



"""simple docstring"""




import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


_UpperCamelCase:				str    							=  logging.get_logger(__name__)  # pylint: disable=invalid-name


_UpperCamelCase:				int    							=  'lfs-multipart-upload'


class   a__		(						SCREAMING_SNAKE_CASE__     ):


   @staticmethod
   def        lowercase				(							lowerCAmelCase      :							ArgumentParser			)    ->       Optional[int]:
         lowercase  :      Union[str, Any]					=   parser.add_parser(
             'lfs-enable-largefiles',     help=(
                 'Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB.'
             ),     )
         enable_parser.add_argument('path',     type=lowerCAmelCase,     help='Local path to repository you want to configure.'			)
         enable_parser.set_defaults(func=lambda lowerCAmelCase			: LfsEnableCommand(lowerCAmelCase			)			)

         lowercase  :      int					=   parser.add_parser(
             lowerCAmelCase,     help=(
                 'Deprecated: use `huggingface-cli` instead. '
                 'Command will get called by git-lfs, do not call it directly.'
             ),     )
         upload_parser.set_defaults(func=lambda lowerCAmelCase			: LfsUploadCommand(lowerCAmelCase			)			)




class   a__		:


   def __init__(							self      :							Tuple,     lowerCAmelCase      :							int			)    ->       Tuple:
         lowercase  :      Any					=   args






   def        lowercase				(							self      :							Optional[Any]			)    ->       int:
         warnings.warn(
             'Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead.'			)
         lowercase  :      Optional[int]					=   os.path.abspath(self.args.path			)
         if not os.path.isdir(lowerCAmelCase			):
               print('This does not look like a valid git repo.'			)
               exit(1			)
         subprocess.run(
             'git config lfs.customtransfer.multipart.path transformers-cli'.split(),     check=lowerCAmelCase,     cwd=lowerCAmelCase			)
         subprocess.run(
             f'''git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'''.split(),     check=lowerCAmelCase,     cwd=lowerCAmelCase,     )
         print('Local repo set up for largefiles'			)




def   lowercase__     (			_UpperCAmelCase				)     ->			Any:






      '''simple docstring'''


      lowercase  :      Tuple					=   json.dumps(_UpperCAmelCase				) + '\n'
      sys.stdout.write(_UpperCAmelCase				)
      sys.stdout.flush()


def   lowercase__     (			)     ->			Optional[Dict]:






      '''simple docstring'''


      lowercase  :      Any					=   json.loads(sys.stdin.readline().strip()				)

      if "terminate" in (msg.get('type'				), msg.get('event'				)):
            # terminate message received
            return None

      if msg.get('event'				) not in ("download", "upload"):
            logger.critical('Received unexpected message'				)
            sys.exit(1				)

      return msg


class   a__		(						SCREAMING_SNAKE_CASE__     ):


   def __init__(							self      :							Union[str, Any],     lowerCAmelCase      :							str,     lowerCAmelCase      :							int,     lowerCAmelCase      :							int			)    ->       Tuple:
         lowercase  :      int					=   filepath
         lowercase  :      str					=   seek_from
         lowercase  :      int					=   read_limit
         lowercase  :      Optional[Any]					=   0


   def __enter__(							self      :							Optional[int]			)    ->       Optional[int]:
         lowercase  :      Tuple					=   open(self.filepath,     'rb'			)
         self.f.seek(self.seek_from			)
         return self


   def __len__(							self      :							Dict			)    ->       Dict:
         lowercase  :      Union[str, Any]					=   os.fstat(self.f.fileno()			).st_size
         return min(self.read_limit,     total_length - self.seek_from			)


   def        lowercase				(							self      :							List[Any],     lowerCAmelCase      :							Union[str, Any]=-1			)    ->       Union[str, Any]:
         if self.n_seen >= self.read_limit:
               return b""
         lowercase  :      List[str]					=   self.read_limit - self.n_seen
         lowercase  :      int					=   self.f.read(remaining_amount if n < 0 else min(lowerCAmelCase,     lowerCAmelCase			)			)
         self.n_seen += len(lowerCAmelCase			)
         return data


