
'''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,
)
A							     = logging.getLogger(__name__)
def 	snake_case_							(				_a  :				Any      ):
      np.random.seed(args.seed      )
      torch.manual_seed(args.seed      )
      if args.n_gpu > 0:
            torch.cuda.manual_seed_all(args.seed      )
def 	snake_case_							(				):
      snake_case_    							=				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=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("--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=42			,       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_    							=				parser.parse_args()

      snake_case_    							=				torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu"      )
      snake_case_    							=				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_    							=				AutoTokenizer.from_pretrained(args.model_name_or_path      )
      snake_case_    							=				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_    							=				args.prompt if args.prompt else input("Model prompt >>> "      )

      snake_case_    							=				tokenizer(_a			,       return_tensors="pt"			,       add_special_tokens=_a      )
      snake_case_    							=				{key: value.to(args.device      ) for key, value in inputs.items()}

      snake_case_    							=				model.generate(
          **_a			,       max_length=args.length + len(inputs["input_ids"][0]      )			,       penalty_alpha=args.penalty_alpha			,       top_k=args.k			,       )

      snake_case_    							=				[]
      for generated_sequence_idx, generated_sequence in enumerate(_a      ):
            print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''      )
            snake_case_    							=				generated_sequence.tolist()

            # Decode text
            snake_case_    							=				tokenizer.decode(_a			,       clean_up_tokenization_spaces=_a			,       add_special_tokens=_a      )

            # Remove all text after the stop token
            snake_case_    							=				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_    							=				(
                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()






'''simple docstring'''





import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union


class 							_lowerCAmelCase	(  enum.Enum							):


 """simple docstring"""

 lowerCamelCase									=     '''timm'''
 lowerCamelCase									=     '''transformers'''



def     UpperCAmelCase     (    a_      ,		a_      ,		a_		)   ->       Optional[Any]:



   """simple docstring"""






   if stage_names is None:
      raise ValueError("""Stage_names must be set for transformers backbones"""		)

   if out_features is not None:
      if not isinstance(a_      ,		(list,)		):
         raise ValueError(F"out_features must be a list {type(a_		)}"		)
      if any(feat not in stage_names for feat in out_features		):
         raise ValueError(F"out_features must be a subset of stage_names: {stage_names} got {out_features}"		)

   if out_indices is not None:
      if not isinstance(a_      ,		(list, tuple)		):
         raise ValueError(F"out_indices must be a list or tuple, got {type(a_		)}"		)
      if any(idx >= len(a_		) for idx in out_indices		):
         raise ValueError("""out_indices must be valid indices for stage_names {stage_names}, got {out_indices}"""		)

   if out_features is not None and out_indices is not None:
      if len(a_		) != len(a_		):
         raise ValueError("""out_features and out_indices should have the same length if both are set"""		)
      if out_features != [stage_names[idx] for idx in out_indices]:
         raise ValueError("""out_features and out_indices should correspond to the same stages if both are set"""		)



def     UpperCAmelCase     (    a_      ,		a_      ,		a_      ,		)   ->       Dict:



   """simple docstring"""






   if out_indices is None and out_features is None:
      A_  :		Optional[Any]						=					[len(a_		) - 1]
      A_  :		Optional[int]						=					[stage_names[-1]]
   elif out_indices is None and out_features is not None:
      A_  :		Optional[Any]						=					[stage_names.index(a_		) for layer in out_features]
   elif out_features is None and out_indices is not None:
      A_  :		int						=					[stage_names[idx] for idx in out_indices]
   return out_features, out_indices



def     UpperCAmelCase     (    a_      ,		a_      ,		a_      ,		)   ->       Tuple[List[str], List[int]]:



   """simple docstring"""






   verify_out_features_out_indices(out_features=a_      ,		out_indices=a_      ,		stage_names=a_		)
   A_						,  A_  :		List[Any]						=					_align_output_features_output_indices(
       out_features=a_      ,		out_indices=a_      ,		stage_names=a_		)
   # Verify that the aligned out_features and out_indices are valid
   verify_out_features_out_indices(out_features=a_      ,		out_indices=a_      ,		stage_names=a_		)
   return output_features, output_indices


class 							_lowerCAmelCase	:


 """simple docstring"""

 lowerCamelCase									=     None







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    None:
    if getattr(self					,    """_backbone"""					,    _lowerCamelCase  ) is None:
       raise ValueError("""self._backbone must be set before calling _init_timm_backbone"""  )

