code: 
  
  
  
  
  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
  
  
  _A							    =	5e-5  # larger error tolerance than in our internal tests, to avoid flaky user-facing errors
  def 						lowerCAmelCase_       (	__a )       ->			Dict:
  
  
  
  
  
  							"""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 							_lowerCAmelCase		(     A_	):
  
  
  
  
  				@staticmethod
  				def     __a		( snake_case_							)    ->      Optional[int]:
  											SCREAMING_SNAKE_CASE			: Any	     =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=snake_case_		,		required=snake_case_		,		help='''The model name, including owner/organization, as seen on the hub.'''		,		)
  											train_parser.add_argument(
  											    '''--local-dir'''		,		type=snake_case_		,		default=''''''		,		help='''Optional local directory of the model repository. Defaults to /tmp/{model_name}'''		,		)
  											train_parser.add_argument(
  											    '''--max-error'''		,		type=snake_case_		,		default=snake_case_		,		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=snake_case_		,		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=snake_case_		,		default=snake_case_		,		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=snake_case_							)
  
  
  
  
  				@staticmethod
  				def     __a		( snake_case_		,		snake_case_							)    ->      Dict:
  											SCREAMING_SNAKE_CASE			: Dict	     =set(pt_outputs.keys()							)
  											SCREAMING_SNAKE_CASE			: str	     =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(snake_case_		,		snake_case_		,		snake_case_		,		snake_case_=""							):
  																		# 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(snake_case_		,		torch.Tensor							):
  																									SCREAMING_SNAKE_CASE			: str	     =np.max(np.abs(pt_out.numpy() - tf_out.numpy()							)							)
  																									SCREAMING_SNAKE_CASE			: Dict	     =tensor_difference
  																		else:
  																									SCREAMING_SNAKE_CASE			: Tuple	     =attr_name
  																									for i, pt_item in enumerate(snake_case_							):
  																																# If it is a named attribute, we keep the name. Otherwise, just its index.
  																																if isinstance(snake_case_		,		snake_case_							):
  																																							SCREAMING_SNAKE_CASE			: List[str]	     =root_name + pt_item
  																																							SCREAMING_SNAKE_CASE			: Dict	     =tf_out[pt_item]
  																																							SCREAMING_SNAKE_CASE			: Tuple	     =pt_out[pt_item]
  																																else:
  																																							SCREAMING_SNAKE_CASE			: Dict	     =root_name + F'[{i}]'
  																																							SCREAMING_SNAKE_CASE			: List[Any]	     =tf_out[i]
  																																SCREAMING_SNAKE_CASE			: Any	     =_find_pt_tf_differences(snake_case_		,		snake_case_		,		snake_case_		,		snake_case_							)
  
  																		return differences
  
  											return _find_pt_tf_differences(snake_case_		,		snake_case_		,		{}							)
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		*snake_case_		,		)    ->      Tuple:
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =logging.get_logger('''transformers-cli/pt_to_tf'''							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =model_name
  											SCREAMING_SNAKE_CASE			: int	     =local_dir if local_dir else os.path.join('''/tmp'''		,		snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =max_error
  											SCREAMING_SNAKE_CASE			: List[Any]	     =new_weights
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =no_pr
  											SCREAMING_SNAKE_CASE			: Tuple	     =push
  											SCREAMING_SNAKE_CASE			: Tuple	     =extra_commit_description
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =override_model_class
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      List[Any]:
  
  
  
  											def _get_audio_input():
  																		SCREAMING_SNAKE_CASE			: str	     =load_dataset('''hf-internal-testing/librispeech_asr_dummy'''		,		'''clean'''		,		split='''validation'''							)
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =ds.sort('''id'''							).select(range(2							)							)[:2]['''audio''']
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =[x['''array'''] for x in speech_samples]
  																		return raw_samples
  
  											SCREAMING_SNAKE_CASE			: List[str]	     =type(pt_model.config							)
  											if model_config_class in PROCESSOR_MAPPING:
  																		SCREAMING_SNAKE_CASE			: str	     =AutoProcessor.from_pretrained(self._local_dir							)
  																		if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
  																									SCREAMING_SNAKE_CASE			: str	     =processor.tokenizer.eos_token
  											elif model_config_class in IMAGE_PROCESSOR_MAPPING:
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =AutoImageProcessor.from_pretrained(self._local_dir							)
  											elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =AutoFeatureExtractor.from_pretrained(self._local_dir							)
  											elif model_config_class in TOKENIZER_MAPPING:
  																		SCREAMING_SNAKE_CASE			: Any	     =AutoTokenizer.from_pretrained(self._local_dir							)
  																		if processor.pad_token is None:
  																									SCREAMING_SNAKE_CASE			: Tuple	     =processor.eos_token
  											else:
  																		raise ValueError(F'Unknown data processing type (model config type: {model_config_class})'							)
  
