


import os
import pathlib
import tempfile
import uuid

import numpy as np

from ..utils import is_soundfile_availble, is_torch_available, is_vision_available, logging


__a   				=      logging.get_logger(__name__)

if is_vision_available():
  import PIL.Image
  from PIL import Image
  from PIL.Image import Image as ImageType
else:
  __a   				=      object

if is_torch_available():
  import torch

if is_soundfile_availble():
  import soundfile as sf







class 				__SCREAMING_SNAKE_CASE   :


      def __init__(							self							,						SCREAMING_SNAKE_CASE__		):
            lowercase			:   List[str]					   =		value


      def __str__(							self		):
            return self.to_string()


      def        __lowerCamelCase			(							self		):
            logger.error(
                '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''		)
            return self._value






      def        __lowerCamelCase			(							self		):
            logger.error(
                '''This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'''		)
            return str(self._value		)







class 				__SCREAMING_SNAKE_CASE   (			A__  ,						A__   ):


      def        __lowerCamelCase			(							self		):
            return self._value






      def        __lowerCamelCase			(							self		):
            return self._value







class 				__SCREAMING_SNAKE_CASE   (			A__  ,						A__   ):


      def __init__(							self							,						SCREAMING_SNAKE_CASE__		):
            super().__init__(SCREAMING_SNAKE_CASE__		)

            if not is_vision_available():
                  raise ImportError('''PIL must be installed in order to handle images.'''		)

            lowercase			:   Tuple					   =		None
            lowercase			:   str					   =		None
            lowercase			:   Dict					   =		None

            if isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
                  lowercase			:   Tuple					   =		value
            elif isinstance(SCREAMING_SNAKE_CASE__							,						(str, pathlib.Path)		):
                  lowercase			:   List[str]					   =		value
            elif isinstance(SCREAMING_SNAKE_CASE__							,						torch.Tensor		):
                  lowercase			:   Dict					   =		value
            else:
                  raise ValueError(f"""Unsupported type for {self.__class__.__name__}: {type(SCREAMING_SNAKE_CASE__		)}"""		)


      def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__=None							,						SCREAMING_SNAKE_CASE__=None		):
            from IPython.display import Image, display

            display(Image(self.to_string()		)		)


      def        __lowerCamelCase			(							self		):
            if self._raw is not None:
                  return self._raw

            if self._path is not None:
                  lowercase			:   List[Any]					   =		Image.open(self._path		)
                  return self._raw






      def        __lowerCamelCase			(							self		):
            if self._path is not None:
                  return self._path

            if self._raw is not None:
                  lowercase			:   Tuple					   =		tempfile.mkdtemp()
                  lowercase			:   List[str]					   =		os.path.join(SCREAMING_SNAKE_CASE__							,						str(uuid.uuida()		) + '''.png'''		)
                  self._raw.save(self._path		)

                  return self._path

            if self._tensor is not None:
                  lowercase			:   Optional[int]					   =		self._tensor.cpu().detach().numpy()

                  # There is likely simpler than load into image into save
                  lowercase			:   Tuple					   =		Image.fromarray((array * 255).astype(np.uinta		)		)

                  lowercase			:   Union[str, Any]					   =		tempfile.mkdtemp()
                  lowercase			:   Tuple					   =		os.path.join(SCREAMING_SNAKE_CASE__							,						str(uuid.uuida()		) + '''.png'''		)

                  img.save(self._path		)

                  return self._path







class 				__SCREAMING_SNAKE_CASE   (			A__   ):


      def __init__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__=16000		):
            super().__init__(SCREAMING_SNAKE_CASE__		)

            if not is_soundfile_availble():
                  raise ImportError('''soundfile must be installed in order to handle audio.'''		)

            lowercase			:   Union[str, Any]					   =		None
            lowercase			:   List[Any]					   =		None

            lowercase			:   Union[str, Any]					   =		samplerate

            if isinstance(SCREAMING_SNAKE_CASE__							,						(str, pathlib.Path)		):
                  lowercase			:   str					   =		value
            elif isinstance(SCREAMING_SNAKE_CASE__							,						torch.Tensor		):
                  lowercase			:   Dict					   =		value
            else:
                  raise ValueError(f"""Unsupported audio type: {type(SCREAMING_SNAKE_CASE__		)}"""		)


      def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__=None							,						SCREAMING_SNAKE_CASE__=None		):
            from IPython.display import Audio, display

            display(Audio(self.to_string()							,						rate=self.samplerate		)		)


      def        __lowerCamelCase			(							self		):
            if self._tensor is not None:
                  return self._tensor

            if self._path is not None:
                  lowercase   ,			lowercase			:   Dict					   =		sf.read(self._path		)
                  lowercase			:   Dict					   =		torch.tensor(SCREAMING_SNAKE_CASE__		)
                  return self._tensor






      def        __lowerCamelCase			(							self		):
            if self._path is not None:
                  return self._path

            if self._tensor is not None:
                  lowercase			:   Optional[Any]					   =		tempfile.mkdtemp()
                  lowercase			:   int					   =		os.path.join(SCREAMING_SNAKE_CASE__							,						str(uuid.uuida()		) + '''.wav'''		)
                  sf.write(self._path							,						self._tensor							,						samplerate=self.samplerate		)
                  return self._path





