


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


lowerCamelCase		      = logging.get_logger(__name__)

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

if is_torch_available():
 import torch

if is_soundfile_availble():
 import soundfile as sf




class A						:






      def __init__(      self		:  Tuple    ,     lowercase_		:  Any				)      ->		Optional[Any]:
             """simple docstring"""




             _lowerCamelCase :	List[Any]						=value






      def __str__(      self		:  List[str]				)      ->		Tuple:
             """simple docstring"""




             return self.to_string()






      def  lowerCamelCase (      self		:  List[Any]				)      ->		int:
             """simple docstring"""




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






      def  lowerCamelCase (      self		:  str				)      ->		str:
             """simple docstring"""




             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						(     UpperCamelCase_					,							UpperCamelCase_  ):






      def  lowerCamelCase (      self		:  str				)      ->		Tuple:
             """simple docstring"""




             return self._value






      def  lowerCamelCase (      self		:  Tuple				)      ->		int:
             """simple docstring"""




             return self._value




class A						(     UpperCamelCase_					,							UpperCamelCase_  ):






      def __init__(      self		:  Dict    ,     lowercase_		:  Optional[int]				)      ->		List[str]:
             """simple docstring"""




             super().__init__(lowercase_				)

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

             _lowerCamelCase :	int						=None
             _lowerCamelCase :	Dict						=None
             _lowerCamelCase :	Any						=None

             if isinstance(lowercase_    ,     lowercase_				):
                    _lowerCamelCase :	str						=value
             elif isinstance(lowercase_    ,     (str, pathlib.Path)				):
                    _lowerCamelCase :	List[str]						=value
             elif isinstance(lowercase_    ,     torch.Tensor				):
                    _lowerCamelCase :	int						=value
             else:
                    raise ValueError(F'''Unsupported type for {self.__class__.__name__}: {type(lowercase_				)}'''				)






      def  lowerCamelCase (      self		:  Dict    ,     lowercase_		:  int=None    ,     lowercase_		:  int=None				)      ->		List[Any]:
             """simple docstring"""




             from IPython.display import Image, display

             display(Image(self.to_string()				)				)






      def  lowerCamelCase (      self		:  List[str]				)      ->		Dict:
             """simple docstring"""




             if self._raw is not None:
                    return self._raw

             if self._path is not None:
                    _lowerCamelCase :	Dict						=Image.open(self._path				)
                    return self._raw






      def  lowerCamelCase (      self		:  str				)      ->		int:
             """simple docstring"""




             if self._path is not None:
                    return self._path

             if self._raw is not None:
                    _lowerCamelCase :	Any						=tempfile.mkdtemp()
                    _lowerCamelCase :	Any						=os.path.join(lowercase_    ,     str(uuid.uuida()				) + '.png'				)
                    self._raw.save(self._path				)

                    return self._path

             if self._tensor is not None:
                    _lowerCamelCase :	str						=self._tensor.cpu().detach().numpy()

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

                    _lowerCamelCase :	List[Any]						=tempfile.mkdtemp()
                    _lowerCamelCase :	str						=os.path.join(lowercase_    ,     str(uuid.uuida()				) + '.png'				)

                    img.save(self._path				)

                    return self._path




class A						(     UpperCamelCase_  ):






      def __init__(      self		:  Dict    ,     lowercase_		:  List[Any]    ,     lowercase_		:  Optional[int]=1_6000				)      ->		List[Any]:
             """simple docstring"""




             super().__init__(lowercase_				)

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

             _lowerCamelCase :	str						=None
             _lowerCamelCase :	Optional[Any]						=None

             _lowerCamelCase :	List[Any]						=samplerate

             if isinstance(lowercase_    ,     (str, pathlib.Path)				):
                    _lowerCamelCase :	Optional[int]						=value
             elif isinstance(lowercase_    ,     torch.Tensor				):
                    _lowerCamelCase :	List[Any]						=value
             else:
                    raise ValueError(F'''Unsupported audio type: {type(lowercase_				)}'''				)






      def  lowerCamelCase (      self		:  str    ,     lowercase_		:  List[Any]=None    ,     lowercase_		:  Optional[int]=None				)      ->		List[str]:
             """simple docstring"""




             from IPython.display import Audio, display

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






      def  lowerCamelCase (      self		:  Tuple				)      ->		Optional[Any]:
             """simple docstring"""




             if self._tensor is not None:
                    return self._tensor

             if self._path is not None:
                    _lowerCamelCase			,  _lowerCamelCase :	List[str]						=sf.read(self._path				)
                    _lowerCamelCase :	Any						=torch.tensor(lowercase_				)
                    return self._tensor






      def  lowerCamelCase (      self		:  Optional[int]				)      ->		Any:
             """simple docstring"""




             if self._path is not None:
                    return self._path

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




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

if is_vision_available():
 lowerCamelCase		      = AgentImage




def 			a_   (					*SCREAMING_SNAKE_CASE__      :   Tuple	,    **SCREAMING_SNAKE_CASE__      :   Tuple  ):
       '''simple docstring'''





