'''simple docstring'''





from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


SCREAMING_SNAKE_CASE_:					Any  =logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_:					List[Any]  =OrderedDict(
    [
        # Base model mapping
        ('albert', 'FlaxAlbertModel'),
        ('bart', 'FlaxBartModel'),
        ('beit', 'FlaxBeitModel'),
        ('bert', 'FlaxBertModel'),
        ('big_bird', 'FlaxBigBirdModel'),
        ('blenderbot', 'FlaxBlenderbotModel'),
        ('blenderbot-small', 'FlaxBlenderbotSmallModel'),
        ('clip', 'FlaxCLIPModel'),
        ('distilbert', 'FlaxDistilBertModel'),
        ('electra', 'FlaxElectraModel'),
        ('gpt-sw3', 'FlaxGPT2Model'),
        ('gpt2', 'FlaxGPT2Model'),
        ('gpt_neo', 'FlaxGPTNeoModel'),
        ('gptj', 'FlaxGPTJModel'),
        ('longt5', 'FlaxLongT5Model'),
        ('marian', 'FlaxMarianModel'),
        ('mbart', 'FlaxMBartModel'),
        ('mt5', 'FlaxMT5Model'),
        ('opt', 'FlaxOPTModel'),
        ('pegasus', 'FlaxPegasusModel'),
        ('regnet', 'FlaxRegNetModel'),
        ('resnet', 'FlaxResNetModel'),
        ('roberta', 'FlaxRobertaModel'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormModel'),
        ('roformer', 'FlaxRoFormerModel'),
        ('t5', 'FlaxT5Model'),
        ('vision-text-dual-encoder', 'FlaxVisionTextDualEncoderModel'),
        ('vit', 'FlaxViTModel'),
        ('wav2vec2', 'FlaxWav2Vec2Model'),
        ('whisper', 'FlaxWhisperModel'),
        ('xglm', 'FlaxXGLMModel'),
        ('xlm-roberta', 'FlaxXLMRobertaModel'),
    ]
)

SCREAMING_SNAKE_CASE_:					List[Any]  =OrderedDict(
    [
        # Model for pre-training mapping
        ('albert', 'FlaxAlbertForPreTraining'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForPreTraining'),
        ('big_bird', 'FlaxBigBirdForPreTraining'),
        ('electra', 'FlaxElectraForPreTraining'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
        ('wav2vec2', 'FlaxWav2Vec2ForPreTraining'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

SCREAMING_SNAKE_CASE_:					Any  =OrderedDict(
    [
        # Model for Masked LM mapping
        ('albert', 'FlaxAlbertForMaskedLM'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForMaskedLM'),
        ('big_bird', 'FlaxBigBirdForMaskedLM'),
        ('distilbert', 'FlaxDistilBertForMaskedLM'),
        ('electra', 'FlaxElectraForMaskedLM'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

SCREAMING_SNAKE_CASE_:					int  =OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('blenderbot', 'FlaxBlenderbotForConditionalGeneration'),
        ('blenderbot-small', 'FlaxBlenderbotSmallForConditionalGeneration'),
        ('encoder-decoder', 'FlaxEncoderDecoderModel'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('marian', 'FlaxMarianMTModel'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('pegasus', 'FlaxPegasusForConditionalGeneration'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
    ]
)

SCREAMING_SNAKE_CASE_:					str  =OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

SCREAMING_SNAKE_CASE_:					str  =OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

SCREAMING_SNAKE_CASE_:					List[Any]  =OrderedDict(
    [
        # Model for Causal LM mapping
        ('bart', 'FlaxBartForCausalLM'),
        ('bert', 'FlaxBertForCausalLM'),
        ('big_bird', 'FlaxBigBirdForCausalLM'),
        ('electra', 'FlaxElectraForCausalLM'),
        ('gpt-sw3', 'FlaxGPT2LMHeadModel'),
        ('gpt2', 'FlaxGPT2LMHeadModel'),
        ('gpt_neo', 'FlaxGPTNeoForCausalLM'),
        ('gptj', 'FlaxGPTJForCausalLM'),
        ('opt', 'FlaxOPTForCausalLM'),
        ('roberta', 'FlaxRobertaForCausalLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForCausalLM'),
        ('xglm', 'FlaxXGLMForCausalLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForCausalLM'),
    ]
)

