

'''simple docstring'''



import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM

def   lowerCamelCase__       (      a__)		->    Tuple:



    """simple docstring"""







    if "model" in orig_key:
        _snake_case : Any												=		orig_key.replace('model.'			,      '')
    if "norm1" in orig_key:
        _snake_case : List[str]												=		orig_key.replace('norm1'			,      'attention.output.LayerNorm')
    if "norm2" in orig_key:
        _snake_case : List[Any]												=		orig_key.replace('norm2'			,      'output.LayerNorm')
    if "norm" in orig_key:
        _snake_case : int												=		orig_key.replace('norm'			,      'LayerNorm')
    if "transformer" in orig_key:
        _snake_case : str												=		orig_key.split('.')[0].split('_')[-1]
        _snake_case : Optional[Any]												=		orig_key.replace(F"""transformer_{layer_num}"""			,      F"""encoder.layer.{layer_num}""")
    if "mha.attn" in orig_key:
        _snake_case : Tuple												=		orig_key.replace('mha.attn'			,      'attention.self')
    if "mha" in orig_key:
        _snake_case : List[Any]												=		orig_key.replace('mha'			,      'attention')
    if "W_q" in orig_key:
        _snake_case : Optional[Any]												=		orig_key.replace('W_q'			,      'self.query')
    if "W_k" in orig_key:
        _snake_case : Union[str, Any]												=		orig_key.replace('W_k'			,      'self.key')
    if "W_v" in orig_key:
        _snake_case : str												=		orig_key.replace('W_v'			,      'self.value')
    if "ff1" in orig_key:
        _snake_case : Union[str, Any]												=		orig_key.replace('ff1'			,      'intermediate.dense')
    if "ff2" in orig_key:
        _snake_case : str												=		orig_key.replace('ff2'			,      'output.dense')
    if "ff" in orig_key:
        _snake_case : List[Any]												=		orig_key.replace('ff'			,      'output.dense')
    if "mlm_class" in orig_key:
        _snake_case : Any												=		orig_key.replace('mlm.mlm_class'			,      'cls.predictions.decoder')
    if "mlm" in orig_key:
        _snake_case : Optional[int]												=		orig_key.replace('mlm'			,      'cls.predictions.transform')
    if "cls" not in orig_key:
        _snake_case : List[Any]												=		'nystromformer.' + orig_key

    return orig_key

def   lowerCamelCase__       (      a__			,      a__)		->    int:



    """simple docstring"""







    for key in orig_state_dict.copy().keys():
        _snake_case : Dict												=		orig_state_dict.pop(a__)

        if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
            continue
        else:
            _snake_case : str												=		val

    _snake_case : Union[str, Any]												=		orig_state_dict['cls.predictions.decoder.bias']
    _snake_case : str												=		(
        torch.arange(config.max_position_embeddings).expand((1, -1)) + 2
    )

    return orig_state_dict

def   lowerCamelCase__       (      a__			,      a__			,      a__)		->    Union[str, Any]:



    """simple docstring"""







    _snake_case : List[Any]												=		torch.load(a__			,      map_location='cpu')['model_state_dict']
    _snake_case : List[str]												=		NystromformerConfig.from_json_file(a__)
    _snake_case : str												=		NystromformerForMaskedLM(a__)

    _snake_case : Optional[Any]												=		convert_checkpoint_helper(a__			,      a__)

    model.load_state_dict(a__)
    model.eval()
    model.save_pretrained(a__)

    print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""")


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_    =   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--pytorch_model_path", default=None, type=str, required=True, help="Path to Nystromformer pytorch checkpoint."
   )
   parser.add_argument(
       "--config_file",
       default=None,
       type=str,
       required=True,
       help="The json file for Nystromformer model config.",
   )
   parser.add_argument(
       "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   SCREAMING_SNAKE_CASE_    =   parser.parse_args()
   convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






'''simple docstring'''



import argparse
import logging

import numpy as np
import torch

from transformers import AutoModelForCausalLM, AutoTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)
SCREAMING_SNAKE_CASE_    =   logging.getLogger(__name__)

def   lowerCamelCase__       (      a__)		->    List[Any]:



    """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   lowerCamelCase__       (      )		->    Tuple:



    """simple docstring"""







    _snake_case : Optional[int]												=		argparse.ArgumentParser()
    parser.add_argument(
        '--model_name_or_path'			,      default=a__			,      type=a__			,      required=a__			,      )
    parser.add_argument('--prompt'			,      type=a__			,      default='')
    parser.add_argument('--length'			,      type=a__			,      default=2_0)
    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('--penalty_alpha'			,      type=a__			,      default=0.0)
    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=4_2			,      help='random seed for initialization')
    parser.add_argument('--no_cuda'			,      action='store_true'			,      help='Avoid using CUDA when available')
    parser.add_argument(
        '--fp16'			,      action='store_true'			,      help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'			,      )
    _snake_case : Optional[int]												=		parser.parse_args()

    _snake_case : Optional[int]												=		torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu')
    _snake_case : 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(a__)

    # Initialize the model and tokenizer
    _snake_case : Dict												=		AutoTokenizer.from_pretrained(args.model_name_or_path)
    _snake_case : List[Any]												=		AutoModelForCausalLM.from_pretrained(args.model_name_or_path)

    # tokenizer = GPT2Tokenizer.from_pretrained(args.model_name_or_path)
    # model = OPTForCausalLM.from_pretrained(args.model_name_or_path)
    model.to(args.device)

    if args.fpaa:
        model.half()

    logger.info(a__)
    _snake_case : Any												=		args.prompt if args.prompt else input('Model prompt >>> ')

    _snake_case : List[str]												=		tokenizer(a__			,      return_tensors='pt'			,      add_special_tokens=a__)
    _snake_case : List[str]												=		{key: value.to(args.device) for key, value in inputs.items()}

    _snake_case : Optional[int]												=		model.generate(
        **a__			,      max_length=args.length + len(inputs['input_ids'][0])			,      penalty_alpha=args.penalty_alpha			,      top_k=args.k			,      )

    _snake_case : int												=		[]
    for generated_sequence_idx, generated_sequence in enumerate(a__):
        print(F"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ===""")
        _snake_case : Any												=		generated_sequence.tolist()

        # Decode text
        _snake_case : str												=		tokenizer.decode(a__			,      clean_up_tokenization_spaces=a__			,      add_special_tokens=a__)

        # Remove all text after the stop token
        _snake_case : str												=		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
        _snake_case : Tuple												=		(
            prompt_text + text[len(tokenizer.decode(inputs['input_ids'][0]			,      clean_up_tokenization_spaces=a__)) :]
        )

        generated_sequences.append(a__)
        print(a__)

    return generated_sequences


if __name__ == "__main__":
   main()



