SNAKE_CASE  :  str								=		max_sequence_length  # No generation bigger than model size
 elif length < 0:
  SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		MAX_LENGTH  # avoid infinite loop
 return length



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	List[Any]							)       ->	Any:
 SCREAMING_SNAKE_CASE  :  Any								=		None
 if hasattr(snake_case_   ,     'hidden_size'							):
  SCREAMING_SNAKE_CASE  :  int								=		model_config.hidden_size
 elif hasattr(snake_case_   ,     'n_embed'							):
  SCREAMING_SNAKE_CASE  :  Optional[int]								=		model_config.n_embed
 elif hasattr(snake_case_   ,     'n_embd'							):
  SCREAMING_SNAKE_CASE  :  Tuple								=		model_config.n_embd

 SCREAMING_SNAKE_CASE  :  Optional[Any]								=		None
 if hasattr(snake_case_   ,     'num_attention_heads'							):
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		model_config.num_attention_heads
 elif hasattr(snake_case_   ,     'n_head'							):
  SCREAMING_SNAKE_CASE  :  List[str]								=		model_config.n_head

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

 SCREAMING_SNAKE_CASE  :  Tuple								=		int(embedding_size / num_head							)
 if hasattr(snake_case_   ,     'n_layer'							):
  SCREAMING_SNAKE_CASE  :  Dict								=		model_config.n_layer
 elif hasattr(snake_case_   ,     'num_hidden_layers'							):
  SCREAMING_SNAKE_CASE  :  Optional[Any]								=		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        SCREAMING_SNAKE_CASE_  (	snake_case_				:	List[Any]   ,     snake_case_				:	Any   ,     snake_case_				:	int							)       ->	int:
 SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  str								=		sparse_model_config(model.config							)
 if model.config.model_type == "bloom":
  SCREAMING_SNAKE_CASE  :  Any								=		tuple(
      (
          torch.empty(int(num_attention_heads * batch_size							)   ,     snake_case_   ,     snake_case_							)
          .to(model.dtype							)
          .to(model.device							),
          torch.empty(int(num_attention_heads * batch_size							)   ,     snake_case_   ,     snake_case_							)
          .to(model.dtype							)
          .to(model.device							),
      )
      for _ in range(snake_case_							)							)
 else:
  SCREAMING_SNAKE_CASE  :  List[Any]								=		tuple(
      (
          torch.empty(snake_case_   ,     snake_case_   ,     snake_case_   ,     snake_case_							)
          .to(model.dtype							)
          .to(model.device							),
          torch.empty(snake_case_   ,     snake_case_   ,     snake_case_   ,     snake_case_							)
          .to(model.dtype							)
          .to(model.device							),
      )
      for _ in range(snake_case_							)							)
 return past_key_values



def        SCREAMING_SNAKE_CASE_  (	snake_case_				:	Dict   ,     snake_case_				:	Tuple   ,     snake_case_				:	int							)       ->	Optional[int]:
 SCREAMING_SNAKE_CASE  :  Optional[int]								=		len(snake_case_							)
 SCREAMING_SNAKE_CASE  :  Optional[int]								=		tokenizer.batch_encode_plus(snake_case_   ,     return_tensors='pt'							)
 SCREAMING_SNAKE_CASE  :  Any								=		dummy_input.to(model.device							)
 if model.config.use_cache:
  SCREAMING_SNAKE_CASE  :  str								=		generate_past_key_values(snake_case_   ,     snake_case_   ,     1							)
 SCREAMING_SNAKE_CASE  :  str								=		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 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   A		= ('_optimized', '_default')