SCREAMING_SNAKE_CASE_:					Optional[int]  =OrderedDict(
    [
        # Model for Sequence Classification mapping
        ('albert', 'FlaxAlbertForSequenceClassification'),
        ('bart', 'FlaxBartForSequenceClassification'),
        ('bert', 'FlaxBertForSequenceClassification'),
        ('big_bird', 'FlaxBigBirdForSequenceClassification'),
        ('distilbert', 'FlaxDistilBertForSequenceClassification'),
        ('electra', 'FlaxElectraForSequenceClassification'),
        ('mbart', 'FlaxMBartForSequenceClassification'),
        ('roberta', 'FlaxRobertaForSequenceClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForSequenceClassification'),
        ('roformer', 'FlaxRoFormerForSequenceClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForSequenceClassification'),
    ]
)

SCREAMING_SNAKE_CASE_:					Any  =OrderedDict(
    [
        # Model for Question Answering mapping
        ('albert', 'FlaxAlbertForQuestionAnswering'),
        ('bart', 'FlaxBartForQuestionAnswering'),
        ('bert', 'FlaxBertForQuestionAnswering'),
        ('big_bird', 'FlaxBigBirdForQuestionAnswering'),
        ('distilbert', 'FlaxDistilBertForQuestionAnswering'),
        ('electra', 'FlaxElectraForQuestionAnswering'),
        ('mbart', 'FlaxMBartForQuestionAnswering'),
        ('roberta', 'FlaxRobertaForQuestionAnswering'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForQuestionAnswering'),
        ('roformer', 'FlaxRoFormerForQuestionAnswering'),
        ('xlm-roberta', 'FlaxXLMRobertaForQuestionAnswering'),
    ]
)

SCREAMING_SNAKE_CASE_:					Any  =OrderedDict(
    [
        # Model for Token Classification mapping
        ('albert', 'FlaxAlbertForTokenClassification'),
        ('bert', 'FlaxBertForTokenClassification'),
        ('big_bird', 'FlaxBigBirdForTokenClassification'),
        ('distilbert', 'FlaxDistilBertForTokenClassification'),
        ('electra', 'FlaxElectraForTokenClassification'),
        ('roberta', 'FlaxRobertaForTokenClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForTokenClassification'),
        ('roformer', 'FlaxRoFormerForTokenClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForTokenClassification'),
    ]
)

SCREAMING_SNAKE_CASE_:					Optional[Any]  =OrderedDict(
    [
        # Model for Multiple Choice mapping
        ('albert', 'FlaxAlbertForMultipleChoice'),
        ('bert', 'FlaxBertForMultipleChoice'),
        ('big_bird', 'FlaxBigBirdForMultipleChoice'),
        ('distilbert', 'FlaxDistilBertForMultipleChoice'),
        ('electra', 'FlaxElectraForMultipleChoice'),
        ('roberta', 'FlaxRobertaForMultipleChoice'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMultipleChoice'),
        ('roformer', 'FlaxRoFormerForMultipleChoice'),
        ('xlm-roberta', 'FlaxXLMRobertaForMultipleChoice'),
    ]
)

SCREAMING_SNAKE_CASE_:					int  =OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

SCREAMING_SNAKE_CASE_:					List[Any]  =OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

SCREAMING_SNAKE_CASE_:					Optional[int]  =OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

