
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()
_A						      =				logging.get_logger(__name__)

_A						      =				"Hello world! cécé herlolip"



def 		lowercase_    (     A__				,	A__				,	A__	)							->							Optional[int]:
    """simple docstring"""

    snake_case				  =      FairseqRobertaModel.from_pretrained(A__	)
    roberta.eval()  # disable dropout
    snake_case				  =      roberta.model.encoder.sentence_encoder
    snake_case				  =      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=514				,	type_vocab_size=1				,	layer_norm_eps=1e-5				,	)
    if classification_head:
        snake_case				  =      roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

    print("Our RoBERTa config:"				,	A__	)

    snake_case				  =      XLMRobertaXLForSequenceClassification(A__	) if classification_head else XLMRobertaXLForMaskedLM(A__	)
    model.eval()

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

    snake_case				  =      roberta_sent_encoder.layer_norm.weight
    snake_case				  =      roberta_sent_encoder.layer_norm.bias

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

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

        # self attention
        snake_case				  =      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)	)
        )

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

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

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

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

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

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

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

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

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


if __name__ == "__main__":
   _A						      =				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."
   )
   _A						      =				parser.parse_args()
   convert_xlm_roberta_xl_checkpoint_to_pytorch(
       args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
   )



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class   lowerCamelCase	(						unittest.TestCase						):





     @slow
     def 	UpperCAmelCase(self   :					List[str]     )		->		Optional[int]:
         snake_case				  =      AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       ,					return_dict=_A     ).to(_A     )
         snake_case				  =      AutoTokenizer.from_pretrained("google/mt5-small"     )

         snake_case				  =      tokenizer("Hello there"       ,					return_tensors="pt"     ).input_ids
         snake_case				  =      tokenizer("Hi I am"       ,					return_tensors="pt"     ).input_ids

         snake_case				  =      model(input_ids.to(_A     )       ,					labels=labels.to(_A     )     ).loss
         snake_case				  =      -(labels.shape[-1] * loss.item())

         snake_case				  =      -84.91_27
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1E-4     )