   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    SCREAMING_SNAKE_CASE  :  Tuple								=		optimized
    SCREAMING_SNAKE_CASE  :  List[Any]								=		default






   def __call__(       self    ,*__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      ):
    if kwargs["past_key_values"] is None and self._default.config.use_cache:
     SCREAMING_SNAKE_CASE  :  List[str]								=		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      )
    SCREAMING_SNAKE_CASE  :  Dict								=		self._optimized(**__SCREAMING_SNAKE_CASE      )
    SCREAMING_SNAKE_CASE  :  Tuple								=		outputs[0]
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		outputs[1]
    SCREAMING_SNAKE_CASE  :  int								=		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      ):
    return getattr(self._default    ,__SCREAMING_SNAKE_CASE      )






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=None    ,**__SCREAMING_SNAKE_CASE      ):
    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 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      ):
    return self._default._reorder_cache(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE      )



def        SCREAMING_SNAKE_CASE_  (	)       ->	Optional[Any]:
 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		argparse.ArgumentParser()
 parser.add_argument(
     '--model_type'   ,     default=snake_case_   ,     type=snake_case_   ,     required=snake_case_   ,     help='Model type selected in the list: ' + ', '.join(MODEL_CLASSES.keys()							)   ,     )
 parser.add_argument(
     '--model_name_or_path'   ,     default=snake_case_   ,     type=snake_case_   ,     required=snake_case_   ,     help='Path to pre-trained model or shortcut name selected in the list: ' + ', '.join(MODEL_CLASSES.keys()							)   ,     )

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

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

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

 parser.add_argument('--seed'   ,     type=snake_case_   ,     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=snake_case_   ,     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'							)
 SCREAMING_SNAKE_CASE  :  str								=		parser.parse_args()

 SCREAMING_SNAKE_CASE  :  List[str]								=		torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'							)
 SCREAMING_SNAKE_CASE  :  Optional[int]								=		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(snake_case_							)

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

 SCREAMING_SNAKE_CASE  :  Tuple								=		tokenizer_class.from_pretrained(args.model_name_or_path							)
 if tokenizer.pad_token is None:
  SCREAMING_SNAKE_CASE  :  int								=		tokenizer.eos_token
 SCREAMING_SNAKE_CASE  :  Any								=		model_class.from_pretrained(args.model_name_or_path							)
 model.to(args.device							)

 if args.fpaa:
  model.half()
 SCREAMING_SNAKE_CASE  :  int								=		getattr(model.config   ,     'max_position_embeddings'   ,     0							)
 SCREAMING_SNAKE_CASE  :  Dict								=		adjust_length_to_model(args.length   ,     max_sequence_length=snake_case_							)
 logger.info(snake_case_							)

 SCREAMING_SNAKE_CASE  :  Any								=		args.prompt if args.prompt else input('Model prompt >>> '							)

 # Different models need different input formatting and/or extra arguments
 SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		args.model_type in PREPROCESSING_FUNCTIONS.keys()
 if requires_preprocessing:
  SCREAMING_SNAKE_CASE  :  List[str]								=		PREPROCESSING_FUNCTIONS.get(args.model_type							)
  SCREAMING_SNAKE_CASE  :  str								=		prepare_input(snake_case_   ,     snake_case_   ,     snake_case_   ,     snake_case_							)

  if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
   SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		{'add_space_before_punct_symbol': True}
  else:
   SCREAMING_SNAKE_CASE  :  Optional[int]								=		{}

  SCREAMING_SNAKE_CASE  :  List[str]								=		tokenizer.encode(
      snake_case_   ,     add_special_tokens=snake_case_   ,     return_tensors='pt'   ,     **snake_case_							)
 else:
  SCREAMING_SNAKE_CASE  :  Dict								=		args.prefix if args.prefix else args.padding_text
  SCREAMING_SNAKE_CASE  :  int								=		tokenizer.encode(prefix + prompt_text   ,     add_special_tokens=snake_case_   ,     return_tensors='pt'							)
 SCREAMING_SNAKE_CASE  :  int								=		encoded_prompt.to(args.device							)

 if encoded_prompt.size()[-1] == 0:
  SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		None
 else:
  SCREAMING_SNAKE_CASE  :  int								=		encoded_prompt