SCREAMING_SNAKE_CASE_:					str  =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_:					str  =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_:					List[str]  =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_:					List[Any]  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Optional[Any]  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Dict  =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_:					Optional[Any]  =_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_:					Any  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					List[str]  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Dict  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Any  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Dict  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Dict  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_:					Optional[Any]  =_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						int	       =  FLAX_MODEL_MAPPING




SCREAMING_SNAKE_CASE_:					Dict  =auto_class_update(FlaxAutoModel)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						str	       =  FLAX_MODEL_FOR_PRETRAINING_MAPPING




SCREAMING_SNAKE_CASE_:					Dict  =auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Optional[Any]	       =  FLAX_MODEL_FOR_CAUSAL_LM_MAPPING




SCREAMING_SNAKE_CASE_:					Tuple  =auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Optional[Any]	       =  FLAX_MODEL_FOR_MASKED_LM_MAPPING




SCREAMING_SNAKE_CASE_:					Optional[Any]  =auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						List[Any]	       =  FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING




SCREAMING_SNAKE_CASE_:					Optional[Any]  =auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Union[str, Any]	       =  FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING




SCREAMING_SNAKE_CASE_:					Optional[int]  =auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Optional[int]	       =  FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING




SCREAMING_SNAKE_CASE_:					List[Any]  =auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Tuple	       =  FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING




SCREAMING_SNAKE_CASE_:					List[Any]  =auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Dict	       =  FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING




SCREAMING_SNAKE_CASE_:					Any  =auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Union[str, Any]	       =  FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING




SCREAMING_SNAKE_CASE_:					int  =auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						int	       =  FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING




SCREAMING_SNAKE_CASE_:					Dict  =auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						Any	       =  FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING




SCREAMING_SNAKE_CASE_:					Optional[int]  =auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')




class 	__A				(			_BaseAutoModelClass     ):
    a__	:						List[Any]	       =  FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING




SCREAMING_SNAKE_CASE_:					Union[str, Any]  =auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)




'''simple docstring'''





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 XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
    raise Exception('requires fairseq >= 1.0.0a')

logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:					List[Any]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					Tuple  ='Hello world! cécé herlolip'





def      lowerCAmelCase_    ( snake_case_	:			str ,   snake_case_	:			str ,   snake_case_	:			bool      )   ->				Any:


     '''simple docstring'''




     UpperCAmelCase_	   =       FairseqRobertaModel.from_pretrained(snake_case_      )
     roberta.eval()  # disable dropout
     UpperCAmelCase_	   =       roberta.model.encoder.sentence_encoder
     UpperCAmelCase_	   =       XLMRobertaConfig(
         vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings ,   hidden_size=roberta.cfg.model.encoder_embed_dim ,   num_hidden_layers=roberta.cfg.model.encoder_layers ,   num_attention_heads=roberta.cfg.model.encoder_attention_heads ,   intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim ,   max_position_embeddings=5_14 ,   type_vocab_size=1 ,   layer_norm_eps=1E-5 ,   )
     if classification_head:
          UpperCAmelCase_	   =       roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

     print("Our RoBERTa config:" ,   snake_case_      )

     UpperCAmelCase_	   =       XLMRobertaXLForSequenceClassification(snake_case_      ) if classification_head else XLMRobertaXLForMaskedLM(snake_case_      )
     model.eval()

     # Now let's copy all the weights.
     # Embeddings
     UpperCAmelCase_	   =       roberta_sent_encoder.embed_tokens.weight
     UpperCAmelCase_	   =       roberta_sent_encoder.embed_positions.weight
     UpperCAmelCase_	   =       torch.zeros_like(
         model.roberta.embeddings.token_type_embeddings.weight      )  # just zero them out b/c RoBERTa doesn't use them.

