
"""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()
snake_case    =		logging.get_logger(__name__)

snake_case    =		'Hello world! cécé herlolip'

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	FairseqRobertaModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
					roberta.eval()  # disable dropout
					SCREAMING_SNAKE_CASE				=	roberta.model.encoder.sentence_encoder
					SCREAMING_SNAKE_CASE				=	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=5_1_4,     type_vocab_size=1,     layer_norm_eps=1E-5,     )
					if classification_head:
										SCREAMING_SNAKE_CASE				=	roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]
					print('Our BERT config:',     SCREAMING_SNAKE_CASE_			)

					SCREAMING_SNAKE_CASE				=	RobertaForSequenceClassification(SCREAMING_SNAKE_CASE_			) if classification_head else RobertaForMaskedLM(SCREAMING_SNAKE_CASE_			)
					model.eval()

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

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

										# self attention
										SCREAMING_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)			)
										)

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

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

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

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

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

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

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






'''simple docstring'''







import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


_lowerCamelCase							:						int								=  get_logger(__name__)


if TYPE_CHECKING:
						from .builder import DatasetBuilder






def 		_lowercase		(	lowercase_						,							lowercase_ = None  ):



		'''simple docstring'''







		_snake_case	       =       importlib.import_module(lowercase_  )

		# TODO(QL): always update the module to add subsequent new authentication
		if hasattr(lowercase_						,							"_patched_for_streaming"  ) and module._patched_for_streaming:
				return

		def wrap_auth(lowercase_  ):
				@wraps(lowercase_  )
				def wrapper(*lowercase_						,							**lowercase_  ):
						return function(*lowercase_						,							use_auth_token=lowercase_						,							**lowercase_  )

				_snake_case	       =       "wrap_auth"
				return wrapper

		# open files in a streaming fashion
		patch_submodule(lowercase_						,							"open"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.listdir"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.walk"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"glob.glob"						,							wrap_auth(lowercase_  )  ).start()
		# allow to navigate in remote zip files
		patch_submodule(lowercase_						,							"os.path.join"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.dirname"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.basename"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.relpath"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.split"						,							lowercase_  ).start()
		patch_submodule(lowercase_						,							"os.path.splitext"						,							lowercase_  ).start()
		# allow checks on paths
		patch_submodule(lowercase_						,							"os.path.exists"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.path.isdir"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.path.isfile"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"os.path.getsize"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"pathlib.Path"						,							lowercase_  ).start()
		# file readers
		patch_submodule(lowercase_						,							"gzip.open"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"numpy.load"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"pandas.read_csv"						,							wrap_auth(lowercase_  )						,							attrs=["__version__"]  ).start()
		patch_submodule(lowercase_						,							"pandas.read_excel"						,							wrap_auth(lowercase_  )						,							attrs=["__version__"]  ).start()
		patch_submodule(lowercase_						,							"scipy.io.loadmat"						,							wrap_auth(lowercase_  )						,							attrs=["__version__"]  ).start()
		patch_submodule(lowercase_						,							"xml.etree.ElementTree.parse"						,							wrap_auth(lowercase_  )  ).start()
		patch_submodule(lowercase_						,							"xml.dom.minidom.parse"						,							wrap_auth(lowercase_  )  ).start()
		_snake_case	       =       True






def 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		extend_module_for_streaming(builder.__module__						,							use_auth_token=builder.use_auth_token  )
		# if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
		if not builder.__module__.startswith("datasets."  ):  # check that it's not a packaged builder like csv
				for imports in get_imports(inspect.getfile(builder.__class__  )  ):
						if imports[0] == "internal":
								_snake_case	       =       imports[1]
								_snake_case	       =       ".".join(builder.__module__.split("."  )[:-1] + [internal_import_name]  )
								extend_module_for_streaming(lowercase_						,							use_auth_token=builder.use_auth_token  )

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
		from .builder import DatasetBuilder

		_snake_case	       =       [
		    cls.__module__
		    for cls in type(lowercase_  ).__mro__[1:]  # make sure it's not the same module we've already patched
		    if issubclass(cls						,							lowercase_  ) and cls.__module__ != DatasetBuilder.__module__
		]  # check it's not a standard builder from datasets.builder
		for module in parent_builder_modules:
				extend_module_for_streaming(lowercase_						,							use_auth_token=builder.use_auth_token  )