    # These will diagree with the defaults for the transformers models e.g. for resnet50
    # the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
    # the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
    A_  :		str						=					[stage["""module"""] for stage in self._backbone.feature_info.info]
    A_  :		Any						=					[stage["""num_chs"""] for stage in self._backbone.feature_info.info]
    A_  :		Tuple						=					self._backbone.feature_info.out_indices
    A_  :		str						=					self._backbone.feature_info.module_name()

    # We verify the out indices and out features are valid
    verify_out_features_out_indices(
        out_features=_lowerCamelCase					,    out_indices=_lowerCamelCase					,    stage_names=self.stage_names  )
    A_						,  A_  :		List[Any]						=					out_features, out_indices







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    None:
    A_  :		List[Any]						=					getattr(_lowerCamelCase					,    """stage_names"""  )
    A_  :		int						=					getattr(_lowerCamelCase					,    """out_features"""					,    _lowerCamelCase  )
    A_  :		Optional[int]						=					getattr(_lowerCamelCase					,    """out_indices"""					,    _lowerCamelCase  )

    A_  :		Union[str, Any]						=					stage_names
    A_						,  A_  :		List[str]						=					get_aligned_output_features_output_indices(
        out_features=_lowerCamelCase					,    out_indices=_lowerCamelCase					,    stage_names=_lowerCamelCase  )
    # Number of channels for each stage. This is set in the transformer backbone model init
    A_  :		int						=					None







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    None:
    A_  :		Optional[Any]						=					config

    A_  :		Optional[Any]						=					getattr(_lowerCamelCase					,    """use_timm_backbone"""					,    _lowerCamelCase  )
    A_  :		Optional[int]						=					BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

    if self.backbone_type == BackboneType.TIMM:
       self._init_timm_backbone(_lowerCamelCase  )
    elif self.backbone_type == BackboneType.TRANSFORMERS:
       self._init_transformers_backbone(_lowerCamelCase  )
    else:
       raise ValueError(F"backbone_type {self.backbone_type} not supported."  )







 @property
 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    return self._out_features







 @out_features.setter
 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Any:
    A_						,  A_  :		Union[str, Any]						=					get_aligned_output_features_output_indices(
        out_features=_lowerCamelCase					,    out_indices=_lowerCamelCase					,    stage_names=self.stage_names  )







 @property
 def       UpperCAmelCase_       (      self  )    ->    Any:
    return self._out_indices







 @out_indices.setter
 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    Union[str, Any]:
    A_						,  A_  :		List[str]						=					get_aligned_output_features_output_indices(
        out_features=_lowerCamelCase					,    out_indices=_lowerCamelCase					,    stage_names=self.stage_names  )







 @property
 def       UpperCAmelCase_       (      self  )    ->    Dict:
    # the current backbones will output the number of channels for each stage
    # even if that stage is not in the out_features list.
    return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names  )}







 @property
 def       UpperCAmelCase_       (      self  )    ->    Any:
    return [self.out_feature_channels[name] for name in self.out_features]







 def       UpperCAmelCase_       (      self					,    *_lowerCamelCase					,    **_lowerCamelCase  )    ->    List[Any]:
    A_  :		Optional[int]						=					dict(inspect.signature(self.forward  ).parameters  )
    A_  :		str						=					{k: v for k, v in kwargs.items() if k in signature}
    return self(*_lowerCamelCase					,    **_lowerCamelCase  )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase = None					,    _lowerCamelCase = None					,    _lowerCamelCase = None					,    )    ->    List[Any]:
    raise NotImplementedError("""This method should be implemented by the derived class."""  )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		Optional[int]						=					super().to_dict()
    A_  :		Union[str, Any]						=					output.pop("""_out_features"""  )
    A_  :		int						=					output.pop("""_out_indices"""  )
    return output


class 							_lowerCAmelCase	:


 """simple docstring"""

 @property
 def       UpperCAmelCase_       (      self  )    ->    Union[str, Any]:
    return self._out_features







 @out_features.setter
 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    List[str]:
    A_						,  A_  :		List[str]						=					get_aligned_output_features_output_indices(
        out_features=_lowerCamelCase					,    out_indices=_lowerCamelCase					,    stage_names=self.stage_names  )







 @property
 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    return self._out_indices







 @out_indices.setter
 def       UpperCAmelCase_       (      self					,    _lowerCamelCase  )    ->    str:
    A_						,  A_  :		Union[str, Any]						=					get_aligned_output_features_output_indices(
        out_features=_lowerCamelCase					,    out_indices=_lowerCamelCase					,    stage_names=self.stage_names  )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		Optional[int]						=					super().to_dict()
    A_  :		Tuple						=					output.pop("""_out_features"""  )
    A_  :		Optional[Any]						=					output.pop("""_out_indices"""  )
    return output