__a   				=      {'''text''': AgentText, '''image''': AgentImage, '''audio''': AgentAudio}
__a   				=      {str: AgentText}

if is_vision_available():
  __a   				=      AgentImage




def 			__lowercase	(						*_UpperCamelCase,    **_UpperCamelCase     )    ->str:







      """simple docstring"""

      lowercase			:   List[Any]					   =		[(arg.to_raw() if isinstance(_UpperCamelCase,    _UpperCamelCase     ) else arg) for arg in args]
      lowercase			:   str					   =		{k: (v.to_raw() if isinstance(_UpperCamelCase,    _UpperCamelCase     ) else v) for k, v in kwargs.items()}
      return args, kwargs




def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase=None     )    ->Optional[int]:







      """simple docstring"""

      if isinstance(_UpperCamelCase,    _UpperCamelCase     ):
            lowercase			:   Tuple					   =		{}
            for i, (k, v) in enumerate(outputs.items()     ):
                  if output_types is not None:
                        # If the class has defined outputs, we can map directly according to the class definition
                        if output_types[i] in AGENT_TYPE_MAPPING:
                              lowercase			:   Optional[int]					   =		AGENT_TYPE_MAPPING[output_types[i]](_UpperCamelCase     )
                        else:
                              lowercase			:   str					   =		AgentType(_UpperCamelCase     )

                  else:
                        # If the class does not have defined output, then we map according to the type
                        for _k, _v in INSTANCE_TYPE_MAPPING.items():
                              if isinstance(_UpperCamelCase,    _k     ):
                                    lowercase			:   List[Any]					   =		_v(_UpperCamelCase     )
                        if k not in decoded_outputs:
                              lowercase			:   Optional[Any]					   =		AgentType[v]

      elif isinstance(_UpperCamelCase,    (list, tuple)     ):
            lowercase			:   str					   =		type(_UpperCamelCase     )()
            for i, v in enumerate(_UpperCamelCase     ):
                  if output_types is not None:
                        # If the class has defined outputs, we can map directly according to the class definition
                        if output_types[i] in AGENT_TYPE_MAPPING:
                              decoded_outputs.append(AGENT_TYPE_MAPPING[output_types[i]](_UpperCamelCase     )     )
                        else:
                              decoded_outputs.append(AgentType(_UpperCamelCase     )     )
                  else:
                        # If the class does not have defined output, then we map according to the type
                        lowercase			:   Union[str, Any]					   =		False
                        for _k, _v in INSTANCE_TYPE_MAPPING.items():
                              if isinstance(_UpperCamelCase,    _k     ):
                                    decoded_outputs.append(_v(_UpperCamelCase     )     )
                                    lowercase			:   Dict					   =		True

                        if not found:
                              decoded_outputs.append(AgentType(_UpperCamelCase     )     )

      else:
            if output_types[0] in AGENT_TYPE_MAPPING:
                  # If the class has defined outputs, we can map directly according to the class definition
                  lowercase			:   Any					   =		AGENT_TYPE_MAPPING[output_types[0]](_UpperCamelCase     )

            else:
                  # If the class does not have defined output, then we map according to the type
                  for _k, _v in INSTANCE_TYPE_MAPPING.items():
                        if isinstance(_UpperCamelCase,    _k     ):
                              return _v(_UpperCamelCase     )
                  return AgentType(_UpperCamelCase     )

      return decoded_outputs





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 			__lowercase	(						_UpperCamelCase     )    ->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 			__lowercase	(						)    ->List[str]:







      """simple docstring"""

      lowercase			:   List[str]					   =		argparse.ArgumentParser()
      parser.add_argument(
          '''--model_name_or_path''',    default=_UpperCamelCase,    type=_UpperCamelCase,    required=_UpperCamelCase,    )
      parser.add_argument('''--prompt''',    type=_UpperCamelCase,    default=''''''     )
      parser.add_argument('''--length''',    type=_UpperCamelCase,    default=20     )
      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('''--penalty_alpha''',    type=_UpperCamelCase,    default=0.0     )
      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=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''',    )
      lowercase			:   Any					   =		parser.parse_args()

      lowercase			:   int					   =		torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''     )
      lowercase			:   List[str]					   =		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
      lowercase			:   Optional[Any]					   =		AutoTokenizer.from_pretrained(args.model_name_or_path     )
      lowercase			:   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(_UpperCamelCase     )
      lowercase			:   Union[str, Any]					   =		args.prompt if args.prompt else input('''Model prompt >>> '''     )

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

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

      lowercase			:   Union[str, Any]					   =		[]
      for generated_sequence_idx, generated_sequence in enumerate(_UpperCamelCase     ):
            print(f"""=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==="""     )
            lowercase			:   Optional[Any]					   =		generated_sequence.tolist()

            # Decode text
            lowercase			:   int					   =		tokenizer.decode(_UpperCamelCase,    clean_up_tokenization_spaces=_UpperCamelCase,    add_special_tokens=_UpperCamelCase     )

            # Remove all text after the stop token
            lowercase			:   Optional[int]					   =		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
            lowercase			:   Optional[Any]					   =		(
                prompt_text + text[len(tokenizer.decode(inputs['''input_ids'''][0],    clean_up_tokenization_spaces=_UpperCamelCase     )     ) :]
            )

            generated_sequences.append(_UpperCamelCase     )
            print(_UpperCamelCase     )

      return generated_sequences


if __name__ == "__main__":
  main()

