





'''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 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      :   Tuple								=     logging.get_logger(__name__)

__UpperCamelCase      :   Union[str, Any]								=     '''Hello world! cécé herlolip'''





def      lowercase  (  lowerCAmelCase :						str   , lowerCAmelCase :						str   , lowerCAmelCase :						bool):



				"""simple docstring"""


				_A :				Any				=  FairseqRobertaModel.from_pretrained(lowerCAmelCase)
				roberta.eval()  # disable dropout
				_A :				List[Any]				=  roberta.model.encoder.sentence_encoder
				_A :				Dict				=  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:
								_A :				Any				=  roberta.model.classification_heads['''mnli'''].out_proj.weight.shape[0]
				print('''Our BERT config:'''   , lowerCAmelCase)

				_A :				Optional[Any]				=  RobertaForSequenceClassification(lowerCAmelCase) if classification_head else RobertaForMaskedLM(lowerCAmelCase)
				model.eval()

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

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

								# self attention
								_A :				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))
								)

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

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

								# intermediate
								_A :				BertIntermediate				=  layer.intermediate
								assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
								_A :				Union[str, Any]				=  roberta_layer.fca.weight
								_A :				int				=  roberta_layer.fca.bias

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

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

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

				_A :				Any				=  model(lowerCAmelCase)[0]
				if classification_head:
								_A :				Union[str, Any]				=  roberta.model.classification_heads['''mnli'''](roberta.extract_features(lowerCAmelCase))
				else:
								_A :				Union[str, Any]				=  roberta.model(lowerCAmelCase)[0]
				print(our_output.shape   , their_output.shape)
				_A :				List[str]				=  torch.max(torch.abs(our_output - their_output)).item()
				print(f"""max_absolute_diff = {max_absolute_diff}""")  # ~ 1e-7
				_A :				str				=  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      :   List[str]								=     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      :   List[Any]								=     parser.parse_args()
	convert_roberta_checkpoint_to_pytorch(
	    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)







'''simple docstring'''






import textwrap

import pyarrow as pa
import pytest

from datasets import Features, Image
from datasets.packaged_modules.text.text import Text

from ..utils import require_pil





@pytest.fixture
def      lowercase  (  lowerCAmelCase :						Union[str, Any]):



				"""simple docstring"""


				_A :				int				=  tmp_path / '''text.txt'''
				_A :				int				=  textwrap.dedent(
				    '''\
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.

        Second paragraph:
        Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
        Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
        Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
        Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
        ''')
				with open(lowerCAmelCase   , '''w'''   , encoding='''utf-8''') as f:
								f.write(lowerCAmelCase)
				return str(lowerCAmelCase)





@pytest.fixture
def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Tuple):



				"""simple docstring"""


				_A :				str				=  tmp_path / '''text_with_image.txt'''
				with open(lowerCAmelCase   , '''w'''   , encoding='''utf-8''') as f:
								f.write(lowerCAmelCase)
				return str(lowerCAmelCase)





@pytest.mark.parametrize('''keep_linebreaks'''   , [True, False])
def      lowercase  (  lowerCAmelCase :						Optional[Any]   , lowerCAmelCase :						Union[str, Any]):



				"""simple docstring"""


				with open(lowerCAmelCase   , encoding='''utf-8''') as f:
								_A :				Any				=  f.read().splitlines(keepends=lowerCAmelCase)
				_A :				Any				=  Text(keep_linebreaks=lowerCAmelCase   , encoding='''utf-8''')
				_A :				Optional[Any]				=  text._generate_tables([[text_file]])
				_A :				Dict				=  pa.concat_tables([table for _, table in generator]).to_pydict()['''text''']
				assert generated_content == expected_content





@require_pil
def      lowercase  (  lowerCAmelCase :						Dict):



				"""simple docstring"""


				with open(lowerCAmelCase   , encoding='''utf-8''') as f:
								_A :				Tuple				=  f.read().splitlines()[0]
				_A :				Optional[int]				=  Text(encoding='''utf-8'''   , features=Features({'''image''': Image()}))
				_A :				Any				=  text._generate_tables([[text_file_with_image]])
				_A :				str				=  pa.concat_tables([table for _, table in generator])
				assert pa_table.schema.field('''image''').type == Image()()
				_A :				List[str]				=  pa_table.to_pydict()['''image''']
				assert generated_content == [{"path": image_file, "bytes": None}]





@pytest.mark.parametrize('''sample_by'''   , ['''line''', '''paragraph''', '''document'''])
def      lowercase  (  lowerCAmelCase :						int   , lowerCAmelCase :						Optional[int]):



				"""simple docstring"""


				with open(lowerCAmelCase   , encoding='''utf-8''') as f:
								_A :				Any				=  f.read()
				if sample_by == "line":
								_A :				str				=  expected_content.splitlines()
				elif sample_by == "paragraph":
								_A :				str				=  expected_content.split('''\n\n''')
				elif sample_by == "document":
								_A :				Tuple				=  [expected_content]
				_A :				str				=  Text(sample_by=lowerCAmelCase   , encoding='''utf-8'''   , chunksize=100)
				_A :				Optional[Any]				=  text._generate_tables([[text_file]])
				_A :				int				=  pa.concat_tables([table for _, table in generator]).to_pydict()['''text''']
				assert generated_content == expected_content
