



"""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    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Optional[int]    ,		lowerCamelCase_:       Union[str, Any]    ,		lowerCamelCase_:       Dict   ):
						"""simple docstring"""






						snake_case      : List[str]							=   FairseqRobertaModel.from_pretrained(lowercase__   )
						roberta.eval()  # disable dropout
						snake_case      : Tuple							=   roberta.model.encoder.sentence_encoder
						snake_case      : Optional[Any]							=   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:
												snake_case      : int							=   roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
						print("Our BERT config:"    ,		lowercase__   )

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

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

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

												# self attention
												snake_case      : Optional[Any]							=   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)   )
												)

												snake_case      : Any							=   roberta_layer.self_attn.q_proj.weight
												snake_case      : Optional[Any]							=   roberta_layer.self_attn.q_proj.bias
												snake_case      : Any							=   roberta_layer.self_attn.k_proj.weight
												snake_case      : Dict							=   roberta_layer.self_attn.k_proj.bias
												snake_case      : List[str]							=   roberta_layer.self_attn.v_proj.weight
												snake_case      : List[str]							=   roberta_layer.self_attn.v_proj.bias

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

												# intermediate
												snake_case      : List[Any]							=   layer.intermediate
												assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
												snake_case      : List[str]							=   roberta_layer.fca.weight
												snake_case      : Union[str, Any]							=   roberta_layer.fca.bias

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

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

						# Let's check that we get the same results.
						snake_case      : Any							=   roberta.encode(lowercase__   ).unsqueeze(0   )  # batch of size 1

						snake_case      : List[str]							=   model(lowercase__   )[0]
						if classification_head:
												snake_case      : Tuple							=   roberta.model.classification_heads["mnli"](roberta.extract_features(lowercase__   )   )
						else:
												snake_case      : int							=   roberta.model(lowercase__   )[0]
						print(our_output.shape    ,		their_output.shape   )
						snake_case      : Dict							=   torch.max(torch.abs(our_output - their_output   )   ).item()
						print(f'''max_absolute_diff = {max_absolute_diff}'''   )  # ~ 1e-7
						snake_case      : Optional[int]							=   torch.allclose(lowercase__    ,		lowercase__    ,		atol=1e-3   )
						print("Do both models output the same tensors?"    ,		"🔥" if success else "💩"   )
						if not success:
												raise Exception("Something went wRoNg"   )

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


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"""







import unittest

from cer import CER


A    =	CER()

class 			_a     (   unittest.TestCase):



			def 							__lowercase				( self       :							Optional[Any]		)       ->				Tuple:
									snake_case      : Union[str, Any]							=   ["White House"]
									snake_case      : Tuple							=   ["white house"]
									# S = 2, D = 0, I = 0, N = 11, CER = 2 / 11
									snake_case      : int							=   cer.compute(predictions=_lowercase      ,  references=_lowercase		)
									self.assertTrue(abs(char_error_rate - 0.1818181818		) < 1E-6		)



			def 							__lowercase				( self       :							Optional[Any]		)       ->				Dict:
									snake_case      : Tuple							=   ["were wolf"]
									snake_case      : Union[str, Any]							=   ["werewolf"]
									# S = 0, D = 0, I = 1, N = 9, CER = 1 / 9
									snake_case      : List[str]							=   cer.compute(predictions=_lowercase      ,  references=_lowercase		)
									self.assertTrue(abs(char_error_rate - 0.1111111		) < 1E-6		)

									snake_case      : List[str]							=   ["werewolf"]
									snake_case      : Optional[Any]							=   ["weae     wolf"]
									# S = 1, D = 1, I = 0, N = 8, CER = 0.25
									snake_case      : Optional[int]							=   cer.compute(predictions=_lowercase      ,  references=_lowercase		)
									self.assertTrue(abs(char_error_rate - 0.25		) < 1E-6		)

									# consecutive whitespaces case 1
									snake_case      : Optional[int]							=   ["were wolf"]
									snake_case      : Dict							=   ["were               wolf"]
									# S = 0, D = 0, I = 0, N = 9, CER = 0
									snake_case      : Dict							=   cer.compute(predictions=_lowercase      ,  references=_lowercase		)
									self.assertTrue(abs(char_error_rate - 0.0		) < 1E-6		)