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

  SCREAMING_SNAKE_CASE  :  List[str]								=		_ModelFallbackWrapper(snake_case_   ,     snake_case_							)

 SCREAMING_SNAKE_CASE  :  str								=		model.generate(
     input_ids=snake_case_   ,     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=snake_case_   ,     num_return_sequences=args.num_return_sequences   ,     )

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

 SCREAMING_SNAKE_CASE  :  int								=		[]

 for generated_sequence_idx, generated_sequence in enumerate(snake_case_							):
  print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""							)
  SCREAMING_SNAKE_CASE  :  int								=		generated_sequence.tolist()

  # Decode text
  SCREAMING_SNAKE_CASE  :  List[Any]								=		tokenizer.decode(snake_case_   ,     clean_up_tokenization_spaces=snake_case_							)

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

  generated_sequences.append(snake_case_							)
  print(snake_case_							)

 return generated_sequences


if __name__ == "__main__":
   main()




'''simple docstring'''

from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__UpperCAmelCase												=   logging.get_logger(__name__)

__UpperCAmelCase												=   {
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}


class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   A		= 'gpt2'
   A		= ['past_key_values']
   A		= {
       'hidden_size': 'n_embd',
       'max_position_embeddings': 'n_positions',
       'num_attention_heads': 'n_head',
       'num_hidden_layers': 'n_layer',
   }






   def __init__(       self    ,__SCREAMING_SNAKE_CASE=50257    ,__SCREAMING_SNAKE_CASE=1024    ,__SCREAMING_SNAKE_CASE=768    ,__SCREAMING_SNAKE_CASE=12    ,__SCREAMING_SNAKE_CASE=12    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE="gelu_new"    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=1e-5    ,__SCREAMING_SNAKE_CASE=0.02    ,__SCREAMING_SNAKE_CASE="cls_index"    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=None    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=0.1    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=True    ,__SCREAMING_SNAKE_CASE=50256    ,__SCREAMING_SNAKE_CASE=50256    ,__SCREAMING_SNAKE_CASE=False    ,__SCREAMING_SNAKE_CASE=False    ,**__SCREAMING_SNAKE_CASE    ,):
    SCREAMING_SNAKE_CASE  :  Optional[Any]								=		vocab_size
    SCREAMING_SNAKE_CASE  :  Tuple								=		n_positions
    SCREAMING_SNAKE_CASE  :  int								=		n_embd
    SCREAMING_SNAKE_CASE  :  Dict								=		n_layer
    SCREAMING_SNAKE_CASE  :  Dict								=		n_head
    SCREAMING_SNAKE_CASE  :  List[Any]								=		n_inner
    SCREAMING_SNAKE_CASE  :  Dict								=		activation_function
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		resid_pdrop
    SCREAMING_SNAKE_CASE  :  str								=		embd_pdrop
    SCREAMING_SNAKE_CASE  :  int								=		attn_pdrop
    SCREAMING_SNAKE_CASE  :  Tuple								=		layer_norm_epsilon
    SCREAMING_SNAKE_CASE  :  Tuple								=		initializer_range
    SCREAMING_SNAKE_CASE  :  Any								=		summary_type
    SCREAMING_SNAKE_CASE  :  List[Any]								=		summary_use_proj
    SCREAMING_SNAKE_CASE  :  int								=		summary_activation
    SCREAMING_SNAKE_CASE  :  int								=		summary_first_dropout
    SCREAMING_SNAKE_CASE  :  List[Any]								=		summary_proj_to_labels
    SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		scale_attn_weights
    SCREAMING_SNAKE_CASE  :  Any								=		use_cache
    SCREAMING_SNAKE_CASE  :  List[str]								=		scale_attn_by_inverse_layer_idx
    SCREAMING_SNAKE_CASE  :  Any								=		reorder_and_upcast_attn

