




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()
__UpperCamelCase   :			Any          = logging.get_logger(__name__)

__UpperCamelCase   :			List[str]          = "Hello world! cécé herlolip"





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Tuple:
	a									=				FairseqRobertaModel.from_pretrained(lowerCamelCase_       )
	roberta.eval()  # disable dropout
	a									=				roberta.model.encoder.sentence_encoder
	a									=				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:
		a									=				roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

	print("""Our RoBERTa config:"""				,     lowerCamelCase_       )

	a									=				XLMRobertaXLForSequenceClassification(lowerCamelCase_       ) if classification_head else XLMRobertaXLForMaskedLM(lowerCamelCase_       )
	model.eval()

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

	a									=				roberta_sent_encoder.layer_norm.weight
	a									=				roberta_sent_encoder.layer_norm.bias

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

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

		# self attention
		a									=				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									=				roberta_layer.self_attn.q_proj.weight
		a									=				roberta_layer.self_attn.q_proj.bias
		a									=				roberta_layer.self_attn.k_proj.weight
		a									=				roberta_layer.self_attn.k_proj.bias
		a									=				roberta_layer.self_attn.v_proj.weight
		a									=				roberta_layer.self_attn.v_proj.bias

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

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

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

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

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

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

	a									=				model(lowerCamelCase_       )[0]
	if classification_head:
		a									=				roberta.model.classification_heads["mnli"](roberta.extract_features(lowerCamelCase_       )       )
	else:
		a									=				roberta.model(lowerCamelCase_       )[0]
	print(our_output.shape				,     their_output.shape       )
	a									=				torch.max(torch.abs(our_output - their_output       )       ).item()
	print(f'max_absolute_diff = {max_absolute_diff}'       )  # ~ 1e-7
	a									=				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   :			Tuple          = 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   :			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
		)






"""simple docstring"""







import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


A		=						logging.get_logger(__name__)

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int]     ,		lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = set()
						snake_case   :			Optional[int]						    = []

						def parse_line(lowerCamelCase_:							Union[str, Any] ):
												for line in fp:
																		if isinstance(lowerCamelCase_     ,		lowerCamelCase_ ):
																								snake_case   :			Optional[Any]						    = line.decode("UTF-8" )
																		if "warnings summary (final)" in line:
																								continue
																		# This means we are outside the body of a warning
																		elif not line.startswith(" " ):
																								# process a single warning and move it to `selected_warnings`.
																								if len(lowerCamelCase_ ) > 0:
																														snake_case   :			str						    = "\n".join(lowerCamelCase_ )
																														# Only keep the warnings specified in `targets`
																														if any(f''': {x}: ''' in warning for x in targets ):
																																				selected_warnings.add(lowerCamelCase_ )
																														buffer.clear()
																								continue
																		else:
																								snake_case   :			Dict						    = line.strip()
																								buffer.append(lowerCamelCase_ )

						if from_gh:
												for filename in os.listdir(lowerCamelCase_ ):
																		snake_case   :			Tuple						    = os.path.join(lowerCamelCase_     ,		lowerCamelCase_ )
																		if not os.path.isdir(lowerCamelCase_ ):
																								# read the file
																								if filename != "warnings.txt":
																														continue
																								with open(lowerCamelCase_ ) as fp:
																														parse_line(lowerCamelCase_ )
						else:
												try:
																		with zipfile.ZipFile(lowerCamelCase_ ) as z:
																								for filename in z.namelist():
																														if not os.path.isdir(lowerCamelCase_ ):
																																				# read the file
																																				if filename != "warnings.txt":
																																										continue
																																				with z.open(lowerCamelCase_ ) as fp:
																																										parse_line(lowerCamelCase_ )
												except Exception:
																		logger.warning(
																		    f'''{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.''' )

						return selected_warnings

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any]     ,		lowerCamelCase_:							Any ):
						"""simple docstring"""






						snake_case   :			Dict						    = set()

						snake_case   :			str						    = [os.path.join(lowerCamelCase_     ,		lowerCamelCase_ ) for p in os.listdir(lowerCamelCase_ ) if (p.endswith(".zip" ) or from_gh)]
						for p in paths:
												selected_warnings.update(extract_warnings_from_single_artifact(lowerCamelCase_     ,		lowerCamelCase_ ) )

						return selected_warnings


if __name__ == "__main__":

			def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Dict ):
									"""simple docstring"""






									return values.split("," )

			A		=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
			parser.add_argument(
			    '--output_dir',
			    type=str,
			    required=True,
			    help='Where to store the downloaded artifacts and other result files.',
			)
			parser.add_argument('--token', default=None, type=str, help='A token that has actions:read permission.')
			# optional parameters
			parser.add_argument(
			    '--targets',
			    default='DeprecationWarning,UserWarning,FutureWarning',
			    type=list_str,
			    help='Comma-separated list of target warning(s) which we want to extract.',
			)
			parser.add_argument(
			    '--from_gh',
			    action='store_true',
			    help='If running from a GitHub action workflow and collecting warnings from its artifacts.',
			)

			A		=						parser.parse_args()

			A		=						args.from_gh
			if from_gh:
						# The artifacts have to be downloaded using `actions/download-artifact@v3`
						pass
			else:
						os.makedirs(args.output_dir, exist_ok=True)

						# get download links
						A		=						get_artifacts_links(args.workflow_run_id, token=args.token)
						with open(os.path.join(args.output_dir, 'artifacts.json'), 'w', encoding='UTF-8') as fp:
									json.dump(artifacts, fp, ensure_ascii=False, indent=4)

						# download artifacts
						for idx, (name, url) in enumerate(artifacts.items()):
									print(name)
									print(url)
									print('=' * 8_0)
									download_artifact(name, url, args.output_dir, args.token)
									# Be gentle to GitHub
									time.sleep(1)

    # extract warnings from artifacts
			A		=						extract_warnings(args.output_dir, args.targets)
			A		=						sorted(selected_warnings)
			with open(os.path.join(args.output_dir, 'selected_warnings.json'), 'w', encoding='UTF-8') as fp:
						json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)



