



"""simple docstring"""




import inspect
import os
from argparse import ArgumentParser, Namespace
from importlib import import_module

import huggingface_hub
import numpy as np
from packaging import version

from .. import (
    FEATURE_EXTRACTOR_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoImageProcessor,
    AutoProcessor,
    AutoTokenizer,
    is_datasets_available,
    is_tf_available,
    is_torch_available,
)
from ..utils import TF2_WEIGHTS_INDEX_NAME, TF2_WEIGHTS_NAME, logging
from . import BaseTransformersCLICommand


if is_tf_available():
     import tensorflow as tf

     tf.config.experimental.enable_tensor_float_aa_execution(False)

if is_torch_available():
     import torch

if is_datasets_available():
     from datasets import load_dataset


_UpperCamelCase:				Union[str, Any]    							=  5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors


def   lowercase__     (			_UpperCAmelCase				)     ->			List[str]:






      '''simple docstring'''


      return PTtoTFCommand(
          args.model_name	,      args.local_dir	,      args.max_error	,      args.new_weights	,      args.no_pr	,      args.push	,      args.extra_commit_description	,      args.override_model_class	,      )





class   a__		(						SCREAMING_SNAKE_CASE__     ):


   @staticmethod
   def        lowercase				(							lowerCAmelCase      :							ArgumentParser			)    ->       Any:
         lowercase  :      Dict					=   parser.add_parser(
             'pt-to-tf',     help=(
                 'CLI tool to run convert a transformers model from a PyTorch checkpoint to a TensorFlow checkpoint.'
                 ' Can also be used to validate existing weights without opening PRs, with --no-pr.'
             ),     )
         train_parser.add_argument(
             '--model-name',     type=lowerCAmelCase,     required=lowerCAmelCase,     help='The model name, including owner/organization, as seen on the hub.',     )
         train_parser.add_argument(
             '--local-dir',     type=lowerCAmelCase,     default='',     help='Optional local directory of the model repository. Defaults to /tmp/{model_name}',     )
         train_parser.add_argument(
             '--max-error',     type=lowerCAmelCase,     default=lowerCAmelCase,     help=(
                 f'''Maximum error tolerance. Defaults to {MAX_ERROR}. This flag should be avoided, use at your own risk.'''
             ),     )
         train_parser.add_argument(
             '--new-weights',     action='store_true',     help='Optional flag to create new TensorFlow weights, even if they already exist.',     )
         train_parser.add_argument(
             '--no-pr',     action='store_true',     help='Optional flag to NOT open a PR with converted weights.'			)
         train_parser.add_argument(
             '--push',     action='store_true',     help='Optional flag to push the weights directly to `main` (requires permissions)',     )
         train_parser.add_argument(
             '--extra-commit-description',     type=lowerCAmelCase,     default='',     help='Optional additional commit description to use when opening a PR (e.g. to tag the owner).',     )
         train_parser.add_argument(
             '--override-model-class',     type=lowerCAmelCase,     default=lowerCAmelCase,     help='If you think you know better than the auto-detector, you can specify the model class here. '
             'Can be either an AutoModel class or a specific model class like BertForSequenceClassification.',     )
         train_parser.set_defaults(func=lowerCAmelCase			)


   @staticmethod
   def        lowercase				(							lowerCAmelCase      :							List[Any],     lowerCAmelCase      :							Dict			)    ->       Any:
         lowercase  :      Union[str, Any]					=   set(pt_outputs.keys()			)
         lowercase  :      Optional[int]					=   set(tf_outputs.keys()			)
         if pt_out_attrs != tf_out_attrs:
               raise ValueError(
                   f'''The model outputs have different attributes, aborting. (Pytorch: {pt_out_attrs}, TensorFlow:'''
                   f''' {tf_out_attrs})'''			)