    SCREAMING_SNAKE_CASE  :  Tuple								=		bos_token_id
    SCREAMING_SNAKE_CASE  :  Optional[int]								=		eos_token_id

    super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE    ,eos_token_id=__SCREAMING_SNAKE_CASE    ,**__SCREAMING_SNAKE_CASE      )



class 		_a     (							SCREAMING_SNAKE_CASE  ):







   """simple docstring"""




   def __init__(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = "default"    ,__SCREAMING_SNAKE_CASE = None    ,__SCREAMING_SNAKE_CASE = False    ,):
    super().__init__(__SCREAMING_SNAKE_CASE    ,task=__SCREAMING_SNAKE_CASE    ,patching_specs=__SCREAMING_SNAKE_CASE    ,use_past=__SCREAMING_SNAKE_CASE      )
    if not getattr(self._config    ,'pad_token_id'    ,__SCREAMING_SNAKE_CASE      ):
     # TODO: how to do that better?
     SCREAMING_SNAKE_CASE  :  int								=		0






   @property
   def 					__a			(       self      ):
    SCREAMING_SNAKE_CASE  :  Dict								=		OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}      )
    if self.use_past:
     self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE    ,direction='inputs'      )
     SCREAMING_SNAKE_CASE  :  Dict								=		{0: 'batch', 1: 'past_sequence + sequence'}
    else:
     SCREAMING_SNAKE_CASE  :  Optional[Any]								=		{0: 'batch', 1: 'sequence'}

    return common_inputs






   @property
   def 					__a			(       self      ):
    return self._config.n_layer






   @property
   def 					__a			(       self      ):
    return self._config.n_head






   def 					__a			(       self    ,__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE = -1    ,__SCREAMING_SNAKE_CASE = -1    ,__SCREAMING_SNAKE_CASE = False    ,__SCREAMING_SNAKE_CASE = None    ,):
    SCREAMING_SNAKE_CASE  :  List[str]								=		super(__SCREAMING_SNAKE_CASE    ,self      ).generate_dummy_inputs(
        __SCREAMING_SNAKE_CASE    ,batch_size=__SCREAMING_SNAKE_CASE    ,seq_length=__SCREAMING_SNAKE_CASE    ,is_pair=__SCREAMING_SNAKE_CASE    ,framework=__SCREAMING_SNAKE_CASE      )

    # We need to order the input in the way they appears in the forward()
    SCREAMING_SNAKE_CASE  :  int								=		OrderedDict({'input_ids': common_inputs['input_ids']}      )

    # Need to add the past_keys
    if self.use_past:
     if not is_torch_available():
      raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'      )
     else:
      import torch

      SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE  :  List[str]								=		common_inputs['input_ids'].shape
      # Not using the same length for past_key_values
      SCREAMING_SNAKE_CASE  :  str								=		seqlen + 2
      SCREAMING_SNAKE_CASE  :  Dict								=		(
          batch,
          self.num_attention_heads,
          past_key_values_length,
          self._config.hidden_size // self.num_attention_heads,
      )
      SCREAMING_SNAKE_CASE  :  Union[str, Any]								=		[
          (torch.zeros(__SCREAMING_SNAKE_CASE      ), torch.zeros(__SCREAMING_SNAKE_CASE      )) for _ in range(self.num_layers      )
      ]

    SCREAMING_SNAKE_CASE  :  Optional[int]								=		common_inputs['attention_mask']
    if self.use_past:
     SCREAMING_SNAKE_CASE  :  Tuple								=		ordered_inputs['attention_mask'].dtype
     SCREAMING_SNAKE_CASE  :  Any								=		torch.cat(
         [ordered_inputs['attention_mask'], torch.ones(__SCREAMING_SNAKE_CASE    ,__SCREAMING_SNAKE_CASE    ,dtype=__SCREAMING_SNAKE_CASE      )]    ,dim=1      )

    return ordered_inputs






   @property
   def 					__a			(       self      ):
    return 13