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




def 			a_   (					SCREAMING_SNAKE_CASE__      :   Tuple	,    SCREAMING_SNAKE_CASE__      :   List[Any]=None  ):
       '''simple docstring'''





       if isinstance(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__  ):
              _lowerCamelCase :	Optional[int]						={}
              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:
                                   _lowerCamelCase :	Tuple						=AGENT_TYPE_MAPPING[output_types[i]](SCREAMING_SNAKE_CASE__  )
                            else:
                                   _lowerCamelCase :	Tuple						=AgentType(SCREAMING_SNAKE_CASE__  )

                     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(SCREAMING_SNAKE_CASE__	,    _k  ):
                                          _lowerCamelCase :	Any						=_v(SCREAMING_SNAKE_CASE__  )
                            if k not in decoded_outputs:
                                   _lowerCamelCase :	Optional[int]						=AgentType[v]

       elif isinstance(SCREAMING_SNAKE_CASE__	,    (list, tuple)  ):
              _lowerCamelCase :	Tuple						=type(SCREAMING_SNAKE_CASE__  )()
              for i, v in enumerate(SCREAMING_SNAKE_CASE__  ):
                     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]](SCREAMING_SNAKE_CASE__  )  )
                            else:
                                   decoded_outputs.append(AgentType(SCREAMING_SNAKE_CASE__  )  )
                     else:
                            # If the class does not have defined output, then we map according to the type
                            _lowerCamelCase :	List[str]						=False
                            for _k, _v in INSTANCE_TYPE_MAPPING.items():
                                   if isinstance(SCREAMING_SNAKE_CASE__	,    _k  ):
                                          decoded_outputs.append(_v(SCREAMING_SNAKE_CASE__  )  )
                                          _lowerCamelCase :	List[Any]						=True

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

       else:
              if output_types[0] in AGENT_TYPE_MAPPING:
                     # If the class has defined outputs, we can map directly according to the class definition
                     _lowerCamelCase :	List[str]						=AGENT_TYPE_MAPPING[output_types[0]](SCREAMING_SNAKE_CASE__  )

              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(SCREAMING_SNAKE_CASE__	,    _k  ):
                                   return _v(SCREAMING_SNAKE_CASE__  )
                     return AgentType(SCREAMING_SNAKE_CASE__  )

       return decoded_outputs






import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.9.0'):
 raise Exception('requires fairseq >= 0.9.0')


logging.set_verbosity_info()
lowerCamelCase		      = logging.get_logger(__name__)

lowerCamelCase		      = 'Hello world! cécé herlolip'




def 			a_   (					SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   str	,    SCREAMING_SNAKE_CASE__      :   bool  ):
       '''simple docstring'''





       _lowerCamelCase :	Dict						=FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE__  )
       roberta.eval()  # disable dropout
       _lowerCamelCase :	int						=roberta.model.encoder.sentence_encoder
       _lowerCamelCase :	Union[str, Any]						=RobertaConfig(
           vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings	,    hidden_size=roberta.args.encoder_embed_dim	,    num_hidden_layers=roberta.args.encoder_layers	,    num_attention_heads=roberta.args.encoder_attention_heads	,    intermediate_size=roberta.args.encoder_ffn_embed_dim	,    max_position_embeddings=514	,    type_vocab_size=1	,    layer_norm_eps=1e-5	,    )
       if classification_head:
              _lowerCamelCase :	int						=roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
       print('Our BERT config:'	,    SCREAMING_SNAKE_CASE__  )

       _lowerCamelCase :	Optional[int]						=RobertaForSequenceClassification(SCREAMING_SNAKE_CASE__  ) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE__  )
       model.eval()

       # Now let's copy all the weights.
       # Embeddings
       _lowerCamelCase :	List[str]						=roberta_sent_encoder.embed_tokens.weight
       _lowerCamelCase :	int						=roberta_sent_encoder.embed_positions.weight
       _lowerCamelCase :	Tuple						=torch.zeros_like(
           model.roberta.embeddings.token_type_embeddings.weight  )  # just zero them out b/c RoBERTa doesn't use them.
       _lowerCamelCase :	Optional[Any]						=roberta_sent_encoder.emb_layer_norm.weight
       _lowerCamelCase :	int						=roberta_sent_encoder.emb_layer_norm.bias

       for i in range(config.num_hidden_layers  ):
              # Encoder: start of layer
              _lowerCamelCase :	BertLayer						=model.roberta.encoder.layer[i]
              _lowerCamelCase :	TransformerSentenceEncoderLayer						=roberta_sent_encoder.layers[i]