  											SCREAMING_SNAKE_CASE			: int	     =set(inspect.signature(pt_model.forward							).parameters.keys()							)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     ={}
  											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:
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =load_dataset('''cifar10'''		,		'''plain_text'''		,		split='''test'''							)[:2]['''img''']
  																		processor_inputs.update({'''images''': sample_images}							)
  											if "input_features" in model_forward_signature:
  																		SCREAMING_SNAKE_CASE			: Any	     =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:
  																									SCREAMING_SNAKE_CASE			: str	     =feature_extractor_signature['''padding'''].default
  																									if default_strategy is not False and default_strategy is not None:
  																																SCREAMING_SNAKE_CASE			: Dict	     =default_strategy
  																									else:
  																																SCREAMING_SNAKE_CASE			: int	     =True
  																		else:
  																									SCREAMING_SNAKE_CASE			: 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}							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =processor(**snake_case_		,		return_tensors='''pt'''							)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =processor(**snake_case_		,		return_tensors='''tf'''							)
  
  											# Extra input requirements, in addition to the input modality
  											if (
  											    config.is_encoder_decoder
  											    or (hasattr(snake_case_		,		'''encoder'''							) and hasattr(snake_case_		,		'''decoder'''							))
  											    or "decoder_input_ids" in tf_dummy_inputs
  											):
  																		SCREAMING_SNAKE_CASE			: Any	     =np.asarray([[1], [1]]		,		dtype=snake_case_							) * (pt_model.config.decoder_start_token_id or 0)
  																		pt_input.update({'''decoder_input_ids''': torch.tensor(snake_case_							)}							)
  																		tf_input.update({'''decoder_input_ids''': tf.convert_to_tensor(snake_case_							)}							)
  
  											return pt_input, tf_input
  
  
  
  
  
  
  
  				def     __a		( self							)    ->      Any:
  
  
  
  											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
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =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
  											SCREAMING_SNAKE_CASE			: List[str]	     =AutoConfig.from_pretrained(self._local_dir							)
  											SCREAMING_SNAKE_CASE			: Tuple	     =config.architectures
  											if self._override_model_class is not None:
  																		if self._override_model_class.startswith('''TF'''							):
  																									SCREAMING_SNAKE_CASE			: int	     =[self._override_model_class[2:]]
  																		else:
  																									SCREAMING_SNAKE_CASE			: Union[str, Any]	     =[self._override_model_class]
  																		try:
  																									SCREAMING_SNAKE_CASE			: str	     =getattr(import_module('''transformers'''							)		,		architectures[0]							)
  																		except AttributeError:
  																									raise ValueError(F'Model class {self._override_model_class} not found in transformers.'							)
  																		try:
  																									SCREAMING_SNAKE_CASE			: str	     =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
  																		SCREAMING_SNAKE_CASE			: Tuple	     =getattr(import_module('''transformers'''							)		,		'''AutoModel'''							)
  																		SCREAMING_SNAKE_CASE			: Any	     =getattr(import_module('''transformers'''							)		,		'''TFAutoModel'''							)
  																		self._logger.warning('''No detected architecture, using AutoModel/TFAutoModel'''							)
  											else:  # Architecture defined -- use it
  																		if len(snake_case_							) > 1:
  																									raise ValueError(F'More than one architecture was found, aborting. (architectures = {architectures})'							)
  																		self._logger.warning(F'Detected architecture: {architectures[0]}'							)
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =getattr(import_module('''transformers'''							)		,		architectures[0]							)
  																		try:
  																									SCREAMING_SNAKE_CASE			: Dict	     =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
  											SCREAMING_SNAKE_CASE			: List[str]	     =tf_class.from_config(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =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
  											SCREAMING_SNAKE_CASE			: List[Any]	     =pt_class.from_pretrained(self._local_dir							)
  											pt_model.eval()
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.get_inputs(snake_case_		,		snake_case_		,		snake_case_							)
  
  											with torch.no_grad():
  																		SCREAMING_SNAKE_CASE			: Tuple	     =pt_model(**snake_case_		,		output_hidden_states=snake_case_							)
  											del pt_model  # will no longer be used, and may have a large memory footprint
  