   def __iter__(							self      :							int			)    ->       Any:
         yield self.read(n=4 * 1024 * 1024			)






   def __exit__(							self      :							Any,     *lowerCAmelCase      :							Optional[Any]			)    ->       str:
         self.f.close()




class   a__		:


   def __init__(							self      :							int,     lowerCAmelCase      :							Dict			)    ->       Any:
         lowercase  :      Tuple					=   args






   def        lowercase				(							self      :							List[str]			)    ->       List[str]:
         # Immediately after invoking a custom transfer process, git-lfs
         # sends initiation data to the process over stdin.
         # This tells the process useful information about the configuration.
         lowercase  :      Optional[int]					=   json.loads(sys.stdin.readline().strip()			)
         if not (init_msg.get('event'			) == "init" and init_msg.get('operation'			) == "upload"):
               write_msg({'error': {'code': 32, 'message': 'Wrong lfs init operation'}}			)
               sys.exit(1			)

         # The transfer process should use the information it needs from the
         # initiation structure, and also perform any one-off setup tasks it
         # needs to do. It should then respond on stdout with a simple empty
         # confirmation structure, as follows:
         write_msg({}			)

         # After the initiation exchange, git-lfs will send any number of
         # transfer requests to the stdin of the transfer process, in a serial sequence.
         while True:
               lowercase  :      Optional[int]					=   read_msg()
               if msg is None:
                     # When all transfers have been processed, git-lfs will send
                     # a terminate event to the stdin of the transfer process.
                     # On receiving this message the transfer process should
                     # clean up and terminate. No response is expected.
                     sys.exit(0			)

               lowercase  :      List[Any]					=   msg['oid']
               lowercase  :      Any					=   msg['path']
               lowercase  :      str					=   msg['action']['href']
               lowercase  :      Dict					=   msg['action']['header']
               lowercase  :      List[str]					=   int(header.pop('chunk_size'			)			)
               lowercase  :      List[str]					=   list(header.values()			)

               lowercase  :      Dict					=   []
               for i, presigned_url in enumerate(lowerCAmelCase			):
                     with FileSlice(lowerCAmelCase,     seek_from=i * chunk_size,     read_limit=lowerCAmelCase			) as data:
                           lowercase  :      Dict					=   requests.put(lowerCAmelCase,     data=lowerCAmelCase			)
                           r.raise_for_status()
                           parts.append(
                               {
                                   'etag': r.headers.get('etag'			),
                                   'partNumber': i + 1,
                               }			)
                           # In order to support progress reporting while data is uploading / downloading,
                           # the transfer process should post messages to stdout
                           write_msg(
                               {
                                   'event': 'progress',
                                   'oid': oid,
                                   'bytesSoFar': (i + 1) * chunk_size,
                                   'bytesSinceLast': chunk_size,
                               }			)
                           # Not precise but that's ok.

               lowercase  :      Any					=   requests.post(
                   lowerCAmelCase,     json={
                       'oid': oid,
                       'parts': parts,
                   },     )
               r.raise_for_status()

               write_msg({'event': 'complete', 'oid': oid}			)





"""simple docstring"""




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


_UpperCamelCase:				List[str]    							=  logging.get_logger(__name__)

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

if is_torch_available():
     import torch

if is_soundfile_availble():
     import soundfile as sf


class   a__		:


   def __init__(							self      :							Union[str, Any],     lowerCAmelCase      :							Optional[int]			)    ->       Any:
         lowercase  :      Dict					=   value


   def __str__(							self      :							List[str]			)    ->       Union[str, Any]:
         return self.to_string()


   def        lowercase				(							self      :							Dict			)    ->       Optional[Any]:
         logger.error(
             'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'			)
         return self._value






   def        lowercase				(							self      :							List[Any]			)    ->       str:
         logger.error(
             'This is a raw AgentType of unknown type. Display in notebooks and string conversion will be unreliable'			)
         return str(self._value			)




class   a__		(						SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__     ):


   def        lowercase				(							self      :							List[Any]			)    ->       List[Any]:
         return self._value






   def        lowercase				(							self      :							str			)    ->       Optional[int]:
         return self._value




class   a__		(						SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__     ):


   def __init__(							self      :							int,     lowerCAmelCase      :							Optional[Any]			)    ->       Any:
         super().__init__(lowerCAmelCase			)