									# consecutive whitespaces case 2
									snake_case      : Dict							=   ["were   wolf"]
									snake_case      : Dict							=   ["were               wolf"]
									# S = 0, D = 0, I = 0, N = 9, CER = 0
									snake_case      : Optional[int]							=   cer.compute(predictions=_lowercase      ,  references=_lowercase		)
									self.assertTrue(abs(char_error_rate - 0.0		) < 1E-6		)



			def 							__lowercase				( self       :							List[Any]		)       ->				Union[str, Any]:
									snake_case      : str							=   ["werewolf"]
									snake_case      : List[Any]							=   ["weaewolf"]
									# S = 1, D = 0, I = 0, N = 8, CER = 0.125
									snake_case      : str							=   cer.compute(predictions=_lowercase      ,  references=_lowercase		)
									self.assertTrue(abs(char_error_rate - 0.125		) < 1E-6		)



			def 							__lowercase				( self       :							int		)       ->				int:
									snake_case      : Dict							=   ["werewolf"]
									snake_case      : Optional[Any]							=   ["wereawolf"]
									# S = 0, D = 1, I = 0, N = 8, CER = 0.125
									snake_case      : List[str]							=   cer.compute(predictions=_lowercase      ,  references=_lowercase		)
									self.assertTrue(abs(char_error_rate - 0.125		) < 1E-6		)



			def 							__lowercase				( self       :							Union[str, Any]		)       ->				List[Any]:
									snake_case      : Tuple							=   ["werewolf"]
									snake_case      : Union[str, Any]							=   ["wereolf"]
									# S = 0, D = 0, I = 1, N = 8, CER = 0.125
									snake_case      : str							=   cer.compute(predictions=_lowercase      ,  references=_lowercase		)
									self.assertTrue(abs(char_error_rate - 0.125		) < 1E-6		)



			def 							__lowercase				( self       :							int		)       ->				List[str]:
									snake_case      : int							=   ["werewolf"]
									snake_case      : Optional[Any]							=   cer.compute(predictions=_lowercase      ,  references=_lowercase		)
									self.assertEqual(_lowercase      ,  0.0		)



			def 							__lowercase				( self       :							List[Any]		)       ->				Optional[Any]:
									snake_case      : str							=   ["werewolf", "I am your father"]
									snake_case      : str							=   cer.compute(predictions=_lowercase      ,  references=_lowercase		)
									self.assertEqual(_lowercase      ,  0.0		)

									snake_case      : Union[str, Any]							=   ["werewolf", "I am your father", "doge"]
									snake_case      : str							=   ["werxwolf", "I       am your father", "doge"]
									# S = 1, D = 0, I = 0, N = 28, CER = 1 / 28
									snake_case      : Dict							=   cer.compute(predictions=_lowercase      ,  references=_lowercase		)
									self.assertTrue(abs(char_error_rate - 0.03571428		) < 1E-6		)



			def 							__lowercase				( self       :							Any		)       ->				Union[str, Any]:
									snake_case      : Union[str, Any]							=   ["My hovercraft", "is full of eels"]
									snake_case      : List[str]							=   ["My hovercraft is full", " of eels"]
									# S = 0, D = 0, I = 2, N = 28, CER = 2 / 28
									# whitespace at the front of " of eels" will be strip during preporcessing
									# so need to insert 2 whitespaces
									snake_case      : int							=   cer.compute(predictions=_lowercase      ,  references=_lowercase      ,  concatenate_texts=_lowercase		)
									self.assertTrue(abs(char_error_rate - 0.071428		) < 1E-6		)



			def 							__lowercase				( self       :							Any		)       ->				Dict:
									snake_case      : Dict							=   ["我能吞下玻璃而不伤身体"]
									snake_case      : List[str]							=   [" 能吞虾玻璃而 不霜身体啦"]
									# S = 3, D = 2, I = 0, N = 11, CER = 5 / 11
									snake_case      : str							=   cer.compute(predictions=_lowercase      ,  references=_lowercase		)
									self.assertTrue(abs(char_error_rate - 0.4545454545		) < 1E-6		)

									snake_case      : List[Any]							=   ["我能吞下玻璃", "而不伤身体"]
									snake_case      : int							=   ["我    能 吞 下 玻 璃", "而不伤身体"]
									# S = 0, D = 5, I = 0, N = 11, CER = 5 / 11
									snake_case      : Dict							=   cer.compute(predictions=_lowercase      ,  references=_lowercase		)
									self.assertTrue(abs(char_error_rate - 0.454545454545		) < 1E-6		)

									snake_case      : int							=   ["我能吞下玻璃而不伤身体"]
									snake_case      : int							=   cer.compute(predictions=_lowercase      ,  references=_lowercase		)
									self.assertFalse(_lowercase      ,  0.0		)



			def 							__lowercase				( self       :							List[Any]		)       ->				List[Any]:
									snake_case      : int							=   [""]
									snake_case      : Optional[int]							=   ["Hypothesis"]
									with self.assertRaises(_lowercase		):
															cer.compute(predictions=_lowercase      ,  references=_lowercase		)


if __name__ == "__main__":
			unittest.main()