  											SCREAMING_SNAKE_CASE			: Dict	     =tf_class.from_pretrained(self._local_dir		,		from_pt=snake_case_							)
  											SCREAMING_SNAKE_CASE			: List[Any]	     =tf_from_pt_model(**snake_case_		,		output_hidden_states=snake_case_		,		training=snake_case_							)
  
  											# Confirms that cross loading PT weights into TF worked.
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =self.find_pt_tf_differences(snake_case_		,		snake_case_							)
  											SCREAMING_SNAKE_CASE			: Any	     ={k: v for k, v in crossload_differences.items() if '''hidden''' not in k}
  											SCREAMING_SNAKE_CASE			: List[str]	     ={k: v for k, v in crossload_differences.items() if '''hidden''' in k}
  											if len(snake_case_							) == 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\''''							)
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =max(output_differences.values()							) if output_differences else 0.0
  											SCREAMING_SNAKE_CASE			: Tuple	     =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
  											SCREAMING_SNAKE_CASE			: Any	     =os.path.join(self._local_dir		,		snake_case_							)
  											SCREAMING_SNAKE_CASE			: int	     =os.path.join(self._local_dir		,		snake_case_							)
  											if (not os.path.exists(snake_case_							) and not os.path.exists(snake_case_							)) 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
  
  											SCREAMING_SNAKE_CASE			: int	     =tf_class.from_pretrained(self._local_dir							)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =tf_model(**snake_case_		,		output_hidden_states=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: int	     =self.find_pt_tf_differences(snake_case_		,		snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     ={k: v for k, v in conversion_differences.items() if '''hidden''' not in k}
  											SCREAMING_SNAKE_CASE			: int	     ={k: v for k, v in conversion_differences.items() if '''hidden''' in k}
  											if len(snake_case_							) == 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\''''							)
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =max(output_differences.values()							) if output_differences else 0.0
  											SCREAMING_SNAKE_CASE			: int	     =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]							)							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     ='''Update TF weights''' if self._new_weights else '''Add TF weights'''
  											if self._push:
  																		repo.git_add(auto_lfs_track=snake_case_							)
  																		repo.git_commit(snake_case_							)
  																		repo.git_push(blocking=snake_case_							)  # 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...'''							)
  																		SCREAMING_SNAKE_CASE			: 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(snake_case_							):
  																									SCREAMING_SNAKE_CASE			: List[str]	     =[
  																									    CommitOperationAdd(path_in_repo=snake_case_		,		path_or_fileobj=snake_case_							)
  																									]
  																									for shard_path in tf.io.gfile.glob(self._local_dir + '''/tf_model-*.h5'''							):
  																																operations += [
  																																    CommitOperationAdd(path_in_repo=os.path.basename(snake_case_							)		,		path_or_fileobj=snake_case_							)
  																																]
  																		else:
  																									SCREAMING_SNAKE_CASE			: Any	     =[CommitOperationAdd(path_in_repo=snake_case_		,		path_or_fileobj=snake_case_							)]
  
  																		SCREAMING_SNAKE_CASE			: List[str]	     =create_commit(
  																		    repo_id=self._model_name		,		operations=snake_case_		,		commit_message=snake_case_		,		commit_description=snake_case_		,		repo_type='''model'''		,		create_pr=snake_case_		,		).pr_url
  																		self._logger.warning(F'PR open in {hub_pr_url}'							)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  import torch
  from torch import nn
  from torch.nn import CrossEntropyLoss, MSELoss
  
  from ....modeling_outputs import BaseModelOutputWithPooling, SequenceClassifierOutput
  from ....modeling_utils import ModuleUtilsMixin
  from ....utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
  
  
  _A							    =	logging.get_logger(__name__)
  
