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()
UpperCamelCase													=		logging.get_logger(__name__)

UpperCamelCase													=		'''Hello world! cécé herlolip'''

def     lowercase_       (    _lowerCamelCase							: str	,			_lowerCamelCase							: str	,			_lowerCamelCase							: bool):
    lowercase__				:     Union[str, Any]					      =       FairseqRobertaModel.from_pretrained(_lowerCamelCase)
    roberta.eval()  # disable dropout
    lowercase__				:     Any					      =       roberta.model.encoder.sentence_encoder
    lowercase__				:     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:
        lowercase__				:     Any					      =       roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
    print("Our BERT config:"	,			_lowerCamelCase)

    lowercase__				:     Union[str, Any]					      =       RobertaForSequenceClassification(_lowerCamelCase) if classification_head else RobertaForMaskedLM(_lowerCamelCase)
    model.eval()

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

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

        # self attention
        lowercase__				:     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))
        )

        lowercase__				:     Union[str, Any]					      =       roberta_layer.self_attn.q_proj.weight
        lowercase__				:     List[str]					      =       roberta_layer.self_attn.q_proj.bias
        lowercase__				:     Any					      =       roberta_layer.self_attn.k_proj.weight
        lowercase__				:     List[str]					      =       roberta_layer.self_attn.k_proj.bias
        lowercase__				:     Any					      =       roberta_layer.self_attn.v_proj.weight
        lowercase__				:     Tuple					      =       roberta_layer.self_attn.v_proj.bias

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

        # intermediate
        lowercase__				:     BertIntermediate					      =       layer.intermediate
        assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
        lowercase__				:     str					      =       roberta_layer.fca.weight
        lowercase__				:     Tuple					      =       roberta_layer.fca.bias

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

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

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

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

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


if __name__ == "__main__":
      UpperCamelCase													=		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.'''
      )
      UpperCamelCase													=		parser.parse_args()
      convert_roberta_checkpoint_to_pytorch(
          args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
      )




import unittest

from datasets import load_dataset

from transformers import load_tool

from .test_tools_common import ToolTesterMixin



class 						snake_case_ (	unittest.TestCase						,__A ):






   def    __UpperCamelCase  (   self       :  Optional[int]						)      ->		List[Any]:
       lowercase__				:     List[Any]					      =       load_tool("document-question-answering"						)
       self.tool.setup()
       lowercase__				:     Optional[int]					      =       load_tool("document-question-answering"			,		remote=lowercase_						)






   def    __UpperCamelCase  (   self       :  int						)      ->		Optional[int]:
       lowercase__				:     List[Any]					      =       load_dataset("hf-internal-testing/example-documents"			,		split="test"						)
       lowercase__				:     List[Any]					      =       dataset[0]["image"]

       lowercase__				:     int					      =       self.tool(lowercase_			,		"When is the coffee break?"						)
       self.assertEqual(lowercase_			,		"11-14 to 11:39 a.m."						)






   def    __UpperCamelCase  (   self       :  Optional[Any]						)      ->		List[Any]:
       lowercase__				:     str					      =       load_dataset("hf-internal-testing/example-documents"			,		split="test"						)
       lowercase__				:     str					      =       dataset[0]["image"]

       lowercase__				:     List[str]					      =       self.remote_tool(lowercase_			,		"When is the coffee break?"						)
       self.assertEqual(lowercase_			,		"11-14 to 11:39 a.m."						)






   def    __UpperCamelCase  (   self       :  List[Any]						)      ->		Dict:
       lowercase__				:     List[str]					      =       load_dataset("hf-internal-testing/example-documents"			,		split="test"						)
       lowercase__				:     List[str]					      =       dataset[0]["image"]

       self.tool(document=lowercase_			,		question="When is the coffee break?"						)







   def    __UpperCamelCase  (   self       :  str						)      ->		Optional[Any]:
       lowercase__				:     Optional[Any]					      =       load_dataset("hf-internal-testing/example-documents"			,		split="test"						)
       lowercase__				:     int					      =       dataset[0]["image"]

       lowercase__				:     Tuple					      =       self.remote_tool(document=lowercase_			,		question="When is the coffee break?"						)
       self.assertEqual(lowercase_			,		"11-14 to 11:39 a.m."						)