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

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

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


   def        lowercase				(							self      :							int,     lowerCAmelCase      :							Union[str, Any]=None,     lowerCAmelCase      :							Optional[Any]=None			)    ->       List[Any]:
         from IPython.display import Image, display

         display(Image(self.to_string()			)			)


   def        lowercase				(							self      :							int			)    ->       List[str]:
         if self._raw is not None:
               return self._raw

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






   def        lowercase				(							self      :							List[Any]			)    ->       List[Any]:
         if self._path is not None:
               return self._path

         if self._raw is not None:
               lowercase  :      Tuple					=   tempfile.mkdtemp()
               lowercase  :      Dict					=   os.path.join(lowerCAmelCase,     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  :      Dict					=   Image.fromarray((array * 255).astype(np.uinta			)			)

               lowercase  :      Optional[int]					=   tempfile.mkdtemp()
               lowercase  :      Any					=   os.path.join(lowerCAmelCase,     str(uuid.uuida()			) + '.png'			)

               img.save(self._path			)

               return self._path




class   a__		(						SCREAMING_SNAKE_CASE__     ):


   def __init__(							self      :							Dict,     lowerCAmelCase      :							Any,     lowerCAmelCase      :							Any=16000			)    ->       Tuple:
         super().__init__(lowerCAmelCase			)

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

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

         lowercase  :      Optional[Any]					=   samplerate

         if isinstance(lowerCAmelCase,     (str, pathlib.Path)			):
               lowercase  :      List[Any]					=   value
         elif isinstance(lowerCAmelCase,     torch.Tensor			):
               lowercase  :      List[str]					=   value
         else:
               raise ValueError(f'''Unsupported audio type: {type(lowerCAmelCase			)}'''			)


   def        lowercase				(							self      :							Optional[int],     lowerCAmelCase      :							Optional[Any]=None,     lowerCAmelCase      :							int=None			)    ->       Optional[Any]:
         from IPython.display import Audio, display

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


   def        lowercase				(							self      :							Any			)    ->       Union[str, Any]:
         if self._tensor is not None:
               return self._tensor

         if self._path is not None:
               lowercase	,    lowercase  :      List[Any]					=   sf.read(self._path			)
               lowercase  :      int					=   torch.tensor(lowerCAmelCase			)
               return self._tensor






   def        lowercase				(							self      :							Tuple			)    ->       Tuple:
         if self._path is not None:
               return self._path

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




_UpperCamelCase:				Optional[int]    							=  {'text': AgentText, 'image': AgentImage, 'audio': AgentAudio}
_UpperCamelCase:				Optional[int]    							=  {str: AgentText}

if is_vision_available():
     _UpperCamelCase:				str    							=  AgentImage


def   lowercase__     (			*_UpperCAmelCase	,      **_UpperCAmelCase				)     ->			Any:






      '''simple docstring'''


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





def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase=None				)     ->			Tuple:






      '''simple docstring'''


      if isinstance(_UpperCAmelCase	,      _UpperCAmelCase				):
            lowercase  :      List[Any]					=   {}
            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  :      int					=   AGENT_TYPE_MAPPING[output_types[i]](_UpperCAmelCase				)
                        else:
                              lowercase  :      Union[str, Any]					=   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  :      Optional[int]					=   _v(_UpperCAmelCase				)
                        if k not in decoded_outputs:
                              lowercase  :      str					=   AgentType[v]

      elif isinstance(_UpperCAmelCase	,      (list, tuple)				):
            lowercase  :      Union[str, Any]					=   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  :      Any					=   False
                        for _k, _v in INSTANCE_TYPE_MAPPING.items():
                              if isinstance(_UpperCAmelCase	,      _k				):
                                    decoded_outputs.append(_v(_UpperCAmelCase				)				)
                                    lowercase  :      Optional[Any]					=   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  :      List[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
