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

a							='Hello world! cécé herlolip'





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[Any]:




						'''simple docstring'''



						lowerCamelCase__ =FairseqRobertaModel.from_pretrained(__lowerCAmelCase    )
						roberta.eval()  # disable dropout
						lowerCamelCase__ =roberta.model.encoder.sentence_encoder
						lowerCamelCase__ =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:
												lowerCamelCase__ =roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
						print("Our BERT config:"       ,   __lowerCAmelCase    )

						lowerCamelCase__ =RobertaForSequenceClassification(__lowerCAmelCase    ) if classification_head else RobertaForMaskedLM(__lowerCAmelCase    )
						model.eval()

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

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

												# self attention
												lowerCamelCase__ =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)    )
												)

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

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

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

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

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

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

						lowerCamelCase__ =model(__lowerCAmelCase    )[0]
						if classification_head:
												lowerCamelCase__ =roberta.model.classification_heads["mnli"](roberta.extract_features(__lowerCAmelCase    )    )
						else:
												lowerCamelCase__ =roberta.model(__lowerCAmelCase    )[0]
						print(our_output.shape       ,   their_output.shape    )
						lowerCamelCase__ =torch.max(torch.abs(our_output - their_output    )    ).item()
						print(F'''max_absolute_diff = {max_absolute_diff}'''    )  # ~ 1e-7
						lowerCamelCase__ =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__":
	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_roberta_checkpoint_to_pytorch(
	    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
	)



"""simple docstring"""


from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

a							={
    name: (getattr(transformers, name), getattr(transformers, name + 'Fast')) for name in SLOW_TO_FAST_CONVERTERS
}

a							=datasets.load_dataset('xnli', split='test+validation')

a							=0
a							=0
a							=0
a							=0





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						if spm_diff == list(reversed(__lowerCAmelCase    )    ):
												# AAA -> AA+A vs A+AA case.
												return True
						elif len(__lowerCAmelCase    ) == len(__lowerCAmelCase    ) and fast.decode(__lowerCAmelCase    ) == fast.decode(__lowerCAmelCase    ):
												# Second order OK
												# Barrich -> Barr + ich vs Bar + rich
												return True
						lowerCamelCase__ =slow.encode(slow.decode(__lowerCAmelCase    )    )
						lowerCamelCase__ =fast.encode(fast.decode(__lowerCAmelCase    )    )
						if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
												# Type 3 error.
												# Snehagatha ->
												#       Sne, h, aga, th, a
												#       Sne, ha, gat, ha
												# Encoding the wrong with sp does not even recover what spm gave us
												# It fits tokenizer however...
												return True
						return False





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



						lowerCamelCase__ =fast.encode_plus(__lowerCAmelCase    )[0]
						lowerCamelCase__ =enc.offsets
						lowerCamelCase__ ,				lowerCamelCase__ =offsets[idx], offsets[idx - 1]
						if curr is not None and line[curr[0] : curr[1]] == "\u200f":
												return True
						if prev is not None and line[prev[0] : prev[1]] == "\u200f":
												return True





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Tuple:




						'''simple docstring'''



						for i, (spm_id, tok_id) in enumerate(zip(__lowerCAmelCase       ,   __lowerCAmelCase    )    ):
												if spm_id != tok_id:
																		break
						lowerCamelCase__ =i
						for i, (spm_id, tok_id) in enumerate(zip(reversed(__lowerCAmelCase    )       ,   reversed(__lowerCAmelCase    )    )    ):
												if spm_id != tok_id:
																		break
						lowerCamelCase__ =len(__lowerCAmelCase    ) - i

						lowerCamelCase__ =spm_ids[first:last]
						lowerCamelCase__ =tok_ids[first:last]

						if check_diff(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
												return True

						if check_LTR_mark(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
												return True

						if last - first > 5:
												# We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
												lowerCamelCase__ =Counter(spm_ids[first:last]    )
												lowerCamelCase__ =Counter(tok_ids[first:last]    )

												lowerCamelCase__ ={spm_ for (spm_, si) in spms.items() if toks.get(spm_       ,   0    ) == si}
												lowerCamelCase__ =3
												for i in range(last - first - min_width    ):
																		if all(spm_ids[first + i + j] in removable_tokens for j in range(__lowerCAmelCase    )    ):
																								lowerCamelCase__ =[
																								    k
																								    for k in range(last - first - min_width    )
																								    if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
																								]
																								for j in possible_matches:
																														if check_diff(spm_ids[first : first + i]       ,   tok_ids[first : first + j]       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ) and check_details(
																														    __lowerCAmelCase       ,   spm_ids[first + i : last]       ,   tok_ids[first + j : last]       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   ):
																																				return True

						print(F'''Spm: {[fast.decode([spm_ids[i]]    ) for i in range(__lowerCAmelCase       ,   __lowerCAmelCase    )]}'''    )
						try:
												print(F'''Tok: {[fast.decode([tok_ids[i]]    ) for i in range(__lowerCAmelCase       ,   __lowerCAmelCase    )]}'''    )
						except Exception:
												pass

						lowerCamelCase__ =fast.decode(spm_ids[:first]    )
						lowerCamelCase__ =fast.decode(spm_ids[last:]    )
						lowerCamelCase__ =fast.decode(spm_ids[first:last]    )
						print()
						print(__lowerCAmelCase    )
						return False





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )							-> Optional[int]:




						'''simple docstring'''



						global perfect
						global imperfect
						global wrong
						global total

						lowerCamelCase__ =slow.encode(__lowerCAmelCase    )
						lowerCamelCase__ =fast.encode(__lowerCAmelCase    )

						lowerCamelCase__ =False
						total += 1

						if slow_ids != fast_ids:
												if check_details(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    ):
																		lowerCamelCase__ =True
																		imperfect += 1
												else:
																		wrong += 1
						else:
												perfect += 1

						if total % 1_0000 == 0:
												print(F'''({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})'''    )

						if skip_assert:
												return

						assert (
						    slow_ids == fast_ids
						), F'''line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(__lowerCAmelCase    )}\n{fast.tokenize(__lowerCAmelCase    )}'''





def       lowerCamelCase_			( __lowerCAmelCase       ,   __lowerCAmelCase    )							-> List[str]:




						'''simple docstring'''



						global batch_total
						for i in range(len(__lowerCAmelCase    )    ):
												# premise, all languages
												for text in dataset[i]["premise"].values():
																		test_string(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )

												# hypothesis, all languages
												for text in dataset[i]["hypothesis"]["translation"]:
																		test_string(__lowerCAmelCase       ,   __lowerCAmelCase       ,   __lowerCAmelCase    )


if __name__ == "__main__":
	for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
		a							=list(slow_class.max_model_input_sizes.keys())
		for checkpoint in checkpoint_names:
			a							=0
			a							=0
			a							=0
			a							=0

			print(F'''========================== Checking {name}: {checkpoint} ==========================''')
			a							=slow_class.from_pretrained(checkpoint, force_download=True)
			a							=fast_class.from_pretrained(checkpoint, force_download=True)
			test_tokenizer(slow, fast)
			print(F'''Accuracy {perfect * 100 / total:.2f}''')