  _A							    =	"""MMBTConfig"""
  
  
  class 							_lowerCAmelCase		(     nn.Module	):
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      str:
  											super().__init__()
  											SCREAMING_SNAKE_CASE			: str	     =config
  											SCREAMING_SNAKE_CASE			: Dict	     =encoder
  											SCREAMING_SNAKE_CASE			: Tuple	     =nn.Linear(config.modal_hidden_size		,		config.hidden_size							)
  											SCREAMING_SNAKE_CASE			: Any	     =embeddings.position_embeddings
  											SCREAMING_SNAKE_CASE			: List[str]	     =embeddings.token_type_embeddings
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =embeddings.word_embeddings
  											SCREAMING_SNAKE_CASE			: Any	     =embeddings.LayerNorm
  											SCREAMING_SNAKE_CASE			: Any	     =nn.Dropout(p=config.hidden_dropout_prob							)
  
  
  
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None							)    ->      Dict:
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.proj_embeddings(self.encoder(snake_case_							)							)
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =token_embeddings.size(1							)
  
  											if start_token is not None:
  																		SCREAMING_SNAKE_CASE			: List[str]	     =self.word_embeddings(snake_case_							)
  																		seq_length += 1
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =torch.cat([start_token_embeds.unsqueeze(1							), token_embeddings]		,		dim=1							)
  
  											if end_token is not None:
  																		SCREAMING_SNAKE_CASE			: List[str]	     =self.word_embeddings(snake_case_							)
  																		seq_length += 1
  																		SCREAMING_SNAKE_CASE			: Any	     =torch.cat([token_embeddings, end_token_embeds.unsqueeze(1							)]		,		dim=1							)
  
  											if position_ids is None:
  																		SCREAMING_SNAKE_CASE			: Any	     =torch.arange(snake_case_		,		dtype=torch.long		,		device=input_modal.device							)
  																		SCREAMING_SNAKE_CASE			: Dict	     =position_ids.unsqueeze(0							).expand(input_modal.size(0							)		,		snake_case_							)
  
  											if token_type_ids is None:
  																		SCREAMING_SNAKE_CASE			: Any	     =torch.zeros(
  																		    (input_modal.size(0							), seq_length)		,		dtype=torch.long		,		device=input_modal.device							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.position_embeddings(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.token_type_embeddings(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Dict	     =token_embeddings + position_embeddings + token_type_embeddings
  											SCREAMING_SNAKE_CASE			: Any	     =self.LayerNorm(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.dropout(snake_case_							)
  											return embeddings
  
  
  
  
  
  
  
  _A							    =	r"""
      MMBT model was proposed in [Supervised Multimodal Bitransformers for Classifying Images and
      Text](https://github.com/facebookresearch/mmbt) by Douwe Kiela, Suvrat Bhooshan, Hamed Firooz, Davide Testuggine.
      It's a supervised multimodal bitransformer model that fuses information from text and other image encoders, and
      obtain state-of-the-art performance on various multimodal classification benchmark tasks.
  
      This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
      library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
      etc.)
  
      This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
      Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
      and behavior.
  
      Parameters:
          config ([`MMBTConfig`]): Model configuration class with all the parameters of the model.
              Initializing with a config file does not load the weights associated with the model, only the
              configuration.
          transformer (`nn.Module`): A text transformer that is used by MMBT.
              It should have embeddings, encoder, and pooler attributes.
          encoder (`nn.Module`): Encoder for the second modality.
              It should take in a batch of modal inputs and return k, n dimension embeddings.
  """
  
  _A							    =	r"""
      Args:
          input_modal (`torch.FloatTensor` of shape `(batch_size, ***)`):
              The other modality data. It will be the shape that the encoder for that type expects. e.g. With an Image
              Encoder, the shape would be (batch_size, channels, height, width)
          input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
              Indices of input sequence tokens in the vocabulary. It does not expect [CLS] token to be added as it's
              appended to the end of other modality embeddings. Indices can be obtained using [`AutoTokenizer`]. See
              [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details.
  
              [What are input IDs?](../glossary#input-ids)
          modal_start_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
              Optional start token to be added to Other Modality Embedding. [CLS] Most commonly used for classification
              tasks.
          modal_end_tokens (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
              Optional end token to be added to Other Modality Embedding. [SEP] Most commonly used.
          attention_mask (*optional*) `torch.FloatTensor` of shape `(batch_size, sequence_length)`:
              Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
              [What are attention masks?](../glossary#attention-mask)
          token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, sequence_length)`:
              Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
              1]`:
  
              - 0 corresponds to a *sentence A* token,
              - 1 corresponds to a *sentence B* token.
  
