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()
_SCREAMING_SNAKE_CASE:						Dict    	=       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE:						Tuple    	=       '''Hello world! cécé herlolip'''






def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      FairseqRobertaModel.from_pretrained(lowerCamelCase_  )
			roberta.eval()  # disable dropout
			_snake_case              =      roberta.model.encoder.sentence_encoder
			_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=514							,	type_vocab_size=1							,	layer_norm_eps=1E-5							,	)
			if classification_head:
						_snake_case              =      roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]
			print("Our BERT config:"							,	lowerCamelCase_  )

			_snake_case              =      RobertaForSequenceClassification(lowerCamelCase_  ) if classification_head else RobertaForMaskedLM(lowerCamelCase_  )
			model.eval()

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

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

						# self attention
						_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)  )
						)

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

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

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

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

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

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

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





import unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property

class 	_SCREAMING_SNAKE_CASE							(     unittest.TestCase							):






		"""simple docstring"""







		@cached_property
		def 				_a				(			self    :				Tuple   ):






					"""simple docstring"""
					return BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"   )



		@cached_property
		def 				_a				(			self    :				Tuple   ):






					"""simple docstring"""
					return BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B"   )



		def 				_a				(			self    :				Optional[Any]   ):






					"""simple docstring"""
					_snake_case              =      self.tokenizer_ab
					_snake_case              =      " I am a small frog."
					_snake_case              =      tok([src_text]  ,					padding=__SCREAMING_SNAKE_CASE  ,					truncation=__SCREAMING_SNAKE_CASE   )["input_ids"]
					_snake_case              =      tok.batch_decode(__SCREAMING_SNAKE_CASE  ,					skip_special_tokens=__SCREAMING_SNAKE_CASE  ,					clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )[0]
					assert src_text == decoded



		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					_snake_case              =      self.rust_tokenizer_ab
					_snake_case              =      " I am a small frog."
					_snake_case              =      tok([src_text]  ,					padding=__SCREAMING_SNAKE_CASE  ,					truncation=__SCREAMING_SNAKE_CASE   )["input_ids"]
					_snake_case              =      tok.batch_decode(__SCREAMING_SNAKE_CASE  ,					skip_special_tokens=__SCREAMING_SNAKE_CASE  ,					clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )[0]
					assert src_text == decoded



		def 				_a				(			self    :				Optional[Any]   ):






					"""simple docstring"""
					assert self.tokenizer_ab.add_prefix_space
					assert self.tokenizer_ab([" Sam", "Sam"]   ).input_ids == [[5_502, 2], [5_502, 2]]







		def 				_a				(			self    :				str   ):






					"""simple docstring"""
					assert self.rust_tokenizer_ab.add_prefix_space
					assert self.rust_tokenizer_ab([" Sam", "Sam"]   ).input_ids == [[5_502, 2], [5_502, 2]]