         # 2. For each output attribute, computes the difference
         def _find_pt_tf_differences(lowerCAmelCase      :							Dict,     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							Union[str, Any],     lowerCAmelCase      :							List[str]=""			):
               # If the current attribute is a tensor, it is a leaf and we make the comparison. Otherwise, we will dig in
               # recursivelly, keeping the name of the attribute.
               if isinstance(lowerCAmelCase,     torch.Tensor			):
                     lowercase  :      Union[str, Any]					=   np.max(np.abs(pt_out.numpy() - tf_out.numpy()			)			)
                     lowercase  :      Any					=   tensor_difference
               else:
                     lowercase  :      List[Any]					=   attr_name
                     for i, pt_item in enumerate(lowerCAmelCase			):
                           # If it is a named attribute, we keep the name. Otherwise, just its index.
                           if isinstance(lowerCAmelCase,     lowerCAmelCase			):
                                 lowercase  :      Any					=   root_name + pt_item
                                 lowercase  :      str					=   tf_out[pt_item]
                                 lowercase  :      List[str]					=   pt_out[pt_item]
                           else:
                                 lowercase  :      Optional[Any]					=   root_name + f'''[{i}]'''
                                 lowercase  :      str					=   tf_out[i]
                           lowercase  :      int					=   _find_pt_tf_differences(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)

               return differences

         return _find_pt_tf_differences(lowerCAmelCase,     lowerCAmelCase,     {}			)


   def __init__(							self      :							Optional[int],     lowerCAmelCase      :							str,     lowerCAmelCase      :							str,     lowerCAmelCase      :							float,     lowerCAmelCase      :							bool,     lowerCAmelCase      :							bool,     lowerCAmelCase      :							bool,     lowerCAmelCase      :							str,     lowerCAmelCase      :							str,     *lowerCAmelCase      :							Tuple,     )    ->       Any:
         lowercase  :      List[Any]					=   logging.get_logger('transformers-cli/pt_to_tf'			)
         lowercase  :      Optional[Any]					=   model_name
         lowercase  :      Optional[Any]					=   local_dir if local_dir else os.path.join('/tmp',     lowerCAmelCase			)
         lowercase  :      List[Any]					=   max_error
         lowercase  :      Dict					=   new_weights
         lowercase  :      List[Any]					=   no_pr
         lowercase  :      Any					=   push
         lowercase  :      Optional[int]					=   extra_commit_description
         lowercase  :      Optional[int]					=   override_model_class


   def        lowercase				(							self      :							int,     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							int,     lowerCAmelCase      :							Any			)    ->       Optional[Any]:

         def _get_audio_input():
               lowercase  :      Optional[Any]					=   load_dataset('hf-internal-testing/librispeech_asr_dummy',     'clean',     split='validation'			)
               lowercase  :      Union[str, Any]					=   ds.sort('id'			).select(range(2			)			)[:2]['audio']
               lowercase  :      Optional[Any]					=   [x['array'] for x in speech_samples]
               return raw_samples

         lowercase  :      List[Any]					=   type(pt_model.config			)
         if model_config_class in PROCESSOR_MAPPING:
               lowercase  :      Union[str, Any]					=   AutoProcessor.from_pretrained(self._local_dir			)
               if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
                     lowercase  :      int					=   processor.tokenizer.eos_token
         elif model_config_class in IMAGE_PROCESSOR_MAPPING:
               lowercase  :      List[Any]					=   AutoImageProcessor.from_pretrained(self._local_dir			)
         elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
               lowercase  :      Optional[int]					=   AutoFeatureExtractor.from_pretrained(self._local_dir			)
         elif model_config_class in TOKENIZER_MAPPING:
               lowercase  :      Tuple					=   AutoTokenizer.from_pretrained(self._local_dir			)
               if processor.pad_token is None:
                     lowercase  :      int					=   processor.eos_token
         else:
               raise ValueError(f'''Unknown data processing type (model config type: {model_config_class})'''			)

         lowercase  :      List[str]					=   set(inspect.signature(pt_model.forward			).parameters.keys()			)
         lowercase  :      Tuple					=   {}
         if "input_ids" in model_forward_signature:
               processor_inputs.update(
                   {
                       'text': ['Hi there!', 'I am a batch with more than one row and different input lengths.'],
                       'padding': True,
                       'truncation': True,
                   }			)
         if "pixel_values" in model_forward_signature:
               lowercase  :      int					=   load_dataset('cifar10',     'plain_text',     split='test'			)[:2]['img']
               processor_inputs.update({'images': sample_images}			)
         if "input_features" in model_forward_signature:
               lowercase  :      Optional[int]					=   inspect.signature(processor.feature_extractor			).parameters
               # Pad to the largest input length by default but take feature extractor default
               # padding value if it exists e.g. "max_length" and is not False or None
               if "padding" in feature_extractor_signature:
                     lowercase  :      str					=   feature_extractor_signature['padding'].default
                     if default_strategy is not False and default_strategy is not None:
                           lowercase  :      str					=   default_strategy
                     else:
                           lowercase  :      List[Any]					=   True
               else:
                     lowercase  :      Union[str, Any]					=   True
               processor_inputs.update({'audio': _get_audio_input(), 'padding': padding_strategy}			)
         if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
               processor_inputs.update({'audio': _get_audio_input(), 'padding': True}			)
         lowercase  :      Union[str, Any]					=   processor(**lowerCAmelCase,     return_tensors='pt'			)
         lowercase  :      Dict					=   processor(**lowerCAmelCase,     return_tensors='tf'			)

         # Extra input requirements, in addition to the input modality
         if (
             config.is_encoder_decoder
             or (hasattr(lowerCAmelCase,     'encoder'			) and hasattr(lowerCAmelCase,     'decoder'			))
             or "decoder_input_ids" in tf_dummy_inputs
         ):
               lowercase  :      Optional[int]					=   np.asarray([[1], [1]],     dtype=lowerCAmelCase			) * (pt_model.config.decoder_start_token_id or 0)
               pt_input.update({'decoder_input_ids': torch.tensor(lowerCAmelCase			)}			)
               tf_input.update({'decoder_input_ids': tf.convert_to_tensor(lowerCAmelCase			)}			)

         return pt_input, tf_input






   def        lowercase				(							self      :							Optional[Any]			)    ->       Any:
         # hub version 0.9.0 introduced the possibility of programmatically opening PRs with normal write tokens.
         if version.parse(huggingface_hub.__version__			) < version.parse('0.9.0'			):
               raise ImportError(
                   'The huggingface_hub version must be >= 0.9.0 to use this command. Please update your huggingface_hub'
                   ' installation.'			)
         else:
               from huggingface_hub import Repository, create_commit
               from huggingface_hub._commit_api import CommitOperationAdd

         # Fetch remote data
         lowercase  :      Dict					=   Repository(local_dir=self._local_dir,     clone_from=self._model_name			)

         # Load config and get the appropriate architecture -- the latter is needed to convert the head's weights
         lowercase  :      str					=   AutoConfig.from_pretrained(self._local_dir			)
         lowercase  :      List[Any]					=   config.architectures
         if self._override_model_class is not None:
               if self._override_model_class.startswith('TF'			):
                     lowercase  :      Optional[Any]					=   [self._override_model_class[2:]]
               else:
                     lowercase  :      Any					=   [self._override_model_class]
               try:
                     lowercase  :      Optional[Any]					=   getattr(import_module('transformers'			),     architectures[0]			)
               except AttributeError:
                     raise ValueError(f'''Model class {self._override_model_class} not found in transformers.'''			)
               try:
                     lowercase  :      Tuple					=   getattr(import_module('transformers'			),     'TF' + architectures[0]			)
               except AttributeError:
                     raise ValueError(f'''TF model class TF{self._override_model_class} not found in transformers.'''			)
         elif architectures is None:  # No architecture defined -- use auto classes
               lowercase  :      str					=   getattr(import_module('transformers'			),     'AutoModel'			)
               lowercase  :      Dict					=   getattr(import_module('transformers'			),     'TFAutoModel'			)
               self._logger.warning('No detected architecture, using AutoModel/TFAutoModel'			)
         else:  # Architecture defined -- use it
               if len(lowerCAmelCase			) > 1:
                     raise ValueError(f'''More than one architecture was found, aborting. (architectures = {architectures})'''			)
               self._logger.warning(f'''Detected architecture: {architectures[0]}'''			)
               lowercase  :      List[Any]					=   getattr(import_module('transformers'			),     architectures[0]			)
               try:
                     lowercase  :      int					=   getattr(import_module('transformers'			),     'TF' + architectures[0]			)
               except AttributeError:
                     raise AttributeError(f'''The TensorFlow equivalent of {architectures[0]} doesn\'t exist in transformers.'''			)

        # Check the TF dummy inputs to see what keys we need in the forward pass
         lowercase  :      Dict					=   tf_class.from_config(lowerCAmelCase			)
         lowercase  :      List[str]					=   tf_from_pt_model.dummy_inputs

         del tf_from_pt_model  # Try to keep only one model in memory at a time

         # Load the model and get some basic inputs
         lowercase  :      Optional[Any]					=   pt_class.from_pretrained(self._local_dir			)
         pt_model.eval()

         lowercase	,    lowercase  :      Optional[Any]					=   self.get_inputs(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)

         with torch.no_grad():
               lowercase  :      Dict					=   pt_model(**lowerCAmelCase,     output_hidden_states=lowerCAmelCase			)
         del pt_model  # will no longer be used, and may have a large memory footprint

         lowercase  :      Tuple					=   tf_class.from_pretrained(self._local_dir,     from_pt=lowerCAmelCase			)
         lowercase  :      Optional[int]					=   tf_from_pt_model(**lowerCAmelCase,     output_hidden_states=lowerCAmelCase,     training=lowerCAmelCase			)

         # Confirms that cross loading PT weights into TF worked.
         lowercase  :      Dict					=   self.find_pt_tf_differences(lowerCAmelCase,     lowerCAmelCase			)
         lowercase  :      Optional[int]					=   {k: v for k, v in crossload_differences.items() if 'hidden' not in k}
         lowercase  :      Optional[Any]					=   {k: v for k, v in crossload_differences.items() if 'hidden' in k}
         if len(lowerCAmelCase			) == 0 and architectures is not None:
               raise ValueError(
                   f'''Something went wrong -- the config file has architectures ({architectures}), but no model head'''
                   ' output was found. All outputs start with \'hidden\''			)
         lowercase  :      Optional[Any]					=   max(output_differences.values()			) if output_differences else 0.0
         lowercase  :      Dict					=   max(hidden_differences.values()			)
         if max_crossload_output_diff > self._max_error or max_crossload_hidden_diff > self._max_error:
               raise ValueError(
                   'The cross-loaded TensorFlow model has different outputs, something went wrong!\n'
                   + f'''\nList of maximum output differences above the threshold ({self._max_error}):\n'''
                   + '\n'.join([f'''{k}: {v:.3e}''' for k, v in output_differences.items() if v > self._max_error]			)
                   + f'''\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n'''
                   + '\n'.join([f'''{k}: {v:.3e}''' for k, v in hidden_differences.items() if v > self._max_error]			)			)

         # Save the weights in a TF format (if needed) and confirms that the results are still good
         lowercase  :      Tuple					=   os.path.join(self._local_dir,     lowerCAmelCase			)
         lowercase  :      List[str]					=   os.path.join(self._local_dir,     lowerCAmelCase			)
         if (not os.path.exists(lowerCAmelCase			) and not os.path.exists(lowerCAmelCase			)) or self._new_weights:
               tf_from_pt_model.save_pretrained(self._local_dir			)
         del tf_from_pt_model  # will no longer be used, and may have a large memory footprint

         lowercase  :      Tuple					=   tf_class.from_pretrained(self._local_dir			)
         lowercase  :      Optional[Any]					=   tf_model(**lowerCAmelCase,     output_hidden_states=lowerCAmelCase			)