     UpperCAmelCase_	   =       roberta_sent_encoder.layer_norm.weight
     UpperCAmelCase_	   =       roberta_sent_encoder.layer_norm.bias

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

          UpperCAmelCase_	   =       layer.attention
          UpperCAmelCase_	   =       roberta_layer.self_attn_layer_norm.weight
          UpperCAmelCase_	   =       roberta_layer.self_attn_layer_norm.bias

          # self attention
          UpperCAmelCase_	   =       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)      )
          )

          UpperCAmelCase_	   =       roberta_layer.self_attn.q_proj.weight
          UpperCAmelCase_	   =       roberta_layer.self_attn.q_proj.bias
          UpperCAmelCase_	   =       roberta_layer.self_attn.k_proj.weight
          UpperCAmelCase_	   =       roberta_layer.self_attn.k_proj.bias
          UpperCAmelCase_	   =       roberta_layer.self_attn.v_proj.weight
          UpperCAmelCase_	   =       roberta_layer.self_attn.v_proj.bias

          # self-attention output
          UpperCAmelCase_	   =       layer.attention.output
          assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
          UpperCAmelCase_	   =       roberta_layer.self_attn.out_proj.weight
          UpperCAmelCase_	   =       roberta_layer.self_attn.out_proj.bias

          # this one is final layer norm
          UpperCAmelCase_	   =       roberta_layer.final_layer_norm.weight
          UpperCAmelCase_	   =       roberta_layer.final_layer_norm.bias

          # intermediate
          UpperCAmelCase_	   =       layer.intermediate
          assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
          UpperCAmelCase_	   =       roberta_layer.fca.weight
          UpperCAmelCase_	   =       roberta_layer.fca.bias

          # output
          UpperCAmelCase_	   =       layer.output
          assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
          UpperCAmelCase_	   =       roberta_layer.fca.weight
          UpperCAmelCase_	   =       roberta_layer.fca.bias
          # end of layer

     if classification_head:
          UpperCAmelCase_	   =       roberta.model.classification_heads["mnli"].dense.weight
          UpperCAmelCase_	   =       roberta.model.classification_heads["mnli"].dense.bias
          UpperCAmelCase_	   =       roberta.model.classification_heads["mnli"].out_proj.weight
          UpperCAmelCase_	   =       roberta.model.classification_heads["mnli"].out_proj.bias
     else:
          # LM Head
          UpperCAmelCase_	   =       roberta.model.encoder.lm_head.dense.weight
          UpperCAmelCase_	   =       roberta.model.encoder.lm_head.dense.bias
          UpperCAmelCase_	   =       roberta.model.encoder.lm_head.layer_norm.weight
          UpperCAmelCase_	   =       roberta.model.encoder.lm_head.layer_norm.bias
          UpperCAmelCase_	   =       roberta.model.encoder.lm_head.weight
          UpperCAmelCase_	   =       roberta.model.encoder.lm_head.bias

     # Let's check that we get the same results.
     UpperCAmelCase_	   =       roberta.encode(snake_case_      ).unsqueeze(0      )  # batch of size 1

     UpperCAmelCase_	   =       model(snake_case_      )[0]
     if classification_head:
          UpperCAmelCase_	   =       roberta.model.classification_heads["mnli"](roberta.extract_features(snake_case_      )      )
     else:
          UpperCAmelCase_	   =       roberta.model(snake_case_      )[0]
     print(our_output.shape ,   their_output.shape      )
     UpperCAmelCase_	   =       torch.max(torch.abs(our_output - their_output      )      ).item()
     print(f"""max_absolute_diff = {max_absolute_diff}"""      )  # ~ 1e-7
     UpperCAmelCase_	   =       torch.allclose(snake_case_ ,   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(snake_case_      ).mkdir(parents=snake_case_ ,   exist_ok=snake_case_      )
     print(f"""Saving model to {pytorch_dump_folder_path}"""      )
     model.save_pretrained(snake_case_      )


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					Optional[int]  =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.'
    )
    SCREAMING_SNAKE_CASE_:					Union[str, Any]  =parser.parse_args()
    convert_xlm_roberta_xl_checkpoint_to_pytorch(
        args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
    )