              [What are token type IDs?](../glossary#token-type-ids)
          modal_token_type_ids (*optional*) `torch.LongTensor` of shape `(batch_size, modal_sequence_length)`:
              Segment token indices to indicate different portions of the non-text modality. The embeddings from these
              tokens will be summed with the respective token embeddings for the non-text modality.
          position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
              Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
              config.max_position_embeddings - 1]`.
  
              [What are position IDs?](../glossary#position-ids)
          modal_position_ids (`torch.LongTensor` of shape `(batch_size, modal_sequence_length)`, *optional*):
              Indices of positions of each input sequence tokens in the position embeddings for the non-text modality.
              Selected in the range `[0, config.max_position_embeddings - 1]`.
  
              [What are position IDs?](../glossary#position-ids)
          head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
              Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
  
              - 1 indicates the head is **not masked**,
              - 0 indicates the head is **masked**.
  
          inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, embedding_dim)`, *optional*):
              Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
              is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
              model's internal embedding lookup matrix.
          encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
              Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
              the model is configured as a decoder.
          encoder_attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
              Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
              the cross-attention if the model is configured as a decoder. Mask values selected in `[0, 1]`:
  
              - 1 for tokens that are **not masked**,
              - 0 for tokens that are **masked**.
  
          output_attentions (`bool`, *optional*):
              Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
              tensors for more detail.
          output_hidden_states (`bool`, *optional*):
              Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
              more detail.
          return_dict (`bool`, *optional*):
              Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
  """
  
  
  @add_start_docstrings(
      'The bare MMBT Model outputting raw hidden-states without any specific head on top.'  ,   UpperCamelCase__  ,   )
  class 							_lowerCAmelCase		(     nn.Module  ,   UpperCamelCase__	):
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      Any:
  											super().__init__()
  											SCREAMING_SNAKE_CASE			: str	     =config
  											SCREAMING_SNAKE_CASE			: str	     =transformer
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =ModalEmbeddings(snake_case_		,		snake_case_		,		transformer.embeddings							)
  
  
  
  
  				@add_start_docstrings_to_model_forward(snake_case_							)
  				@replace_return_docstrings(output_type=snake_case_		,		config_class=_CONFIG_FOR_DOC							)
  				def     __a		( self		,		snake_case_		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		)    ->      List[Any]:
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =output_attentions if output_attentions is not None else self.config.output_attentions
  											SCREAMING_SNAKE_CASE			: int	     =(
  											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
  											)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =return_dict if return_dict is not None else self.config.use_return_dict
  
  											if input_ids is not None and inputs_embeds is not None:
  																		raise ValueError('''You cannot specify both input_ids and inputs_embeds at the same time'''							)
  											elif input_ids is not None:
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =input_ids.size()
  											elif inputs_embeds is not None:
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =inputs_embeds.size()[:-1]
  											else:
  																		raise ValueError('''You have to specify either input_ids or inputs_embeds'''							)
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =input_ids.device if input_ids is not None else inputs_embeds.device
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.modal_encoder(
  											    snake_case_		,		start_token=snake_case_		,		end_token=snake_case_		,		position_ids=snake_case_		,		token_type_ids=snake_case_		,		)
  
  											SCREAMING_SNAKE_CASE			: str	     =modal_embeddings.size()[:-1]
  
  											if token_type_ids is None:
  																		SCREAMING_SNAKE_CASE			: Tuple	     =torch.ones(snake_case_		,		dtype=torch.long		,		device=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: List[Any]	     =self.transformer.embeddings(
  											    input_ids=snake_case_		,		position_ids=snake_case_		,		token_type_ids=snake_case_		,		inputs_embeds=snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: Any	     =torch.cat([modal_embeddings, txt_embeddings]		,		1							)
  
  											SCREAMING_SNAKE_CASE			: Any	     =embedding_output.size()[:-1]
  
  											if attention_mask is None:
  																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =torch.ones(snake_case_		,		device=snake_case_							)
  											else:
  																		SCREAMING_SNAKE_CASE			: Optional[int]	     =torch.cat(
  																		    [torch.ones(snake_case_		,		device=snake_case_		,		dtype=torch.long							), attention_mask]		,		dim=1							)
  											if encoder_attention_mask is None:
  																		SCREAMING_SNAKE_CASE			: List[str]	     =torch.ones(snake_case_		,		device=snake_case_							)
  											else:
  																		SCREAMING_SNAKE_CASE			: Tuple	     =torch.cat(
  																		    [torch.ones(snake_case_		,		device=snake_case_							), encoder_attention_mask]		,		dim=1							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.get_extended_attention_mask(snake_case_		,		snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.invert_attention_mask(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.get_head_mask(snake_case_		,		self.config.num_hidden_layers							)
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.transformer.encoder(
  											    snake_case_		,		attention_mask=snake_case_		,		head_mask=snake_case_		,		encoder_hidden_states=snake_case_		,		encoder_attention_mask=snake_case_		,		output_attentions=snake_case_		,		output_hidden_states=snake_case_		,		return_dict=snake_case_		,		)
  
  											SCREAMING_SNAKE_CASE			: str	     =encoder_outputs[0]
  											SCREAMING_SNAKE_CASE			: str	     =self.transformer.pooler(snake_case_							)
  
  											if not return_dict:
  																		return (sequence_output, pooled_output) + encoder_outputs[1:]
  
  											return BaseModelOutputWithPooling(
  											    last_hidden_state=snake_case_		,		pooler_output=snake_case_		,		hidden_states=encoder_outputs.hidden_states		,		attentions=encoder_outputs.attentions		,		)
  
  
  
  
  				def     __a		( self							)    ->      List[str]:
  											return self.embeddings.word_embeddings
  
  
  
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      Optional[int]:
  											SCREAMING_SNAKE_CASE			: str	     =value
  
  
  
  
  
  
  
  @add_start_docstrings(
      '\n    MMBT Model with a sequence classification/regression head on top (a linear layer on top of the pooled output)\n    '  ,   UpperCamelCase__  ,   UpperCamelCase__  ,   )
  class 							_lowerCAmelCase		(     nn.Module	):
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_		,		snake_case_							)    ->      int:
  											super().__init__()
  											SCREAMING_SNAKE_CASE			: Dict	     =config.num_labels
  
  											SCREAMING_SNAKE_CASE			: List[str]	     =MMBTModel(snake_case_		,		snake_case_		,		snake_case_							)
  											SCREAMING_SNAKE_CASE			: int	     =nn.Dropout(config.hidden_dropout_prob							)
  											SCREAMING_SNAKE_CASE			: Dict	     =nn.Linear(config.hidden_size		,		config.num_labels							)
  
  
  
  
  
  
  
  				def     __a		( self		,		snake_case_		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		snake_case_=None		,		)    ->      List[str]:
  											SCREAMING_SNAKE_CASE			: Dict	     =return_dict if return_dict is not None else self.config.use_return_dict
  
  											SCREAMING_SNAKE_CASE			: Dict	     =self.mmbt(
  											    input_modal=snake_case_		,		input_ids=snake_case_		,		modal_start_tokens=snake_case_		,		modal_end_tokens=snake_case_		,		attention_mask=snake_case_		,		token_type_ids=snake_case_		,		modal_token_type_ids=snake_case_		,		position_ids=snake_case_		,		modal_position_ids=snake_case_		,		head_mask=snake_case_		,		inputs_embeds=snake_case_		,		return_dict=snake_case_		,		)
  
  											SCREAMING_SNAKE_CASE			: Optional[int]	     =outputs[1]
  
  											SCREAMING_SNAKE_CASE			: List[str]	     =self.dropout(snake_case_							)
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.classifier(snake_case_							)
  
  											SCREAMING_SNAKE_CASE			: int	     =None
  											if labels is not None:
  																		if self.num_labels == 1:
  																									#  We are doing regression
  																									SCREAMING_SNAKE_CASE			: List[Any]	     =MSELoss()
  																									SCREAMING_SNAKE_CASE			: int	     =loss_fct(logits.view(-1							)		,		labels.view(-1							)							)
  																		else:
  																									SCREAMING_SNAKE_CASE			: List[str]	     =CrossEntropyLoss()
  																									SCREAMING_SNAKE_CASE			: Dict	     =loss_fct(logits.view(-1		,		self.num_labels							)		,		labels.view(-1							)							)
  
  											if not return_dict:
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =(logits,) + outputs[2:]
  																		return ((loss,) + output) if loss is not None else output
  
  											return SequenceClassifierOutput(
  											    loss=snake_case_		,		logits=snake_case_		,		hidden_states=outputs.hidden_states		,		attentions=outputs.attentions		,		)
  
  
  
  
  
  
  