         lowercase  :      int					=   self.find_pt_tf_differences(lowerCAmelCase,     lowerCAmelCase			)
         lowercase  :      Dict					=   {k: v for k, v in conversion_differences.items() if 'hidden' not in k}
         lowercase  :      Dict					=   {k: v for k, v in conversion_differences.items() if 'hidden' in k}
         if len(lowerCAmelCase			) == 0 and architectures is not None:
               raise ValueError(
                   f'''Something went wrong -- the config file has architectures ({architectures}), but no model head'''
                   ' output was found. All outputs start with \'hidden\''			)
         lowercase  :      Any					=   max(output_differences.values()			) if output_differences else 0.0
         lowercase  :      str					=   max(hidden_differences.values()			)
         if max_conversion_output_diff > self._max_error or max_conversion_hidden_diff > self._max_error:
               raise ValueError(
                   'The converted TensorFlow model has different outputs, something went wrong!\n'
                   + f'''\nList of maximum output differences above the threshold ({self._max_error}):\n'''
                   + '\n'.join([f'''{k}: {v:.3e}''' for k, v in output_differences.items() if v > self._max_error]			)
                   + f'''\n\nList of maximum hidden layer differences above the threshold ({self._max_error}):\n'''
                   + '\n'.join([f'''{k}: {v:.3e}''' for k, v in hidden_differences.items() if v > self._max_error]			)			)

         lowercase  :      Optional[Any]					=   'Update TF weights' if self._new_weights else 'Add TF weights'
         if self._push:
               repo.git_add(auto_lfs_track=lowerCAmelCase			)
               repo.git_commit(lowerCAmelCase			)
               repo.git_push(blocking=lowerCAmelCase			)  # this prints a progress bar with the upload
               self._logger.warning(f'''TF weights pushed into {self._model_name}'''			)
         elif not self._no_pr:
               self._logger.warning('Uploading the weights into a new PR...'			)
               lowercase  :      Optional[Any]					=   (
                   'Model converted by the [`transformers`\' `pt_to_tf`'
                   ' CLI](https://github.com/huggingface/transformers/blob/main/src/transformers/commands/pt_to_tf.py). '
                   'All converted model outputs and hidden layers were validated against its PyTorch counterpart.\n\n'
                   f'''Maximum crossload output difference={max_crossload_output_diff:.3e}; '''
                   f'''Maximum crossload hidden layer difference={max_crossload_hidden_diff:.3e};\n'''
                   f'''Maximum conversion output difference={max_conversion_output_diff:.3e}; '''
                   f'''Maximum conversion hidden layer difference={max_conversion_hidden_diff:.3e};\n'''
               )
               if self._max_error > MAX_ERROR:
                     commit_descrition += (
                         f'''\n\nCAUTION: The maximum admissible error was manually increased to {self._max_error}!'''
                     )
               if self._extra_commit_description:
                     commit_descrition += "\n\n" + self._extra_commit_description

               # sharded model -> adds all related files (index and .h5 shards)
               if os.path.exists(lowerCAmelCase			):
                     lowercase  :      Tuple					=   [
                         CommitOperationAdd(path_in_repo=lowerCAmelCase,     path_or_fileobj=lowerCAmelCase			)
                     ]
                     for shard_path in tf.io.gfile.glob(self._local_dir + '/tf_model-*.h5'			):
                           operations += [
                               CommitOperationAdd(path_in_repo=os.path.basename(lowerCAmelCase			),     path_or_fileobj=lowerCAmelCase			)
                           ]
               else:
                     lowercase  :      Any					=   [CommitOperationAdd(path_in_repo=lowerCAmelCase,     path_or_fileobj=lowerCAmelCase			)]

               lowercase  :      Tuple					=   create_commit(
                   repo_id=self._model_name,     operations=lowerCAmelCase,     commit_message=lowerCAmelCase,     commit_description=lowerCAmelCase,     repo_type='model',     create_pr=lowerCAmelCase,     ).pr_url
               self._logger.warning(f'''PR open in {hub_pr_url}'''			)





"""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