              # self attention
              _lowerCamelCase :	BertSelfAttention						=layer.attention.self
              assert (
                  roberta_layer.self_attn.k_proj.weight.data.shape
                  == roberta_layer.self_attn.q_proj.weight.data.shape
                  == roberta_layer.self_attn.v_proj.weight.data.shape
                  == torch.Size((config.hidden_size, config.hidden_size)  )
              )

              _lowerCamelCase :	Dict						=roberta_layer.self_attn.q_proj.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn.q_proj.bias
              _lowerCamelCase :	Optional[Any]						=roberta_layer.self_attn.k_proj.weight
              _lowerCamelCase :	int						=roberta_layer.self_attn.k_proj.bias
              _lowerCamelCase :	Optional[Any]						=roberta_layer.self_attn.v_proj.weight
              _lowerCamelCase :	int						=roberta_layer.self_attn.v_proj.bias

              # self-attention output
              _lowerCamelCase :	BertSelfOutput						=layer.attention.output
              assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
              _lowerCamelCase :	Union[str, Any]						=roberta_layer.self_attn.out_proj.weight
              _lowerCamelCase :	str						=roberta_layer.self_attn.out_proj.bias
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn_layer_norm.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.self_attn_layer_norm.bias

              # intermediate
              _lowerCamelCase :	BertIntermediate						=layer.intermediate
              assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
              _lowerCamelCase :	str						=roberta_layer.fca.weight
              _lowerCamelCase :	Optional[int]						=roberta_layer.fca.bias

              # output
              _lowerCamelCase :	BertOutput						=layer.output
              assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
              _lowerCamelCase :	str						=roberta_layer.fca.weight
              _lowerCamelCase :	List[Any]						=roberta_layer.fca.bias
              _lowerCamelCase :	List[str]						=roberta_layer.final_layer_norm.weight
              _lowerCamelCase :	Optional[int]						=roberta_layer.final_layer_norm.bias
              # end of layer

       if classification_head:
              _lowerCamelCase :	Any						=roberta.model.classification_heads['mnli'].dense.weight
              _lowerCamelCase :	Optional[Any]						=roberta.model.classification_heads['mnli'].dense.bias
              _lowerCamelCase :	List[str]						=roberta.model.classification_heads['mnli'].out_proj.weight
              _lowerCamelCase :	Tuple						=roberta.model.classification_heads['mnli'].out_proj.bias
       else:
              # LM Head
              _lowerCamelCase :	Optional[Any]						=roberta.model.encoder.lm_head.dense.weight
              _lowerCamelCase :	Tuple						=roberta.model.encoder.lm_head.dense.bias
              _lowerCamelCase :	Tuple						=roberta.model.encoder.lm_head.layer_norm.weight
              _lowerCamelCase :	int						=roberta.model.encoder.lm_head.layer_norm.bias
              _lowerCamelCase :	Dict						=roberta.model.encoder.lm_head.weight
              _lowerCamelCase :	str						=roberta.model.encoder.lm_head.bias

       # Let's check that we get the same results.
       _lowerCamelCase :	torch.Tensor						=roberta.encode(SCREAMING_SNAKE_CASE__  ).unsqueeze(0  )  # batch of size 1

       _lowerCamelCase :	List[Any]						=model(SCREAMING_SNAKE_CASE__  )[0]
       if classification_head:
              _lowerCamelCase :	int						=roberta.model.classification_heads['mnli'](roberta.extract_features(SCREAMING_SNAKE_CASE__  )  )
       else:
              _lowerCamelCase :	int						=roberta.model(SCREAMING_SNAKE_CASE__  )[0]
       print(our_output.shape	,    their_output.shape  )
       _lowerCamelCase :	Any						=torch.max(torch.abs(our_output - their_output  )  ).item()
       print(F'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
       _lowerCamelCase :	Optional[Any]						=torch.allclose(SCREAMING_SNAKE_CASE__	,    SCREAMING_SNAKE_CASE__	,    atol=1e-3  )
       print('Do both models output the same tensors?'	,    '🔥' if success else '💩'  )
       if not success:
              raise Exception('Something went wRoNg'  )

       pathlib.Path(SCREAMING_SNAKE_CASE__  ).mkdir(parents=SCREAMING_SNAKE_CASE__	,    exist_ok=SCREAMING_SNAKE_CASE__  )
       print(F'''Saving model to {pytorch_dump_folder_path}'''  )
       model.save_pretrained(SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
 lowerCamelCase		      = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 parser.add_argument(
     '--classification_head', action='store_true', help='Whether to convert a final classification head.'
 )
 lowerCamelCase		      = parser.parse_args()
 convert_roberta_checkpoint_to_pytorch(
     args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )


