"""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()

__magic_name__    				=					{
    name: (getattr(transformers, name), getattr(transformers, name + "Fast")) for name in SLOW_TO_FAST_CONVERTERS
}

__magic_name__    				=					datasets.load_dataset("xnli", split="test+validation")

__magic_name__    				=					0
__magic_name__    				=					0
__magic_name__    				=					0
__magic_name__    				=					0





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)							->     List[Any]:


			'''simple docstring'''


			if spm_diff == list(reversed(UpperCAmelCase__					)					):
						# AAA -> AA+A vs A+AA case.
						return True
			elif len(UpperCAmelCase__					) == len(UpperCAmelCase__					) and fast.decode(UpperCAmelCase__					) == fast.decode(UpperCAmelCase__					):
						# Second order OK
						# Barrich -> Barr + ich vs Bar + rich
						return True
			a__     				=		slow.encode(slow.decode(UpperCAmelCase__					)					)
			a__     				=		fast.encode(fast.decode(UpperCAmelCase__					)					)
			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 ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)							->     Any:


			'''simple docstring'''


			a__     				=		fast.encode_plus(UpperCAmelCase__					)[0]
			a__     				=		enc.offsets
			a__				,       a__     				=		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 ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)							->     Optional[Any]:


			'''simple docstring'''


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

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

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

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

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

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

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

			a__     				=		fast.decode(spm_ids[:first]					)
			a__     				=		fast.decode(spm_ids[last:]					)
			a__     				=		fast.decode(spm_ids[first:last]					)
			print()
			print(UpperCAmelCase__					)
			return False





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)							->     List[str]:


			'''simple docstring'''


			global perfect
			global imperfect
			global wrong
			global total

			a__     				=		slow.encode(UpperCAmelCase__					)
			a__     				=		fast.encode(UpperCAmelCase__					)

			a__     				=		False
			total += 1

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

			if total % 1_00_00 == 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(UpperCAmelCase__					)}\n{fast.tokenize(UpperCAmelCase__					)}'''





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__					)							->     List[Any]:


			'''simple docstring'''


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

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


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

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



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

__magic_name__    				=					"Hello world! cécé herlolip"





def   _lowerCamelCase ( UpperCAmelCase__,UpperCAmelCase__,UpperCAmelCase__					)							->     Dict:


   '''simple docstring'''


   a__     				=		FairseqRobertaModel.from_pretrained(UpperCAmelCase__					)
   roberta.eval()  # disable dropout
   a__     				=		roberta.model.encoder.sentence_encoder
   a__     				=		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_14,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 BERT config:',UpperCAmelCase__					)

   a__     				=		RobertaForSequenceClassification(UpperCAmelCase__					) if classification_head else RobertaForMaskedLM(UpperCAmelCase__					)
   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.emb_layer_norm.weight
   a__     				=		roberta_sent_encoder.emb_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]

      # 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
      a__     				=		roberta_layer.self_attn_layer_norm.weight
      a__     				=		roberta_layer.self_attn_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
      a__     				=		roberta_layer.final_layer_norm.weight
      a__     				=		roberta_layer.final_layer_norm.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(UpperCAmelCase__					).unsqueeze(0					)  # batch of size 1

   a__     				=		model(UpperCAmelCase__					)[0]
   if classification_head:
      a__     				=		roberta.model.classification_heads['mnli'](roberta.extract_features(UpperCAmelCase__					)					)
   else:
      a__     				=		roberta.model(UpperCAmelCase__					)[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(UpperCAmelCase__,UpperCAmelCase__,atol=1e-3					)
   print('Do both models output the same tensors?','🔥' if success else '💩'					)
   if not success:
      raise Exception('Something went wRoNg'					)

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


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


